A new image content authentication algorithm based on Laplace spectra was proposed. Outstanding feature points are extracted from the original image and a cipher point is inserted. A relational graph is then built, and the Laplace spectra of the graph are calculated to serve as image features. The Laplace spectra are quantized then embedded into the original image as a watermark. In the authentication step, the Laplace spectra of the authenticating image are calculated and compared with that of the watermark embedded in the image. If both of the spectra are identical, the image passes the authentication test. Otherwise, the tamper is found. The experimental results show that the proposed authentication algorithm can effectively detect the event and the location when the original image content is tampered viciously.
{"title":"Image Content Authentication Algorithm Based on Laplace Spectra Feature","authors":"Wanli Lv, Jixin Ma, B. Luo","doi":"10.1109/ICNC.2007.426","DOIUrl":"https://doi.org/10.1109/ICNC.2007.426","url":null,"abstract":"A new image content authentication algorithm based on Laplace spectra was proposed. Outstanding feature points are extracted from the original image and a cipher point is inserted. A relational graph is then built, and the Laplace spectra of the graph are calculated to serve as image features. The Laplace spectra are quantized then embedded into the original image as a watermark. In the authentication step, the Laplace spectra of the authenticating image are calculated and compared with that of the watermark embedded in the image. If both of the spectra are identical, the image passes the authentication test. Otherwise, the tamper is found. The experimental results show that the proposed authentication algorithm can effectively detect the event and the location when the original image content is tampered viciously.","PeriodicalId":250881,"journal":{"name":"Third International Conference on Natural Computation (ICNC 2007)","volume":"379 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116575266","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}
We consider a new 4D representation of DNA sequence, which has the advantage of not only containing all the information in the DNA sequence but also avoiding the overlapping. Based on this representation, we define a new common frequency coefficient and apply it to gene identification. The identification result of the S.cerevisiae genome illustrates the superior performance of our approach.
{"title":"Gene Identification Based on Geometrical Representation of DNA Sequence","authors":"Jiawei Luo, Li Yang, Yi Zhou","doi":"10.1109/ICNC.2007.397","DOIUrl":"https://doi.org/10.1109/ICNC.2007.397","url":null,"abstract":"We consider a new 4D representation of DNA sequence, which has the advantage of not only containing all the information in the DNA sequence but also avoiding the overlapping. Based on this representation, we define a new common frequency coefficient and apply it to gene identification. The identification result of the S.cerevisiae genome illustrates the superior performance of our approach.","PeriodicalId":250881,"journal":{"name":"Third International Conference on Natural Computation (ICNC 2007)","volume":"35 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122598646","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}
Dianhui Mao, Zhi-yuan Zeng, Cheng Wang, Weihua Lin
Soil erosion is a very complicated process, and influenced by many correlatively factors, so it is hard to evaluate and predict the condition of soil erosion, especially in those regions where there have not sufficiently observation date. To solve the above problem, this paper proposed a new assessment model based on the support vector machines (SVM), In order to improve the accuracy of the model, the algorithm of particle swarm optimization (PSO) is used to hunt the optimum solution of the parameters sigma, penalty factor C and xi -insensitive loss function of SVM. The model is carried out in Shiqiaopu catchment of Hubei province, the results of training and validation have shown that the model has higher forecasting accuracy, compared with the algorithm of BP artificial neural network model. Thus, the model based on SVM provides a new method for evaluating and predicting the condition of soil erosion.
{"title":"Support Vector Machines with PSO Algorithm for Soil Erosion Evaluation and Prediction","authors":"Dianhui Mao, Zhi-yuan Zeng, Cheng Wang, Weihua Lin","doi":"10.1109/ICNC.2007.697","DOIUrl":"https://doi.org/10.1109/ICNC.2007.697","url":null,"abstract":"Soil erosion is a very complicated process, and influenced by many correlatively factors, so it is hard to evaluate and predict the condition of soil erosion, especially in those regions where there have not sufficiently observation date. To solve the above problem, this paper proposed a new assessment model based on the support vector machines (SVM), In order to improve the accuracy of the model, the algorithm of particle swarm optimization (PSO) is used to hunt the optimum solution of the parameters sigma, penalty factor C and xi -insensitive loss function of SVM. The model is carried out in Shiqiaopu catchment of Hubei province, the results of training and validation have shown that the model has higher forecasting accuracy, compared with the algorithm of BP artificial neural network model. Thus, the model based on SVM provides a new method for evaluating and predicting the condition of soil erosion.","PeriodicalId":250881,"journal":{"name":"Third International Conference on Natural Computation (ICNC 2007)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122645463","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}
Present methods of facial expression recognition usually designate an expression image as one kind of six facial basic expressions. However, a facial expression usually is a complex expression that consists of several basic expressions. This paper proposes a facial complex expression recognition algorithm based on fuzzy kernel clustering and support vector machines. This algorithm designs the binary facial complex expression classification tree by using fuzzy kernel clustering algorithm, trains support vector machines at each node of the binary classification tree and describes the complexity of a facial expression according as the result of support vector machines classification. Experimental results indicate that the proposed algorithm generates higher accuracy for the JAFFE database and achieves better performance than 1-a-r SVMs. In addition, experimental results show that the result of the proposed method is more accord with practice than the result of traditional expression recognition methods.
