Marcin Juszczyński

Arkadiusz Kowalski
email: arkadiusz.kowalski@pwr.wroc.pl

Achieving Desired Cycle Times by Modelling Production Systems

Keywords: simulation of discrete processes, production system, cycle time 1

de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

One of many definitions of simulation is: "Simulation is the art and science of creating representations of a process or system in order to conduct experiments and assessments". [START_REF] Hopp | Factory Physics[END_REF], [START_REF] Ören | The Many Facets of Simulation through a Collection of about 100 Definitions[END_REF] Simulation methodologies are most often classified by time. [START_REF] Helal | A Hybrid System Dynamics-discrete Event Simulation Approach to Simulating the Manufacturing Enterprise[END_REF], [START_REF] Zeigler | Theory of Modeling and Simulation: integrating discrete event and continuous complex dynamic systems[END_REF] In DESS -Differential Equation System Specification both the states and attributes of the model are being update continuously. In DEVS -Discrete Event System Specification on the other hand, system status is approximated for fixed time periods. Those models may be referred to as time-dependent. System status is recognised using the predetermined time periods, hence attributes and system status may be registered. In case of discrete events, time is registered once system status is changed i.e. at least one attribute of an object changes in the modelled system. Moreover, the DEVS&DESS method combines the two above mentioned methods. Simulation has become a commonly used tool to engineer production systems. [START_REF] Chlebus | The Concept of Intelligent System for Horizontal Transport in a Copper Ore Mine[END_REF], [START_REF] Law | Simulation Modeling and Analysis[END_REF] Modelling by simulation is one of the most important supporting techniques allowing to cut production time of a new product and its market roll-out. Naturally, it is possi-ble to investigate real production systems, however, it is not economically viable. Hence additional steps currently used in production system engineering are building best possible simulation model, its validation and computer simulation, review of obtained results testing whether or not theoretical solutions are in fact effective and if necessary making some adjustments.

Simulation methods

DEVS&DESS Discrete Event & Differential Equation System Specification

Discrete time simulation method

DEVS -Discrete Event System Specification

DESS -Differential

Equation System Specification Fig. 1. Classification of simulation methods [START_REF] Zeigler | Theory of Modeling and Simulation: integrating discrete event and continuous complex dynamic systems[END_REF] Combining modelling with simulation in investigating production processes allows among other to:

 streamline processes and eliminate waste of time and resources,  improve efficiency and cut production costs,  analyse and simulate different but critical cases and develop adequate procedures to action,  create excellent environment for training and understanding processes taking place in an organisation. [START_REF] Biniek | Elements of the systems, modelling and simulation theoretics[END_REF], [START_REF] Chlebus | Concept of a Data Exchange Agent System for Automatic Construction of Simulation Models of Manufacturing Processes[END_REF], [START_REF] Ulrich | Product Design and Development[END_REF] Computer simulation is also one of the most important tools for planning production. It allows for addressing market demand in a flexible manner and cut costs of production. [START_REF] Tisza | Recent achievements in computer aided process planning and numerical modelling of sheet metal forming processes[END_REF], [START_REF] Krenczyk | Integration Production Planning and Scheduling Systems for Determination of Transitional Phases in Repetitive Production[END_REF], [START_REF] Westkämper | Fabrikdigitalisierung -Ein angepasster Ansatz für die digitale Fabrikplanungin bestehenden Fabriken und Gebäuden[END_REF] Currently available simulation suites are versatile and userfriendly. The most popular systems are FACTOR/AIM, ProModel, Taylor II, Arena and WITNESS. The most useful features include model building environment using predefined graphical elements, considerably improving the process of building a simulation model. Presented below example projects regarding Technical and Organisational Production Set-up prove it is possible to combine production system simulation techniques with Lean Management tools, here with the Kanban scheduling system. Objective of the project The primary objective of the project was to analyse and streamline material flow for purposes of starting-up electric scooter production line.

Simulation models of investigated production system were based on production plans, drafted prior to starting production. It considered among other adding machinery, increase production capacity, process of production and transport, transport routes, layout, material flow, human resources and means of transport. The main criterion for designing the layout was minimum transporting distance for products and materials.

The project took two stages to complete, which focused on expanding production area, adding machinery and increasing manufacturing capacity. The first stage included modelling current layout of the facilities in order to determine current manufacturing capacity and pin-point bottle necks in production lines. The second stage comprised consecutive model modifications until objective defined for particular production cycle was achieved.

Description of technological process

The main production process if the process of assembly. The scooter, assembled on the main assembly line, is fixed to a carrier platform (Figure 2) which is moved by operators between consecutive workstations.

Fig. 2. Platform for assembling the scooter

The platform for assembling the scooter consists of three independently operating surfaces which are lifted depending on currently assembled element. Material flow along the assembly line was designed to allow preliminary component assembly in side assembly cells (both left and right to the main assembly line) for them later to be directly moved to the point where they would be assembled on the main assembly line. Below schematically represented in the scooter production line (Figure 3). Traffic management workstation is also on the shop floor, which stores all tools necessary for the event of production line failure.

Development and implementation of production system model

ProModel software was used to build the simulation model. Its functionalities enable to effectively simulate production processes by using predefined objects representing workstations, processed goods and production resources [START_REF] Harrell | Simulation Using ProModel[END_REF]. For purposes of discussed project, the graphics library was built with particular attention to details to assure good quality visualisations, whereas the shop floor layout was imported from AutoCad (Figure 4). Advantages of that solution are among other that the scale of investigated workstation layout is retained, thus allowing later, in simulation software, to factor in real distance travelled by production resources during transport along transport routes. For instance, modelling cell 10L included the following activities:

 Worker no 1preliminary assembly and encoder fitting, pressing bushing onto swingarm and transmission gear disassembly,  Worker no 2fitting transmission gear onto the swingarm, screwing the breather on, pouring oil and fixing wheel to the swingarm. That worker also transports the swingarm to the main line and fits it to the frame on the platform,  Worker no 3pressing small bushings into rear rim, fitting tyres and wheel balancing on the balancing machine.

Stage I -as-is state

When building simulation models, it is critical to provide correct data about the modelled production process, e.g. about times of technological operations. Here, time taken by each worker to complete operations at individual stations was measured. Time per each operation was measured three times to calculate an average. Total time of all activities was 354.1 min. Below chart (Figure 6) presents time to completion for operations at individual production cells which were then factored into simulation model.

Stage II -verification of proposed solutions

Impact of modifications and improvements was tested during consecutive simulations of the production system model in order to determine their effect and achieve the objective of 12 minute cycle time. Types of proposed improvements and their performance is presented in Table 1. The final model factors in all 12 proposed organisational improvements. It was then subject to testing in order to verify viability of proposed changes. The final outcome was that time to completion of technological operations at each individual workstation was lower than assumed cycle time. Chart in figure 7 presents time to completion per operation at individual workstations. Notice, times of consecutive operations at each workstation were lower than the initial target time of 12 minutesthe cross-over point is marked by red line. Some workstations take several minutes, hence one worker can attend to several workstations. For comparing the two simulation models, a function calculating median proved useful. Median for worker utilisation before changes was 24%, whereas after optimisation increase up to 66%approx. threefold. The number of workers in the factory grew from 30 to 42.

Summary

The project objective was to analyse and streamline material flow for purposes of scooter production line. The main criterion was to achieve a 12 minute cycle time.

Reaching target time was possible by making changes to the machinery, increase manufacturing capacity, transport routes and increasing human resources. The graphics received particular attention. Image quality and resemblance with real objects were prioritised. All workstations, processed goods and lengths of transport routes were modelled to be identical in terms of dimensions and shapes with real objects. The underlying idea was to represent technological process comparable to the real system, thus enabling to obtain detailed information about material flow.

The project was completed in two stages. The first stage included building the as-is state model in order to pin-point bottlenecks and validate obtained results. At the second stage proposed solutions were verified by assessing their efficiency.

An additional outcome of the projectapart from achieving target cycle timewas achieving more balanced worker utilisation. Figure 11 presents worker utilisation before and after modifying the production line. Simulation models and data they generated proved very useful for production system engineering. Initial workstation and resources layout allowed for production of 44 scooters per week, producing cycle time of 48 minutes. After reconfiguration, that time was shortened down to 12 minutes, and the number of scooters rolling off production line jumped to 140 units per week, thus increasing utilisation of resources owned by the company.

Fig. 3 .

 3 Fig. 3. Assembly line diagram

Fig. 4 .

 4 Fig. 4. Simulation model of investigated manufacturing system -workstations

Fig. 5 .

 5 Fig. 5. Details factored into modelling production cells

Fig. 6 .

 6 Fig. 6. Time to completion for operations at individual cells

Fig. 7 .

 7 Fig. 7. Time to completion for operations at individual workstations

Fig. 8 .Fig. 9 .

 89 Fig. 8. Time to completion for operations at individual cells

Fig. 10 .

 10 Fig. 10. Worker utilisation before and after optimisation

Fig. 11 .

 11 Fig. 11. Worker utilisation before and after streamlining individual production cells After optimisation

Table 1 .

