Mobile location service of dynamic geographic information space application that supported school teachers and students is a currently a research hotspot for universities. The applications of classroom navigation, teacher-student tracking monitoring, registration management, etc. it will meet the school management which offers convenience for personal position information retrieval, recommend services and individualized maps constructed. How to use mobile network to organically integrate LBS and WebGIS, and to construct a new generation of mobile information services platform and system environment based on the campus network location perception, this paper presents several key technologies of system framework and implementation.
{"title":"Interactive Mobile Campus Based on Position Perception","authors":"Wenzhi Liu, Zhaobin Liu, Yan Zhang","doi":"10.1109/ISCID.2011.45","DOIUrl":"https://doi.org/10.1109/ISCID.2011.45","url":null,"abstract":"Mobile location service of dynamic geographic information space application that supported school teachers and students is a currently a research hotspot for universities. The applications of classroom navigation, teacher-student tracking monitoring, registration management, etc. it will meet the school management which offers convenience for personal position information retrieval, recommend services and individualized maps constructed. How to use mobile network to organically integrate LBS and WebGIS, and to construct a new generation of mobile information services platform and system environment based on the campus network location perception, this paper presents several key technologies of system framework and implementation.","PeriodicalId":224504,"journal":{"name":"2011 Fourth International Symposium on Computational Intelligence and Design","volume":"289 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116402783","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 paper applies chaotic encryption to information document, and designed a simple and fast encryption algorithm based on Sine Square Mapping. This kind of mapping can be chaotic in a wide parameter range and the sequences generated possess larger Lyapunov exponent and excellent dynamical properties. The keys of system's parameters, initial value and iterations are selected in order to let the chaotic encryption algorithm has a higher security and increase the key space. The simulation results show that the encryption algorithm of this paper can effectively resist the common assault of chaotic systems--statistical attack and brute-force attack.
{"title":"An Encryption Algorithm of Chaos Based on Sine Square Mapping","authors":"Yongyi Mao, Xiang Chen","doi":"10.1109/ISCID.2011.41","DOIUrl":"https://doi.org/10.1109/ISCID.2011.41","url":null,"abstract":"The paper applies chaotic encryption to information document, and designed a simple and fast encryption algorithm based on Sine Square Mapping. This kind of mapping can be chaotic in a wide parameter range and the sequences generated possess larger Lyapunov exponent and excellent dynamical properties. The keys of system's parameters, initial value and iterations are selected in order to let the chaotic encryption algorithm has a higher security and increase the key space. The simulation results show that the encryption algorithm of this paper can effectively resist the common assault of chaotic systems--statistical attack and brute-force attack.","PeriodicalId":224504,"journal":{"name":"2011 Fourth International Symposium on Computational Intelligence and Design","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116295933","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}
Data fusion is an important technology in WSN, But with the expansion of the scope object sensor and complexity increase, the fusion function is more and more complex, Energy consumption of the fusion operation is required more and more. Therefore, the energy consumption of the data fusion and the data transmission has been can't ignored already. Using the compressive sensing theory in the WSN data fusion, It's using auto-adapted data fusion algorithm. Collect and fusion the correlation node sensation data in the route process, To keep the total cost of the transmission and integration of energy close to the minimum.
{"title":"Research of Compressed Sensing Theory in WSN Data Fusion","authors":"Li Li, Jian Li","doi":"10.1109/ISCID.2011.133","DOIUrl":"https://doi.org/10.1109/ISCID.2011.133","url":null,"abstract":"Data fusion is an important technology in WSN, But with the expansion of the scope object sensor and complexity increase, the fusion function is more and more complex, Energy consumption of the fusion operation is required more and more. Therefore, the energy consumption of the data fusion and the data transmission has been can't ignored already. Using the compressive sensing theory in the WSN data fusion, It's using auto-adapted data fusion algorithm. Collect and fusion the correlation node sensation data in the route process, To keep the total cost of the transmission and integration of energy close to the minimum.","PeriodicalId":224504,"journal":{"name":"2011 Fourth International Symposium on Computational Intelligence and Design","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124374039","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}
Hu Yan, Lixin Li, Fangchun Di, Jin Hua, Qiqiang Sun
Identification of perimeter events enables smarter perimeter security systems. This paper presents a multi classifier. Support Vector Machine (SVM) and Artificial Neural Network (ANN) are the bottom to build the classifier. The top level employs voting mechanism to identify intrusions, taking time evolution characters into account. In addition, to make the classifier be more self-adaptive, an incremental learning module is introduced. The proposed classifier has been successfully applied to oil and gas pipeline intrusion detection systems. Practical results show that it can distinguish nuisance events from intrusion events at a high rate of 94.86% and for seven kinds of intrusions, the recognition rate is 95.29%, fully satisfies the real application requirement.
