Sanmao Xie
email: xiesanmao@ecjtu.jx.cn

Study of Intelligent Diagnosis System for Mechanism Wear Fault Based on Fuzzy-Neural Networks

Keywords: wear, fault diognosis, fuzzy-neural networks, study 2. Structure of fuzzy-neural network

According to fuzziness and complexity of mechanism wear fault,a kind of intelligent diagnosis system for mechanism wear fault based on fuzzyneural network is put forward. The structure and learning method of fuzzyneural network are introduced. This paper analysises how to create the characteristic vector of wear particles and standard wear particles spectrum by combine the characteristics of mechanism wear fault,and the fuzzy-neural network of mechanism wear fault intelligent diagnosis is built. Mechanism wear faults can be diagnosed to apply the fuzzy-neural network and fault causes are determined. This system is developed by applying MATLAB software and the GUI functione of MATLAB for the simplicity and intuition of diagnosis.

Introduction

In mechanical faults, the wear faults occupy a large proportion of faults. For this type of fault diagnosis, common method is to use the oil sample analysis. However, oil sample analysis often can'nt obtained complete wear particles information, we required abundant experience to identify wear particles and wear. Therefore, the study on computer recognition of wear particle, that is intelligent diagnosis, has important significance. Common identification theory and diagnostic techniques of intelligent diagnosis have gray theory, fuzzy diagnosis, neural network diagnostic technology and so on [START_REF] Heji | Intelligent Diagnosis Based on Neural Networks[END_REF] . Fuzzy-neural network is combined with fuzzy theory and neural networks . It not only can describe the problem with fuzzy concepts, but also has strong learning ability and processing capabilities of data. According to fuzziness and complexity of mechanism wear fault,this paper analysises how to create the characteristic vector of wear particles and standard wear particles spectrum by combine the characteristics of mechanism wear fault, a fuzzy-neural network is introduced and an intelligent particale recognition system is attained.

In general,the combination of fuzzy systems and neural networks has four modes: loose combination, parallel combination ,Series combination and learning combination. The Series combination is simple and easy to be achieved, so in this paper, Series combination is used.

According to fuzzy logic and neural network features, fuzzy-neural network is built as Figure1. It is composed of three modules [START_REF] Li Guo-Yong | FNN-based Intelligent Fault Diagnosis System for Vehicle Engine[END_REF] :

(1) Input fuzzy module: to complete the transformation form fault symptoms to input vector.

(2) ANN inference module: to apply BP neural network to complete the reasoning diagnosis process .

(3) Output clarity module: to complete the process from the output of BP neural network to the diagnosis result.

In fuzzy-neural network ,first, the fault symptoms are quantified based on fuzzy theory, the input vector is attained,second,output vector is attained by the learning and reasoning of BP neural network,finally the results of fault diagnosis is attained by comparing output datda with each other.

Establishment of standard particale spectrum

The priori knowledge of typical wear particls must be determined to recognize wear particles and classify wear,that is to establish standard particle spectrum. According to research, wear usually are divided into five basic types: normality, fatigue, abrasive,cutting and sliding . Relating to wear,wear particles are classified into ' normality ' , ' expanse slice ' , ' stick ' , ' sphere ' , ' cutting ' and 'severe sliding'. Wear particle of normality is 'normality'. Wear particle of fatigue is'expanse slice'or'stick'. Wear particle of abrasive is 'sphere'. Wear particles of cutting is 'cutting'. Wear particle of sliding is 'severe sliding'.This six kinds of particles can be described with the feature vector of five parameters such as outline shape, edge detail, surface texture, size, ratio of length to thickness [START_REF] Huang | The Application of Artificial Neural Networks to Wear Debris Recognition in Ferrography Technology[END_REF] . As follows: K 1 -outline shape,K 2 -edge details, K 3 -surface texture,K 4 -size,K 5 -ratio of long-thickness Based on the reference [1], [2] and linear principles, the following quantization values of particle features are obtained by combination of the particle characteristics and the typical morphology [START_REF] Heji | Intelligent Diagnosis Based on Neural Networks[END_REF] [START_REF] Huang | The Application of Artificial Neural Networks to Wear Debris Recognition in Ferrography Technology[END_REF] : outline shape: K 11 -spherical shape(1.0),K 12 -flake shape (0.8),K 13 -circular shape (0.6), K 14 -elongated shape (0.4).

edge details: K 21 -smooth edge(1.0), K 22 -straight edge (0.8), K 23 -irregular edge(0.6) surface texture: K 31 -smooth surface (1.0) , K 32 -rough surface (0.8), K 33striated surface (0.6), K 34 -holed surface (0.4), K 35 -pitted surface (0.2) size: K 41 <15μm (1.0), K 42 : 15 ~ 25μm (0.8), K 43 :25 ~ 100μm (0.6), K 44 > 100μm (0.4) ratio of length to thickness: K 51 <5:1 (1.0), K 52 :5:1 ~ 10:1 (0.8), K 53 > 10:1 (0.6)

The data in brackets express the quantization values of particle features. According to expert experience and knowledge, the following typical particles feature vectors are obtained:

'normality'particle feature vector: X 1 = (0.8,1.0,1.0,1.0,0.8) 'expanse slice 'particle feature vector : X 2 = (0.8,0.6,0.4,0.6,0.6) 'stick'particle feature vector : X 3 = (0.6,0.6,0.8,0.6,1.0) 'sphere'particle feature vector: X 4 = (1.0,1.0,1.0,0.8,1.0) 'cutting'particle feature vector: X 5 = (0.6,0.6,0.8,0.6,0.6) 'severe sliding'particle feature vector: X 6 = (0.4,0.6,0.6,0.4,1.0) Thus the quantified standard particle spectrum is shown in table 1:

Table 1. Standard particle spectrum K m X i K 1 K 2 K 3 K 4 K 5 X 1 0.8 1.0 1.0 1.0 0.

Procedure of wear fault diagnosis based on fuzzy-neural network

Procedure of wear fault diagnosis based on fuzzy-neural network is shown in figure 2. Wear particle characteristic information are obtained from the oil sample analysis.After fuzzy processing, the feature vector of particles is provided to the diagnosis system by man-machine interaction. The diagnosis result of wear fault is shown by learning and reasoning of neural network.

Design of ANN inference module

Structure of ANN inference module

In this module ,a three-layer BP neural network with input-layer,hidden-layer and output-layer is used and hybrid algorithm of 'BP-random' is adopted to train the network. Parameters of BP neural network are selected as following: (1) the number of input-layer node: As the five particle morphology parameters are as input after fuzzy, so 5 nodes are selected in input-layer; (2) the number of output-layer node: Since there are six kinds of wear particle, so 6 nodes are selected in output-layer; (3) the number of hidden-layer node: According to the reference [1], the following formula can be used to determine the number of hidden-layer node:

a m n l + + =
, n is the number of input-layer node, m is the number of outputlayer node, α is a constant between 1 to 10. As a result, 10 nodes are selected in hidden-layer; (4) extraction of network training sample: According to the expert experience and knowledge, features spectrum of the typical wear particles has been shown in Table 1, so the features spectrum is used as the input training sample P of neural network. Table 2 shows the target output training samples T of the neural network, its output value is in the range [0,1], the greater the value, the greater the likelihood of that type of fault.

1 0 0 0 0 0 'expanse slice' 0 1 0 0 0 0 'stick' 0 0 1 0 0 0 'sphere' 0 0 0 1 0 0 'cutting' 0 0 0 0 1 0 'severe sliding' 0 0 0 0 0 1

Implementation of BP neural network

BP neural network's basic idea is that characterized vector of training sample is as input of neural network, an output is get after the forward propagation calculation of neural network, and then the output is compared with a expected sample output, if there is deviation, the deviation is propagated back from the output layer, the network connection weights and thresholds are adjusted, the network's desired output is consistent to the output of the sample [START_REF] Guohua | Machinery fault diagnosis[END_REF] .

MATLAB software has stronge function of mathematical calculation , image processing and computer simulation and has a very high programming efficiency. It is more important that MATLAB concentrated the wisdom of experts and scholars in many fields and successfully expanded the toolbox of more than 30 fields, in which neural network toolbox is based on the theory of neural network. Utility functions of typical neural network is constructed in MATLAB.It provides a powerful and convenience tool for the application of the BP neural network [5][6] .Therefore, this system is developed by applying MATLAB software and the GUI functione of MATLAB for the simplicity and intuition of diagnosis. BP neural network is created as follows: net = newff (minmax (P), [5 10 6], ('pureline''logsig' 'logsig'), 'trainlm') Choose η = 0.2, α = 0.85, and the system error is 0.001, the corresponding network parameters: net.trainParam.mc = 0.85; net.trainParam.lr = 0.2; net.trainParam.goal = 0.001; The neural network is initialized and trained according to the samples of Table 1 andTable 2 .As following: net = train (net, P, T).

The main function of this intelligent diagnosis system

As MATLAB software has a powerful function of mathematical calculation and friendship GUI visual interface, so this system is developed by applying MATLAB software ,the functions of system includ: 1) the system description; 2) neural network training; 3) wear particle recognition and wear fault diagnosis; 4) the help of system. Testing example of diagnostic system: According to the analysis of a particular ferrography, we obtained the characteristics information of wear particle tested. Feature vector X 0 was attained through fuzzy processing, as follows:

X 0 = [0.6,1.0,0.8,0.6,0.8] In fact , these data is the wear particle membership of characteristics. Feature vector X 0 was as the input of diagnosis system, the network output value is: Y = [0.0025 0.0061 0.0016 0.9332 0.0064 0.0086] Obviously, the fourth is largest data of output nodes, the particle is 'sphere', the corresponding wear is abrasive wear.The diagnosis result is shown as Figure 3.

