João Dias Ferreira

Luis Ribeiro

Mauro Onori
email: onori@kth.se

José Barata

Challenges and Properties for Bio-inspiration in Manufacturing

Keywords: Bio-inspiration, Self-Organization, Manufacturing Systems

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

Nowadays, manufacturing companies are facing a very challenging reality. Not only society is demanding more customized high quality products than ever, but also markets are becoming increasingly characterized by an erratic behavior. Hence, sustainability and responsiveness at the shop-floor level are fundamental factors to survive and thrive in such harsh and challenging conditions. The short life-cycle of products and the need to promptly respond to product demand fluctuations and shortterm business opportunities is compelling companies to shift from traditional shopfloor approaches, towards more modular and reusable systems.

Mainly in the last two decades, academia has been developing a number of innovative production philosophies that provide the theoretical background necessary for the implementation of such distributed and decoupled systems. As result of this effort a number of manufacturing paradigms focused on agility emerged, supported by the advances in ICT (Information and Communication Technology) and based on AI (Artificial Intelligence) and complexity science concepts. Some examples are Bionic Manufacturing Systems (BMS) [START_REF] Ueda | A concept for bionic manufacturing systems based on dna-type information[END_REF], Holonic Manufacturing Systems (HMS) [START_REF] Gou | Holonic manufacturing scheduling: architecture, cooperation mechanism, and implementation[END_REF], [START_REF] Bussmann | Rationales for holonic manufacturing control[END_REF], Reconfigurable Manufacturing System (RMS) [START_REF] Koren | Reconfigurable manufacturing systems[END_REF], [START_REF] Mehrabi | Reconfigurable manufacturing systems and their enabling technologies[END_REF] and Evolvable Production Systems (EPS) [START_REF] Onori | Evolvable assembly systems -a new paradigm?[END_REF], [START_REF] Onori | An architecture development approach for evolvable assembly systems[END_REF], etc. Despite some conceptual differences, most modern manufacturing paradigms theoretically rely on bio-inspired principles to attain the necessary adaptation and responsiveness to unpredictable scenarios. Nevertheless their application, so far, has been limited to specific manufacturing problems, such as: scheduling, planning, layout formation, modeling, fault detection, etc [START_REF] Ferreira | Bio-inspired Self-Organisation in Evolvable Production Systems[END_REF]. Consequently the resulting systems are typically distributed, but not necessarily highly scalable, reconfigurable and able to tackle problems in a distributed manner. The network-like architecture and modular functional complexity of modern shop-floors supported by the innovative production paradigms closely follow the structural patterns presented by complex natural systems. In this context, the following question is raised: what are the main challenges and required properties necessary to foster the development of real production systems that closely mimic biological systems behavior and consequently present overall characteristics of complex natural systems?

To some extent, this paper attempts to answer this question by presenting a critical analysis on how bio-inspired concepts are currently being explored and used under the scope of modern production systems. For this purpose, a brief review of some applications of bio-inspired approaches in manufacturing is presented, in order to highlight the advantages of bio-inspiration, leading to the formulation of a set of challenges that need to be tackled and to the identification of a number of required properties necessary to enact a production system as bio-inspired.

Relationship to Collective Awareness Systems

The origins of Life can be traced back to more than 3500 Million years ago. Since then, primitive organisms were subjected to an evolutionary process in which the occurrence of changes in the environment conditions fostered the reproduction of individuals that better matched their characteristics against the environment functional requirements. Hence, generation after generation individuals developed more adequate characteristics to handle the challenges posed by their natural habitat.

Today's natural diversity is the reflection of such evolutionary process. Biological systems are typically distributed and usually composed by numerous individuals that exhibit simple behaviors. However, through the exchange of meaningful local information the individual entities are able to foster the emergence of a collective action that drives the system towards common goals. The collective behavior itself is greater and considerably more complex than the sum of the behaviors of its parts [START_REF] Holland | Emergence: From chaos to order[END_REF].

Swarms of insects, such as ants, bees, termites, fireflies, among others, are recurrent examples of biological collective systems present in the literature.

Nonetheless, collective systems can be encountered in all sizes, forms and scales. Critical to the emergence of the collective behavior is however the self-awareness of the individual entities. Each entity, despite its simplicity and limited cognitive ability, needs to be able to assess the local surroundings and its internal state in order to make adequate decisions and consequently establish meaningful interactions in response to internal or environmental changes. In this context, collective awareness should not be perceived as an intrinsic characteristic of collective systems per se, but instead as a property that emerges in result of the awareness and social abilities of the individual entities. Even though optimal decisions can only be enacted with a global perspective, the local nature of the interactions and the efficiency of the communication mechanisms endow biological collective systems with the necessary edge to quickly respond as a whole to environmental perturbations. Collective biological systems can therefore be perceived as a unique entity that is aware of the environment and consequently act according to it.

Modern manufacturing systems, as they are idealized by modern manufacturing paradigms also consist on many 'intelligent' autonomous modular entities with social capabilities, that dynamically establish interactions with each other, in order to achieve common objectives. However, the development and implementation of innovative industrial applications which rely on bio-inspired principles tend to stumble in some important gaps between the conceptual academic models and their technological realization, as pointed out in [START_REF] Ribeiro | Self-organizing multiagent mechatronic systems in perspective[END_REF]. Hence, one as to master the intricacies of biological complex systems in order to effectively translate their regulatory principles into modern modular manufacturing structures. Only in this way it is possible to truly profit from their dynamics, and achieve a collective level of awareness that allows the system to successfully face environmental changes and uncertainties.

On the Application of Bio-inspired Concepts in Manufacturing

Considering the before mentioned paradigms, the increasing structural modularity and autonomy of manufacturing systems means that production floors are becoming evermore complex, rendering traditional control approaches insufficient. On the other hand, ICT-based decentralized control solutions that support advanced AI, are naturally designed to efficiently support the current requirements imposed by modern manufacturing systems. However these solutions are naturally of complex nature. Bio-inspired mechanisms are currently recognized as a powerful and viable solution to tackle complex engineering problems, particularly in situations where there is a lack of available information or in cases where the search space is so large that bruteforce optimization algorithms are out of the question [START_REF] Floreano | Bio-inspired artificial intelligence: theories, methods, and technologies[END_REF]. In this context, it is the purpose of this section to discuss and analyze how bio-inspired techniques are currently being applied and explored within the manufacturing context.

Evolutionary concepts have been widely used in schedule optimization, such as flexible manufacturing scheduling problem [START_REF] Kumar | Scheduling of flexible manufacturing systems using genetic algorithm: A heuristic approach[END_REF] and flow shop scheduling [START_REF] Wang | A modified evolutionary programming for flow shop scheduling[END_REF]. Furthermore many evolutionary applications have also been reported in planning [START_REF] Zhang | Using genetic algorithms in process planning for job shop machining[END_REF], [START_REF] Routroy | Differential evolution algorithm for supply chain inventory planning[END_REF], [START_REF] Brezocnik | Prediction of steel machinability by genetic programming[END_REF], manufacturing cell formation problems [START_REF] Stawowy | Evolutionary strategy for manufacturing cell design[END_REF], [START_REF] Wu | A simulated annealing algorithm for manufacturing cell formation problems[END_REF] and modeling manufacturing processes [START_REF] Chan | A genetic programming based fuzzy regression approach to modelling manufacturing processes[END_REF], [START_REF] Chan | Modeling manufacturing processes using a genetic programming-based fuzzy regression with detection of outliers[END_REF], among others. Biological collective systems behaviors, commonly colonies of insects, have also been applied to a number of manufacturing problems. An application based on stigmergy to regulate the self-organizing behavior is presented in [START_REF] Leitão | Adacor: A holonic architecture for agile and adaptive manufacturing control[END_REF]. Ants' behaviors are also an option to tackle layout problems [START_REF] Solimanpur | Ant colony optimization algorithm to the intercell layout problem in cellular manufacturing[END_REF] and vehicle routing problems [START_REF] Yu | An improved ant colony optimization for vehicle routing problem[END_REF]. Bee's food foraging behavior has also been the source of inspiration to solve job scheduling problems [START_REF] Pham | Using the bees algorithm to schedule jobs for a machine[END_REF], and the printed circuit board (PCB) assembly planning problem [START_REF] Pham | Application of the bees algorithm to pcb assembly optimisation[END_REF]. In addition, bacterial foraging behavior provides adequate principles to tackle assembly line balancing [START_REF] Atasagun | Assembly line balancing using bacterial foraging optimization algorithm[END_REF]. The flashing lights of fireflies were also explored in the development of scheduling mechanisms [START_REF] Sanaei | Using firefly algorithm to solve resource constrained project scheduling problem[END_REF], cell formation problems [START_REF] Sayadi | Firefly-inspired algorithm for discrete optimization problems: An application to manufacturing cell formation[END_REF] and solving machining models [START_REF] Aungkulanon | Simulated manufacturing process improvement via particle swarm optimisation and firefly algorithms[END_REF]. Mechanisms based on the neural behavior have been widely applied in recognition problems [START_REF] Rangwala | Sensor integration using neural networks for intelligent tool condition monitoring[END_REF][START_REF] Sunil | Automatic recognition of machining features using artificial neural networks[END_REF] and fault detection [START_REF] Eski | Fault detection on robot manipulators using artificial neural networks[END_REF], among others.