 1 Types of proposed improvements and their effect

				Outcome
	No.	Improvement	Cycle Time [min]	No of scooters [units/week]	No of workers
	1	Two workers were added to the 10R cell, where the frame is assembled	44	48	31
		Two workers were added for assembly
	2	of ICM (Input Control Module) and	43	48	33
		wheel fitting at the main line
		Bottlenecks at brake fitting station
	3	were eliminated by adding two more	27	71	35
		workers
	4	One worker was added to the 30L sta-tion	27	71	36
	5	Two workers were added to the 30R stations and one to the 30M station	26	71	38
	6	Workers were added to the 30R30 station, where the battery is fitted	25	71	41
	7	15M station was added to the main line. Activities at the 20M station were	17	106	41
		partially relocated to the 15M station
	8	Technological operations completed at 50M station were redistributed	15	114	41
	9	One worker was added to the 10R sta-tion where Motor Controller is fitted.	15	114	42
	10	5M stations were added to decrease utilisation of the 10M station.	15	114	42
		25M station was added to decrease
	11	utilisation of the 20M and 30M work-	15	114	42
		station
		45M station was added to the main line
	12	to decrease utilisation of the 40M sta-	11.4	140
		tion.


Anna Burduk
email: anna.burduk@pwr.wroc.pl

Artificial neural networks as tools for controlling production systems and ensuring their stability

Keywords: production system, production process stability, neural networks

Models of artificial neural networks can be used to control a production system, and thus to ensure its stability. Such models are very useful tools, because they can be built quickly and easily. The only issue is a large amount of data needed in the neural network training process. However, in the era of common availability of IT systems, the parameterization and standardization of production processes is not a problem anymore. Contemporary production systems are mostly automated and metered. This paper presents a method for building a model of an artificial neural network for controlling a wire harness production system and determining its stability.

Introduction

A contemporary customer requires that a product should be not only of good quality and sold at a low price, but also diversified, i.e. available in different versions and variants. In order to meet these requirements, manufacturers are forced to manufacture products in small batches and quickly deliver them to the market. However, a problem with ensuring the stability of production appears here. Production in small batches with highly diversified products and low inventory levels is characterised by a much lower stability as compared with the large-batch manufacturing. Although these disturbances are usually temporary, they may lead to a loss of the functioning or manufacturing stability in a company, which in turn translates into financial losses as well as a loss of customers [START_REF] Chlebus | Modelling and computer simulation as tools for reorganization of production processes[END_REF].

In order to ensure smooth functioning of a production system, the stability of its processes must be ensured and, on the other hand, quick decisions, which would be encumbered with the lowest possible risk and uncertainty, should be made. The concept of stability is derived from systems theory and means the ability of a system to return to the steady state after the disturbances have ceased. The assessment of the system stability is a typical task of the qualitative analysis. Stability in a very broad sense means permanency (invariability over time) of a system feature or a certain series of states of the system [START_REF] Frumusanu | Cutting process stability evaluation by process parameters monitoring[END_REF][START_REF] Chlebus | The concept of intelligent system for horizontal transport in a copper ore mine[END_REF].

The steady state is a system state that meets certain conditions. Each system is in a certain state, and because production systems are of dynamic character, a continuous transformation, which causes transition from one state to another, takes place in them. In other words, parameters of a production process may have a different value at any moment [START_REF] Frumusanu | Cutting process stability evaluation by process parameters monitoring[END_REF][START_REF] Mahmood | Performance analysis of a RLS-based MLP-DFE in time-invariant and time-varying channels[END_REF]. Taking into account any time interval (provisionally adopted unit), it can be said that there is a sequence of output or input states.

When transposing the definition of stability to production systems it can be said that a production system may be considered stable if values of the parameters defining it are within the ranges defined in the planning function and registered in a standard (usually a production plan). Steady state of a production system is presented in Fig. 1. The range of values of the P parameters, at which the process is in the steady state

System parameter (P)

The system is unstable, because P(t3) > P2

Fig. 1. Steady state of a system

If the value of the parameter in the instant is within a defined range, that is , it proves that the course of the process is correct. Otherwise, corrective measures should be taken. Corrective measures usually consist in changing the values of control variables (inputs to the system X) in such a way, so that the values of the parameters characterizing the controlled variables (outputs from the system Y) return to the process course standards established in the planning function. A correct decision will cause that the system will return to the steady state [START_REF] Burduk | Methods of risk evaluation in manufacturing systems[END_REF][START_REF] Sankar | Modified approach for prioritization of failures in a system failure mode and effects analysis[END_REF].

In connection with the above, the stability of a production system at the assumed margin of variability will be understood as maintaining the steady state by the system for a certain assumed period. A production system is in the steady state, if values of the parameters characterizing the system are within the ranges defined in the planning function and registered in a standard (usually a production plan).

Production systems are not only of technical nature, but also of economic character, and one of their purposes is to generate profit through a continuous increase of the market share. If the development and an increase in the market share are included in company's plans, the stability of a production system should be then understood as the ability to maintain or increase the values assumed in the parameters plans. Artificial neural networks in production system control Decisions are based on objectives with different planning perspectives, while possible variants are verified on various types of models of products, processes and even entire systems. A model is a simplified representation of the reality. It constitutes the main source of knowledge about the object being modelled and the impact of planned solution on the object. A model describes and explains the method of operation and functioning of an object or a system under specific conditions. Studies performed on a model do not cause disturbances in the functioning stability of a real object or system. It offers the possibility to verify the effects of the decisions planned both in relation to the current operations of an enterprise and its future [START_REF] Bilski | The UD RLS algorithm for training feedforward neural networks[END_REF][START_REF] Bilski | A fast training algorithm for neural networks[END_REF][START_REF] Rutkowski | Methods and Techniques of Arificial Inteligence[END_REF].

Usability of models is influenced by their accuracy, possibility of populating them with reliable data, and a short time of building them. It is possible thanks to the progressing unification, standardization and parameterization of production processes and products. Standardization, apart from shortening the time needed to build a model, also lowers the costs of product development or production process improvement.

In the case of management or optimization of a production process, a model is used to verify the decisions planned. Thanks to the use of IT systems, a model can be populated with appropriate data from a real system. Experiments conducted on a model in a way that does not disturb the functioning of the real system allow learning about the effects and selecting the most optimal decision variant concerning the quantities and types of inputs to the system.

The entire 19th century and most of the 20th century are characterized by predominance of mathematical modelling. Such methods usually allow obtaining good results from models, however the complexity and dynamics of contemporary enterprises cause that there is a need to look for new modelling methods such as artificial neural networks (ANN, NN). They belong to the group of so-called empirical modelling techniques, and the main issue associated with them is to provide measurement data. In the computerized environment of today's companies, a lack of data is not a problem, because most of them have different types of IT systems, while production processes are generality metered and automated. It can be said that companies often have a problem not with a lack of data but with their excess.

In turn, the market requirements and the dynamics of the business environment caused that new, faster methods of production system modelling are searched for. Such methods include models of artificial neural networks, which provide solutions that take into account the process of learning on the basis of available data. They allow skipping the stage of modelling complex relationships between elements of a production system. In order to build a model of a production system, it is enough to define the problem: selection of input and output variables, and a set of training data.

Artificial neural networks are usually used to solve problems related to approximation, interpolation, prediction, classification, recognition, and control. Image recognition, which also includes classification, grouping and processing, accounts for approx. 70% of all industrial applications. In the management and operation of production systems, artificial neural network are more and more often used for [START_REF] Bilski | The UD RLS algorithm for training feedforward neural networks[END_REF][START_REF] Rutkowski | Methods and Techniques of Arificial Inteligence[END_REF][START_REF] Wieczorek | Neural models of technological processes[END_REF][START_REF] Tung-Hsu | Intelligent remote monitoring and diagnosis of manufacturing processes using an integrated approach of neural networks and rough sets[END_REF]:

 control of production processes, robots,  analysis of manufacturing problems,  diagnostics of electronic systems of machines,  selection of personnel and input materials,  optimization of the business activity, waste disposal, robot movements,  planning overhauls of machines,  forecasting.

The primary objective of modelling the dynamics of a production process is to identify the temporal variability of its physical quantities or states. To this end, a time series, i.e. an ordered sequence of values of a certain variable over time, should be determined. A time series may have a form of a vector ( ) ( ) ( ) . Due to the fact that process parameters may differ in separate phases of the process, the time series vector can take the form of a vector defined in -dimensional space. Individual components of this vector will be the states of the production process stages in the past, which in turn can be regarded as points in a multi-dimensional output space. Thus the task of analysing the temporal variability of the production process can be reduced to searching -dimensional space for a certain trajectory, on which the analysed output variable of the process "moves". Therefore, a given quantity in the form of a time series is determined in order to predict its value in future moments [START_REF] Bilski | A fast training algorithm for neural networks[END_REF][START_REF] Wieczorek | Neural models of technological processes[END_REF].

3

A multilayer perceptron network for determining the stability of the wire harness assembly process

Characteristics of the wiring harness assembly process

A growing company producing wiring harnesses for Electrolux dishwashers planned to increase the production plan by 30 to 40% in the coming years to meet the increasing orders. An extension of the production program will lead to an increase in the load on production resources, while the production system will be more vulnerable to disturbances and risk factors occurring at the production line.