Conclusion

In this paper, mechanical wear faults were diagnosed effectively after obtaining characteristics information of wear particles and processing them with fuzzy theory.The effective combination of fuzzy theory and neural networks can handle ambiguous and even erroneous information.

Figure 1 .

 1 Figure 1. Structure chart of fuzzy-neural network

Figure 2 .

 2 Figure 2. Flow chart of wear fault diagnosis

Figure 3 .

 3 Figure 3. Diagnosis result of wear fault

Table 2 .

 2 The output matrix of the training sample

	Fuzzy processing of particle characteristics	Input feature vector of particle	Reasoning and Learning	Result Diagnosis
	Quantitative standard particle spectrum


Yajuan Liu
email: liuyajuanlyj@tom.com

Application of computer technology in advanced material science and processing

Keywords: Computer Technology, Material Processing, Material Science 1

Computer technology is an actual system model, which is largely unaffected by experimental conditions, time and space constraints, and is of great flexibility. Nowadays, computer technology has thoroughly penetrated in the various areas of material processing and research, which becomes one of the important frontiers in the field of material manufacturing industry. At the same time, material science and technology are also developing rapidly and constantly giving birth to the new industrial field, such as nanotechnology, optoelectronic, magnetic electronic technologies, which are inseparable of computer technology. Hence, in this article, the application of computer technology in advanced material science and processing, which includes material science database, computational material science, computer-aided design or processing etc are reviewed.

Introduction

With the continuously deepening research of material science, material science occupies an important position in the national economy; however, material science is still an immature interdisciplinary, which mainly depends on the facts and the experience of the current study. The systematic studies need a very long process [START_REF] Flaszka | Computer-aided forging design using model material simulation[END_REF].

Computer as a modern tool plays an increasingly significant role in various areas of the world, which has penetrated into many fields. With respect to the material science and engineering, the computer is also becoming a very important tool and becomes one of the reasons for the accumulation of the rapid development of material science.

For example, computer technology has been widely used in the variety field of material forming technology, including application in liquid forming, plastic forming, polymer material forming, powder forming et al, which can basically provide a qualitative description toward to quantitative prediction for material processing [START_REF] Joshi | Impact of information technology on users' work environment: A case of computer aided design (CAD) system implementation[END_REF][START_REF] Sapuan | A knowledge-based system for material selection in mechanical engineering design[END_REF]. Furthermore, computer application in material science is the trend of multi-scale simulation and integration [START_REF] Gates | Computational materials: Multi-scale modeling and simulation of nanostructured materials[END_REF][START_REF] Hao | Multi-scale constitutive model and computational framework for the design of ultra-high strength, high toughness steels[END_REF]. In this article, the application of computer technology in material science and processing including material science literature search practice, computational material science, computer-aided design and processing are mainly introduced.

Computer technology in material science databases

A reasonable choice of material, accurate design and scientific processing are directly impacting on product cost and quality, which even affect the social development and progress. In recent years, with computer technology development, particularly in the development of database technology, material science databases in scientific research has become increasingly emphasized and get more and more widely used. A sample material database was illustrated in Figure 1. Material science database can be divided into the data type, numeric type as well as the map database. Otherwise, it is also divided into the online and offline database type. The literature database is of mainly online services, while numerical databases were more used in off-line. Moreover, according to the point view of material, it can be classified into metallic and nonmetallic material databases.

Public material databases have been constructed every year and the developed countries continue to make the information on this strategic. Numeric material database has been established in China since 1992 and has accumulated in recent years [6]. Though after several decades of development and accumulated, however, the following deficiencies still exist: [START_REF] Flaszka | Computer-aided forging design using model material simulation[END_REF] The lack of the data of mechanical properties. In the development of metallic material database, it focuses on the iron and steel materials, however, with respect to the light alloys such as aluminum, magnesium alloys, it was still lack of the mechanical properties' database. [START_REF] Joshi | Impact of information technology on users' work environment: A case of computer aided design (CAD) system implementation[END_REF] The lack of the data of material processes performance. Cold and hot processing of material data is rarely found. (3) Although some scientific databases of materials have built at home and abroad, its research and development still lags far behind the actual rate of application requirements. Generally, network, standardization, intelligence and commercial trends will make the application material database, extended to broader areas of material research and development.

Computational Material science

Computational material science (CMS) is a typical interdisciplinary of material science and computer science, which is the material scientific research about the "computer design" and "computer experiment" in material composition, structure, performance, service performance [START_REF] Sokrates | Frontiers in computational materials science[END_REF]. According to the literatures [START_REF] Richard | Computational materials science -a personal perspective of an industrial scientist[END_REF], CMS could be mainly included into two aspects: one is calculation and simulation, which is starting from the experimental data, through the establishment of mathematical models and numerical calculations to simulate the actual process; the other is the computer added material design, which is directly through the theoretical model to calculate, predict or design the new structure and properties of material. Therefore, CMS is a bridge to connect theory and experimental material.

It is well known that the material composition, structure, performance and service performance are the four elements of material research. The traditional research is based material experimental results in the laboratory, which is an experimental science. However, with the requirements of high material performance increasing, especially because the material sciences research object is constantly changing spatial scale into small, the micro-level studies do not reveal the nature of material properties, nano-structures and atoms scale and even electronic level become the studied content when the functional material are studied [START_REF] Hafner | Atomic-scale computational materials science[END_REF]. Therefore, material research is increasingly dependent on high-level testing technology, the research difficulty and costs are getting much higher.

In addition, the service performance is increasing attention in material research, which is to study the interaction of material and service environment and its impact on material performance. As the material was serviced in an increasingly complex environment, laboratory studies of service performance have become more and more difficult. In short, it was difficult that the new and modern material research and development relying solely on laboratory experiments to conduct material research.

Computer simulation technology, however, according to the basic theory, from the inclusive concept of a virtual environment, micro-, meso-, macro-scale, multi-level research on the material in the computer, but also can simulate the ultra-high temperature, high pressure material under extreme environments such as service performance to simulate material properties under service conditions, failure mechanism, so as to realize the performance of material in service improvements and material design. Thus, in the field of modern material science, the computer "experiment" has the same important position in research methods.

It is necessary to point that CMS is closely related to computer development. In the past, even if the use of large computers is also very difficult to calculate a number of materials, such as material, quantum mechanic calculations, now it can complete this on PC. In addition, with the continuous progress of CMS and mature, material, computer simulation and design is no longer a hot research topic of just theoretical material physics and material scientists calculated, but also will become an important research tool of general-purpose material researchers.

Computer aided design and manufacture

The initial processing of computer used for complex engineering analysis and calculation is followed by the development process in the modern industrial product design, such as the aircraft surface flow field calculation, the stress analysis of the complex structure [START_REF] Wan | Application of computers in materials science[END_REF]. The system can do a lot of complex calculations in a very short period of time, and it is possible for many programs for rapid analysis and evaluation to choose the best design.

Material processing CAD can be divided into casting CM, plastic forming CAD, welding forming CAD, injection molding CAD, as well as mold CAD [START_REF] Bata | Computer integrated material processing (CIMP) -A generic application of CAD/CAM technology[END_REF].

Conclusion

In summary, material science is a cross-emerging for the development of immature discipline. At present, its research is largely depended on the facts and the experience.

The systematic studies need a very long process. The computer is becoming an extremely important tool, which is one of the important reasons for rapid development of material science. The use of computers for design of new material has gradually been recognized and used. However, this understanding and effort are still insufficient.

Facing the future, computer simulation technology, material calculation and design will become an inevitable trend.

Figure 1 A

 1 Figure 1 A sample of material database

Table 1 .

 1 The computer simulation of casting process was carried out earlier and the technically is more mature, which has been into the micro-macro simulation stage. From the early 90's in 20 centuries, it has launched the computer simulation of micro-morphology, in which it can simulate the nucleation, growth the process of casting solidification process of forecasting[START_REF] Jerald | Application of computer-aided engineering techniques to tooling for castings[END_REF][START_REF] Im | A computer-aided-design system for forming processes[END_REF][START_REF] Karima | A knowledge-based systems framework for computer-aided technologies in metal forming[END_REF]. After years of research and development, a large number of casting process simulation software has been the commercialization, which is shown in Table1[START_REF] Cao | The application and development of Computer Simulation of Casting Process[END_REF]. Overview of main foreign casting special software

	Software name	Developer	Function
	Mavls software	Alphacast software Ltd	Predicted melt flow temperature, pressure,
			velocity distribution, macro-and
			micro-shrinkage, dendrite arm spacing,
			steady-state temperature distribution,
	Flow-3d	Flow science, Inc	Automatically predicted solidification
			shrinkage, binary segregation and tracking of
			surface defects
	ProCast	UES software,Inc	formation of micro-structure such as porosity,
			pore aggregation
	Cast CAE4	Finland	Calculated solidification shrinkage, formation
			of 3-D view

* Corresponding author: Yajuan Liu;Tel:+86-791-7046184;Fax:+86-791-7046185 E-Mail: liuyajuanlyj@tom.com


L U Liming
email: luliming@ecjtu.jx.cn

Yang Yuchuan

L U Jinfu

Study on the online Control System to Prevent Drunk Driving Based on Photoelectric Detection Technology

Keywords: Photoelectric Technology, Drunk Driving, Control System

A drink driving online control system based on photoelectric detection technology is introduced in the paper. The system can take the initiative to force motor vehicle drivers to test their drinking status, and reasonably control the driver's behavior accordingly. The basic principle is to use the characteristic that 1185nm wavelength IR is absorbed by alcohol the most easily to detect the motor vehicle driver's alcohol concentration and to determine the extent of motor vehicle drivers drinking. The alcohol concentration detected is converted into photoelectric signal that is handled by 89C2051 MCU. The signal handled is exported to voice devices to get voice prompts and warning, and exported to the relay of ignition circuit system to cut off the ignition system to control the engine's starting and prevent the driver drunk driving, and exported to electro-hydraulic proportional flow valve to control the maximum speed of motor vehicle according to the driver's alcohol content. The purpose of this study is to be able to effectively solve the problem of drink driving and avoid serious traffic accidents resulting from drunken driving.