Finally, the dynamics of the biological immune systems have also been a source of inspiration in manufacturing applications. Similarly to other biological sources, immune behavior has been explored to handle scheduling [START_REF] Ong | Applying the clonal selection principle to find flexible jobshop schedules[END_REF], layout problems [START_REF] Ulutas | Parameter setting for clonal selection algorithm in facility layout problems[END_REF] and fault diagnosis [START_REF] Hao | Artificial immune network classification algorithm for fault diagnosis of power transformer[END_REF] and detection [START_REF] Dasgupta | Tool breakage detection in milling operations using a negative-selection algorithm[END_REF].

Bio-inspired methods, as the previous short literature review also illustrates, are generally focused on the parallel resolution of problems, not necessarily computationally distributed. Moreover, these methods are typically explored as optimization techniques. Hence, it is only natural that the majority of the applications of these techniques within the industrial context are also applied in an optimization perspective. In this sense, and according to the searching approaches generally followed by metaheuristic methods, the algorithms iterate over several possible solutions until a specific stopping criteria is satisfied. Once the stopping criterion is satisfied, the solution is assumed by the system. It is however, in the variety of solutions, distributiveness of information, type of interactions and operators used to explore and select the solutions that the biological inspiration lays. Nevertheless, bioinspired approaches are generally not envisaged to consider the manufacturing problem from a distributed problem resolution and holistic perspective, but instead to simply tackle narrow and specific manufacturing problems. For a more extensive review on bio-inspired methods and applications please refer to [START_REF] Ferreira | Bio-inspired Self-Organisation in Evolvable Production Systems[END_REF].

The Relevance of Evolution and Adaptation

As previously stated, modern manufacturing paradigms clearly support the introduction of bio-inspired concepts in the so traditional field of industrial production. Some of the more desirable from a manufacturing point of view are: adaptation, evolution, self-organization and emergence. These are the key fundamental concepts behind the success of biological systems. Evolution in the natural world is a long term optimization/adaptation process. Evolutionary adaptation is a pervasive feature of biological organisms and it is the outcome of natural selection, mutation and genetic drift. The conjugation of these properties leads to an optimization process which is only constrained by the genetic code itself. In such evolutionary process, the individuals with features that better match the environmental requirements, have higher probability to survive and therefore reproduce. Consequently, those features that foster the survivability of particular individuals are passed to the next generations increasing their adequacy towards the environment. The existence of biological diversity implies however that populations do not reach adaptive peaks and that evolutionary adaptation is an open-ended optimization process that is always reacting to the environmental changing conditions, in order to provide the best functional solutions to the current state of the environment. Since manufacturing systems cannot physically adopt a similar genetic evolutionary process, evolution in this case implies the development and integration/removal of new equipment/modules in the system, to explore alternatives in the system reconfiguration, leading to new evolutionary states through which the system should be able to meet the new manufacturing requirements.

On the other hand, short term adaptation (behavioral adaptation) is the concept behind the responsiveness and agility of biological systems. It is therefore, the critical property that endows the system the ability to handle changes and uncertainties of dynamic environments. From a manufacturing perspective, adaptation is similarly a short-term process that concerns the system aptitude to revise its logical parameters and reorganize or redesign its set of processes. The extension of the adaptation process should be as big as necessary to overcome requirements or disturbances, within the constraints of the actual physical system.

Collective living systems are characteristically composed by numerous simple 'homogeneous' (possess a common interface or means to seamlessly interact with other components of the system) constructs. Typically, these systems rely on simple principles and local information, without requiring any centralization of the control and information flow. In this sense, behavioral adaptation is mainly attained by selforganization mechanisms. Interaction patterns which are locally and asynchronously established, according to the individuals internal and surrounding status, support the main regulatory principles that lead to the emergence of the necessary individuals functional adaptation and coherent global system behavior. It is however curious that the biggest advantage of these collective systems, is also their greatest weakness. As stressed before, an optimal decision can only be taken when there is a general overview of all the system information. Even though, it is possible to compose a consistent global view by the exchange of local information, it is impractical and inefficient with the increasing number of entities [START_REF] Ribeiro | Self-organizing multiagent mechatronic systems in perspective[END_REF]. Nevertheless, the distribution of both the knowledge and decision nodes ensures the system responsiveness and robustness to malfunctions, reducing the effect of deviations or catastrophic failures of the individuals. In other words, the system is not dependent on specific individuals. In a certain extent, every individual unit is relatively negligible to the proper functioning of the system. Yet, every entity contributes to the whole, and the whole supports the individual entities.

This represents one of the major gaps of most modern manufacturing paradigms. The centralization of the knowledge and regulatory control on certain hierarchical structures, simply results in a computationally distributed system in which the control mechanisms are still dependent on key higher hierarchical entities to manage the execution of critical processes. Although the effects of any malfunctioning or removal in lower level entities are easily suppressed by relocating the process, if there is sufficient redundancy or simply by replacing the component. The failure or removal of a key hierarchical node however is critical to the correct functioning of the system.

Similarly to the biological systems, central to manufacturing systems' evolution and behavioral adaptation are the concepts of self-organization and emergence that need to be properly supported by the control architectures, in order to endow the system with the ability to autonomously handle both logical and physical changes.

Towards Bio-Inspired Complex Manufacturing Systems

The currently growing interest and development of modular mechatronic production systems provides the opportunity to explore highly reconfigurable and scalable architectures with simultaneous and distributed resolution of various manufacturing problems. In this context, the integration of regulating bio-inspired principles, particularly bio-inspired self-organization, may be the way to unravel the potential of modern manufacturing paradigms. Even though these techniques have been fairly used in modern approaches [START_REF] Ferreira | Bio-inspired Self-Organisation in Evolvable Production Systems[END_REF], [START_REF] Ribeiro | Self-organizing multiagent mechatronic systems in perspective[END_REF], when down to the implementation, bio-inspired methods tend to be replaced by negotiation-based procedures which tend to be efficiently poor. Consequently, current modern manufacturing systems architectures performance and scalability is typically constrained by the architecture's artificially devised self-organizing approaches and control structures. In this sense and in order to attain biological-like agility bio-inspired mechanism should be considered from a considerably more holistic perspective. This necessarily introduces a number of challenges that need to be tackled:

• Despite the technological and distributed support provided by modular mechatronic systems, the bio-inspired structural characteristics and concepts need to be properly abstracted by a robust technical architecture.

• Production requirements, typically centralized on single entities, should be distributed over the manufacturing system components in order to reduce the necessary specification and simultaneously foster the optimization and distributed execution of production workflows.