The analysed factory manufactures approximately 700 different types of products. All the products are characterized by a high similarity of the structure and the manufacturing process. Each wiring harness consists of so-called modules, while a module -of wires ended with terminals. Both the number of modules and the number of wires may differ depending on the type of wiring harness. Some wires may be joined with the use of insulating tape. Individual modules are connected in the enclosure. Assembly operations are performed on a rotary table by three workers. Worker 1 lays out the prepared modules on the assembly table in accordance with the drawing of the product to be assembled, which is shown on the table. Worker 2 applies two additional wires into connectors, while Worker 3 inserts modules into enclosures. The wire harnesses are then taken from the table and transferred for the taping operation, after which an electrical test is carried out. If one of the modules or additional wires was not installed correctly, the product is considered defective. All assembly operations require precision and high skills of the workers. An incorrect arrangement of modules causes a significant extension of time for application of additional wires in the subsequent operation. Both the operation of laying out the modules and taking off the wiring harnesses must be performed very carefully because the wires may slip out from the connectors or enclosure. Because final assembly centres operate on the principle of a swivel, the skills of the operators re very important as they need to work with the same pace.

3.2

The purpose and method of building the artificial neural network

The purpose of building the artificial neural network was to ensure the stability of a wiring harness assembly process. An assembly process can be deemed stable, if the production volume during a shift is consistent with the adopted production plan. Otherwise, corrective actions, which consist in changing the values of input parameters of the production resources used in the process, should be taken.

The assembly time of a wiring harness depends primarily on the number of the modules it consists of. This is associated both with the fact that more elements need to be assembled and taped. The boundaries of stability and the productivity of the assembly process for different numbers of modules were determined. These values are presented in Table 1. Basing on an observation of the process, an analysis of the documentation and interviews with the workers, it has been found that the factors affecting the assembly process include experience and skills of the workers designated in Fig. 3 as Workers 1, 2, 3 and 4. Therefore, the absences and turnover of workers are the factors that disturb the course of the assembly process the most. A new worker slows down the operation of an entire assembly centre and increases the number of defective elements found. It has been found that a new worker is able to acquire an adequate efficiency only after 1 month of work at a given assembly centre.

In order to predict the quantity of the products produced at the input parameters set, a unidirectional neural network (multilayer perceptron) was built. The quantity of assembled wiring harnesses of good quality, i.e. those which passed the electrical test successfully, was to be the dependent variable. The independent variables were selected as follows:

X 1the number of modules in the wire harness, X 2 -the skills level of Worker 1, X 3 -the skills level of Worker 2, X 4 -the skills level of Worker 3, X 5 -the skills level of Worker 4, X 6taping time, X 7the number of defective elements detected at the electrical test station.

In order to evaluate the parameter of workers' skills levels, 4 values have been introduced:

1 -a worker who works less than 1 week, 2 -a worker who works less than 2 week, 3 -a worker who works less than 4 week, 4 -an experienced worker.

The data were collected from observations and measurements of an actual process as well as from the analysis of the organizational documentation and quality control reports. In total, 378 measurements were available for each variable. This set was divided into two parts, one of which served as a training set, while the second part was used for testing the network.

The experiment was performed in the SAS Enterprise Miner 6.2 environment. The first step was to investigate the correlation between independent variables and the dependent variable. The results containing the correlation value are shown in Table 2.

Table 2. The values of the correlation between variables

Independent attribute (variable)

Correlation value number of modules in the wiring harness 0.16583 the skills level of Worker The obtained results indicate that it is pointless to use a linear regression method (absolute values of the correlation are below 0.5) for the analysed problem. Therefore it is justified to the use neural networks which build non-linear regression models.

As a part of further experiments, a model of a multilayer perceptron network was built, for which the values of the number of neurons in the hidden layer were changed. In order to confirm the results of the correlation analysis, a neural network according to the generalized linear model was also built.

For the neural network models built, a series of experiments for different numbers of independent variables was performed. Their goal was to establish the combination of independent variables, for which the neural network will provide the best prediction of the number of wiring harnesses manufactured per shift. When building the models, different numbers of independent variables were considered. Their selection was dictated by previous experiments, i.e. it depended on the absolute value of the correlation (see: Table 2). Under the experiment no. 1, all input attributes were used, while in the experiment no. 2 the 'taping time' attribute (the lowest absolute value of the correlation) was discarded. In the experiment no. 3 the 'number of defective elements found at the electrical test station' attribute (the next lowest absolute value of the correlation) was discarded in addition. The results are presented in Table 3, where the values obtained represent a network selection criterion, which is a mean square error. These results concern the analysis of the input data set, which was also used for network training process. Where MPN -a multilayer perceptron network, NN -number of neurons in the hidden layer, GLM -generalized linear model.

The analysis of the results confirms that linear models are not suitable for solving this problem. For each experiment, the worst results (with the highest mean square error) were obtained for a neural network built according to the generalized linear model. The best results were obtained for a multilayer perceptron network with 48 neurons under the experiment no. 3, a schematic diagram of which is shown in Fig. 4. As it results from Błąd! Nie można odnaleźć źródła odwołania. and Fig. 5, the process loses the steady state, if Worker 3 works for a period shorter than two weeks. This result confirms the observations made when collecting the data and analysing the process. It also confirms the opinions of the workers and process managers that only after a month of performing assembly operations, a new employee is able to work in accordance with the pace adopted for the assembly centre, and the number of defective products returns to the assumed level. The purpose of the next experiment was to check whether the stability of the process would be similar for a larger number of modules in a wiring harness and at the same skills levels of the workers. A wiring harness consisting of 28 modules was used as an example. The production plan for this product was at the level of 300 pcs per shift, while the boundaries of the process stability were set at 290 -310 pcs/shift. Table 6Błąd! Nie można odnaleźć źródła odwołania. presents the production volume predicted by the ANN model for these assumptions. The data included in Błąd! Nie można odnaleźć źródła odwołania. are presented additionally in the context of the process stability in Fig. 6. As it appears from Błąd! Nie można odnaleźć źródła odwołania. and Fig. 6 at the same skills levels of the workers but for an increased number of modules in a wiring harness, the process is stable only if all workers at the assembly centre are experienced, i.e. work at this centre for more than 4 weeks. If Worker 3 works at this centre for a period shorter than 4 weeks, the process is no longer stable. The results of the experiment confirm the observations and previous assumptions that only experienced workers (i.e. those who work at a given centre for more than 4 weeks) are able to work in accordance with the adopted pace, and the number of defective elements manufactured by them decreases to a minimum.

ANN MODEL

Summary

Models of artificial neural networks can be used to control a production system, and thus to ensure its stability. Such models are very useful tools, because they can be built quickly and easily. The only issue is a large amount of data needed in the neural network training process. However, in the era of common availability of IT systems, the parameterization and standardization of production processes is not a problem anymore.

The development of IT systems led to an increase in the amount of information collected in enterprises. In turn, the market requirements and the dynamics of the business environment caused that new, faster methods of production system modelling are searched for. Such methods include models of artificial neural networks, which provide solutions that take into account the process of training on the basis of available data. They allow skipping the stage of modelling complex relationships between elements of a production system. In order to build a model of a production system, it is enough to define the problem, select input and output variables, and prepare a training data set.

Artificial neural networks (ANN) constitute a modern method for mathematical modelling of phenomena and processes, which in the last dozen or so years found many applications in different areas of human activity. The reason why ANN are used in the science and engineering is the possibility to treat dynamic, complex variables or imprecisely defined processes as a black box. In other words, ANN allow finding a relationship between many input variables and the output variable in the process without a need to build complex mathematical equations.

Fig. 2 .Fig. 3 .

 23 Fig. 2. a) Structure of a wiring harness, b) schematic diagram of the selected wiring harness

(Fig. 4 .Fig. 5 .

 45 Fig. 4. Independent variables and the dependent variable used to build the artificial neural network (ANN)

Fig. 6 .

 6 Fig. 6. The predicted production volume for wiring harnesses with 28 modules for different skills levels of Worker 3

Table 1 .

 1 The productivity and the ranges of stability in the wiring harness assembly process depending on the number of modules

	Number of modules in the wiring harness [pcs]	Production plan [pcs/shift]	Boundaries of stability [pcs/shift]
	8 -14	370	(360 -380)
	14 -20	350	(340 -360)
	21 -25	320	(310 -330)
	26 -30	300	(290 -310)

Table 3 .

 3 The results of the experiments for different variants of the neural network built

	Model of the		Mean squared error
	neural	Experiment	Experiment	Experiment	Experiment
	network	No. 1	No. 2	No. 3	No. 4
	MPN -NN=3	999.05	2443.71	1056.1	427.08
	MPN -NN=16	2537.86	1369.98	1437.86	1019.25
	MPN -NN=32	327.08	767.69	375.39	526.14
	MPN -NN=48	1219.25	754.22	327.15	2088.12
	MPN -NN=64	2375.39	872.49	999.05	368.14
	GLM	1851.50	1450.28	1851.50	2569.8

Table 5 .