1.Introduction

The danger of drink driving motor vehicle is known as we all. "Drivers shall not drive a motor vehicle after drinking" is clearly stated in Article 2 of "The People's Republic of China Road Traffic Safety Law.", but some motor vehicle drivers have luck idea in their brain, resulting in the provisions existing in name only, causing a number of serious accidents. To avoid this incident happening, a drink driving online control system based on photoelectric detection technology is introduced in the paper. 2.The overall program of realizing the online detection control system to prevent drunk driving By a gas absorption device constituted by an airflow sensor and a temperature sensor, motor vehicle drivers are forced to detect their breath alcohol concentration before driving, to prevent them to avoid alcohol testing by ventilating through the windows and covering the test probes and other means. The test of alcohol concentration of motor vehicle drivers is done by the equipment detecting breath alcohol concentration based on IR spectroscopy. The optical signal obtained by the test is turned into electric signal by photoelectric detector and magnified by amplifier. The output signal of the alcohol concentration testing device and the output signal of the gas absorption device are respectively inputted control circuit device and processed by the 89C2051 MCU. The new signal got is exported to voice devices to get voice prompts and warning messages, exported to the ignition circuit of the start system to control the motor vehicle start, exported to electro-hydraulic proportional flow valve to limit vehicle maximum speed. The specific procedure is shown in figure 1.

3.The gas absorption device to force motor vehicle drivers to detect their breath alcohol concentration before driving

The gas absorption device accepting the motor vehicle driver's breath gas mainly consists of a temperature sensor and an airflow sensor, to control the relay turning on and off the ignition system. The purpose of using the temperature sensor is to Identify whether the air is human body's gas. The basic principle of the temperature sensor generating electrical signal is to compare gas flow temperature and ambient temperature, because the gas temperature of human body breathing is higher than ambient temperature, to determine whether the electrical signal is generated. The purpose of using the airflow sensors also is to Identify whether the air is human body's gas. The basic principle of the airflow sensor generating electrical signal is to compare the gas flow rate of human body breathing and the flow rate of the environment air to determine whether the electrical signal is generated, because the gas flow of human body breath is faster than the gas flow of environment air in cab. When the two sensors generate signals at the same time, the motor vehicle driver's breath can be regarded as being valid to prevent the driver to avoid detection.

4.The alcohol concentration detector Based on Spectroscopy Technology

It was found by experiment that the most IR wavelength absorbed by alcohol is [1] 1185nm , and the most IR wavelength absorbed by carbon dioxide is 1575nm , and the most IR wavelength absorbed by water vapor is 1315nm . Under normal circumstances the gas of human body exhale mainly contains of carbon dioxide and water vapor. The extent of motor vehicle drivers drinking can be determined by observing the absorbed situation of wavelength [1] 1185nm IR when it passes through the gas region of the driver exhale. Testing device is shown in Figure 2. Using hollow cathode lamp to generate wavelength 1185nm IR, when it passes through the gas region of drivers exhale, alcohol will absorb some of its spectral energy. Using wavelength selector further to filter out other wavelengths of the spectral, the remaining spectral energy is converted into electrical signals by the optical converter. The electrical signal is amplified by the amplifier and then is sent to the control circuit device.

5.drunk driving control circuit system

The voltage of control circuit is +5V. The control system mainly consists of 89C2051 MCU [START_REF] Zongrui | Active breath alcohol detector[END_REF] 、MTP337A temperature sensors、speed sensor 、gas flow sensor、 voice prompt module、 voice warning module、 on-off relay、 electro-hydraulic proportional flow valve etc. The core is 89C2051 MCU. Their mutual relation is shown in Figure 3. If the alcohol content exceeded the standard, the alarm module will issue a voice warning and the on-off relay control module will cut off the vehicle's ignition circuit so that the driver can not start the car. If the alcohol content was not exceeded the standard, the voice prompt module will prompt the driver to slow and the electro-hydraulic proportional flow valve will Limit vehicle maximum speed.

6.drunk driving speed control system

The optical signal produced by the alcohol concentration detector is enlarged, then is processed by 89C2051 MCU, is compared with pre-set the alcohol maximum concentration signal. Final the vehicle is controlled accordingly.

If the driver's alcohol concentration measured was higher than the pre-set the alcohol maximum concentration, the 89C2051 MCU will output signal, and supply power to the relay of ignition circuit system. The relay will cut off the ignition system to the engine not be start.

If the driver's alcohol concentration measured was less than pre-set the alcohol maximum concentration and speed measured by speed sensor reached up to the speed needing to control under the alcohol concentration, the 89C2051 MCU will export signal to the drunk driving speed control system so that the vehicle speed can be within the speed that the driver can safely travel under the alcohol concentration.

The drunk driving speed control system mainly consists of accelerator pedal、 Spring 、hydraulic cylinder、 hydraulic control valve、 check valve、 relief valve、 oil tank and some tubing etc. The relationship between the various components is shown in Figure .

7.Summary

Above the study shows that the online control system to prevent drunk driving can take the initiative to force motor vehicle drivers to test their drinking status, and reasonably control the driver's driving behavior accordingly.

The basic principle is to use the characteristic that 1185nm wavelength IR is absorbed by alcohol the most easily to detect the motor vehicle driver's alcohol concentration and to determine the extent of motor vehicle drivers drinking. The alcohol concentration detected is converted into photoelectric signal that is handled by 89C2051 MCU. The signal handled is exported to voice devices to get voice prompts and warning, and exported to the relay of ignition circuit system to cut off the ignition system to control the engine's starting and prevent the driver drunk driving, and exported to electro-hydraulic proportional flow valve to control the maximum speed of motor vehicle according to the driver's alcohol level. The study shows the system can effectively solve the problem to prevent drunk driving and avoid serious traffic accidents due to drunk.

  Hydraulic cylinder is a special cylinder. It has some holes in different locations and is connected with the hydraulic control valve by some tubing. Hydraulic control valve is an electro-hydraulic proportional valve. At different currents, the spool of hydraulic control valve can move different distances to seal different the holes.Spring has the role to reset the automobile accelerator pedal. Check valve has a complementary role to oil. Relief valve has a role to set oil pressure.The control principle of the drunk driving speed control system is that 89C2051 MCU exports corresponding signal to the electro-hydraulic proportional flow valve according to the driver's the alcohol concentration measured, so that its the spool can produce different mobile to seal different holes which connect with hydraulic cylinder, to control the piston of hydraulic cylinder to move different distances, to control the opening size of the vehicle throttle. The size can control vehicle speed.

Acknowledgements:

The was supported by the National Natural Science Foundation of China "The Study of the Principle and tribological design basis of the roll and slide blend bearing in circle pound situation. " (Grant No.51065009 ).


Hubin Deng

Guiyuan Liu

Lei Zhang

Analysis and Implementation of Embedded SNMP Agent

Keywords: Embedded technology, Network management, SNMP Agent, MIB modules, NET-SNMP development kit

With the extensive application of network devices and the rapid development of embedded technology, network management of embedded devices becomes increasingly complicated. SNMP management is the most widely used network management system(NMS). Most of the network components used in enterprise network have built-in network agents that can respond to an SNMP network management system.By analyzing the SNMP (Simple Network Management Protocol)and base on NET-SNMP development kit, discuss the construction of MIB modules and code conversion and complete embedded SNMP Agent extension. Through network management tools to verify the SNMP agent on the network management functions of the embedded device.

Introduction

Along with the popularization of the network application and the network equipment, it is also gradually increasing to the network management demand.Because Simple Network Management Protocol(SNMP) has obtained the widespread application in the industrial world by its simplicity.The TCP/IP major part router and the switchboard all support SNMP in the protocol standard certain main management information database (MIB).In addition, in other private network equipment management domain, the SNMP network management has also obtained the widespread application.In the SNMP management model, the management station is carries on the management and the monitoring center to AGENT, Agent is managed to the equipment to carry on the monitoring and front end the operation network management.Therefore, in the network equipment, to increase SNMP the network management to act AGENT adapts the network supervising and managing development essential work. This article takes SNMP and the existing system resources as a foundation, analyzes the embedded SNMP proxy software with emphasis of the function module, with the aid of opens source tool development package NET-SNMP, has constructed the MIB storehouse module, and has developed the embedded equipment SNMP agent software.

Simple Network Management Model

Simple Network Management Protocol (SNMP) is a UDP-based network protocol. It is used mostly in network management systems to monitor networkattached devices for conditions that warrant administrative attention. SNMP management is also referred to as Internet management. It's called SNMP management because it has matured to the level that it manages more than the Internet, for example, intranet and telecommunication networks. Any network that uses the TCP/IP protocol suite is an ideal candidate for SNMP management. SNMP network management system can manage even non-TCP/IP network elements through proxy agents.