• The reconfigurable ability, scalability and distributed coherent execution of production plans must be the outcome of efficient and semantic interaction patterns that support the implementation of regulatory bio-inspired self-organizing principles. Although the feasibility of the state transitions is not so relevant for the normal execution of the bio-inspired algorithms itself (providing that the viability of the solutions is ensured), it is critical for systems that holistically follow the same principles. Every transition may have a direct impact in the physical shopfloor consequently the devised regulatory principles need to ensure the transition feasibility. In this way the system should be able to closely mimic the robustness and adaptation of biological systems.

• As opposed to natural systems, production systems evolutionary cycles have to be proportional to the very demanding production time frames [START_REF] Ribeiro | Self-organizing multiagent mechatronic systems in perspective[END_REF].

Recent developments in distributed manufacturing architectures have attempted to attain agility and sustainability through the implementation of complexity abstraction layers, based on dynamic self-organizing logical hierarchical structures. Nevertheless, these efforts have only been partially successful. In this sense, through the literature review and analysis of the most common biological inspiration sources, a set of properties required by distributed systems to support biological-like structures and dynamics were identified: 1. Use of genetic operators -Similarly to nature, crossover and mutation operators are employed to introduce diversity in the population so that the full extent of the solution space can be properly explored. Although most bio-inspired approaches use genetic operators to perform evolutionary adaptation, from a mechatronic perspective these operators could be used to evolve particular parameters and achieve individuals' adaptation. It is important to stress that this is not an essential property to enact an approach as bio-inspired. 2. Large populations -Biological systems are typically characterized by large populations of individuals. Group size is typically determined by the resulting advantage, where the size of the group affects its performance. Small populations imply a magnification of individual mistakes. Too large populations may generate a fearsome resource competition leading to the destruction of population members. Within the midrange is the optimal population size. In the mechatronic context processes and components redundancy should be optimized in order to provide the desired level of robustness, in case critical failures affect the system components. 3. Homogeneity of the individuals -Biological systems are generally composed by identical individuals. This ensures that coherent and semantic interaction patterns are established as a response to disturbances in the environment. Mechatronic components do not necessarily need to present similar physical properties. However, compatible interfaces should be defined so that a meaningful collective behavior is attained. 4. Decoupled nature of the entities -Biological entities usually present high levels of autonomy, in the sense that different individuals interact by using well defined interfaces rather than tightly depending on each other. This implies that the removal or addition of components should have little or no impact in the normal system behavior. In other words, each mechatronic entity should contribute to the collective behavior, whilst every entity should be as negligible as possible to the proper functioning of the system. 5. Stochastic behavior -In nature, as opposed to the engineering world, systems do not have any specific goal rather than reproduce and survive. Individual decisions that influence the overall function and dynamics of the collective are the result of an interplay between deterministic and stochastic events presented by the environment. Similarly, evolutionary adaptation is also the outcome of exposing living systems to some events of stochastic nature such as mutations, genetic drift and natural selection. In this sense, it is therefore important to have, in the mechatronic context, a limited amount of randomness in order to explore all possible, available and feasible solutions (the stochastic process can be virtually simulated [START_REF] Leitão | Bio-inspired multi-agent systems for reconfigurable manufacturing systems[END_REF]). 6. Asynchronous and local interactions -Local interactions are one of the key aspects in the emergence of coherent and meaningful biological collective behaviors. Notoriously, the regulatory principles that foster the emergence of the natural self-organizing mechanisms are supported by the asynchronous establishment, between population individuals, of semantic and efficient interaction patterns. Consequently, comparable interaction patterns need to be devised in artificial systems in order to attain similar behavior. Furthermore, living systems are usually characterized by physical limitations that heavily constrain the individuals perception of the environment. This however implies that they are extremely responsive to environmental disturbances. Thus, despite the fact that self-organization in artificial systems does not necessarily preclude the use of global information, it seriously has a negative impact with the increasing size of the system. Therefore, in order to closely match the mechanisms of biological selforganization, a trade-off between the responsiveness of the system and the optimality of the decision needs to be found. 7. No centralized knowledge or decision nodes -Unlike the approach that has been typically followed by current state-of-the-art architectures, biological systems do not rely on entities with centralized knowledge and decision making capabilities. Instead, each individual is an autonomous decoupled entity that acts according to its internal and surrounding information. In this way, natural systems are able to, in a distributive manner, handle the disturbances or problems posed by the environment. This is a relevant differentiation aspect between natural and artificial distributed systems. Hence, a bio-inspired self-organizing system that presents the agility and robustness of biological systems can only be enacted with the full distribution of both knowledge and decision making capabilities. 8. Emergence of collective behavior -Emergent properties are a hallmark of collective animal behavior. In this context, the adequate implementation of the previous properties should foster the emergence of a robust, coherent, and meaningful collective behavior. Moreover, the system should present characteristics such as responsiveness, agility, robustness, scalability, pluggability, adaptability and evolutionary properties as result of the implemented bio-inspired self-organizing mechanisms.

Conclusions

Organizational sustainability and supply chain agility have become critical for the success of regular manufacturing enterprises. Companies with a pluggable, scalable and highly reconfigurable shop-floor have the ability to better cope with uncertainties and therefore have a faster response to new business opportunities. In this context, modern manufacturing paradigms play a crucial role, since they provide the methodology and background necessary to implement such systems. However, although the importance and validity of modern production paradigms is generally consensual, their implementation in real industrial systems has been, until now, minimal and only partially successful.

To some extent, this problem can be attributed to the fact that modern manufacturing systems heavily rely on biologically inspired concepts that are not properly considered and supported in the following development phases. Modern production systems, as envisioned by innovative paradigms, rely on modular independent constructs that naturally make the system a complex structure. However, the biological mechanisms that critically regulate collective living systems with similar structure are still faced simply as a desirable characteristic of the systems, but not as the main control mechanism. A more holistic integration of these mechanisms in the manufacturing context could possibly not only foster systems autonomy, scalability and reconfigurable ability but also its distributed problem resolution capabilities, enabling the system to more efficiently respond to changes and uncertainties in the environment. It is the authors' belief that considering the highlighted challenges and identified properties is a small step towards that goal.


Giovanni Di Orio

José Barata

Carlos Sousa
email: carlos.sousa@introsys.eu

Luís Flores
email: luis.flores@introsys.eu

The ProFlex Methodology: Agile Manufacturing in Practice

Keywords: Automation, Automotive Industry, Industrial Standards, Controllers, IEC-61131-3, Behavioural Modelling, Code Generation

Today, manufacturing production systems are required to be responsive, re-configurable, adaptable, and flexible. The fulfilment of these requirements is directly related to the method used to design and develop their control applications. The paper presents the result of an investigation study to capture current techniques adopted by logic designers during the design and development of control applications at IntRoSys. The result of this study is then used as the foundation for the development of a methodology and its related tools for achieving agility in programming manufacturing control systems.

Introduction

Today, European manufacturing industries are under high pressure in the global market due to a "pincer effect" as claimed in [START_REF] Jovane | The ManuFuture Road: Towards Competitive and Sustainable High-adding-value Manufacturing[END_REF]: on the one side, the presence of competitors which operate in regions with low-wage and are absorbing very fast the available technologies, and on the other side, the need to keep pace with sciencebased innovation processes and products that are creating new markets and new business. This effect is incredible felt in the automotive industry, where the increasing demand and competition in market sharing are radically changing the way production systems are designed and products are manufactured in terms of improved flexibility/adaptability, reduced lifecycle costs, high products customization and quality, and reduced time-to-market [START_REF] Rosiná | Advanced Maintenance Strategies for a Sustainable Manufacturing[END_REF].

In the recent years, the competition between manufacturing companies is going to be shifted from cost reduction to added value in products. The Original Equipment Manufacturers (OEMs) are demanding for more and more exclusive, efficient and effective production systems capable to produce as many different product variations as quickly as possible. Manufacturing companies are striving to introduce flexible and adaptive manufacturing techniques in order to better meet market needs whilst maintaining the low cost base of heavily automated mass production techniques [START_REF] Hajarnavis | An Assessment of PLC Software Structure Suitability for the Support of Flexible Manufacturing Processes[END_REF]. In this context, the key to competitiveness is represented by the reduction of the production costs during production system lifecycle and the capability to have systems that are able to quickly respond to markets variations and demands for new products. However as stated in [START_REF] Michalos | Automotive assembly technologies review: challenges and outlook for a flexible and adaptive approach[END_REF], the capability of offering more variants per product, and introducing new products faster, is constrained by the adoption of technologies and equipment of mass production operations, implying that frequently changes in products may potentially cause changes in the plant's hardware structure and its functionality.