{"title":"Facial Complex Expression Recognition Based on Fuzzy Kernel Clustering and Support Vector Machines","authors":"H Zhao, Zhiliang Wang, Jihui Men","doi":"10.1109/ICNC.2007.372","DOIUrl":"https://doi.org/10.1109/ICNC.2007.372","url":null,"abstract":"Present methods of facial expression recognition usually designate an expression image as one kind of six facial basic expressions. However, a facial expression usually is a complex expression that consists of several basic expressions. This paper proposes a facial complex expression recognition algorithm based on fuzzy kernel clustering and support vector machines. This algorithm designs the binary facial complex expression classification tree by using fuzzy kernel clustering algorithm, trains support vector machines at each node of the binary classification tree and describes the complexity of a facial expression according as the result of support vector machines classification. Experimental results indicate that the proposed algorithm generates higher accuracy for the JAFFE database and achieves better performance than 1-a-r SVMs. In addition, experimental results show that the result of the proposed method is more accord with practice than the result of traditional expression recognition methods.","PeriodicalId":250881,"journal":{"name":"Third International Conference on Natural Computation (ICNC 2007)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122845076","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}
In this paper, the variable weight combination forecasting approach which both uses genetic algorithm with global searching ability and uses neural network with nonlinear mapping ability is put forward. First, the weight coefficients are gained by means of adaptive genetic algorithm. Second, the neural network is trained by weight -obtained and the intending weighted values are predicted further. The method has character that whole weighted values is positive and the summation of weight values at same time equals to 1. At last, the variable weight combination forecasting model is built and applied into forecasting total consumption expenditure in Shanghai GDP . Simulation shows the effectiveness of the proposed approach.
{"title":"Variable Weighted Combination Forecasting Model Based on Genetic Algorithm and Artificial Neural Network","authors":"Junfengs Li, Wenzhan Dai, Haipeng Pan","doi":"10.1109/ICNC.2007.808","DOIUrl":"https://doi.org/10.1109/ICNC.2007.808","url":null,"abstract":"In this paper, the variable weight combination forecasting approach which both uses genetic algorithm with global searching ability and uses neural network with nonlinear mapping ability is put forward. First, the weight coefficients are gained by means of adaptive genetic algorithm. Second, the neural network is trained by weight -obtained and the intending weighted values are predicted further. The method has character that whole weighted values is positive and the summation of weight values at same time equals to 1. At last, the variable weight combination forecasting model is built and applied into forecasting total consumption expenditure in Shanghai GDP . Simulation shows the effectiveness of the proposed approach.","PeriodicalId":250881,"journal":{"name":"Third International Conference on Natural Computation (ICNC 2007)","volume":"177 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122875859","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 proposed approach is neural-network based and combines the self-tuning principle with reinforcement learning. The proposed control scheme consists of a controller, a utility estimator, an exploration module, a learning module and a rewarding module. The controller and the utility estimator are implemented together in a single radial basis function network (RBFN). The learning method involves structural adaptation (growing RBFN) and parameter adaptation. No prior knowledge of the plant is assumed, and the controller has to begin with exploration of the state space. The exploration versus exploitation dilemma of reinforcement learning is solved through smooth transitions between the two modes. The controller is capable of asymptotically approaching the desired reference trajectory, which is showed in simulation result.
{"title":"A Study on the Control of Nonlinear System Using Growing RBFN and Reinforcement Learning","authors":"Hyun-Seob Cho","doi":"10.1109/ICNC.2007.151","DOIUrl":"https://doi.org/10.1109/ICNC.2007.151","url":null,"abstract":"The proposed approach is neural-network based and combines the self-tuning principle with reinforcement learning. The proposed control scheme consists of a controller, a utility estimator, an exploration module, a learning module and a rewarding module. The controller and the utility estimator are implemented together in a single radial basis function network (RBFN). The learning method involves structural adaptation (growing RBFN) and parameter adaptation. No prior knowledge of the plant is assumed, and the controller has to begin with exploration of the state space. The exploration versus exploitation dilemma of reinforcement learning is solved through smooth transitions between the two modes. The controller is capable of asymptotically approaching the desired reference trajectory, which is showed in simulation result.","PeriodicalId":250881,"journal":{"name":"Third International Conference on Natural Computation (ICNC 2007)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122889457","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}
Rock fracture tracing is very important in many rock-engineering applications. This paper presents a new methodology for rock fracture detection, description and classification based on image processing technique and support vector machine (SVM). The developed algorithm uses a number of rock surface images those were taken by sophisticated CCD cameras. The studied algorithm processes all the images. Then, the fractures are identified and categorized by SVM. The proposed algorithm has been tested, and the results show that the approach is promising.