The SNMP management consist four elements: Network Management System (NMS), Agent, Management Information Base (MIB), and Simple Network Management Protocol (SNMP). It uses the concept of Client/Server application. A network management system (NMS) executes applications that monitor and control managed devices. On each manageable equipment, a SNMP Agent is running. This agent manages information relating to the equipment which is stored in a local database called the MIB.And the SNMP Protocol is used to connect the NMS and the Agents. SNMP is a protocol built on the top of UDP/IP:

The architecture specifies the management messages between the management system and the management agents. 5 types of SNMP messages or SNMP requests can be exchanged (SNMPv1) between a SNMP agent and a SNMP manager:

1.Obtaining the current value of a MIB object managed by an agent: request get-request (GET).

2.Obtaining the current value of the next MIB object managed by an agent: request get-next-request (GETNEXT).

3.Updating of the current value of a MIB object managed by an agent: request set-request (SET).

4.Sending back the value of a MIB object managed by an agent: request get-response . It's the answer to a GET, GETNEXT or SET request. One can see that SNMP is a command/response protocol without state.

5.Signal/alarm emitted by an agent to a manager: message trap (TRAP).

MIB module design and code conversion

SNMP itself does not define which information a managed system should offer. Instead, SNMP uses an extensible design, where the available information is defined by management information baes(MIB). MIB describe the structure of the management data of a device subsystem; they use a hierarchical namespace containing object identifiers (OID). Each OID identifies a variable that can be read or set via SNMP. MIB use the notation defined by ASN.1.

Figure 2. Definiton(skeletal) of SMI for SNMP v2

In telecommunications and computer networking, Abstract Syntax Notation One (ASN.1) is a standard and flexible notation that describes data structures for representing, encoding, transmitting, and decoding data. It provides a set of formal rules for describing the structure of objects that are independent of machinespecific encoding techniques and is a precise, formal notation that removes ambiguities.

ASN.1 is defined as a set of rules used to specify data types and structures for storage of information.Basic Encoding Rules (BER) is defined for the transfer syntax by the ASN.1 syntax.The syntax to create a SMIB module is referred to the description section in SMI(Structure of Management Information).

In this paper, MG-SOFT is used to define a MIB document in ASN.1. MG-SOFT's MIB tools are quite mature and widely used for SNMP development and testing.Its MIB Browser can not only read and write MIB, but also receive the Trap sent by SNMP agents; MIB Compiler can be used to check the legality of MIB, which is useful for the compose of MIB.This MIB document is then transformed into the C language source file by MIB2C.MIB2C is a useful software tool in NET-SNMP. Follow these steps to convert:

First of all copy the MIB module definition files to the mibs directory: cp./modulename /usr/local/share/snmp/mibs; Then, run MIB2C command: /Usr/local/bin/mib2c modulename to translate; Finally,MIB2C generated in the current directory two C source files: modulename.h and modulename.c. These two documents are made under the MIB library module conversion, and also need to be added to the NET-SNMP SNMP Agent Software to extensions of the source code.

SNMP Entity and AGENT Extension

The SNMP Agent consists of a dispatcher, a message processing subsystem, and a securtiy subsystem.

The SNMP Agent consists of a dispatcher, a message processing subsystem, and a securtiy subsystem.

The SNMP message processing subsystem of an SNMP engine interacts with the dispatcher to handle version-specific SNMP message.It contains one or more message processing models.The version is identified by the version field in the header.

The security and access control subsystem provides authentication and privacy protection at he message level.The access control subsystem provides access authorization security.

There is only one dispatcher in an SNMP engine,but it can handle multiple versions of SNMP message.It performs three sets of functions.First,it sends message to and receives message from the network.Second,it determines the version of the message and the interacts with the corresponding message processing model.Third,if provides an abstract interface to SNMP applications to deliver an incoming PDU to the local application and to send a PDU from the local application to a remote entity. Copy the snmpd program to the development board "/ usr / bin" directory and start with the following command: /usr/bin/snmpd -V -c /etc/snmpd.conf

Conclution

The SNMP protocol was developed to facilitate the network management.In this paper the free software package,NET-SNMP is used to extend the NET-SNMP agent to an embedded system under the ARM-LINUX OS in order that the embedded can be controlled remotely by SNMP NMS.NET-SNMP makes it possible to integrate an embedded system into a network and to manage it with SNMP managers like MG-SOFT. The Management station's IP is 192.168.2.4,and the embedded equipment's IP is 192.168.2.120.The testing result of verifying the embedded SNMP agent shows as in Figure 5.

Figure 1 .

 1 Figure 1. Five types of SNMP Operation

Figure 3 .

 3 Figure 3. SNMP Agent EntityTo complete the SNMP Agent to a embedded device takes the steps as follows:In the net-snmp-5.4.2.1 directory configure: ./ Configure -host = arm-linux target = arm --Withcc = /armv4l-unknowlinux-gcc --with-ar = arm-linuxar --disable-shared -with-endianness = little -With-mib-modules = "moudlename" . In the net-snmp-5.4.2.1 directory, compile: make. In net-snmp-5.4.2.1/agent directory, you can see the generated snmpd process, as shown below:

Figure 4 .

 4 Figure 4. Run the snmpd programe

Figure 5 .

 5 Figure 5. Verify the SNMP agent

6.Acknowledement

This work was sponsored by the technology proliferation plan item of Nanchang city (Caiqi[2008], NO.68), science and research foundation of East China Jiaotong University(09XX05).


Shehua Cao
email: shehuacao,shehuacaonc@sina.com

Development and Application of Tennis Match Video Retrieval Technology in Multimedia Education

Keywords: words:Tennis Match Video, Visual Features, Movement Features, Audio Features

In order to improve the application effect of tennis match video in multimedia education, this paper puts forward a kind of method for retrieving the spectacular Rally scenes in tennis match video and classified the shots into Global View Shots and Non-Global View Shots. The Support Vector Machine (SVM) is for identifying the important types of audio in shots, applying the audio information in shots and setting up corresponding rules for retrieval. The results show that applying the retrieved spectacular Rally scenes in tennis match video in tennis multimedia education is line with most students' psychological situation and greatly popular with students.

Introduction

With the emergence of many tennis match videos, the tennis match video retrieval technology has become a study topic urgently and strongly needed in application. The tennis match is very long, the proportion of the Non-play Shots is large and generally, the audience are usually interested in the match scenes or the spectacular Rally scenes in match, therefore, automatically retrieving the spectacular scenes from the tennis match video which the audience is interested in can save the audience's time for appreciation of match and make the audience be able to enjoy the match's spectacular Rally scenes in limited time. In addition, the spectacular Rally scenes extracted can also be applied in tennis multimedia education, which is of great research significance and application value in development of the retrieval technology for the spectacular Rally scenes in tennis match video.

Structure of Tennis Match Video

The Rally shots, the players' close-up shots, the audiences' shots, the playback shots, the shot cuts and other shots which the audiences are interested in tennis match video are with the predicable sequential structure. For the Play Shots in tennis match video, the number of play targets is fixed and the size of play targets can hardly be changed; the color of court accounts for the overwhelming majority of the video and the main color ratio is relatively high. The majority of Play Shots are Global View Shots, however, the Non-play Shots including the players' close-up shots and the audiences' shots are generally regarded as the Non-Global View Shots. The video's major audio events include the sound of ball-striking, the hurrahs of audiences, the sound of applause and the sound of the commentators. These sounds sometimes mix with each other, which even mix with the noise from the auditorium. But the audio information and the match proceeding state are closely related, like the ball-striking sound shows the Play state and after the Rally shots, there often come the sound of applause. It is thus evident that the retrieval of some audio events can help us analyze the tennis match video in a better manner.

Through the analysis on the structure of tennis match video, we can find that the shot boundary detection is more suitable for the retrieval of spectacular Rally scenes in tennis match than the audio segmentation. For this reason, in the retrieval, we should firstly measure the similarity of shots based on the features of movement and major colors and design a two-category classifier, which classified the shots into the Global View Shots and Non-Global View Shots; and then apply the SVM for indentifying the meaningful audio events (like the sound of ball-striking and applause); apply the visual and audio features to set up the rules for heuristic procedures, detect the issues which the audiences are interested in the match video; finally set the spectacular degree model of the Rally scenes and arrange the spectacular degree in the from-high-to-low order.

Classification of Tennis Match Video Shots

Due to the fact that the majority of Play Shots in tennis match video are Global View Shots, generally regarding the Serve scene as the Rally scene is not suitable [START_REF] Ly | A scheme for racquet sports video analysis with the combination of audio-visual information[END_REF], however, when classifying the shots into the Global View Shots and the Non-Global View Shots, we are not required assuming the color of court in advance, which is thus more suitable for the tennis match video shots. Apart from that, the classification of shots is not only required considering the similarity of low-level visual features, but people's perception and attention on movement. It should suit the outdoor tennis match video with relatively apparent changes of bright.

Classification Based on Features of Major Colors

The tennis match video mainly includes four typical types of shots, namely, the Global View Shots, the Medium View Shots, the Close-up View Shots and the Audience View Shots. All Global View Shots have some common features of colors. The color of court in the Global View Shots accounts for the majority of the picture frame, making the main color ratio relatively high; apart from that, when comparing with the Non-Global View Shots, the colors of Global View Shots also distribute evenly. Therefore, the tennis match video can define the features of colors in Global View Shots through the description of key frames and the spatial distribution of major colors. We adopt the HSV color solid as the color models and classify the shots through comparing the key frames of each shot and referring to the similarity of key frames [START_REF] Tong | Analysis on Contents of Shots and Application of Contents of Shots in Video Retrieval[END_REF].