As existing plants cannot be rebuild due to high investment volumes, the changes in hardware components affect particularly the control software. Therefore, control software is the stage of continuous modifications during the production system lifecycle which implies an increasing in its amount and complexity [START_REF] Wagner | Applying Agents for Engineering of Industrial Automation Systems[END_REF]. Furthermore, every hardware/software change in the production environment has heavy consequences on the normal flow of operations: the production system needs to be shut-down, mechanically changed and reprogrammed in order to face the new configuration. The (re)programming task is often commissioned by the OEMs to external suppliers that are then responsible for fast and secure rump-ups. The time needed for this task is directly proportional to the dimension of changes, presence of a well-suited documentation, particular familiarity with the standard used by the OEMs, presence of an up-to-date control software version [START_REF] Barata | Coalition Based Approach For ShopFloor Agility[END_REF]. Moreover, the presence of rigid industrial proprietary standards, encompassing electrical systems and diagnostic as well as hardware types and control software structure, has two fundamental implications as exposed in [START_REF] Hajarnavis | An investigation into programmable logic controller software design techniques in the automotive industry[END_REF]. From one side, standards contribute to reduce the flexibility of the programmer while discouraging any technique/technology other than the defined in the standard. From the other side, rigid and strong software structure aids the development of tools and methodologies for automatically generating the source code.

The present paper is motivated by the need to conduct an analysis of current methods for implementing monitoring and control solutions in the automotive industry in order to outline the challenges faced by the system integrators. The paper also serves as a starting point for research community for providing the baseline of current industrial software design and development practices. The baseline has then been used as the foundation of the ProFlex methodology intended to enhance the (re)programming task of control software during the shut-downs.

Contribution to Collective Awareness Systems

To face globalization challenges manufacturing industry needs to move towards new paradigms and methodologies aiming at enabling distributed knowledge creation and intelligent usage of manufacturing production data from the production environment. The main goal is creating awareness about relevant problems faced by industry in order to elaborate solutions by exploiting collective efforts. To remain competitive, manufacturing companies need to understand where and how to change. This paper aims at contributing to collective awareness by providing a theoretical background about the way current monitoring and control solutions are designed and developed and, most important, presenting a way to model manufacturing components/resources. The findings of this paper have the intent to open the door to the introduction of new technologies and paradigms in industry that are expected to support environmentally awareness by framing the current practices for designing and developing control applications for the automotive sector.

Review on Current Design Methodologies

Today, programmable logic controllers (PLCs) represent the de-facto standard for implementing control and monitoring solutions in many manufacturing companies due to their features and characteristics. Since PLCs are established as the device of choice for automation. Then a good starting point for gaining background information and knowledge on how to develop PLC programs is the introduction of the International Electrotechnical Commission (IEC) 61131-3 standard.

IEC 61131-3 Standard

As stated in [START_REF] Otto | IEC 61131: A general overview and emerging trends[END_REF], after the introduction of the PLCs in 1969, various companies developed its own platform with different run-time environments, operating systems, and programming languages. To reduce the complexity for the PLCs users and their fragmentation, the IEC 61131-3 standard has been published. The IEC 61131-3 contains five programming languages for developing programs to be executed inside a PLC, which are supposed to be supported by any compliant vendor. The main goals of the IEC 61131-3 standard is to unify all the programming concepts for developing industrial control applications while abstracting from proprietary peculiarities and creating a common user experience when programming and configuring industrial controllers. The supported programming languages are:

1. Instruction List (IL) -A textual programming language similar to assembly and consisting of simple operation codes. 2. Structured Text (ST) -A programming language with syntax similar to Pascal, Basic or FORTRAN suited for programming more complex algorithms. 3. Functions Blocks Diagram (FBD) -A graphical language providing a mechanism for encapsulating functionality into a module with a common external interface, and designed to promote the code reusability. 4. Sequential Functional Chart (SFC) -A graphical language derived from the GRAFCET. It is made up of graphical elements called steps and transitions; a step represents a specific state in process and/or machine sequence while a transition represents the set of conditions that allows the evolution from one step to another. 5. Ladder Logic Diagram (LLD) -A graphical language derived from the electrical relay diagrams and created to be easily understood by booth engineers and technician without any software skill. Most of the industrial applications are designed using one of these languages. However as exposed in [START_REF] Lucas | Understanding and assessing logic control design methodologies[END_REF], and recently confirmed a poll realized by the Control Engineering U.S. and Control Engineering Poland magazine [START_REF] Pietrusewicz | control system software programming[END_REF], 96% of industrial developers uses LLDs. FBDs are the next most popular at 67%, followed by IL (37%). The large predominance of the LLD language for developing industrial applications is due to several factors that are well exposed in [START_REF] Walter | Ladder logic: Strengths, weaknesses[END_REF]. However, the main goal in this scope is not the evaluation of current practices for developing monitoring and control solution, but only their analysis in order to enable the identification of the challenges related to the development of logic control programs. With this in mind, this analysis represents the foundation upon which new methodologies can be designed for improving the control logic development process.

Control Logic Development Process

As stated in [START_REF] Hajarnavis | An investigation into programmable logic controller software design techniques in the automotive industry[END_REF], if from one side there is an extensive published material outlining the code structures available to the practitioners of programmable controllers, from the other side, there is little reported on how these techniques are really applied for developing ready-to-use and above all standard compliant industrial control applications.

A practical perspective on the design and development process for control logic has been firstly given by Lucas and Tilbury in their work [START_REF] Lucas | The practice of industrial logic design[END_REF]. During their investigation, conducted from September to December 2001 into logic design techniques for automotive industry, Lucas and Tilbury have noticed that the control logic, needed to monitor and control a manufacturing process/machine, is obtained by gathering and combining information from three sources, namely: project specific specifications, unspecified requirements and standard specifications (see Fig. 1). [START_REF] Lucas | The practice of industrial logic design[END_REF].

Fig. 1. Control Logic Development Process

The project specific specifications comprise several documents that are primary used during the control logic development activity, namely: project specifications (functional requirements), mechanical drawings, electrical drawings and schematics, printout of previous projects, PLC memory map and process/machine timing bar (used to specify the desired behaviour of the process/machine along time during production activities). At this point, logic designers are responsible to integrate fragmented piece of information, extracted from these documents, and combine it with standard specifications (usually in the form of old project) to create the control logic. Finally, the control logic is refined taking into account the unspecified requirements that, in turn, can include late changes and/or unexpected constraints in the process/machine.

ProFlex Methodology

Research Activity

In order to gain a solid knowledge about current control logic development process for the automotive industry, a set of observations were conducted at IntRoSys SA in Portugal. Since 2004 the company has established itself in the market as one of the reference Controls Houses for the European Manufacturing Industry.

The observations were targeted at capturing current practices and activities followed by system integrators when creating control logic. The set of observations will be, then, used as inputs for developing an innovative methodology to improve the control logic development process by pushing software engineering techniques in the world of automation.

The investigation revealed that the control logic development process is today still the same as the one depicted by Lucas and Tilbury. The entire set of documents analysed and used by the logic designer during the development process is the same for all the car manufacturers meaning that all the information needed to create the control and monitoring logic can be extracted from them. Moreover, the particular software structure can be derived from standard template projects provided by the car manufacturers. Finally, new control and monitoring logic could be implemented onsite if late changes in process and/or machine are realized. In this context, the main activities carried out by logic designers are four, namely: Documents analysis, Modification of standard template functions/projects, Human Machine Interface (HMI) development and new code Development.