{"title":"Rock fracture tracing based on image processing and SVM","authors":"Weixing Wang, Haijun Liao, Ying Huang","doi":"10.1109/ICNC.2007.643","DOIUrl":"https://doi.org/10.1109/ICNC.2007.643","url":null,"abstract":"Rock fracture tracing is very important in many rock-engineering applications. This paper presents a new methodology for rock fracture detection, description and classification based on image processing technique and support vector machine (SVM). The developed algorithm uses a number of rock surface images those were taken by sophisticated CCD cameras. The studied algorithm processes all the images. Then, the fractures are identified and categorized by SVM. The proposed algorithm has been tested, and the results show that the approach is promising.","PeriodicalId":250881,"journal":{"name":"Third International Conference on Natural Computation (ICNC 2007)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114423647","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}
Considering about to solve the bottleneck problem of computing ability and power consumption of mobile robot vision system, a DSP and FPGA based intelligent image grabber and a robot vision system using this intelligent image grabber are developed. The configuration and some important characteristics of this robot vision system, which can not only complete the work of image capturing but can also process images using different algorithms in real-time, are described in this paper. It has been shown by experiments and performance comparison that this newly developed robot vision system is more suitable for mobile robots than the traditional PC based robot vision system.
{"title":"A High Performance Low Power Consumption Robot Vision System","authors":"Peng Lu, Kui Yuan, Wei Zou","doi":"10.1109/ICNC.2007.42","DOIUrl":"https://doi.org/10.1109/ICNC.2007.42","url":null,"abstract":"Considering about to solve the bottleneck problem of computing ability and power consumption of mobile robot vision system, a DSP and FPGA based intelligent image grabber and a robot vision system using this intelligent image grabber are developed. The configuration and some important characteristics of this robot vision system, which can not only complete the work of image capturing but can also process images using different algorithms in real-time, are described in this paper. It has been shown by experiments and performance comparison that this newly developed robot vision system is more suitable for mobile robots than the traditional PC based robot vision system.","PeriodicalId":250881,"journal":{"name":"Third International Conference on Natural Computation (ICNC 2007)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114491650","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 paper defines a process model for an ubiquitous commerce. Defined processes include analyzing collected information of customers using questionnaire as well as remotely monitoring customer behavior information. Using the RFID tag, Intermediary collects sensing data about customer's location, frequency of using cosmetics on a continual basis. Cosmetics companies match customer behavior patterns by mapping RFID tag ID in received contextual data and customer ID in customer DB. Using customer behavior patterns, the company can provide the target customers with highly personalized service in so called u-commerce environment.
{"title":"Ubiquitous Commerce Utilizing a Process Model","authors":"Sang-Chan Park, Cheol Young Kim, K. Im","doi":"10.1109/ICNC.2007.788","DOIUrl":"https://doi.org/10.1109/ICNC.2007.788","url":null,"abstract":"This paper defines a process model for an ubiquitous commerce. Defined processes include analyzing collected information of customers using questionnaire as well as remotely monitoring customer behavior information. Using the RFID tag, Intermediary collects sensing data about customer's location, frequency of using cosmetics on a continual basis. Cosmetics companies match customer behavior patterns by mapping RFID tag ID in received contextual data and customer ID in customer DB. Using customer behavior patterns, the company can provide the target customers with highly personalized service in so called u-commerce environment.","PeriodicalId":250881,"journal":{"name":"Third International Conference on Natural Computation (ICNC 2007)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121922261","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 convergence of estimation of distribution algorithms (EDAs) with finite population is analyzed in this paper. At first, the models of EDAs with finite population are designed by incorporating an error into expected distribution of parent population. Then the convergence of the EDAs is proved with finite population under three widely used selection schemes. The results show that EDAs converge to the optimal solutions within the range of error described in this paper.
{"title":"An Analysis of Estimation of Distribution Algorithms with Finite Population Models","authors":"Yan Wu, Yuping Wang, Xiaoxiong Liu","doi":"10.1109/ICNC.2007.174","DOIUrl":"https://doi.org/10.1109/ICNC.2007.174","url":null,"abstract":"The convergence of estimation of distribution algorithms (EDAs) with finite population is analyzed in this paper. At first, the models of EDAs with finite population are designed by incorporating an error into expected distribution of parent population. Then the convergence of the EDAs is proved with finite population under three widely used selection schemes. The results show that EDAs converge to the optimal solutions within the range of error described in this paper.","PeriodicalId":250881,"journal":{"name":"Third International Conference on Natural Computation (ICNC 2007)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122233989","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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