Classification Based on Features of Movement

The tennis match video always follows the attention-focus area and places it in the central part of the picture. Generally speaking, the attention-focus movement area and the non-attention-focus movement area have two differences in the spatial and time domain; in addition, the more intensified movement events are usually more attractive to audiences than the slow movement events [START_REF] Ngo | Video Summarization and Scene Detection by Graph Modeling[END_REF]. Therefore, the conclusion of movement features should be based on the attention-focus movement model and the number and size of attention-focus movement area in the picture frame should be extracted according to the features and rules of tennis match [START_REF] Ma | A model of motion attention for video[END_REF]. When watching the tennis match, people's attention is usually focused on the players and the ball in the match shots, while when appreciating the close-up shots of players, people are interested in the players' some interesting behaviors. Through careful analysis, we have concluded two movement features of tennis match video: namely, (1) there are two attention-focus movement areas in the match shots (the players and tennis' movement areas); the attention-focus movement areas in close-up shots are usually some player's behaviors; for the audience shots, the number of attention-focus areas is uncertain usually. (2) The sizes of players and balls are seldom changed; while the sizes of the attention-focus movement areas in other Non-play Shots are not fixed.

After the color and movement features of key frames are ascertained, we measure the similarity of two shots according to each key frame's movement and main color features and classify the shots into the Global View Shots and the Non-Global View Shots. When comparing with the Non-Global View Shots, the color features of Global View Shots include more colors of play court. Upon defining the main color ratio as the percentage of the main color pixel to the corresponding picture frame's all pixels, we conclude the main color ratio of the Global View Shots is higher.

Audio Identification in Tennis Match Video

Audio Type Identification Based on SVM[5] [6]

Although most Global View Shots in tennis match video are Play Shots and the majority of Non-Global View Shots are Non-play Shots, we can not only depend on the visual features to correctly identify the Play Shots in tennis match video. The typical tennis match video has the special audio features. Applying the audio information of shots into the classifications of shots can detect the Play Shots in a more satisfied manner. The SVM boasts many advantages which is easy for training, needs less samples in training and can give consideration to the inaccuracies in study and the capacity of generalization. Therefore, the SVM is suitable for identifying the types of audio in tennis match video shots.

The tennis match video's audio events include the sound of ball-striking, the sound of applause, the hurrahs of audiences, the sound of the commentators, the sound of referee and other unpredictable sounds from the site. During the whole play process, the commentator is almost commenting the match with a placid sound, even the match becomes brilliant, the commentator seldom sounds emotionally. For this reason, in tennis match video, there are not so much detection information for the sound of the commentator's comment. Furthermore, in tennis match, after each Rally shot, there will usually come the sound of applause. Comparatively speaking, the ratio of hurrahs is relatively low. Therefore, the sound of ball-striking and applause are the most significant audio events in the Rally match video, which can be directly used in differentiating Play Shots and Non-play Shots. For the reason that the sound of ballstriking and applause will not come at the same time, when applying the SVM in indentifying the audio events of the sound of ball-striking and applause, we should separately train a SVM for each audio part, namely, we could train the SVM with two kinds of audio which are separately for identifying the sound of ball-striking and applause.

Detection of Interested Events in Tennis Match Video

In tennis match, the players can get points from counter attacks. The audiences are often interested in Rally shots. Apart from that, serving a ball is also an important way for players to get points. Therefore, in addition to the Rally shots, the serve shots are the events which the audiences are further interested in. The RePlay Shots are often the repeatedly played brilliant scenes in the match, which can be used in retrieval for spectacular scenes. For this reason, the detection of interested events covers the Rally shots, the Ace, the Missed Serve and the RePlay Shots. We can detect the shots in tennis match video according to the visual and audio features of these typical shots and set some heuristic procedures for detection of interested events, for instance, apply the differences of the number of ball-strikes in the shots to differentiate the Rally shots and the Serve shots; differentiate the Ace and the Missed Serve in the serve shots through identifying whether there is he sound of applause at the end of the shots; and differentiate the Play Shots and the Break Shots through identifying whether there is the sound of ball-striking in shots. Four rules can be worked out for the detection of interested events in the tennis match video shots: Rule 1: IF there is no sound of ball striking in one Global View Shot and even there is no sound of applause at the end of such shot, THEN this shot belongs to a Break Shot, ELSE it is a Play Shot. Rule 2: IF there is not only the sound of ball striking in one Global View Shot and even there is the sound of applause at the end of such shot, THEN this shot belongs to a Play Shot, ELSE it is a Break Shot. Rule 3: IF there is no sound of ball striking in one Global View Shot and even there is no sound of applause at the end of such shot, THEN this shot belongs to a Missed Serve Shot, ELSE judge whether the number of ball-striking sound is 1; if the number of ball-striking sound is 1, THEN this shot belongs to an Ace Shot, ELSE it is an Rally Shot.

Rule 4: IF there is gradual change before and after the Break Shot, THEN this shot belongs to a Replay Shot, ELSE it is not a Replay Shot.

Retrieval and Sorting of Spectacular Rally Scenes in Tennis Match Video

The Rally shots in tennis match video are the Play Shots and are also the shots which the audiences are interested at. When there's no shot cut in the Rally scenes, the Rally scene is made up of one Rally shot; therefore, the spectacular Rally scene is actually one spectacular Rally shot. The retrieval for the spectacular Rally scene is retrieving the brilliant shots from the Rally shots detected from the above section. In order to comment on the spectacular degree of the Rally shots, we set the spectacular degree model for the Rally shots according to the features and rules of the tennis match and the judge on the spectacular degree of the Rally shots by most people. When watching the match in tennis education, the students are usually more eager to see the long-time counter attach scenes. Therefore, the lasting time of one brilliant Rally shot is one of the important factors for evaluating the spectacular degree of the shot. Moreover, after one great Rally shot, the audiences usually give more enthusiastic applause. We can define the Rally spectacular degree model based on this law.

Generally, after one great Rally shot, the tennis match video will Replay the brilliant parts of this shot from different visual angles. Therefore, the Rally live broadcast shot which is just after the Replay scene is one spectacular Rally shot, and the Replayed Rally shots are usually the most brilliant shots in the whole match video, which should be arranged in the forefront according to the from-high-to-low order of spectacular degree. The retrieval methods for the spectacular Rally scenes are as follows:

(1) Judge whether the current Rally shot in the set of Rally shots is Replayed. If the answer is positive, mark this shot as RRally shot;

(2) Take all RRally shots marked as spectacular Rally shots;

(3) Calculate the RH values of all RRally shots marked and the Rally shots unmarked;

(4) Set the threshold value T Rally, if the RH of some unmarked Rally shots meet the standard of RH≧T Rally , then this shot should be marked as spectacular Rally shot;

(5) Arrange all the marked RRally shots and Rally shots according to the from-highto-low order of the RH values, and arrange all RRally shots before the Rally shots.

Application Effect of Spectacular Rally Scenes in Tennis Match Video

In order to test the performance of the retrieval method for the spectacular Rally scenes in the tennis match video, we applied the same in the tennis match multimedia education in East China Jiaotong University(ECJTU). All the students having the lesson are the 2007-grade public sports tennis optional course students, who generally expressed that the video effect is very good and beneficial for learning the skills of tennis during the lessons. We sampled 90 students from the total of 260 students watching the tested scenes as the test targets and required them scoring the spectacular degree for the great Rally scenes, with the total score of 100. They respectively scored for the said scenes, making us receive the average score of 84.35, which is relatively satisfied.

Conclusion

(1)The computational complexity obtained only through applying the visual features and adopting the hierarchical clustering algorithm in classifying the video shots is fairly considerable, which is not suitable for the retrieval of tennis match video. Applying the SVM audio identification technology and integrating the movement and visual features in video retrieval is convenient for extracting the size and number of movement areas in picture frame which people are interested at, as well as helpful for classifying the shots in tennis match video and enhance the application effect of the same.

(2)Automatically extracting the reference key frames of Global View Shots based on the color and movement features and applying the similarity of the key frames and reference key frames of shots in classifying shots do not require the prior knowledge of the color of court. Such classification of shots is especially suitable for the tennis match video with the color of court not ascertained and the changes of bright of video relatively obvious.

(3)When applying the SVM in identifying the important types of audio, we worked out four heuristic procedures for detection the interested events, which can improve the detection precision. The high identification rate of Rally events is extremely important for the retrieval of spectacular Rally scenes in the tennis match video.

(4)The visual retrieval technology integrating the visual, movement and audio features can set the model for the spectacular Rally scenes in tennis match video. We can retrieve the same which is in line with most people's thoughts through the spectacular degree model of the Replayed Rally scenes and the spectacular Rally scenes.

Corresponding author. Tel.: +86-13507918839 E-mail address:shehuacaonc@sina.com


Zhixiong Lu

Lanying Zhao

Xiaoqin Li

Jun Yuan

Simulation Simulation Simulation Simulation of of of of R R R Road oad oad oad S S S Surface urface

Keywords: Road surface roughness, Piecewise Fractal model, Contraction factor, Scaleless range

The fractal interpolation theory was used to the research of road surface roughness, and its simulating method was proposed through the piecewise fractal model. The fractal interpolation model was established for the actual road surface roughness, its influential factors and their laws of influencing were also analyzed, and then the model accuracy was confirmed by the fractal parameters and the traditional parameters. The research indicated that the simulated parameters of road surface in different resolutions and the traditional parameters can keep better consistency with the actual road surface. The contraction factor and the road surface sampling resolution are main influential factors of the model. The contraction factor's selection decided directly the fractal dimension of the simulated road , and the simulated precision reduced with the decrease of sample resolution at the same time. In fact, this method is able to obtain a better simulation results, as long as the sample resolution was restrained on the scaleless range of road surface roughness.