 5 The predicted production volume of wiring harnesses with 28 modules for different skills levels of Worker 3

			Network inputs		Network outputs
	Number of mod-ules [pcs]	Skills level of Worker 1	Skills level of Worker 2	Skills level of Worker 3	Skills level of Worker 3	Predicted production volume
	28	4	4	4	3	308
	28	4	4	3	3	288
	28	4	4	2	3	225
	28	4	4	1	3	162

The ANN model presented in Fig. 4 was used for further experiments, i.e. to assess the stability of the wiring harness assembly process for different values of independent variables.

3.3

Determination of the stability of the wiring harness assembly process using a neural network with 48 neurons in the hidden layer

In order to determine the stability of the analysed process, test data were prepared and the "score" node of the SAS Enterprise Miner 6.2 environment was used. The test data contained various variants of changes in input attributes (independent variables). For such data, the selected neural network model predicts the values for the manufactured wiring harnesses, which are interpreted in the context of the stability of the assembly process. Sample test data along with the predicted number of manufactured elements are presented in Błąd! Nie można odnaleźć źródła odwołania., and Table 5.

The purpose of the experiment no. 1 was to examine how the skills levels of the workers at the assembly centre affect the stability of the analysed process. For example, a wiring harness with 12 modules was selected. The production plan for wiring harnesses consisting of 12 modules is at the level of 370 pcs/shift. For the needs of the study it has been assumed that the production process is stable, if the absolute value of the difference between the quantity assumed in the production plan and the quantity produced does not exceed 20 pcs/shift, i.e. is within the range (360 -380 pcs of wiring harnesses per shift). Błąd! Nie można odnaleźć źródła odwołania. shows that the production volume predicted by the ANN model depends on the skills level of Worker 3, assuming that the level of skills of other workers is high. The data included in Błąd! Nie można odnaleźć źródła odwołania. are presented additionally in the context of the process stability in Fig. 5.


Anna Plichta

Tomasz Gaciarz

Szymon Szomiński

Identification of persons by virtue of hand geometry

Keywords: biometry, nearest neighbour method, image processing

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Recently, biometric techniques have been one of the most flourishing branches of the ICT. As the amount of the processed data increases, the tightening of the access control is more and more important. The above-mentioned control pertains both to the room access and to the logical control over the individuals using the particular data or programs. The traditional techniques, based on magnetic or chip cards or on password systems no longer meet the contemporary security requirements.

The biometric methods of identification provide the high security level and are very convenient. They can be applied separately or together with the traditional access control solutions in order to prevent the room and computer networks from the nefarious use by the unauthorized people.

Currently, the most popular biometric techniques are based on:

1. papillar ridges 2. hand geometry 3. speech dynamics 4. iris pattern Access systems based on hand geometry are often used to verify one's identity. Apart from being highly secure, they are quite cheap, easy to use. They are also thought not to breach the privacy of the users. First of all, the shape of the hand is not considered, at least by the majority of people, to be very personal or intimate feature, contrary to the fingerprints which in fact we remain on thousands of surfaces and things, every day. We are accustomed to giving handshakes and touching many things with our hand. Therefore, we are seldom afraid of the measuring appliance, whereas people who use iris-checking systems are often unwilling to look straight into camera lens. Secondly, features pertaining to the hand geometry do not provide the amount of information sufficient to identificate a person. Moreover, systems based on the hand geometry measurement avail of simple measurement methods and hence are inexpensive to make and easy to use. In such systems it takes much less time for the user to learn how to avail of the appliance in comparison to other biometric methods. All these features make the systems based on hand geometry measurement most widely approved biometric methods, as they seemingly do breach almost no cultural, social, psychological or religious rules. Despite the fact that few working people lack hands, the system can be make available for, at least some, handicapped people. If necessary, the system can be integrated with other biometric techniques, especially with papillar ridges measurement or palmprints checking.

The hand biometry can function improperly if the hand is seriously wounded, break or swollen and the upper extremity injuries are perhaps the most frequent effects of accidents while working. To partially solve that problem, both hands of each worker should be put into system.

Biometric systems based on hand geometry avail of the information which pertains to the physical dimensions of the hand such as length and width of the fingers (measured in various places) or of some geometric features of the metacarpus. It should be stressed, that other features such as fingerprints, palmprints pattern, infrared photography of the hand or the pattern of blood vessels of the hand are considered separate biometric methods. They are connected to the hand, but require different processing. The majority of the above-mentioned techniques can be used in one appliance, together with the system based on the hand geometry measurment.

The data pertaining to the shape of the hand are computed from the photography of the hand taken by virtue of some particular techniques of image processing. Then, the geometric features of the hand are determined. In order to compare the hand to the pattern in the system, various classification techniques are used. The process of comparison can be considered as determining the distance between the pattern and the hands being verified. If the hand of the user matches the pattern, the distance is small (the current image of the hand and the pattern are similar enough). Otherwise, the distance is large. [START_REF] Yoruk | Hand biometrics[END_REF][START_REF] Yoruk | Comparative analysis of global hand apperance-based person recognition[END_REF][START_REF] Yoruk | Shape-Based Hand Recognition[END_REF][START_REF] Jiang | Contactless hand recognition[END_REF] 2 Recognition process

Data acquisition methods

The first step to identify the user is to acquire the data pertaining to the features of his hand -by means of taking photograph. It can be taken in many ways.

For the sake of the identification method:

1. the angles between fingers should be proper 2. the photo should be taken perpendicularly to the camera lens; otherwise, the surface of the hand and fingers is different (we avail of the surfaces of fingers and hand -these are some features we want to acquire) 3. the distance between the lens should be constant; otherwise, the hand put too close to the lens would be larger and all the features would be no longer valid.

There are some professional appliances acquiring such features available to buy, for instance Handkey II or ID3D Handkey. They enable for acquiring geometric features of the hands, fingerprints or palmprints. In these appliances the span between fingers is always the same due to used pegs and the hand is isolated from the background, so the taken photograph is of very good quality.

In household, we can fix the camera and the contrastive mat on which we put hand. That method lacks pegs so the additional module correcting finger span is required.

One can also solve that problem via constructing the appliance consisting of pane of glass and the camera. The hand should be put to the glass and the lens should be installed in a fixed distance to the pane. It is typical of background in such solution to vary each time. The finger span can also vary. However, these problems can be sorted out. The most user friendly method of these (and the most esthetic) is rotating the hand when the camera is fixed. For the further proceeding the system chooses the photo image being the most perpendicular to the lens. Unfortunately, the distance between the hand and the lens is problematic in this method. As we avail of many features pertaining to determining distance (fingers, hand length, circumferences, peripheries etc.), the image scaling module is required.

Segmentation

In all methods which do not make use of pegs between the fingers and thus allow for variable finger span the hand segmentation module is mandatory. It is also required that for each hand the lines between the fingertops and points between them do not adjoin. There are some flaws resulting in the necessity of rejecting the particular hand images.

Image processing

The acquired photo image undergoes processing. It is assumed that the image had underwent scaling and segmentation. The processing comprises the following steps:

1. background detection, binarization, 2. rotation and shifting of the hand, 3. possibly, correcting the marks left by the ring, 4. background detection, binarization, 5. correcting the angles between the fingers,

cutting the wrist

The above-mentioned figures show the steps to achieve the images of quality sufficient to be the samples in the carried out tests [START_REF] Yoruk | Shape-Based Hand Recognition[END_REF] The samples are achieved as in the Fig. 2: The samples used in the tests were prepared and normalized by prof. dr Bulent Sankur [START_REF] Bulatov | Hand recognition Rusing geometric classifiers[END_REF] by virtue of the dedicated identification system availing of the hand geometry. The samples consisted of the photographic images taken from 755 individuals. From each individual 3 photographs were taken. The dimension of the photograph was 200 x 200 pixels.

The features of the hand geometry

The following set of features should be found:

1. 5x finger length 2. 5x base area of the finger 3. 4x the largest circle inscribed in the upper part of the fingers (bases of the thumb) 4. 4x the largest circle inscribed in the lower part of the fingers (bases of the thumb) 5. 1x the largest circle inscribed in thumb 6. 5x area of fingers 7. 5x circumference of the fingers 8. 4x distance between the lowest point of the thumb and the centers of the bases of other fingers. 9. 4x the sides of the rectangle around the middle of the palm (red lines in the figure)

The way of comparison of the features

The input data for the application determining palm features is the vector of 39 numbers. These values are not normalized. Their scope depends on the character of the particular feature. Therefore, the features cannot be compared to one another, as each of them has completely different scope of the values. To sort that problem out, the feature vector should undergo normalization [START_REF] Bulatov | Hand recognition Rusing geometric classifiers[END_REF].

Normalization

The normalization process is done separately for each feature. Firstly, the minimal and maximal values of each feature are found. Then, the value of each feature is transferred into the scope 0,0 -1,0. For instance, the value of the certain feature in a sample is 15. The minimal value is 10 and the maximal is 20.

In this case, the new value of the feature is 0,5.

After the normalization each feature is in the scope 0,0 -1,0, so the all features have the same weight.

The nearest neighbour method

The nearest neighbour method was chosen to comparing the samples. In consists in measuring the distance between the points represented as the feature vector in n-dimensional space and choosing the nearest points. In our case, the space has 39 dimensions.