The fulfilment of the first three activities leads to 90% of control and monitoring logic ready to installation and test. The other 10% includes modifications on-site necessary to fit the logic to the real state of things. The conducted investigation has been used as the basis for the formation of the ProFlex methodology by providing necessary information on how to enhance the control logic development process.

Proposed Methodology

The methodology proposed in this paper address the automatic generation of standard compliant and ready-to-use control and monitoring logic starting from the behavioural model of the resources composing the considered manufacturing process. More extensive explanation of the methodology can be found in [13].

The proposed methodology is depicted in Fig. 2. The main activities of the methodology are five, namely:

• A0: Monitoring and Control system requirements analysis.

• A1: Identification of the resources of the considered manufacturing process.

• A2: Construct a universal behavioural model of each resource.

• A3: Software Generation. Furthermore, inside the Model Development Cycle, the Agentification Cycle is executed. During the Agentification Cycle real manufacturing resources are considered as autonomous components which behaviour is modelled using a unique and formal representation. The result of this activity is a self-contained piece of software (agent) to be executed inside the PLC and, thus, implemented using one of the five languages of the IEC-61131 standard. The methodology is focused on a kind of "lingua franca", represented by the behavioural model of the system resources, as a way to enable fast coding, improve industrial acceptance of developed code and, most important, reduce the error during the programming activity.

A Tool for Applying the Model

The following tool has been implemented to assist the logic designers in applying and implementing the ProFlex methodology (see Fig. 3). The tool is in the form of an Excel spreadsheet providing a table which will be used to model the behaviour of the manufacturing resources inside the considered manufacturing process.

The behaviour of the resources is modelled using impulse diagrams that enable the description of the temporal course of states of system resources together with signals among them to activate state changes. To describe the value sequences and relations of signals over time, a global timescale is considered.

4.4

Case Study Fig. 3. Excel Tool for applying the ProFlex methodology.

In order to validate and assess the proposed methodology, two robotized manufacturing cells located at IntRoSys have been used. The cells include a loading station used to load car components, a robot manipulator and a welding station. The cells are identical in terms of functionalities, i.e. once a part is loaded the robots perform the same actions on it according to the sequence: pick-weld-place. However, they have been designed and developed using two different proprietary standards, from two distinct car manufacturers. The usage of different standards directly implies the way the control software is organized, structured and built, even if the process for developing the control logic is the same. As a matter of fact, the project specific specifications are analysed by the logic designer and all the acquired information is directly parsed into LLDs. The time and the effort needed to develop the control logic strictly depend on the logic designer experience, familiarity with the particular used standard, and the way the control logic is organized. In this context, the ProFlex methodology has been applied to develop the control logic for both the presented cells. In a first moment the Model Development Cycle activity is applied aiming to identify the system requirements and according to them to design the behavioural model of the resources of the robotic cell (during this activity the Excel spreadsheet is used Fig. 3). Once the behavioural model is built then the Control Development Cycle activity can be executed. This activity is completely automatic and aims to create a standard compliant ladder logic code based on the designed behavioural model. According to a poll realized at IntRoSys, the methodology enables potentially the reduction of the overall time required to create the control logic and the possible errors related to this activity. Moreover, the initial modelling activity permits to abstract the development process from the particular OEMs standard used, while equalizing the effort needed during the development activity.

Conclusions

The paper present a software engineering approach based on a behavioural model of manufacturing resources for developing PLC-based OEMs standard compliant control logic. The way manufacturing resources are modelled, is the result of the research activity on current control logic development process. The key element and innovative aspect of the ProFlex methodology is the usage of a unique model to generate different structured and organized control logics. Further, a case study has been presented with the goal to show the feasibility and repeatability of the proposed approach. Finally, the approach, denoted with the acronym ProFlex, makes the control systems satisfy requirements for flexibility and reconfiguration while offering competitive advantage to system integrators by enabling faster and securer ramp-ups. As a matter of fact, any change/modification in the hardware configuration of the manufacturing process/machine simply triggers a new modelling activity.

•

  A4: Deployment and Tests. The activities are organized in two macro activities the Model Development Cycle and the Control Development Cycle respectively. The activities A0, A1 and A2 are executed during the Model Development Cycle while the activities A3 and A4 are executed during the Control Development Cycle.

Fig. 2 .

 2 Fig. 2. Conceptual model of the Proposed Methodology.

Acknowledgments. This work was funded by the Regional Operational Programme of Lisbon (POR Lisboa), in the scope of the National Strategic Reference Framework of Portugal (QREN), part of the European Regional Development Fund (FEDER). This work is also supported by FCT Fundação para a Ciência e Tecnologia under project grant Pest-OE/EEI/UI0066/2011.


Reza Vatankhah Barenji

Majid Hashemipour

Enterprise Competency Modeling -A Case Study

Keywords: Enterprise knowledge modelling, enterprise competency, modelling, case study

The purpose of this paper is analyzing the crucial play in carrying out enterprise competency within the enterprise. This study uses the method of case study and has directed the survey on Chika Food Industry (ChFI). It is anticipated through the case study of this company that it will be possible to implement the finding of enablers concluded by other papers, thus showing the inter-relationship between organizational management and information technology management perspectives.

Introduction

In recent years, the artificial intelligence and enterprise modelling communities have developed important enterprise models and/or ontologies, including: the Toronto Virtual Enterprise (TOVE), the Open Information Model (OIM), Computer Integrated Manufacturing Open System Architecture (CIMOSA), IDEON, Business Process Modelling Language (BPML), and Collaborative Network Organisation (CNO) [START_REF] Ljungquist | Core competency beyond identification: presentation of a model[END_REF]. Furthermore, there exist a number of frameworks to model Collaborative Networked Organizations (CNO) in general [START_REF]Collaborative networks: Reference modeling[END_REF] including the Zachman's Frameworks, GERAM -Generalized Enterprise Reference Architecture, and ARCON -A Reference Model for Collaborative Networks. In particular, ARCON consolidates existing frameworks through generic abstractions to model basic elements of CNO [START_REF]Collaborative networks: Reference modeling[END_REF].

In addition to the enterprise model, it is important to capture and manage the knowledge and skills of enterprises' internal competencies [START_REF] Harzallah | IT-based competency modeling and management: from theory to practice in enterprise engineering and operations[END_REF]. Enterprise Competency is a crucial factor in business scenarios, in that it provides a more nuanced description of an enterprise's [START_REF] Huat Lim | Enterprise modelling and integration: a taxonomy of seven key aspects[END_REF] or individual's [START_REF] Trejo | Framework for competency and capability assessment for resource allocation[END_REF][START_REF] Capece | A practical proposal for a "competence plan fulfillment" key performance indicator[END_REF] profile. Such a profile demonstrates the knowledge, skills, experience, and attributes necessary to effectively implement a defined function [START_REF] Ljungquist | Core competency beyond identification: presentation of a model[END_REF][START_REF] Joshi | Cross-functional integration: the role of information systems[END_REF].That Competency is an essential component of enterprise engineering, acting as a new means to consider knowledge capitalisation [START_REF] Barenji | Toward a framework for intra-enterprise competency modeling[END_REF], associated with a new vision of performance [START_REF] Capece | A practical proposal for a "competence plan fulfillment" key performance indicator[END_REF][START_REF] Barenji | Toward a Modeling Framework for Organizational Competency[END_REF], as well as new forms of ontology [START_REF] Barenji | Towards a Capability-Based Decision Support System for a Manufacturing Shop[END_REF][START_REF] Barenji | An RFID-enabled distributed control and monitoring system for a manufacturing system[END_REF]. First, the understanding and auditing of competencies acquired, required, and desired by a company and second, representing them in a structured manner, are beneficial steps for enhancing the company's performance. These issues motivate the co-author of this paper to propose a PhD thesis to the author.

The main aims of this research thesis are: (a) understand capability and competency concepts (b) introduce an approach to store, manage and maintenance capability and competency of an organization in different levels of abstraction (c) suggest some criteria for using competency as ontology for organization integration. The central research questions which are addressed in this research thesis; RQ1) how to model an enterprise with its existing competencies? RQ2) what are the templates, procedures and methods to store, maintain and manage competency of an organization?