Introduction Introduction Introduction Introduction

Road surface longitudinal elevation change is a non-stationary random process with multi-scale features. Therefore road roughness resolution, measured by instrument with a certain resolution, reflects the roughness in the limited level rather than completely and really, which manifests scale correlation of road roughness. Obviously, statistical characterization parameters have certain deviation, when the data was collected by road roughness testers with different resolution, and this deviation will affect the evaluation on road surface roughness and the accuracy of analysis on vehicle vibration response. But all these questions will be solved, if the limited test data can be used to simulate a more precise road vertical elevation.

At present, roughness testers collect longitudinal elevation data of road surface along the vehicles travel direction, which is in certain sampling interval. So the resolution cannot be high enough to meet the require mends achieve of line survey, and the smooth curves, fitted by data tested directly or obtained by the traditional interpolation methods (the Lagrange interpolation, transect interpolation and so on), have good fitting effect, but can not characterize self-similar surface roughness characteristics and fine structure. In this paper, fractal interpolating method was introduced to simulate road roughness with fractal characteristics, and simulative results were verified and analyzed in detail.

. . .1 1 1 1 self-affine self-affine self-affine self-affine fractal fractal fractal fractal interpolation interpolation interpolation interpolation

The basis of fractal interpolation theory is iteration function systems (IFS).IFS consists of a set of limited contraction mapping family on a complete metric space

(Wi:X→X,i= 1,2,…,N) and the corresponding contraction factor Si∈[0,1]. Denoted {X ; Wi , i= 1 , 2 , … , N} , the contraction factor S= max{Si, i= 1 , 2,…,N} .
Fractal interpolation actually constructed a iteration function system, the attractor is the graphical of interpolation function. To the two-dimensional discrete data{(xi,yi

),i=0,1,…,N}∈R 2 , fractal interpolation function is a continuous function f(x),x∈[x0,xn]→R,which interpolated to {(xi,yi),i=0,1,…,N} ,
and for each i ,function f(x) meet with f(xi)= yi , the graph is attractors of a doublecurved iterated function system .

By constructed a iterated function system {R 2 :Wi,i= 1,2,…,N} which attractor is equal to the graph of interpolation f(x), its constructed method as follows: suppose each of iterated function systems Wi is the affine transformation Wi : R 2 →R 2 , then 0

i i i i i i a e x x W d y c y f ⎛ ⎛ ⎞ ⎞ ⎛ ⎞ ⎛ ⎞ = + ⎜ ⎜ ⎟ ⎟ ⎜ ⎟ ⎜ ⎟ ⎝ ⎠ ⎝ ⎠ ⎠ ⎝ ⎝ ⎠ , i= 1,2,…,N (1 1 1 1)
And satisfy the conditions:

0 1 0 1 i i i x x W y y - - ⎛ ⎞ ⎛ ⎞ = ⎜ ⎟ ⎜ ⎟ ⎝ ⎠ ⎝ ⎠ , n i i n i x x W y y ⎛ ⎞ ⎛ ⎞ = ⎜ ⎟ ⎜ ⎟ ⎝ ⎠ ⎝ ⎠ ,i= 1,2,…,N (2 2 2 2) i a , i c , i d , i e , i f
is the real coefficients to determine an affine transformation, by (2) formula to determine the following four linear equations.

0 1 0 0 1 i i i i n i i i i i i i n i n i i a x e x a x e x c x d y f y c x d y f y - - + = ⎧ ⎪ + = ⎪ ⎨ + + = ⎪ ⎪ + + = ⎩ (3 3 3 3)
Under the constrained condition, here given only four equations, however ,five variables ,so there is a free variable ,generally take di as the free variable and call it the vertical scale factor , limited 0≤| i d |≤1(i=0,1,…,N),which is determined by the property of the affine transform. Here let di=0,make the iteration function system Wi always map the line which parallel to the y-axis still to the line which parallel to the y-axis. Then di express the transform proportion. If i d =0, gain the piecewise linear interpolation function. When i d is arbitrary, combination of the above formula, the other factors of the iterated function system can be derived, as follows:

1 0 ( ) /( ) Barnsley [START_REF] Barnsley | Fractals everywere[END_REF] , etc ,who have prove ,IFS which constructed by this method always have displacement attractor, which must be a continuous function graphics, and also through the interpolation points, this continuous function is the fractal interpolation functions.

i i i n a x x x x - = - - (4 4 4 4) 1 0 0 0 ( ) /( ) ( ) /( ) i i i n i n n c y y x x d y y x x - = - - - - - ( 5

2....Recurrent Recurrent Recurrent Recurrent fractal fractal fractal fractal interpolation interpolation interpolation interpolation

Piecewise linear fractal interpolation method is the promotion of linear fractal interpolation. The basic idea is :put the original curve into several paragraphs, which equal or sub-ranges coordinates along the x-axis direction, or several consecutive interpolation points can be put in a group, then each piece of curve according to above fractal interpolation methods get fitting curve [START_REF] Stamps | Fractals, skylines, nature and beauty[END_REF] . Piecewise fractal interpolation is a new fitting method of data, change its parameters, can get the interpolation curve with different dimensions, generated curve in each segment have an independent selfsimilarity and fractal dimension. According to the principles of above fractal interpolation ,be given a set of i d 'values for each, an iterated function system can be completely determined, this iterated function system attractor image is the interpolation image through the known points, how to choose the optimal vertical scale factor to control the shape of the fractal curve, make it possible approximate to the original objective function or discrete data points, which is called the inverse problem of fractal interpolation [START_REF] Zhen | A class of functional equation and fractal interpolation function[END_REF] .

Determination of the optimal i d value is difficult, Mazel and Hayes [4] give the geometric and analytical methods by using fractal interpolation function to fit discrete data, The geometric and analytical methods is the classic algorithm to calculate vertical scale factor. This paper use particle swarm optimal algorithm to achieve the optimization of vertical scale factor.

Fractal Fractal Fractal Fractal interpolation interpolation interpolation interpolation of of of of road road road road surface surface surface surface roughness roughness roughness roughness

As to collected original elevation data of road roughness in the form of discrete points, usually record data by directly connect or traditional interpolation method, then calculate the corresponding deviation index, such as the root mean square, power spectral density and the international roughness index, because of road testing instrument resolution, the evaluation results only reflect the roughness of some cases. In the case of retention measured discrete data, it is the purpose of fractal interpolation of the road roughness original elevation discrete data which obtain the road roughness information between the sampling points, make the fractal interpolation curve far more approximation to the actual road longitudinal curve.

According to preliminary studies, we know that road roughness has obvious fractal characteristics. Compared to several thousand kilometers road surface, the longitudinal elevation data to evaluate the road roughness be collected by surface detection equipment, which be limited by hard disk storage space of the detection equipment, usually only a small part of road data, therefore, on the research that the road roughness data may not have the self-affine properties or similar self-affine properties, fractal interpolation on the data may produce large errors [START_REF] Xie | Study on Generation of Rock Fracture Surface by Using Fractal Interpolation[END_REF][START_REF] Huojun | Parameter identification problem of the fractal interpolation functions[END_REF] . Therefore, we simulated the road roughness elevation data adapting piecewise fractal interpolation method.

Fractal Fractal Fractal Fractal Interpolation Interpolation Interpolation Interpolation Simulation Simulation Simulation Simulation of of of of Road Road Road Road Roughness Roughness Roughness Roughness

In this paper, vertical elevation data of asphalt concrete road surface roughness is the object of the study, experimental data collected is the sequence of discrete points: {(n,yn),n=0,1,…,N}, N is the sampling points, sampling interval of instrument is 0.16m (resolution), 1024 data points is obtained, then the sample length is 1024 * 0.16 = 163.84m. The measured data were displayed in figure 1. In this sample paragraph, points were taken in every 20 point and interval 3.2m, where lowresolution data(figure 2) is obtained. Table Table 1 1 1 (2) Time-domain characterization (root mean square deviation of profile parameters) of fractal interpolation curve and the measured road roughness curve are very close, indicating the two have good statistical self-similarity.

The above two points can determine that fractal characteristics simulation of road roughness by the fractal interpolation function is feasible.

Compared with the measured surface and the interpolated surface graphics in Figure 1 and Figure 3 and the corresponding characterization parameters in table 1, it can be seen that representation parameter values of the measured fractal interpolation curve and surface roughness data graph is very close, both of which are very good Statistical self-similarity, indicating that sub-fractal interpolation function with the road roughness of fractal characteristics is a sound result.

The The The The accuracy accuracy accuracy accuracy factor factor factor factor analyses analyses analyses analyses affecting affecting affecting affecting the the the the fractal fractal fractal fractal interpolation interpolation interpolation interpolation of of of of road road road road surface surface surface surface roughness roughness roughness roughness

Vertical scale factor is the key factor to control the accuracy of the fractal interpolation, which has been described in principle. At the same time, changes in the number of iterations, the size of the sampling point interval (resolution) and sampling length also affect the fractal interpolation results. From Figure 5, it can be found that the simulated curves obtained by interpolation in the first scale have good fractal characteristics, and the slopes are close , but with the selected vertical scale factor increasing, the scaling law curve in the double logarithmic coordinates of the intercept increases, therefore, the physical meaning of the root mean square method calculating fractal dimension can explain the vertical scale factor is directly related to the absolute measure of the value of road roughness size, which is the important factor to effect fractal interpolation. In the measured surface roughness simulation of fractal interpolation, it is needed to consider the vertical scale factor.