Each individual inserted into the system is provided with some samples of his palm. That set of samples is so called class. As the points in such group lie very close to one another, we can assume that it is highly probable, that the currently examined point and the point nearest to it belong to the same class.

Below we provide the trivial example of searching for the nearest neighbour. In this case there are two classes of points 'A' and 'B'. Additionally, the space is two-dimensional.

Metric

The method of measurement of the distances is also crucial. One can avail of various metrices. We have chosen the Euclidean metric. It is very popular, efficient and easy to imagine. Euclidean metric can be represented with the following formula:

d 2 (x, y) = N ∑ i=1 (x i -y i ) 2 for x, y ∈ IR N (1)

Weights

In order to make the method more efficient we introduced the weight for each feature, too. As it is very difficult to choose the proper set of weights, it must be done automatically. The value dispersion of each feature was considered the main factor to be taken account of while determining the weights. The more various the values were, the larger the weight was. Standard deviation is a very good indicator of dispersion and thus we took it for weights.

The standard deviation is represented with the following formula:

s = 1 n -1 n ∑ i=1 (x i -x) 2 (2)
5 Processing of the sample and searching for features

Preparing the contour points vector

Output data -the vector embracing the points defining the contour of the palm (starting from the pixel lying one pixel-position higher than the bottom left point of the palm, clockwise). The last point of that vector is the bottom left point of the palm (Fig. 4 A and B). Creating the palm contour is a two-step process. First of all, all the white pixels are removed from the interior of the palm. Subsequently, the width of the remaining contour is one pixel and each pixel has only two neighbours. Then, the image is turned into the series of points. While searching the points the distances between these points and the referential point are also counted.

Finding fingertips and the spaces between the fingers

The distance between the points of contour and the referential point is presented in the Fig. 5. In that figure the maxima stand for the fingertips whereas the minima stand for the spaces between the fingers. According to the figure, minima and maxima can be flat. Each extremum is considered as an interval, because it cannot be unambiguously determined which pixel has the maximal value. In order to find these intervals one uses the find extremums function.

Finding the features pertaining to the fingers

If the key points are found, calculating the features is usually quite easy. In fact, it consists of simple mathematic operations for instance calculating the distance between the points. The function calc fingers feature calculates the features of the fingers.

However, the find farrest point function is worth mentioning because it gives back the nearest point in the given group to the point given in the second parameter. The middle of the base of the finger is chosen for that particular point.

Finding the points of the base of the palm (bottom left and bottom right point of the palm)

The input data is the vector containing the contour points. It is intended to fill the areas of the bottom left and the bottom right point in the structure of the palm. The bottom left point of the palm is the last point of the contour. The bottom right point can be calculated assuming that it is the point of the same coordinate OY as the bottom left point and the value on the OX coordinate is as high as possible.

Calculating the features of the palm

The total area is equal to the number of the white pixels in the normalized image (Fig. 4A). The total palm legth is equal to the distance between the OY coordinate of the bottom left point of the palm and the minimal OY coordinate among the centres of the nails.

Other palm features are calculatedby means of thecalc distance function.

Calculatinf the area of the fingers.

In order to calculate the area of the fingers we avail of the algorithms pertaining to filling the boundary-areas. The input data is black image with the white palm contour, as in the Fig. 4B.

The first step is to separate the fingers from the whole palm (by means of drawing lines linking the points between the fingers) (Fig. 4C).

Then, each finger is processed with the boundary-fill algorithm. All the pixels surrounded be the contour (by the closed line made of one-colour pixels) are given the same colour)

The pixels given the new colour are counted. The area of the particular finger is equal to the sum of coloured pixels in its boundary-area (Fig. 4D).

The fill-seed is the pixel form which we start filling the boundary-area. It must be placed within the area to fill. In our algorithm it is the point in the middle of the line linking the middle of the nail and the middle of the base of the finger (Fig. 4E).

Other pixels are found thanks to the fact, that each pixel can be reached only from other pixels lying in four directions (up, down, left, right).

The above-mentioned algorithm is one of the seed fill algorithms. The fill seed is placed inside the area to fill and then it is propagated (sowed) in four directions. If the seed meets the fertile ground it fills it with colour and tries to propagate in four directions. The entire area is filled with the particular colour this way. The seed finds the fertile ground only if the pixel to propagate has different colour than the contour colour.

While sowing the number of sown seeds for the particular areas (fingers) is counted. The achieved number is equal to the area of the particular fingers.

Finding circles inscribed into fingers.

Finding circles inscribed into fingers is similar to inscribing the circle into any boundary-area for instance square, trapezoid or pentagon. To calculate the maximal circle inscribed into the boundary-area we used the Euclidean distance transform algorithm which was described in details and optimized by Pedro F. Felzenszwalb and Daniel P. Huttenlocher [START_REF] Jiang | New directions in contact free hand recognition[END_REF]. The principle of operation of the algorithm:

As the input data one should input the binary table (binary image);

1. the zero value stands for the boundary-area in which we want to inscribe the circle.

the one value stands for other pixels

As the output data we obtain the table in which all zeros were replaced with the squares of the maximal distance between the radius of the circle inscribed into the boundary-area and having the centre in a given point. Then we find the maximal value in the obtained table. In our case, it is 9. According to the algorithm, the middle of the largest circle inscribed into the given boundary-area is in the point having the biggest value (in our case, 9) The radius length is equal to the square root of the given number (sqrt (9) =3). [START_REF] Jiang | New directions in contact free hand recognition[END_REF] 6 The carried out tests

The method of carrying out tests

Tests are carried out by means of the dedicated application written in c# language. It loads the test set consisting of the consecutive feature vectors and the names of related files. [START_REF] Felzenszwalb | Distance Transforms of Sampled Functions[END_REF] Because the NN method was chosen, the application takes down nearest neighbours of the sample counting from the one nearest in relation to the Euclidean metric up to the first. Then we seek for the first element of the same class as the examined sample (it means that both samples are the representation of the palm of the same user) When than element is found, we increase the value of the numerator related to the position in which it was adjusted. The first number stands for the number of the currently examined class. The consecutive numbers stand for the classes of the nearest neighbours. As we see, in the second line the nearest neighbour for that sample has the class number 22 and the correct class was found in the fourth position.

The results of the tests

The tests were carried out of three sets of samples. The samples were chosen randomly. Each individual has 3 palm samples so the class size is 3, too. It is a very small value and it should be increased to 5 or 10 to improve the results.

Conclusions

According to the tests, the described method is a safe and effective identification method.

The results of the test 3 are not good. It is because the number of palm samples per each individual was very small. Increasing that number is expected to improve the results. In the test 1 the total number of samples was one less order smaller, which improved the results greatly.

The notation of results may seem dubious. However, the notation 3 -77,08% means that there is 77% probability that there is a sample of the same class as the examined one among the three nearest neighbours. That information is of some importance in relation to the systems aiding the decision whether to grant user the access to the room, content or system or not.

Fig. 1 .

 1 Fig. 1. Examples of the photo images of the hands on glass pane.

Fig. 2 .

 2 Fig. 2. The samples.

Fig. 3 .

 3 Fig. 3. Example of searching for the nearest neighbour.

Fig. 4 .

 4 Fig. 4. A: Input data; B: Normalized palm image; C: Processed with the boundary-fill algorithm; D: Sum of coloured pixels; E: The middle of the line linking the middle of the nail.

Fig. 5 .

 5 Fig. 5. The distance between the points of contour and the referential point.

Table 1 .

 1 Angles between the fingers and the pivot lineT humb Indexf inger M iddlef inger Ringf inger Littlef inger

	150	120	100	80	60

Table 2 .

 2 The exemplary excerpt from the output of the application

	107: 107 22 202 332 227 199 171 41 185 171
	107: 22 22 183 107 332 125 107 197 283 282
	108: 108 70 65 243 65 243 44 557 536 362
	108: 08 65 65 464 65 371 70 44 243 371
	108: 649 509 243 405 620 537 443 141 260 509
	109: 109 109 71 71 5 71 74 117 149 149

Table 3 .

 3 Tests results.

	M odif ication T est1(289samples) T est2(199samples) T est3(2225samples)
	1	90,31%	86,93%	65,66%
	2	93,43%	90,95%	72,58%
	3	94,46%	92,96%	77,08%
	4	96,19%	92,96%	79,60%
	5	96,89%	94,47%	81,84%
	6	97,23%	94,97%	83,42%
	7	97,92%	96,48%	84,54%
	8	98,27%	96,98%	85,35%
	9	98,62%	96,98%	86,25%
	10	98,62%	96,98%	86,97%


Pawel Kasprowski
email: kasprowski@polsl.pl

The Impact of Temporal Proximity between Samples on Eye Movement Biometric Identification

Keywords: eye movement biometrics, behavioral biometrics, classification

Eye movements identification is an interesting alternative to other biometric identification methods. It compiles both physiological and behavioral aspects and therefore it is difficult to forge. However, the main obstacle to popularize this methodology is lack of general recommendations considering eye movement biometrics experiments. Another problem is lack of commonly available databases of eye movements. Different authors present their methodologies using their own datasets of samples recorded with different devices and scenarios. It excludes possibility to compare different approaches. It is obvious that the way the samples were recorded influences the overall results. This work tries to investigate how one of the elementstemporal proximity between subsequent measurementsinfluences the identification results. A dataset of 2556 eye movement recordings collected for over 5 months was used as the basis of analyses. The main purpose of the paper is to identify the impact of sampling and classification scenarios on the overall identification results and to recommend scenarios for creation of future datasets.

