Electric power network injects with amount of harmonic current because of widespread use of nonlinear load, which does great harm to the using electricity consumption. In order to prevent harmonic current from influencing safety of system’s operation, we should know how much the distorted wave contains harmonic and take corresponding measure to make suppression or compensation of it. But due to a lot of noise affect existing, so detection result is inaccuracy, by using multi-resolution wavelet method, we get more accurate network voltage and currency, which can carry on next harmonic detection, etc. By simulation software of MATLAB combing with LabVIEW, wavelet de-noising has better function in filtering high frequency and noise signal, etc than traditional low-passing filter of Butterworth. Through harmonic detection simulation, result is exact through THD% calculation, which difference between standard value and measurement value is very small in THD% measurement error of 0.01%. Wavelet soft threshold de-noising technology can be applied into other monitor, such as three-phase unbalance factor monitor, frequency tracking monitor, fundamental wave monitor, etc.
{"title":"Realization of Wavelet Soft Threshold De-noising Technology Based on Visual Instrument","authors":"Yu Chen","doi":"10.1109/JCAI.2009.135","DOIUrl":"https://doi.org/10.1109/JCAI.2009.135","url":null,"abstract":"Electric power network injects with amount of harmonic current because of widespread use of nonlinear load, which does great harm to the using electricity consumption. In order to prevent harmonic current from influencing safety of system’s operation, we should know how much the distorted wave contains harmonic and take corresponding measure to make suppression or compensation of it. But due to a lot of noise affect existing, so detection result is inaccuracy, by using multi-resolution wavelet method, we get more accurate network voltage and currency, which can carry on next harmonic detection, etc. By simulation software of MATLAB combing with LabVIEW, wavelet de-noising has better function in filtering high frequency and noise signal, etc than traditional low-passing filter of Butterworth. Through harmonic detection simulation, result is exact through THD% calculation, which difference between standard value and measurement value is very small in THD% measurement error of 0.01%. Wavelet soft threshold de-noising technology can be applied into other monitor, such as three-phase unbalance factor monitor, frequency tracking monitor, fundamental wave monitor, etc.","PeriodicalId":154425,"journal":{"name":"2009 International Joint Conference on Artificial Intelligence","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114230543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Error elimination is very important in 3D measurement with ring-structured-light. A method has been proposed, which includes the following procedures. First, acquire ring-structured-light images and extract their stripe center. Second, find the base-circle center of the stripe center locus by curve fitting, using the least squares method. Subsequently unfold the stripe center locus along its base-circle. In the waveform of the unfolded view, there exist an eccentricity error component and an ellipse shape error component. The frequencies of these two error components are different from the frequency of detail component. They can be separated in the unfolded view by power spectrum density (PSD) analysis. Based on their formation principles, eccentricity error and ellipse error can be separated and eliminated, and the accuracy of the measurement can be improved. Experiments have demonstrate its applicability.
{"title":"Applying PSD Analysis in Ring-Structured-Light 3D Measurement","authors":"Huiwen Leng, Chunguang Xu, Zhongwei Feng, D. Xiao","doi":"10.1109/JCAI.2009.131","DOIUrl":"https://doi.org/10.1109/JCAI.2009.131","url":null,"abstract":"Error elimination is very important in 3D measurement with ring-structured-light. A method has been proposed, which includes the following procedures. First, acquire ring-structured-light images and extract their stripe center. Second, find the base-circle center of the stripe center locus by curve fitting, using the least squares method. Subsequently unfold the stripe center locus along its base-circle. In the waveform of the unfolded view, there exist an eccentricity error component and an ellipse shape error component. The frequencies of these two error components are different from the frequency of detail component. They can be separated in the unfolded view by power spectrum density (PSD) analysis. Based on their formation principles, eccentricity error and ellipse error can be separated and eliminated, and the accuracy of the measurement can be improved. Experiments have demonstrate its applicability.","PeriodicalId":154425,"journal":{"name":"2009 International Joint Conference on Artificial Intelligence","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134565674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The special distribution, sampling strategy and responding mechanism of human photoreceptor cells are significant for visual system. Photoreceptor cells layer is the connection between the preceding and the following in visual system,in one hand, it could sample and represent the outside information, in other hand, it could process the data in its specific way and transfer processed data to following layers. Photoreceptor cells could, in some degree, decide the information data, accuracy, processing time, energy,and further the balance among these factors. With the development of high resolution CCD, the CCD could achieve the highest density of photoreceptor cells in fovea, which provides a solid basis for realistic emulation on retina photoreceptor layer. The paper precisely emulate the retina photoreceptor layer based on human real physical data and response mechanism of photoreceptor cells, which could aid in disclose the real mechanism in retina and whole visual system and also contributes to artificial retina design and implementation.