From comparison characterization parameters between measured data and simulated curves under different vertical scale factor in Table 2, it shows that when

i d =0.2、 i d ∈ (0,0.2)
, root mean square deviation by the interpolation curve is close to that by the measured curve, which is consistent with the Analysis of Fractal Interpolation scaling law curve in double logarithmic coordinates log ( ) S τ and logτ in the size of the intercept in Figure 5 and the vertical scale factor values greater , RMS deviation of surface roughness greater and the road more rough. the fractal dimension of the first scale range calculated by RMS is also small difference with the measured data, therefore, when the measured surface roughness is simulated by the fractal interpolation, the vertical scale factor in its allowable range can be neither too large nor too small.

Sampling Sampling Sampling Sampling interval interval interval interval

To be Convenient for the comprehensive analysis, measured road roughness in Figure 1 a) is still as the interpolation object. Sampled low-resolution data on road roughness are based on interval 10, 20, 50 points, Resolution R were: R = 1.6m, R = 3.2m, R = 8m, then take the respective analytical method to calculate the value of the vertical scale factor and simulate discrete data points based on these low-resolution data by fractal interpolation, the result of fractal interpolation simulation of the road roughness is shown in Figure 6. Fractal plots of interpolation curves with different R were shown in figure 7. From the figure 7, we can see that the scaling law of the fractal interpolation curve is greater with the high-resolution than with the low-resolution. The meticulous structures of the simulated curve will reduction, when the resolution is too low according the figure6.

Theoretically the sampling interval is smaller is better. In fact, the sampling interval obtains is too small, does not have affects too greatly to increases the scaleless range and the computation fractal dimension value, because of the gathering system's precision or the resolution limit. According to the fractal interpolation theory, so long as the sampling interval in the non-scale sector, the good simulation effect can be obtained with the reasonable influence parameter. But because errors of measure, the vertical scale factor counting and the limit of times of the iterative, the sampling interval is smaller than the one third of scaleless range. interpolation graph structure more precise. However, when the number of iterations increases, the computation also increases and the run time is too long. So when the calculation process, the sample length, sampling interval and vertical scale factor are confirmed, to adjust appropriately the number of iterations to get good simulation results is to avoid difficulties in the cost of computation taken place by the infinite number of iterations.

. . Conclusion Conclusion Conclusion Conclusion

Fractal interpolation, which is based on affine transformation iteration function system, can fully reflect the self-similarity structure 。 Based on vertical elevation discrete data of road surface roughness, continuous simulation curves are produced through fractal interpolation simulation, and these curves not only retain the information of original discrete points but also characterized more detailed information on road roughness between the discrete points, so instrumentations' resolution of road roughness could been reduced.

From the preparation of characterizations of fractal interpolation curve and the measured road roughness curve, we can see that the application of piecewise fractal function was feasible in self-similarity simulation of the measured road surface with low resolution.

The accuracy of the road fractal interpolation is mainly decided by the computation of the vertical scale factor. At the same time, the iterative number of times and the primary data sampling interval (i.e. resolution) also affects the effect of road fractal interpolation.

3 3 3 3

 3 The The The The algorithm algorithm algorithm algorithm of of of of vertical vertical vertical vertical scale scale scale scale factor factor factor factor Based on constructed curve on fractal interpolation function, fractal dimension D is determined by vertical scale factor i d . If known data {(xi,yi); i=0,1,…,n} isn't collinear, then, D 's value is the only solution of equation points collinear , D=1;under the conditions of 0≤| i d |≤1,appropriately select i d ,make D arbitrarily close to 2. however ,in the linear fractal interpolation computations, given random the vertical scale factor i d ' values, it is difficult to make the fractal curve by interpolation more accurately approximate to the real curve or discrete points.

Fig. Fig.

  Fig. Fig. Fig. Fig. 1 1 1 1. . . . The measured road surface with high resolution

Fig.

  Fig. Fig. Fig. Fig. 4 4 4 4. . . . Comparison of primal data and interpolation curve Comparison with fractal characteristics of curve of the original data and simulation of fractal interpolation in Figure 4 can be found: (1) scaling law relation between simulation curve obtained in the first interpolation interval scale in Figure 4 a) with the original data has crossover phenomenon. It is known by calculating the number of dimensions : the fractal dimensions are still relatively close, both of which fractal error is 0.76%; Scaling law relation between simulation curve obtained in the first interpolation interval scale in Figure 4 b) with the original data is very close. This could explain that the simulation of surface roughness by the fractal interpolation function can be appreciatively simulated with the original data, self-similarity.(2) Time-domain characterization (root mean square deviation of profile parameters) of fractal interpolation curve and the measured road roughness curve are very close, indicating the two have good statistical self-similarity.The above two points can determine that fractal characteristics simulation of road roughness by the fractal interpolation function is feasible.Compared with the measured surface and the interpolated surface graphics in

4 4 4 4

 4 .3.1 .3.1 .3.1 .3.1 Vertical Vertical Vertical Vertical scale scale scale scale factor factor factor factor According to the requirements principles of fractal interpolation on the vertical scale factor, different i d values is selected in the condition: 0 | | 1 i d ≤ ≤ , In order to analyze directly the vertical scale factor on the effect of the interpolation, this method of direct value is used, which are taken i d = 0.05, i d ∈ (0,0.2) , i d =0.2 , i d ∈ (0,1) the measured data (Figure 1 data1) Figure 20 points sampling interval) are fractal interpolated, interpolation process selects the same number of iterations (interpolation results Figure omitted). Root mean square method is used to analyze fractal characteristics of the interpolated curve of shown in Figure 5, the characterization parameter of fractal interpolation curve calculation is shown in table 2 below.

Fig. Fig. Fig. Fig. 5 5 5 5 .

 5 Fig. Fig. Fig. Fig. 5 5 5 5. . . . Fractal plots of interpolation curves with different vertical scale factor

  Fig. Fig. Fig. Fig. 6 6 6 6. . . . Effects of resolution of original data to interpolation result. a) R = 1.6m; b) R = 3.2m; c) R = 8m

Table Table TableTable 2

 Table2

	2 2 2. . . . Comparison of parameters with different di
	parameters Measured data	=0.05 i d	i d ∈(0,0.2)	=0.2 i d	i d ∈(0,1)
	root mean	1.6606	1.5931	1.6299	1.6453 1.8443
	square
	fractal	1.6298	1.4081	1.6113	1.6093 1.5698
	dimension

4 4 4.3.3 .3.3 .3.3 .3.3 T T T The he he he Sampling Sampling Sampling Sampling length length length length and and and and number number number number of of of of iterations iterations iterations iterationsBecause the curve interpolated in too long curve segment does not have the same as fractal law of the actual road roughness, when the sample length of the original data curve is too large, piecewise interpolation should be needed, each should be less than the length of the largest scale of a fractal rough surface characteristic.According to the definition of fractal interpolation, with the number of iterations increasing, the number of interpolation points is increased to M times every time (M is the number of interpolation points) which is based on the rules of affine transformation. With the increase of the number of iterations, when the interpolation points are enough, the graph of the attractors of iterated function systems is established by the intensive interpolation points. Therefore, when the selected couples of original data interpolation points are less, which the sampling interval is large, it should increase the number of the iteration to generate more points so as to make a

According to the fractal interpolation theory and the vertical scale factor values estimated by analytic method, the result that high-resolution and low resolution data in Figure 2 is sub-fractal interpolated is shown in Figure 3, the algorithm procedure is implemented by MATLAB7.0.

.2 .2 .2 Validity Validity Validity Validity of of of of Fractal Fractal Fractal Fractal Interpolation Interpolation Interpolation Interpolation

In order to test the validity of the results of fractal interpolation, the curves of the characterization of road roughness parameters(root mean square deviation of profile, fractal dimension,) showed in Figure 1 and Figure 3 is calculated and the values is shown in Table 1, in which D is calculated by root mean square method. Fractal characteristics of curve of the original data and simulation of fractal interpolation were compared in Figure 4.


Xiangang Jiang

Qing Liang

Tao Shen

A New Color Information Entropy Retrieval Method for Pathological Cell Image

Keywords: Mutual information, Color moments, Information entropy, Content based image retrieval

For the distribution characteristics in a slice of pathological cell image, the system transforms them into the characteristic vectores by quantization and clustering in HSV color model, it promotes the concerned isolated pixel color description into the color feature ralative to its neighborhood's color histogram and color moments. By choosing appropriate neighborhood window size, it uses color information entropy method to gain efficient primary classification of gastric slice image and the similar retrieval results from the sample library images. Experiments show that this method has a better image classification and retrieval result.

Introduction

With the unceasing development of the modern imaging technology and information processing technology, the number of images in medical scopes are more and more. By the submitting image, how to use the existing technical methods to retrieve and classify the comparative images from the qualitative sample library is crucial, which is used for clinical diagnosis and contrast, it is becoming one of the research task for efficient medical diagnosis.There are many kinds of image retrieval methods which focus on textual ,geometrical, and color features.In allusion to the pathological cell images, the color information are special and unique compered with other properties,and in the image registration and retrieval, Shannon mutual information is a exact representation of information of interactivity implicated in two color images. The paper makes use of neighborhood color moment histogram to extract color information and utilizes the theory of information entropy to realize shortcut image retrieval. ,then the Shannon Entropy of I is ( )

Shannon Mutual Information

H I : 1 ( ) ( ) log ( ) i i I i H I p i p i ∈ = × ∑ (1)
Where as , supplementary definition: ( ) 0

i p i = 1 ( ) log 0 ( ) i i p i p i = ,as: 0 1 lim log 0 p p p + → = .