This study uses a case-study of one organization to examine the dynamics of successful competency modelling practices, and to consider the extent to which such practices can be generalized and adapted by others. Therefore, the overall effect of this theoretical approach is to bridge a gap between the abstract concepts that we employ to understand enterprise competency and the practical, context-dependent realities facing business organizations.

Contribution of the Paper to "Collective Awareness Systems"

Competency modeling is an approach for configuring an organizations knowledgebase scheme. In contrast to the other organizational knowledge modeling approaches that covers only the information and knowledge of the organization from only one perspective, competency modeling has the capability of providing an organizational knowledge model from different viewpoints. Similar to the other organizational data models, it can be used on BtoB context in order to provide a platform for sharing information among the stockholders of a network. Furthermore, the competency-based knowledge model can provide an appropriate context for doing better collaboration. The authors believe is that, competency modeling approach, can be employed as an appropriate infrastructure for developing a robust "Collective Awareness System" on organizational management context.

Sector's Capability -Concepts and Model

The formalization of sector capability is as follows. Let's consider for subsequent modelling a set of sectors at an enterprise E= {S1, S2, S3, …}. Definition 1 (Sector capability)-Capability can be understood as sector's ability to perform activities, tasks, acts or processes possible through corresponding resources and knowledge, aimed at achieving a specified number of outcomes. For modelling the remaining concept, let's consider the set of capabilities at sector α:

C = ሼ C ଵ , C ଶ , … , C ୬ ሽ in which each element C ୧
stands for a capability. The following definition introduces the concept of capability, which is built upon three building aspects. It can be specified as a set

C ୧ = ሼX ୧ , R ୧ , K ୧ ሽ , i=1…n; Such that: X ୧ = ൛x ଵ, x ଶ , x ଷ , . . , x ୨ ൟ = ൛x ୨ หx ୨ is a activity for i ୲୦ capability at sector αሽ , i=1,…,n , j=1,…,m R ୧ = ൛r ଵ, r ଶ , r ଷ , . . , r ୨ ൟ = ൛r ୨ หr ୨ is a resource for i ୲୦ capability at sector αሽ , i=1…n, j=1,…,m ; K ୧ = ൛k ଵ, k ଶ , k ଷ , . . , k ୨ ൟ൛k ୨ หk ୨ is a knowledge for i ୲୦ capability at αሽ,i=1…n, j=1,…, m
Definition 2 (Sector's task-oriented capability) -is a sub-set of a sector capability set, this sub-set represents capabilities which are needed to run a specific outcome or specific goal. For sector α it can be shown as C * where:

C * ⊆ C ୧ ; C * = ሼC ∝ଵ , C ∝ଶ , C ∝ଷ , … , C ∝୬ ሽ = {C ∝୩ | C ∝୩ is a selected capability at sector α for a specific task ሽ ; k=1,…, n 4

Cross-functional Coordination and Integration Processes

Cross-functional co-ordination of capabilities of a sector has been identified as a key operation for enterprise competency creation process [START_REF] Barenji | Structural Modeling of a RFIDenabled Reconfigurable Architecture for a Flexible Manufacturing System[END_REF]. The successful achievement of the enterprise's global goals depends not only on the appropriate co-ordination of sectors' capabilities, but the proper integration of the capabilities at enterprise level is also vital. Additionally, a potential defect in one node (sector capabilities) may jeopardise the enterprise competency model [START_REF] Barenji | Toward a Modeling Framework for Organizational Competency[END_REF][START_REF] Pépiot | UECML: Unified enterprise competence modelling language[END_REF]. The interdependencies (sequence/parallelism, synchronisation, data flow, precedence conditions) among capabilities, at the various sectors, must be properly integrated in order to achieve the enterprise global goals. 'Crossfunctional co-ordination' and 'Cross-functional integration' of capabilities is defined as: Definition 3 (Cross Functional Co-ordination (CFC) of capabilities) -is a link among capabilities within a sector, this link seeks to fund relations between the activities of the capabilities using sector's 'product/service workflow diagram.' CFC is act as union for the other component of the capability (i.e. resourceሼR

ଵ ∪ R ଶ ∪ R ଷ ∪ … ∪ R ୬ ሽ, knowledgeሼK ଵ ∪ K ଶ ∪ K ଷ ∪ … ∪ K ୫ ሽ,).
CFC is the set of ordered pairsሺx , ५ሻ; where x is the independent activity and the ५ is dependent on x. CFCሺCሻ = ሼሺx , ५ሻ | x ∈ C and CFCሺxሻ = ५ሽ; FCሺxሻ ൜ = 0 ; if a is not sector to the other activities = ई ; is reachable from product/service workflow diagram ;

where: C-is a capability set; x, ५, ईis a activity, task, act or process Definition 4 (Cross Functional Integration (CFI) of capabilities) -CFI is a link among capabilities of sectors within an enterprise. This link seeks to fund relations among the activities of the capabilities at the enterprise using enterprise's 'product or service structural model'. CFI acts as union for the other component of the capability between sectors (i.e. resourceሼR

ଵ ∪ R ଶ ∪ R ଷ ∪ … ∪ R ୬ ሽ, knowledgeሼK ଵ ∪ K ଶ ∪ K ଷ ∪ … ∪ K ୫ ሽ,). CFI ஒ ൫C , C ஒ ൯ = ሼሺx , ५ ஒ ሻ | x ∈ C and CFIሺx ሻ = ५ ஒ ሽ
CFIሺx ሻ ൜ = 0 ; if x is not sector on the other activities at sector β = ई ஒ ; is reachable based on product/service structural model Definition 5 (Enterprise's competency) -Is defined as cross functional co-ordination and integration of task-oriented capabilities aimed at achieving a global outcome or goal. Enterprise's competency definition can be formulated as:

Competency | ୋ ଵ,ଶ = C ଵ * C ଶ * = CFI ଵଶ [CFC൫ U ሼC ଵ୧ * ሽ ୧ୀଵ ୬ ൯ , CFC൫ U ሼC ଶ୧ * ሽ ୧ୀଵ ୫ ൯] Competency | ୋ ଵ,ଶ,ଷ = C ଵ * C ଶ * C ଷ * = CFI ଵଶ ൣCFC൫ U ሼC ଵ୧ * ሽ ୧ୀଵ ୬ ൯ , CFC൫ U ሼC ଶ୧ * ሽ ୧ୀଵ ୫ ൯൧, CFCCompetency | ୋ ଵ,ଶ,..,୬ = C ଵ * C ଶ * … C ୬ * = CFI ଵଶ…ሺ୬ିଵሻ [CFI ଵଶ…ሺ୬ିଶሻ ൣ… [CFI ଵଶ [CFC൫ U ሼC ଵ୧ * ሽ ୧ୀଵ ୬ ൯ , CFC൫ U ሼC ଶ୧ * ሽ ୧ୀଵ ୫ ൯൧, … ], CFCቀ U ሼC ୬୧ * ሽ ୧ୀଵ ୩ ቁ]
Where: G-Represents a specific outcome or goal; 1, 2, 3,…, n-Is an index for representing sectors; C ୫ * -Task-oriented capability for Sector m as defined previously; U ሼC ୧ * ሽ ୧ୀଵ ୬

= ሼC ଵ ∪ C ଶ ∪ C ଷ ∪ … ∪ C ୬ ሽ; cross functional integration and co-ordination; CFI ୬୫ -Cross Function Integration between sector n and sector m; CFC-Cross Function Co-ordination.