Introduction

The main problem of visual perception is that eyes register scene with uneven acuity.

Only the part of the scene that falls on the fovearegion in the middle of the retinais seen with correct sharpness. All other regions of retina are able to register only contours and fast movements. Therefore, eye movements are very important for correct recognition of objects in visual field. That is why eye movements are one of the fastest and the most accurate movements of a human being [START_REF] Duchowski | Eye Tracking Methodology. Theory and Practice[END_REF]. Eye movements may be divided into voluntary and involuntary. Voluntary eye movements are the effect of our willwe want to look at something. Involuntary eye movement is reflex action, automatic response to some stimulus, for instance sudden movement near the edge of vision. Both movements have physiological aspects but also depend on our previous knowledge or experiencehaving also important behavioral elements. That is why human identification using eye movements should be classified as behavioral biometrics [START_REF] Keesey | Effects of involuntary eye movements on visual acuity[END_REF] [START_REF] Leigh | The Neurology of Eye Movements[END_REF].

Human Identification Using Eye Movements

Eye movements are yet another possibility to perform human identification. The idea that eye movements may be used for human identification is about 10 years old [START_REF] Kasprowski | Eye Movement in Biometrics[END_REF] [START_REF] Maeder | A visual attention approach to personal identification[END_REF]. There have been several publications showing that the method is promising, however it is still on the very early research stage because collecting eye movements' data is difficult and eye movement capturing devices (eye trackers) are still relatively expensive. That is why in most cases the datasets collected by researchers are not publicly available. Most of the eye movements recording experiments have used a 'jumping point' pattern originally introduced in [START_REF] Kasprowski | Eye Movement in Biometrics[END_REF]. In such kind of experiment the stimulus is forcing eye movements -the examined individuals should follow the point on the screen with their eyes. Such a recording is easy to analyze, because it requires that fixations and saccades happen in specific moments. However, there are several interesting experiments with different scenarios, including faces observation [START_REF] Rigas | Biometric identification based on the eye movements and graph matching techniques[END_REF] or text reading [START_REF] Holland | Biometric Identification via Eye Movement Scanpaths in Reading[END_REF]. There is also an attempt to perform identification without any information about a stimulus [START_REF] Kinnunen | Towards task-independent person authentication using eye movement signals[END_REF].

The problem common for all biometric methods using behavioral traits is so called learning effect [START_REF] Kasprowski | Enhancing eye movement based biometric identification method by using voting classifiers[END_REF]. When using the same stimulus for several times the person familiarizes with it and eye movements tend to become automatic. It is for instance clearly visible for textseye movements of a person reading the text that she already knows are very different from eye movements of the person reading a text new for her [START_REF] Rayner | Eye movements in reading and information processing: 20 years of research[END_REF]. Such kind of reading is therefore often called skimming.

The learning effect is especially visible for very short intervals. A person that completes the same task for several times in very short period tends to "learn" the task and the movements (eye movements in our example) are becoming similar to each other. The effect of similarity between subsequent experiments is stronger for shorter periods and becomes invisible for very long periods (because human body "forgets" the task).

In our paper we tried to investigate how the interval between subsequent experiments performed by the same person influences the identification rates. To our best knowledge it is the first paper that analyses that aspect of eye movements' biometrics, however the problem has been already introduced in [START_REF] Kasprowski | First Eye Movement Verification and Identification Competition at BTAS 2012[END_REF].

Eye Movement Verification and Identification Competition

There are several methods to analyze eye movements, but until quite recently it has been difficult to compare them due to lack of publicly available datasets (like for fingerprints [START_REF] Cappelli | Fingerprint Verification Competition at IJCB[END_REF] or faces [START_REF] Ortega-Garcia | MCYT baseline corpus: a bimodal biometric database[END_REF]).

The First Eye Movement Verification and Identification Competition (EMVIC) organized in 2012 as an official BTAS conference competition was the first opportunity to compare different approaches [START_REF] Kasprowski | First Eye Movement Verification and Identification Competition at BTAS 2012[END_REF]. The aim of the competition was to correctly identify individuals on the basis of their eye movements. The organizers prepared four different datasets of eye movements collected with different stimuli and different eye trackers (denominated A, B, C and D).

The Impact of Temporal Proximity between ... All datasets were divided into two parts:

 Training set, containing labeled samples;  Testing set, containing samples with hidden labels.

The aim of the competitors was to build their classification models using labeled samples and then try to use those models to classify unlabeled samples from the testing set. There were about 50 competitors with over 500 separate submissions.

The main problem of EMVIC was that the results were inconsistent for different datasets. For datasets A and B the identification accuracies were better than 90% and for datasets C and D the best accuracies approximated 60%. The question arose as to the reasons of such differences.

One of the obvious reasons was higher number of samples per person for datasets A and B. Another reason could be binocular data in A and B (which was already studied in [START_REF] Rigas | Human eye movements as a trait for biometrical identification[END_REF] and [START_REF] Nguyen | Mel-frequency Cepstral Coefficients for Eye Movement Identification[END_REF]). In [START_REF] Kasprowski | First Eye Movement Verification and Identification Competition at BTAS 2012[END_REF] authors suggested also that low quality of data in A and B could be the reason of better performance. In this paper we investigate another of the possible reasons: impact of proximity between consecutive samples.

As eye movements measurement has very important behavioral aspect, we may expect that measurements taken in short periods of time may share some common information that is unwanted for identification purposes. For instance it has been proven that the person's attitude may highly influence eye movements. If a person is angry or amused, eye movement patterns are different than for the same person in neutral state [START_REF] Deane | Theoretical examination of the effects of anxiety and electronic performance monitoring on behavioural biometric security systems[END_REF]. Similarly, it is possible to find out the level of tiredness by examining eyes reaction to salience regions of images [START_REF] Schleicher | Blinks and saccades as indicators of fatigue in sleepiness warnings: looking tired?[END_REF][12] [START_REF] Sodhi | Glance analysis of driver eye movements to evaluate distraction[END_REF].

Other obvious time-dependent factors which are not directly connected with human properties but may influence the measurement are e. g. lightning conditions or existence of devices which may interfere with eye tracker like cellular phones, computers etc.

Dataset

To check the impact of short term learning effect we decided to perform experiments that would check if temporal differences in the dataset might change the overall accuracy results. We used a dataset of 2556 eye samples collected for over 5 months. There were 61 different subjects under the test with uneven distribution of number of samples (from 4 to 129 samples per subject). The dataset was originally a part of dataset B from EMVIC. Samples were taken with 250Hz frequency using Ober2 eye tracker. It was a jumping point on 2x2 matrix used as stimulus. One experiment lasted for 8128 ms and the stimulus was exactly the same for every experiment (Fig. 1). Every sample consisted of 2048 measurements of horizontal position of the left eye and 2048 measurements of vertical position of the left eye giving 4096 attributes. Additionally every sample had two properties: timestamp of measurement in seconds (t) and subject id (id). 2

Calculation of Time Interval Influence

To check the theory that time interval between samples influences the accuracy results several datasets were created with the same number of samples and the same distribution of samples per subjects. The only element that differed datasets was the minimal time interval between samples belonging to the same person.

Data Preparation

We started with creating a dataset for which a minimal time interval (MTI) between subsequent measurements of the same person was one week (604,800 seconds The new dataset W consisted of 222 samples belonging to 37 subjects. The average number of samples per person was 6 with minimal value equal to 4 and maximal value equal to 11. The next step was creation of other datasets. The property that differed datasets was a minimal time interval (MTI) between subsequent measurements of the same person. Every dataset consisted of the same number of samples for each person and was created using a subset of original samples from dataset D. The algorithm to build datasets was almost identical to the presented above. An additional parameter was the way the samples were initially sorted. There were two possibilities: start from the beginning of the dataset (with samples recorded at the beginning of the experiment) or start from the end of the dataset (with samples recorded as the last samples during the whole experiment). Because we wanted to see if it influences results, we decided to use both methods and store samples in datasets denominated as F for the oldest samples and R for the newest.

The above procedure created two datasets ('F' and 'R') for every interval. There were seven different values of MTI used as presented in Table 1. The columns 'real minimal/maximal intervals' show actual intervals for subsequent trials found in datasets. The main idea of the paper was that identification results were dependent on minimal time interval between samples. It was assumed that samples taken in shorter intervals have some additional (usually unwanted) time related information that could improve classification results. The datasets were examined using four different classic classification algorithms, namely: ─ J48 (Java version of C45 algorithm [START_REF] Quinlan | C4.5: Programs for Machine Learning[END_REF]), ─ Random Forest [START_REF] Breiman | Random Forests. Machine Learning[END_REF], ─ Naïve Bayes ─ SVM (using Sequential Minimal Optimization algorithm) [START_REF] Vapnik | Statistical learning theory[END_REF].

Every dataset was validated using standard 10-fold cross-validation method. The result for every dataset-algorithm pair was then stored as accuracy value. Accuracy is the number of correctly classified samples to the overall number of samples. Because it was 37 different classes, probability of random guess was less than 3% and therefore accuracy seemed to be a good and sufficient measure.