×

To two images I 1 and I 2 ,their color values are described by 256 kinds of vectors, their joint entropy is defined as: 255 255

1 2 0 0 1 ( , ) = ( , ( , ) log ( , ) ij i j ij I P i j P i j = = ∑ ∑ H I (2)
Where ) ij P i j is the representation of color joint probability density distribution.

Their Shannon mutual information I(I 1 ,I 2 ) is defined as: 255 255

1 2 0 0 ( , ) ( , ) = ( , ) log ( ) ( ) ij ij i j i j P i j I P i j p i p j = = ∑ ∑ 2 I I (3) 1 2 1 ( ) ( ) ( ,

) H I H I H I I + -=

Where P i (i),p j ( j ) is the color probability density distribution of I 1 and I 2 , P ij (i,j) is color joint probability density distribution,H(I 1 ,I 2 ) is Shannon joint entropy. When I(I 1 ,I 2 )=0, it shows that I 1 and I 2 are independent mutually, if the value of I(I 1 ,I 2 ) is bigger, which means the similarity is higher in the color distribution of two images.

Color Space Conversions

RGB color models can be better matched with the fact that people can fiercely percept the tricolor such as R, G, and B and express them in software developing language. However, RGB color model is not accorded to the human beings' visual perception dealing with distinguishing color similarity better than HIS, HSV, Luv, Lab and so on.

Commonly used HSV's three component separately represent Hue, Saturation and Value [START_REF] Yeqin | Expression on the Basis of the Timber Surface Color Characteristic of the Histogram and Color Moment Method[END_REF]. This system firstly transformes r, g, and b in RGB color model to h,s and v in HSV color model to define color vector. For a cell image such as shown in Fig. 1,the h component in the HSV color model is mainly concentrated in range and[ for a cell image, but the distribution of s and v component are relative uniformity. According to the characteristics, it will use the method in reference [START_REF] Qin | Gastroscope Image Retrieval Based on Histogram of Neighborhood Color Moments[END_REF] for quantization and clustering of colors. Suppose the color vectors of pixel are .

[0,100] 300,360]

[0, 255 ij ∈ ( , i j) ] p 4 Neighborhood Color Moment Mutual Information 4.

Description of color moment

Any color distribution can be represented by its moments. In addition, as the color distribution information is mainly concentrated in lower-order-moments, so it is enough to express the color distribution of an image only by its first moment, second moment and third moment of colors. Comparing to color histogram, this method is good at the aspect that no needs to quantize the color characteristics. The lower-ordermoments of color are expressed in mathematics as follows [START_REF] Weijun | Image Retrieval Technique Based on Combined Features[END_REF]

: 1 1 2 2 1 1 3 3 1 1 1 ( ( ) 1 ( ( ) ) N j j A N j j A N j j A p N p N s p N μ σ μ μ = = = ⎧ = ⎪ ⎪ ⎪ ⎪ = - ⎨ ⎪ ⎪ ⎪ = - ⎪ ⎩ ∑ ∑ ∑ ) (4)
Where j p is the definition of color value of pixel j , N A represents the whole number of pixels in an image. As the ability to distinguish a image based on lower-order-moments of single pixel color is suboptimal, it doesn't make comprehensive consideration to its local color distribution of pixels. It is better to consider the neighborhood of a pixel as an analysis unit and it concerns the local color spatial distribution information in a pixel's around window. Suppose ij p is the color value of concerned pixel ( , , which is quantified and clustered in HSV color model, then its first, second and third order central moment are as follows: ) i j

1 1 2 2 1 1 2 2 1 1 1 2 2 2 2 1 1 2 2 1 1 1 2 2 3 3 1 1 2 2 1 1 [ ( 1 [ ( N M i j M ij m n N M m i n j N M i j M M ij m n ij N M m i n j N M i j M M ij m n ij N M m i n j p M N p u M N s p M N μ σ - - + + - - = - = - - - + + - - = - = - - - + + - - = - = - ⎧ ⎪ = ⎪ × ⎪ ⎪ ⎪ ⎪ = - ⎨ × ⎪ ⎪ ⎪ ⎪ = - ⎪ × ⎪ ⎩ ∑ ∑ ∑ ∑ ∑ ∑ ) ] ) ] u (5)
The moving window is respectively selected as 3 3

× , 5 5 × , 7 7 × ,

The calculation of mutual information of each order multi-neighborhood color moments

In order to define the mutual information of multi-neighborhood color moments, instead , of , to obtain the definition of entropy and mutual information of multi-neighborhood color moments [START_REF] Xiaoling | Color Image Retrieval Based on Color and Texture[END_REF].Then the definition of entropy of each order multi-neighborhood color moments is:

ik ijk q q ( ) i p i ( , ) ij P i j 1 ( ) ( ik i I q ∈ ∑ 1 2 , ) log ( 1,2,3) ( ) ik H I i k q i = × = (7)
The mutual information of each order multi-neighborhood color moments between two images I I is defined as:

1 2 0 0 ( , ) ( , ) ( , )log ( 1,2,3) ( ) ( ) ijk ijk i j ik jk q i j I I I q i j k q i q j = = = = ∑∑ 1 2 1 ( ) ( ) ( , ) H I H I H I I = + - 2 (8)
In all, the classification and retrieval for the pathological cell images can be gained not only by the mutual information of each order multi-neighborhood color moments, but also by the mutual information of synthesis vector normalized by each order neighborhood color moment.

The Experimental Analysis and Conclusions

In allusion to three hundred images downloaded from www.phoenixpeptide.com &www. microscopyu.com, sixty cancer images are included and the other ones are normal. It clearly shows that the main distinctions between cancer images and normal ones are the differences of their color distribution by comparing and analyzing their information entropy of color moment. In the experiment, it employs a new color information entropy retrieval method which promotes the concerned isolated pixel color description into the color feature of its neighborhood region color histogram and makes use of the theory of information entropy to gain the retrieval and primary classification to a further compared classification between the key image and the qualitative sample library. There are three test patterns designed in this experiment: (1)based on color mutual information of single pixel; (2)by making use of the isolated mutual information of the pre-three order color moment in window size of , , separately;

(3)based on the mutual information of the normalized characteristics vector of the integerated three order color moments. This system uses a as a key aimage and finds out the most similar eight images from the sample image library. [START_REF] Weijun | Image Retrieval Technique Based on Combined Features[END_REF] 3 × 5 5 × 7 7 ×

This system implements the retrieval and classification for the stomach cell images,it uses the mutual information between a key image's and sample library image's based on the single pixel to obtain the most similar eight images being showed in Fig. 3. Fig. 4 shows the retrieval results by employing the method based on the mutual information of the first order color moment in the window size of .Each method's retrieval efficiency are showed as the table 1. 5 5 × From above experiments and above comparson table, we know that the retrieval efficiencies of the methods of mutual information of each order color moment regarding neighborhood region are higher than the one based on the single pixel, meanwhile, the retrieval efficiencies relative to the color moment order trend to be positive growth, the way based on synthesis of mutual information of each order neighborhood color moment has the best precision. At the point of consuming-time, the method of each color moment is longer relatively to the one based on the single pixel and it is in direct proportion to the order. Considering the neighborhood window sizes, while the geometric size of each tissue in an image is small, it will be better to choose a smaller neighborhood window, which has a high retrieval efficiency and low consuming-time, otherwise selecting a bigger neighborhood window to a bigger average tissue size in an image, it would avoid the characteristic fuzzy of each tissue in an image by adopting a flexible window size.

i j 3 3 ×

 3 the range of numerical values in extraction of three lower-order-moments of all pixels ( , is limited in [0,255].In allusion to each pixel in an image, it makes use of quantization and clustering color histogram of image referred above to obtain nine neighborhood color moments, (for a window) ), which are described in the region [0,255] just by rounding all float numbers. It makes the global statistics and normalization to the entire image and gets normalization histograms of nine neighborhood lower-order central moments.Fig.2shows as:the normalization histograms of the first, second and third order central moment in 3 3× neighborhood window, where the red σ ,the blue one represents and the black one represents the color histogram by quantified and clustered color vectors.

Fig. 1 . 2 .

 12 Fig.1. Pathological cell image Fig.2. The compared histograms of the first, second,third order moment and the entire image's quantified and clustered color vectors in a 3 3 × neighborhood window Where represents the occurrence times of the k order color central moment, is the representation of all pixel numbers in an image. Define the color joint probability density distribution of the k order central moment of two images by the same methods, which is supposed as , where

Fig. 3 . 4 .

 34 Fig.3. The retrieval result by the method Fig.4. The retrieval result by the method

Table 1 .

 1 The comparison of methods of color mutual information retrieval

					Precision	Precision of		Total time of
	Retrieval method	(%)	using	Time	using
						moments	(ms)	moments
	Window		Single pixel color		synthesis (%)		synthesis(ms)
	size		moment	80.65		6141
	3	×	3	The first order color moment	88.25	91.23	8828	18012
	window	The second order
				color moment	90.19		8875
				The third order
				color moment	90.19		9234
	5	×	5	The first order color moment	87.88		8625	26412
	window		The second order		92.56
				color moment	91.30		12375
				The third order
				color moment	92.65		13025