Case Study

The competency modeling implementation is a part of knowledge management strategy for a corporation and with knowledge as an intangible asset, the usefulness of it usually cannot be seen in the short run. Therefore, this research uses the method of a case study and has directed our survey on Chika Food Industry (MFI). The reason that we have chosen this company is that it has already carried out knowledge management strategy so a part of knowledge for competency modeling proposes is accessible for the enterprise knowledge base system. (A)Identify and list required capabilities of sector: In this example the goal of the sectors is producing a conserve base 'Macaroni &Sauce' food. After identification process, the listed capabilities are then sequenced so that they follow the order in which they will be performed. Successful completion of these attempts often requires a good knowledge of process planning, manufacturing features and manufacturing resources. 'production' and 'Laboratory' of ChFI have sets of capabilities:

C ୰୭ ={'
ሽ ሼr ଵ, r ଶ, r ଷ ሽ ሼk ଵ, k ଶ, k ଷ ሽ ቑ 'Microbiological test @ୟୠ ' = ቐ ሼMicrobiologic testingሽ ሼSample Precreation Machine, Oven, Testing Machineሽ ሼManual1, Manual2, Manual2, Worksheetሽ ቑ=ቐ ሼx ଵ ሽ ൛r ଵ, r ଶ, r ଷ ൟ ሼk ଵ , k ଶ , k ଷ , k ଷ ሽ ቑ (C)
Interactions of capabilities within sectors and between the sectors: The sequence diagram in Fig. 1 demonstrates the interaction of capabilities among these sectors.

(D)Capability modeling: A capability knowledgebase is developed to assure that the knowledge of capabilities at the sectors is capitalized. At present, the knowledgebase is developed under ACCESS and is operational. The relational model of the capability knowledgebase is represented by Fig. 3. The use of a standard incoming application adds knowledge gathering process to the capability knowledgebase system. (E)'Cross-functional co-ordination' and 'Cross-functional integration' of capabilities :The 'crossfunctional co-ordination' and 'cross-functional integration' sub-categories concerns the linking of enterprise competency aspects. The 'Cross-functional co-ordination' process (definition 3) was adapted to all the identified capabilities at the sectors. For do this, the sector's capabilities sequence diagram (figure 1) is used. As an example: Cross Functional

Conclusions

Under the influence of the enterprise engineering paradigm with enterprise's productability, companies need to start to actively implement competency management with the goal of obtaining important information for their future decision making processes. This research first concluded that 'cross-functional co-ordination', 'crossfunctional integration', and 'sector capability' are three of the sub-categories in enterprise competency modelling. Furthermore, based on past published papers, resource, activity, and strategy (knowledge related resource and activity) are three of the aspects for sector capability modelling. A generic sector capability model is proposed; also cross-functional co-ordination and cross-functional integration of the capabilities are defied as major advancements for intra-enterprise competency modelling. Through the case study of ChFI, we implement the academic 'enterprise associated' concepts with real practice in the industry. The developed experimental system for the case study of ChFI offers four benefits, in that they a) enhance the organizations willingness to collaborate, b) boost the organization's competitiveness, c) facilitate appropriate decision-making, and d) finally help to integrate the entire organization.

Fig. 1 .

 1 Fig. 1. Interactions of capabilities among the design and manufacturing sectors

Fig. 2 .

 2 Fig. 2. Enterprise competency representation

  Cooking','Mixing','Filling','Weighing','Freezing','Sealing','Palletizing'} ; C ୟୠ ={ ′Microbiologic test′ }Since the goal is producing 'Macaroni & Sauce", 'Production' and 'Laboratory' sectors' task oriented capability set is as: C ୰୭ሺ&ௌሻ * ={ ′Microbiologic testing′}. (B)Assign resources, activities and knowledge to the sequenced capabilities: For the resources, activity and knowledge assign processes of acquired capabilities, interviews of personal appreciation, samples, references is used. For instance, the 'Cooking' and 'Mixing' capabilities at 'Production' sector also 'Microbiological test' at 'Laboratory' sector has the following sub elements (raw ingredients Code are:

					={
	Cooking'	,'Mixing',	'Weighing',	'Sealing',	'Palletizing'};
	C ୟୠሺ&ௌሻ *
	1,2,3,4,5):'Cooking @୰୭. '= ቐ ሼCombine&݇ܥሺ1,2ሻ, Combine&݇ܥ ሺ3,4,5ሻሽ ሼCooking kettleA, Cooking kettleB, Technician1ሽ ሼManuals/KettleA, Manuals/KettleB, Recipe1, Recipe2, Bill of Material1 ቐ ሼx େଵ, x େଶ, ሽ ሼr େଵ, r େଶ, , r େଷ, ሽ ቑ; ሼk େଵ, k େଶ, k େଷ, k େସ, k େହ, ሽ 'Mixing @୰୭. '=ቐ ሼDraining, Mixingሽ ሼMixinging kettle, Draining kettle, Technician2ሽ ሼMixingKettleManuals, Recipe3, Bill of Material2 ቑ=ቐ ሼx ଵ, , x ଶ ቑ =


Tomasz Grzejszczak
email: tomasz.grzejszczak@polsl.pl

Adam Galuszka
email: adam.galuszka@polsl.pl

Michal Niezabitowski
email: michal.niezabitowski@polsl.pl

Krystian Radlak
email: krystian.radlak@polsl.pl

Adam Gałuszka

Michał Niezabitowski

Comparison of Hand Feature Points Detection Methods

Keywords: Hand gestures, HCI, Characteristic points detection, Sign language

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

Introduction

From the time when first machine was created, there was a need of controlling it. From the levers, through buttons to the present touchscreens, the way of controlling our devices is tending to be more and more intuitive. On the other hand the most intuitive way of communication between people were body language and gestures. Thus in order to create the future, there is a need of gesture detection algorithms. This lead to the question: is it possible to detect the human hand characteristic points accurate enough to classify the shown gestures? What methods of computer vision approach are the best for this problem?

This paper contains the first author's method of hand characteristic points detection [START_REF] Grzejszczak | Real-time wrist localization in hand silhouettes[END_REF]. Moreover, the authors of the paper compare the above mentioned method with 3 other algorithms [2 -4]. All of presented methods were modified in order to fit them into the common testing environment that was performed on created by author gestures base [1, 5 -7].

Relationship to Collective Awareness Systems

The presented solutions on hand feature points detection can be the core of human computer interaction program that can have many applications, such as sign language gestures classification or intelligent home control, and it means that the main contributions of our work coincide with the main theme of the conference "Collective Awareness Systems".

Presented research analyzes the hand detection algorithms which are the core of human computer interaction. Having the good algorithm of hand detection can lead to accurate computer control program, which can lead to various applications, from simple advertisement or information systems to control of a robotic arm manipulator in a sterile environment. All of those applications are the part of extended user communities, linking humans and smart objects.

Data Base Tests

The presented solutions on hand feature detection were tested on database [START_REF] Kawulok | Database for hand gesture recognition[END_REF] for hand gesture recognition (HGR). The data are organized into three series acquired in different conditions containing over 1500 images with hands of over 30 individuals presenting about 30 gestures from Polish Sign Language, American Sign Language and some special gestures, such as pointing or grabbing.

Each single gesture that is referred as a one image from database is in fact a set of few things:

• original RGB images (jpg files);

• ground truth binary skin presence masks (bmp files);

• hand feature points location (xml files).

The original RGB image is a photo taken during the base creation. Basing on this image, an expert created a ground truth binary image, where value 0 is referred to background, and 1 is referred to skin region. Finally, expert marked up to 25 characteristic points, such as:

• wrist points; <IMAGE WIDTH="400" HEIGHT="604" COLOR="TRUE"> <hand> <FeaturePoint x="312" y="333" category="Wrist" type="WristThumb"/> <FeaturePoint x="258" y="345" category="Wrist" type="WristPinky"/> <FeaturePoint x="309" y="260" category="Thumb" type="Knuckle1"/> <FeaturePoint x="328" y="228" category="Thumb" type="Knuckle2"/> <FeaturePoint x="347" y="206" category="Thumb" type="Fingertip"/> …… File 1. Part of XML File with ground troth feature points localization.

All tests of the presented methods were performed on the newest HGR1 database, containing 899 images of 24 gestures from Polish Sign Language.