It is very important to emphasize that all algorithms were used with standard parameters [START_REF] Hall | The WEKA data mining software: an update[END_REF] without any optimizations towards results improvements. As the main purpose of the paper was to compare different datasets in the same environment (and not to obtain the best result), additional parameters tuning could introduce some biases. Nevertheless, the results are quite good as for identification (one-to-many) task.

Results

The results of the experiment are presented in Table 2. Datasets with F suffix were created by taking samples starting from the earliest while datasets with R suffix were created by taking samples from the last experiments (as it was described in the previous section). Every dataset had the same number of samples (222) and the same distribution of samples among 37 different subjects.

What can be seen clearly from the results is strong negative correlation between the interval and accuracy (-0.61). Because so called "forgetting curve" [START_REF] Ebbinghaus | Memory: A contribution to experimental psychology[END_REF] is considered to be non-linear we also calculated correlation of accuracy with logarithm of the interval and obtained the correlation equal to -0.94. Fig 2 shows logarithmic regression of the average results with coefficient of determination (R 2 ) equal to 0.8637. For linear regression coefficient of determination was 0.8088 and for exponential regression was 0.8433 so logarithmic trend line was chosen as the best fitting option.

Fig. 2. Averaged accuracy results with logarithmic trend line

The accuracies of datasets classification were averaged for all datasets with the same interval and all classification methods. When calculating mutual significance of differences between these results it occurred that the only significant difference may be found between 0 and 1 minute interval (p=0.02). It means that memory effect is clearly visible only for very short intervals.

Another comparison was performed between samples of type 'F' (i.e. first samples of the specific person) and samples of type 'R' (i.e. last samples of the person). The hypothesis was that samples taken later -when a person is already familiarized with stimulus -will be more stable and therefore easier to classify. Indeed, average accuracy for 'R' datasets was better for every classification method. However, the differences were not significant, with the highest significance for SVM method (p=0.068).

To see if the results are stable for different signal conversions we repeated the same classification experiments on datasets converted using different algorithms previously used in eye movement biometric identification [START_REF] Bednarik | Eye-movements as a biometric[END_REF] [START_REF] Kasprowski | Enhancing eye movement based biometric identification method by using voting classifiers[END_REF]. The results were similar, always showing negative correlation, however for some conversions the correlation was not strong. The experiment presented in the previous section showed clearly that samples taken in short intervals should not be mixed in training and testing sets for classification.

[4][11][14][15][16][18] [21][25][20]
On the other hand, closer look into the dataset that was used for experiments revealed that in most cases samples of the same person were recorded in series. It is natural and it is probably a common strategy for every biometric experiment, because with this scenario only one equipment setup is required to obtain several samples. However, as it was proven in the previous section, samples taken in the same session are not independent. Therefore, we decided to divide the original dataset into sessions and check how it influences classification results.

The session was defined as a set of samples taken from the same person with minimal interval between two samples less than 10 minutes. Preprocessing algorithm found 685 sessions in the dataset. The number of sessions per subject differed from 1 to 26 sessions with average number of sessions equal to 11. Every session consisted of one to eight samples (see Fig. 3).

Fig. 3. Histogram of number of samples per session

To check identification results using 10-fold cross-validation and to obtain reliable results it was necessary to remove samples of subjects for which number of sessions was too low. Therefore samples of all subjects with less than 10 sessions were removed from dataset. The reduced dataset consisted of 2195 samples from 29 subjects divided into 567 sessions.

Because samples from the same session were considered to be dependent there were three classification experiments proposed: ─ Experiment using only first samples from each session (referred as first sample in Table 4). ─ Experiment using only last samples from each session (last sample). ─ Experiment using all samples from dataset but with folding algorithm that doesn't divide samples from the same session to different folds (all samples).

number of sessions number of samples per session

Contrary to cross validations used in experiment described in Section 2, where folds were created by stratifying basing on number of samples per subject, this time stratification was done basing on sessions. It means that all samples from the same session had to be in the same fold. Table 4 shows that there are no significant differences between datasets build from first and last samples from the session. However, when all samples from the session were taken, it significantly improved results. It must be remembered that the latter classification used much more samples both for training and testing (2195 versus 567). It shows that collecting samples in series is not generally a bad idea, but care must be taken how samples from the same session are used.

Conclusions

The results of analyzes presented in the paper clearly show that the data collecting scenario may significantly influence the overall results and classification possibilities for a dataset. Especially time related factors were carefully studied and impact of so called memory effect was analyzed. All calculations used only eye movements datasets but it may be assumed that the conclusions could be extended to other behavioral biometric experiments.

Basing on our findings we advise that every behavioral biometric sample should be stored together with information about the exact timestamp when it was collected.

It is possible to collect more than one sample during one session with subject but these samples should never be mixed in training and testing set when evaluating performance. Additionally, we showed that using all samples collected during one session in the training set improves the overall performance of the system. Even if samples from the same session were considered dependent, multiplying the number of samples would give effect similar to bootstrap samples used in bagging algorithm [START_REF] Breiman | Bagging predictors[END_REF].

Fig. 1 .

 1 Fig. 1. Graphical representation of dataset[START_REF] Kasprowski | First Eye Movement Verification and Identification Competition at BTAS 2012[END_REF]

Table 1 .

 1 Minimal time intervals used for experiments

	index	MTI	real minimal interval	real maximal interval
	0	0 (no minimal interval)	11s	13d, 21h, 31min, 26s
	1	1 minute	61s	46d, 3min, 5s
	2	10 minutes	13min, 25s	46d, 3min, 5s
	3	1 hour	1h, 1s	57d, 19h, 44min, 45s
	4	6 hours	6h, 10min, 11s	66d, 23h, 13min, 9s
	5	1 day	24h, 33min, 42 s	66d, 23h, 13min, 9s
	6	1 week	7d, 6s	66d, 23h, 13min, 9s

Table 2 .

 2 Accuracy of each classification method for every dataset.

	dataset	nb	j48	rf	smo
	0F	22,97	20,27	28,38	52,25
	0R	28,37	21,17	37,39	54,04
	1F	18,46	13,51	18,02	31,53
	1R	26,12	17,56	24,32	44,59
	2F	17,11	11,26	23,87	33,78
	2R	25,67	20,72	22,97	48,65
	3F	17,56	12,16	22,97	34,23
	3R	22,97	22,97	21,17	45,95
	4F	18,46	13,96	18,47	32,43
	4R	16,66	12,16	18,47	33,33
	5F	18,01	13,51	20,27	31,08
	5R	18,01	16,21	14,41	36,94
	6F	12,16	12,61	15,31	29,73
	6R	12,16	13,06	16,67	29,73

Table 3 .

 3 Correlation of classification accuracy and minimal time interval between samples for different signal conversions (time means correlation to time in seconds, log is correlation to logarithm of time in seconds).

	applied conversion	time	log(time)
	fourier spectrum	-0.42	-0.91
	cepstrum	-0.7	-0.81
	first derivate (velocity)	-0.22	-0.74
	second derivate (acceleration)	-0.48	-0.92
	direction (in radians)	-0.8	-0.84
	wavelet transform (DWT)	-0.46	-0.86
	high pass filter	-0.56	-0.93
	low pass filter	-0.63	-0.94

Table 4 .

 4 Accuracies of classification for three datasets

	method	first sample last sample	all samples
	J48	23,27	21,02	29,33
	NB	28,39	25,80	27,94
	RF	34,41	29,52	40,58
	SMO	50,09	52,12	64,83
	average	34,04	32,12	40,67


Marek Penhaker
email: marek.penhaker@vsb.cz

Vladimir Kasik
email: vladimir.kasik@vsb.cz

Vaclav Snasel
email: vaclav.snasel@vsb.cz

Distributed Signal Processing and Analysis

Keywords: Ultra Fast, Parallel, Signal Analysis, Web Services FPGA, Biomedical 1 Signal Handling

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

system for storage and exchange biomedical data is based on ASP.NET 2.0 platform .NET Framework and Microsoft SQL Server.

For storage of biomedical data is used a data storage server. Every file uploaded to the server has its own unique name that is used as ID in the database. The basic functions of Web applications provide: display a list of all records stored in a database, add a new record and manage users. The Web application has its own biomedical data browser that allows graphical display of such an EEG records from XML file.

A significant part of the application is the application stored biomedical data, providing by display the 20-channels EEG records. It is also possible to modify the scale of the axis and to move the view in a direction of x axis.