{"title":"Realistic Simulation on Retina Photoreceptor Layer","authors":"X. Guan, Hui Wei","doi":"10.1109/JCAI.2009.148","DOIUrl":"https://doi.org/10.1109/JCAI.2009.148","url":null,"abstract":"The special distribution, sampling strategy and responding mechanism of human photoreceptor cells are significant for visual system. Photoreceptor cells layer is the connection between the preceding and the following in visual system,in one hand, it could sample and represent the outside information, in other hand, it could process the data in its specific way and transfer processed data to following layers. Photoreceptor cells could, in some degree, decide the information data, accuracy, processing time, energy,and further the balance among these factors. With the development of high resolution CCD, the CCD could achieve the highest density of photoreceptor cells in fovea, which provides a solid basis for realistic emulation on retina photoreceptor layer. The paper precisely emulate the retina photoreceptor layer based on human real physical data and response mechanism of photoreceptor cells, which could aid in disclose the real mechanism in retina and whole visual system and also contributes to artificial retina design and implementation.","PeriodicalId":154425,"journal":{"name":"2009 International Joint Conference on Artificial Intelligence","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133779481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Based on bin packing problem algorithm, A revised Best Fit Decreasing algorithm for container ship’s stowage problem was proposed. The simulation result indicates that the performance of the proposed algorithm is sound.
{"title":"A New Algorithm for Container Ship's Stowage","authors":"Jia-jun Wei","doi":"10.1109/JCAI.2009.153","DOIUrl":"https://doi.org/10.1109/JCAI.2009.153","url":null,"abstract":"Based on bin packing problem algorithm, A revised Best Fit Decreasing algorithm for container ship’s stowage problem was proposed. The simulation result indicates that the performance of the proposed algorithm is sound.","PeriodicalId":154425,"journal":{"name":"2009 International Joint Conference on Artificial Intelligence","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130514461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
An improved Fish-Search algorithm was proposed for airplane route planning of a class of Plane-Missile cooperation. The mathematical description of this class of cooperation was introduced. Comprehensively considered such key factors of the cooperation as inter-visibility, threat, maximum distance and relative orientation, the constraint conditions and evaluation index were constructed. According to the characteristics of the problem, the Fish-Search algorithm was used to solve the problem, and was improved by introducing a ta-boo bulletin board and the survival mechanism. As is shown in the comparison of the simulation results of the original and the improved algorithm, the convergence rate was improved.
{"title":"Airplane Route Planning for Plane-Missile Cooperation Using Improved Fish-Search Algorithm","authors":"Tao Sun, Xiaofang Xie, Yong-qin Sun, Song-yang Li","doi":"10.1109/JCAI.2009.73","DOIUrl":"https://doi.org/10.1109/JCAI.2009.73","url":null,"abstract":"An improved Fish-Search algorithm was proposed for airplane route planning of a class of Plane-Missile cooperation. The mathematical description of this class of cooperation was introduced. Comprehensively considered such key factors of the cooperation as inter-visibility, threat, maximum distance and relative orientation, the constraint conditions and evaluation index were constructed. According to the characteristics of the problem, the Fish-Search algorithm was used to solve the problem, and was improved by introducing a ta-boo bulletin board and the survival mechanism. As is shown in the comparison of the simulation results of the original and the improved algorithm, the convergence rate was improved.","PeriodicalId":154425,"journal":{"name":"2009 International Joint Conference on Artificial Intelligence","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123659575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Adaptive PID controller based on back propagation(BP) neural network has many merits like that simple algorithm of PID controller and self-study and adaptive functions of neural network. According the requirements of system output performance, the BP neural network can auto-adjust its weights to vary , and . The simulation results of an electro-hydraulic position servo control system using adaptive PID controller based on BP neural network show that it can get better control characteristics and adaptability, strong robustness in the nonlinear and time vary system. At the same time, simulate results provided a theoretical basis for the design and application of electro-hydraulic position servo control system.