It is also important to point out that all of the presented methods assume a binary mask as the initial image. In order to obtain a mask, one should perform a segmentation algorithm such as skin detection [START_REF] Kawulok | Fast propagation-based skin regions segmentation in color images[END_REF][START_REF] Kawulok | Skin detection using spatial analysis with adaptive seed[END_REF][START_REF] Kovac | Human skin color clustering for face detection, IEEE REGION 8 EUROCON[END_REF][START_REF] Jones | Statistical color models with application to skin detection[END_REF][START_REF] Kakumanu | A survey of skin-color modeling and detection methods[END_REF][START_REF] Phung | Adaptive skin segmentation in color images[END_REF][START_REF] Soriano | Skin detection in video under changing illumination conditions[END_REF][START_REF] Solar | Skin detection using neighborhood information[END_REF][START_REF] Sato | Fast Tracking of Hands and Fingertips in Infrared Images for Augmented Desk Interface[END_REF]. All of presented results are based on analyze of ground truth mask, because analyzing output of skin detection algorithm would carry the error of those algorithms.

Hand Feature Points

Human hand has 27 bones, where 14 of them are phalanges of fingers. In a simple approximation, hand position or gesture can be described by the orientation of hand, position of wrist and mutual relations between knuckles and fingertips. Human anatomy does not allow bone bending, the actual bending is performed in a joints. This fact can lead to some simplifications in hand modeling. The palm region is the biggest, inflexible object in hand, bounded by wrist, finger first knuckles and concave points between fingers. Fingers consist of 3 phalanges (2 in case of thumb), which can be descried as a constant length line segment bounded by two knuckle points or fingertip. In conclusion, those are the reasons why exactly 25 characteristic points have been chosen in database.

However, not all points can be detected. Some of them cannot be seen in particular hand orientation, other are limited by algorithm capabilities. Most of the presented algorithms are able to detect only the wrist points and finger tips.

Method 1. Analyze of Contour Points Distance from Wrist Point

The method was inspired by Nobuhiko Tanibata [START_REF] Tanibata | Extraction of hand features for recognition of sign language words[END_REF]. In this article author described the whole laboratory stand for Japanese Sign Language gesture recognition. The article contains the multiple solutions on whole body gesture detection. One of them is a method of counting raised fingers by analyzing the distance of contour points from wrist point. The method, however assumed that the user would wear a high sleeve, covering the hand, which gives easy palm segmentation. In order to have the method work in any conditions, a set of improvements needed to be added.

In summary, this whole method consists of two curtail steps. First step, developed by the first author and described briefly in paper [START_REF] Grzejszczak | Real-time wrist localization in hand silhouettes[END_REF] provides the hand orientation and wrist detection. Second step, inspired by Nobuhiko Tanibata [START_REF] Tanibata | Extraction of hand features for recognition of sign language words[END_REF], was expanded in order to detect finger tips. The algorithm of the whole method is presented below:

1.

Obtain mask and its contour; 2.

Find the longest diameter of contour; 3.

Calculate the image profile. Each column of the profile image is a sum of pixels perpendicular to longest diameter in a given point; 4.

Find the local minimum of profile and obtain the wrist points in original image; 5.

Calculate the distance of each pixel of hand contour from wrist middle point; 6.

Analyze the extrema of distance function and obtain the fingertip points.

Method 2. Distance Transform

Method developed by Maciej Czupryna [START_REF] Czupryna | Real-time vision pointer interface[END_REF] is based on distance transform. New image is created with the same size as a source image, and each pixel from this new image has a value equal to distance to closest white pixel on source image. As it can be observed on Fig. 3, a white pixel representing a background on source image is also represented as value 0 (white pixel). The pixels from palm region are located far from background. In conclusion, the farther from background, the higher value of output image pixel.

Basing on the distance transform image, one can easily obtain the palm region with center in the maximal value of distance transform, and radius equal to 1.3 times value of maximum. The fingers are detected as a constant line of non-rapidly changing local maxima of distance transform. The beginning of these lines are considered as a knuckle and the end as a fingertip.

Method 3. Template Matching with Use of Cross Correlation

In article [START_REF] Sato | Fast Tracking of Hands and Fingertips in Infrared Images for Augmented Desk Interface[END_REF] author describes a method of template matching. Observing that a fingertip is round ended, the algorithm is based on matching the template image presenting black circle into the source image with use of cross correlation. The finger tips should be located in the local maxima of cross correlation output image. However it is important to filter the false positives. First method is based on closeness of detected points. Each detected point restricts an area in which no other points can be detected. Second method is based on the observation that a fingertip is surrounded by huge amount of background. While detecting a new local maximum, the test is performed if for a black pixel located on a border of template (about 20 pixels in radius from detected point) there exists a white pixel on an opposite side of template border. This rule can be observed on Figure5, where gray points are these which did not applied to the following rule.

Method 4. Analyze of Contour Points Distance from Wrist Point

The rather complicated method of characteristic points detection was presented in [START_REF] Stergiopoulou | Hand gesture recognition using a neural network shape fitting technique[END_REF] where the authors applied the Self Organizing and Self Growing Neural Gas (SGONG) on the image of hand. The neural gas was spread on hand image and in stable state, it uniformly covered the hand region. The neural gas was just a first step of characteristic points detection. The set of rules were applied in order to gather as much information as possible:

1.

Hand profile brings information about palm and wrist localization; 2.

Hand slope is calculated using 2 methods. Slope of leftmost neurons from palm region and a slope between palm and wrist; 3.

Neuron connections passing through background are removed; 4.

Neurons with only one neighbor are considered as fingertips; 5.

From finger tips, neurons with two neighbors are considered as mid-finger region, and with more neighbors are considered as finger base or knuckle; 6.

Fingers are identified with use of hand slope and 3 different measurements.

Results comparison

All of the presented algorithms were implemented and adapted in order to work with our gesture data base. Each algorithm was tested on set of 899 images and the results are presented in Figures 6 and7. On each image, few characteristic points were found, and these points positions were compared to those from database. The output data is presented in form of cumulative distribution function of detected points displacement. For example, method 3 with total of 3789 detected characteristic points has total of 3294 points with displacement error less than 14 pixels. This gives 89% of all points.

Discussion of Results and Critical View

In conclusion to chapters 3 and 4, the following remarks have been done. First method of finger detection with use of distance between contour points and wrist point brings the highest amount of points detection. However, there are images, where the algorithm identify the hand orientation in opposite way, leads to detection in region of elbow. These points are located in last column, with error values above 40.

Second method based on distance transform did not well in images with joined fingers. Bringing the small amount of characteristic points with the lowest accuracy, this method is considered as fast bus inaccurate.

Third method with template matching used to match a finger in sharp mask edges, as it can be shown on example on Fig. 4 where a template is matched on shoulder. This caused the error to be similar to method 1, however with much less characteristic points detected.

Fourth method, that used SGONG, brings the best characteristic points matching, with nearly flat line, while expanding of distribution function. However this method has many limitations with proper hand orientation, causing for example horizontally oriented hand not to be considered in tests. This brings the smallest detection rate. Moreover this algorithm is much slower in comparison to other.

In further work the first algorithm would be used and improved as it brings the most information and detects the highest amount of characteristic points. The improvement should deal with proper hand orientation leading to less false points detection. Moreover an reasoning algorithm would be developed, able to classify the viewed gesture into the proper gesture type.

Fig. 1 .

 1 a) Original hand image, b) Binary mask, c) Hand feature points visualization.

Fig. 2 .

 2 Fig. 2. a) Hand profile along the longest diameter, b) Hand contour with selected wrist region and detected fingers with use of extrema localization on c) function of distance of contour points from wrist point.

Fig. 3 .

 3 Fig. 3. a) Source image, b) distance transform of source image, c) analyzed image.

Fig. 4 .

 4 Fig. 4. a) Source image with annotated results (white -correctly detected points, greyrejected points), b) Cross correlation of source image with c) template.

Fig. 5 .

 5 Fig. 5. Example of hand image with SGONG, hand slope detection, palm region detection and finger recognition.

Fig. 6 .Fig. 7 .

 67 Fig. 6. Distribution of error of characteristic points detection for all presented methods.

Acknowledgements. The research presented here were done by authors as parts of the research projects BKM/514/RAU1/2013/t.2, BK/214/RAU1/2013 and BKM/514/RAU1/2013/t.1, respectively.