Additionally the system were enlarged to evaluate the biomedical data with the detection of real time signals with ultra fast response and accuracy. The precise possibility of the application can be done by FPGA multiparallel system. This hardware is handling data with a data server, but can also be connected to the analog inputs for on-line processing of biomedical signals. [START_REF] Babusiak | The eye-blinking artifacts detection and elimination in the EEG record[END_REF] Nowadays, complex applications are being developed, the scope of which exceeds the ability of one person. The development application is usually involved more programmers. In the work were again used part of their applications or other companies. The programmer thus can no longer rely only on himself, as it was in the days of the first operational system. The first tool for modular application development platform for Microsoft Windows is dynamic libraries. [START_REF] Schalk | Pactical Guide to Brain / Computer Interfacing with BCI[END_REF] - [START_REF] Kukucka | The Frequency and the Shape of Driving Signal Influence in Measurement of the Active Points[END_REF] At the beginning of the 90th years were mostly created separate applications with very little ability to communicate. This deficiency was removed in the mid-90th years, when the company introduced technology Microsoft COM (Component Object Model). The great advantage of component technology is its language neutrality in binary form. For each component was defined interface that facilitates communication between the client and the relevant component. Over time, it became apparent that even the technology has its limitations. Nowadays, the modular architecture of increasingly more used. Components used are usually small and simple. The main disadvantage of COM components is that they hide their internal implementation. The only component that describes the appropriate interface. This prevents inheritance at the source. [START_REF] Kohlish | Physiological changes during computer task: responses to mental load or to motor demands[END_REF]

Problem Definition

Biomedical data representation on different devices was mostly done on local terminals with displaying of measured data using web services. Processing of these data is done either by post-processing after measurement and in minor cases on-line. On-line processing puts high demands on hardware and so this process is destined only for specialized work. Enabling data processing on any number of remote terminals for users working with internet ser currently represents significant progress in training of physician, cooperation and collaborations in real time with ultra fast and inexpensive analysis.

Distance processing of biomedical data was done by using web service terminals is solved several ways. The methods of implementation is described below having a native access in the installation of a powerful multiparalell processor with direct connection to server or database server, providing a web application platform and user cost-effective way of data evaluation.

The proposal of multiparalell ultrafast system for biomedical processing is the tasks that can be used in very large scale of integration composition of FPGA -Field Programmable Gate Array. With the FPGA technology are these features possible not only for off-line processing of saved data, but also the possibility of real time evaluation and data handling to distance physicians by web services. [START_REF] Valentova | Initial Analysis of the EEG, Signal Processing Methods for Studying Correlations between, Muscle and Brain Activity[END_REF] In cases where the digitized signal is processed in several steps, it is preferred to use a computer-operated pipelining. Typically, such calculations are solved by computer, however it does not perform these operations simultaneously but in sequential manner. Such a conventional approach requires higher CPU computing power. In addition the time required to calculate is difficult to predict and the whole situation has an impact on the safety of the calculation. The solution, which better reflects the computational structure for the signal processing is distributed architecture in both the system and the units inside the chip. Therefore, there is the FPGA technology utilized, in which the calculating phases are designed in separate processing modules. [START_REF] Valentova | Initial Analysis of the EEG, Signal Processing Methods for Studying Correlations between, Muscle and Brain Activity[END_REF] 3

New Methods Description

Biomedical data processing requires specifically detection variety in stored measurements. The variety is evaluated qualitative and qualitative parameters and the time parameters. The proper evaluation in variety is usually not often easy, due the biomedical signals volatile on quasi periodic or stochastic characteristics and could also be changed by artificial or physiological artifacts. The way of ultra speed real time data processing should be connected with the computing demands of data handling. Fig. 1.

The multivariance mathematical transformations currently provide a very efficient and effective algorithm for detection and manipulation of biosignals, whatever is used for implementing many of the calculation estimate, especially at the local workstations. Using the multivariance mathematical transforms is easy thanks to the number of steps in one subscription by 2 x n, all the same for use on large real-time huge biomedical data it requires fully ultra fast parallel computing with high discharge. Implementation of this option can be currently done with just two possible architectural designs. First one is a huge supercomputer with many processors linked with tiny data pipelines and the other is a ultra fast parallel computing with FPGA technology, which is nevertheless similar to these with supercomputer ability. In this point of view, there are three alternatives to fast signal processing as shown in Fig. 2. That drown describe the system of transmits saved biomedical data to the coprocessors unit for processing on request. Processed data are giving back after processing of data server, from where may be displayed on the web system. [11] There are the three possibilities of co-processing units: a) FPGA -DSPU (Digital Signal Processing Unit) like Embedded Expansion Card b) FPGA -DSPU like standalone unit with an Ethernet connection and c) supercomputer like an alternative. Possibility and b of all options are established on FPGA programmable logic, which uses the same hardware and software accession in data housing. Hardware design is based realized by parallel implementation of some sub-algorithms in cooperation with the DSP units. Software design allows to process complex calculations and algorithms supporting MicroBlaze soft processor supplement Fig. 3.

In generally is the co-processing unit including FPGA is designed in such ways. The one solution is a PCI flaring card for managing data from server. Mentioned solution is effective and allows to make maximum signal transmission throughput server and the signal processing unit. The main drawback is necessary off a fixed location of the unit and modification of the data server configuration at the same time, which may not be advisable. Differ solution from co-processing unit is a standalone module for to be connected through a communication network. Anywhere the standard Ethernet network with TCP / IP protocol is applied. The advisable of the solution is not just freedom of data server positioning, and also a possibility to use this unit with more servers and / or clients on the network. [START_REF] Krejcar | Complex Biomedical System with Biotelemetric Monitoring of Life Functions[END_REF] Network feature, withal, poses claims on the network communication module, containing requirements for proper safety and maintenance of data transmission in the network. The unit is equipped with a 2x16 channels for digitizing analog biomedical signals and their processing in real time. Data throughput is limited primarily by sampling rate of ADC, which is outside the chip, so it can be further customized according to application requirements. The Ethernet layer circuitry enables the web interface functions extension of the unit. This functionality is used for options parameterization of control units and also for data communication with co-processor unit. An FPGA -DSPU Unit is a powerful computational tool for rapid evaluation of biomedical signals in real time. Individual calculation algorithms as frequency filters, peak detection, envelope detection and further, are implemented in hardware. Target architecture for implementation is Spartan-6 or Virtex-6 FPGA with soft-core MCU option. For fast data storage are available either fast FPGA Block RAM inside FPGA or off-the-chip fast static memory with parallel access. The advantage of using FPGA is also expanded diagnostic possibilities of digital circuits either in FPGA or in digital circuits around the chip. The logic inside the FPGA design also includes a VGA interface for real-time monitoring of calculated data or diagnostic variables of the system. [START_REF] Accortt | Frontal EEG asymmetry and premenstrua dysphoric symptomatology[END_REF] In case of dataflow the FPGA -DSPU co-processing unit come second link between web server and SQL server. The operation of FPGA is driven by a web service that transmits commands to the unit. Partial digital biosignal processing algorithms on signal from SQL Server is realized in on real time. The encountered signals is processed by web services and offered to the authorized physician's web service. [START_REF] Krejcar | Complex Biomedical System with Mobile Clients[END_REF] Fig. 4. Evaluation of medical image in real-time processing of input image gives a result in terms of reference points, whose position is further evaluated.

The advance of the realized tests is the system for the EEG evaluation on patients and their correspondence to epileptic attack. To determine the EEG signal was used multivariance mathematic transform. Also to determine individual patient images in clinical practice were used as a method for digital image processing Fig. 4 line of epileptic attacks was evaluated by detection algorithm that was tested to locate any balanced points in the image. Situation of these points and their movements is based on subsequent in classification the patients signal manifestation Fig. 5. Fig. 5. Power spectral density by CSA for all channels in the selected time period from ultrafast capable system with multiparalel processing algorithms that detected the preliminary signs of epileptic attack in frequencies 7, 11 and 40Hz to 60 Hz.

Application of epileptic attack recognized from EEG records were used multivariance mathematical transform methods implemented on an FPGA gantry. In the same time the fundamental parameters of signal were derived from EEG signal and mainly variety in magnitude and frequency characteristics is evaluated. Likewise, there was examine retardation in the similarity signal propagation within all channels between the first signs of attack in EEG and the corresponding video signals.

Conclusion

The introduced problem solution manipulation, detection of signals in real time with multi processing ultra fast processing by FPGA usage carries new possibilities for common workplace, but also for customer of web applications for access and visualization also with additional possibility of processing the far-out signals in real time. The introduced system was tested on a real polysomnographic video records and well verified by application of this system. There were instead traditional methods of epileptic seizure artifact detection determined. The traditional ways of biosignal processing are very common, but there is a lot of information outside the traditional way of perception. There are hidden information in the measured biosignal especially in high frequencies which are not yet consternate on. We can access the information in the same way of as previous methods for low frequency analyzing, but at can take a lot of human-machine time with weak results. The proposed way of processing introduced in this article is based on multi parallel signal processing with ultra extremely high potential of recognition the even small changes of signal behavior.

Fig. 1 .

 1 Fig. 1. Overall system structure for distributed signal processing.

Fig. 2 .

 2 Fig. 2. FPGA -DSPU Detailed Structure.

Fig. 3 .

 3 Fig. 3. Fundamental structure of DSP units connection with FPGA Device.

Acknowledgment. The work and the contributions were supported by the project SP2013/35 "Biomedical engineering systems IX" and TACR TA01010632 "SCADA system for control and measurement of process in real time". The paper has been elaborated in the framework of the IT4Innovations Centre of Excellence project, reg. no. CZ.1.05/1.1.00/02.0070 supported by Operational Programme 'Research and Development for Innovations' funded by Structural Funds of the European Union and state budget of the Czech Republic.