{"title":"Adaptive PID Controller Based on BP Neural Network","authors":"Beitao Guo, Hongyi Liu, Zhong Luo, Fei Wang","doi":"10.1109/JCAI.2009.86","DOIUrl":"https://doi.org/10.1109/JCAI.2009.86","url":null,"abstract":"Adaptive PID controller based on back propagation(BP) neural network has many merits like that simple algorithm of PID controller and self-study and adaptive functions of neural network. According the requirements of system output performance, the BP neural network can auto-adjust its weights to vary , and . The simulation results of an electro-hydraulic position servo control system using adaptive PID controller based on BP neural network show that it can get better control characteristics and adaptability, strong robustness in the nonlinear and time vary system. At the same time, simulate results provided a theoretical basis for the design and application of electro-hydraulic position servo control system.","PeriodicalId":154425,"journal":{"name":"2009 International Joint Conference on Artificial Intelligence","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122183681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This thesis elaborated the concept, significance and main strategy of machine learning as well as the basic structure of machine learning system. By combining several basic ideas of main strategies, great effort are laid on introducing several machine learning methods, such as Rote learning, Explanation-based learning, Learning from instruction, Learning by deduction, Learning by analogy and Inductive learning, etc. Meanwhile, comparison and analysis are made upon their respective advantages and limitations. At the end of the article, it proposes the research objective of machine learning and points out its development trend.Machine learning is a fundamental way that enable the computer to have the intelligence ; Its application which had been used mainly the method of induction and the synthesis¿rather than the deduction has already reached many fields of Artificial Intelligence.
{"title":"A Study and Application on Machine Learning of Artificial Intellligence","authors":"Ming Xue, Chang-jun Zhu","doi":"10.1109/JCAI.2009.55","DOIUrl":"https://doi.org/10.1109/JCAI.2009.55","url":null,"abstract":"This thesis elaborated the concept, significance and main strategy of machine learning as well as the basic structure of machine learning system. By combining several basic ideas of main strategies, great effort are laid on introducing several machine learning methods, such as Rote learning, Explanation-based learning, Learning from instruction, Learning by deduction, Learning by analogy and Inductive learning, etc. Meanwhile, comparison and analysis are made upon their respective advantages and limitations. At the end of the article, it proposes the research objective of machine learning and points out its development trend.Machine learning is a fundamental way that enable the computer to have the intelligence ; Its application which had been used mainly the method of induction and the synthesis¿rather than the deduction has already reached many fields of Artificial Intelligence.","PeriodicalId":154425,"journal":{"name":"2009 International Joint Conference on Artificial Intelligence","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125130623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A prediction model-based fuzzy neural network (PFNN) approach is proposed, in which a basic FNN is created at first to predict the relative position of the trajectory. Then a FNN is used independently to get the control values of the variables for motor motion according to those variables including trajectory position both from those measured and predicted values, and those speed variables. At last membership functions and network weights of the second FNN are also trained with a BP algorithm. Meanwhile, the measured values of the trajectory are memorized so as to compare them with the memorized values to confirm if the motion is moving in cycles. If it is moving in cycles, a decision making unit would cease the prediction unit. The emulated experiments show that the performance of the proposed approach is higher, the process to train the network is relatively easy, and the control strategy is simple.
{"title":"Control of Mobile Robot Using Prediction-based FNN","authors":"Suiping Qi, Yi Cao, Shou-zhi Yu, Fu-chun Sun","doi":"10.1109/JCAI.2009.136","DOIUrl":"https://doi.org/10.1109/JCAI.2009.136","url":null,"abstract":"A prediction model-based fuzzy neural network (PFNN) approach is proposed, in which a basic FNN is created at first to predict the relative position of the trajectory. Then a FNN is used independently to get the control values of the variables for motor motion according to those variables including trajectory position both from those measured and predicted values, and those speed variables. At last membership functions and network weights of the second FNN are also trained with a BP algorithm. Meanwhile, the measured values of the trajectory are memorized so as to compare them with the memorized values to confirm if the motion is moving in cycles. If it is moving in cycles, a decision making unit would cease the prediction unit. The emulated experiments show that the performance of the proposed approach is higher, the process to train the network is relatively easy, and the control strategy is simple.","PeriodicalId":154425,"journal":{"name":"2009 International Joint Conference on Artificial Intelligence","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125646164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With simple architecture and faster speed, linear feedback shift register often is selected to produce random number in many applications. However, the random number generated by LFSR cannot meet the demand of unpredictability for secure mechanism. The nonlinearity of Genetic algorithm can be used to improve the property of LFSR. We present a novel random number generator by using genetic algorithm to evolve LFSR. This random number generator is convenient for hardware implementation and has longer period and complex architecture. The property of random number generated by it can pass NIST randomness tests and meet the requirement of communication security by test.
{"title":"Evolutionary Design of Random Number Generator","authors":"Yuhua Wang, HongYong Wang, Aihong Guan, Huanguo Zhang","doi":"10.1109/JCAI.2009.46","DOIUrl":"https://doi.org/10.1109/JCAI.2009.46","url":null,"abstract":"With simple architecture and faster speed, linear feedback shift register often is selected to produce random number in many applications. However, the random number generated by LFSR cannot meet the demand of unpredictability for secure mechanism. The nonlinearity of Genetic algorithm can be used to improve the property of LFSR. We present a novel random number generator by using genetic algorithm to evolve LFSR. This random number generator is convenient for hardware implementation and has longer period and complex architecture. The property of random number generated by it can pass NIST randomness tests and meet the requirement of communication security by test.","PeriodicalId":154425,"journal":{"name":"2009 International Joint Conference on Artificial Intelligence","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127543845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Vehicle type automatic recognition is of great important today in Intelligent Transportation System. And neural network is often applied to recognize the vehicle type. However, the network can be very complex and therefore difficult to be trained. In order to cope with such issues, a new developed vehicle type recognition method based on contour feature is presented in this study. It is applied to obtain the vehicle type from the geometrical feature of the vehicle. This enables the implementation of the recognition system only in given geometrical size and simplifies the thinning recognize procedure. The contribution of this work is threefold: At first, a novel evolutionary methodology for extracting vehicle feature is presented. Secondly, a vehicle recognition algorithm consisting of four steps is demonstrated. Finally, the performance of the recognition system is evaluated by not only using static vehicle image but also using dynamic vehicle video.
{"title":"A Study on Contour Feature Algorithm for Vehicle Type Recognition","authors":"Weihua Wang","doi":"10.1109/JCAI.2009.56","DOIUrl":"https://doi.org/10.1109/JCAI.2009.56","url":null,"abstract":"Vehicle type automatic recognition is of great important today in Intelligent Transportation System. And neural network is often applied to recognize the vehicle type. However, the network can be very complex and therefore difficult to be trained. In order to cope with such issues, a new developed vehicle type recognition method based on contour feature is presented in this study. It is applied to obtain the vehicle type from the geometrical feature of the vehicle. This enables the implementation of the recognition system only in given geometrical size and simplifies the thinning recognize procedure. The contribution of this work is threefold: At first, a novel evolutionary methodology for extracting vehicle feature is presented. Secondly, a vehicle recognition algorithm consisting of four steps is demonstrated. Finally, the performance of the recognition system is evaluated by not only using static vehicle image but also using dynamic vehicle video.","PeriodicalId":154425,"journal":{"name":"2009 International Joint Conference on Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129074933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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