{"title":"ANN-based Multi Classifier for Identification of Perimeter Events","authors":"Hu Yan, Lixin Li, Fangchun Di, Jin Hua, Qiqiang Sun","doi":"10.1109/ISCID.2011.141","DOIUrl":"https://doi.org/10.1109/ISCID.2011.141","url":null,"abstract":"Identification of perimeter events enables smarter perimeter security systems. This paper presents a multi classifier. Support Vector Machine (SVM) and Artificial Neural Network (ANN) are the bottom to build the classifier. The top level employs voting mechanism to identify intrusions, taking time evolution characters into account. In addition, to make the classifier be more self-adaptive, an incremental learning module is introduced. The proposed classifier has been successfully applied to oil and gas pipeline intrusion detection systems. Practical results show that it can distinguish nuisance events from intrusion events at a high rate of 94.86% and for seven kinds of intrusions, the recognition rate is 95.29%, fully satisfies the real application requirement.","PeriodicalId":224504,"journal":{"name":"2011 Fourth International Symposium on Computational Intelligence and Design","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131004152","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}
Mobarakol Islam, Arifur Rahaman, M. K. Hasan, M. Shahjahan
Biological brain involves chaos and the structure of artificial neural networks (ANNs) is similar to human brain. In order to imitate the structure and the function of human brain better, it is more logical to combine chaos with neural networks. In this paper we proposed a chaotic learning algorithm called Maximized Gradient function and Modulated Chaos (MGMC). MGMC maximizes the gradient function and also added a modulated version of chaos in learning rate (LR) as well as in activation function. Activation function made adaptive by using chaos as gain factor. MGMC generates a chaotic time series as modulated form of Mackey Glass, Logistic Map and Lorenz Attractor. A rescaled version of this series is used as learning rate (LR) called Modulated Learning Rate (MLR) during NN training. As a result neural network becomes biologically plausible and may get escaped from local minima zone and faster convergence rate is obtained as maximizing the derivative of activation function together with minimizing the error function. MGMC is extensively tested on three real world benchmark classification problems such as australian credit card, wine and soybean identification. The proposed MGMC outperforms the existing BP and BPfast in terms of generalization ability and also convergence rate.
{"title":"An Efficient Neural Network Training Algorithm with Maximized Gradient Function and Modulated Chaos","authors":"Mobarakol Islam, Arifur Rahaman, M. K. Hasan, M. Shahjahan","doi":"10.1109/ISCID.2011.18","DOIUrl":"https://doi.org/10.1109/ISCID.2011.18","url":null,"abstract":"Biological brain involves chaos and the structure of artificial neural networks (ANNs) is similar to human brain. In order to imitate the structure and the function of human brain better, it is more logical to combine chaos with neural networks. In this paper we proposed a chaotic learning algorithm called Maximized Gradient function and Modulated Chaos (MGMC). MGMC maximizes the gradient function and also added a modulated version of chaos in learning rate (LR) as well as in activation function. Activation function made adaptive by using chaos as gain factor. MGMC generates a chaotic time series as modulated form of Mackey Glass, Logistic Map and Lorenz Attractor. A rescaled version of this series is used as learning rate (LR) called Modulated Learning Rate (MLR) during NN training. As a result neural network becomes biologically plausible and may get escaped from local minima zone and faster convergence rate is obtained as maximizing the derivative of activation function together with minimizing the error function. MGMC is extensively tested on three real world benchmark classification problems such as australian credit card, wine and soybean identification. The proposed MGMC outperforms the existing BP and BPfast in terms of generalization ability and also convergence rate.","PeriodicalId":224504,"journal":{"name":"2011 Fourth International Symposium on Computational Intelligence and Design","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131435566","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 takes each weight as a random variable. The values of weight are given by several experts, which are used to determine the distribution of the random variables. Taking the weight random variables as parameters of effectiveness evaluation models, we can calculate their distributions. This method evaluates equipment effectiveness by the expectation and variance of random variable, it can also identify the probability that the effectiveness value fall in a certain interval. The evaluation result coincides with people's understanding about the problem of evaluation. The method of this paper is the improvement of index method and weight sum method.
{"title":"Research on Equipment Effectiveness Evaluation with Weight Random Variables","authors":"Zhao Xin-shuang, N. Kai, Wang Houxiang","doi":"10.1109/ISCID.2011.11","DOIUrl":"https://doi.org/10.1109/ISCID.2011.11","url":null,"abstract":"This paper takes each weight as a random variable. The values of weight are given by several experts, which are used to determine the distribution of the random variables. Taking the weight random variables as parameters of effectiveness evaluation models, we can calculate their distributions. This method evaluates equipment effectiveness by the expectation and variance of random variable, it can also identify the probability that the effectiveness value fall in a certain interval. The evaluation result coincides with people's understanding about the problem of evaluation. The method of this paper is the improvement of index method and weight sum method.","PeriodicalId":224504,"journal":{"name":"2011 Fourth International Symposium on Computational Intelligence and Design","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128359987","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}
Effective porosity is one of the most important parameters in reservoir predication, especially in the carbonate karst reservoirs. In contrast to the calculated results by conventional statistical models, the BP neural network model can predict the porosity of reservoir more accurately because of its high nonlinear mapping ability and very strong abilities of self-adaptation and self-study. In this article, the author unified the different sampling interval of seismic and well logging responses by the mathematical method. Then discussed the correlation of them by the multiple linear regression. On that basis, the authors established the BP neural network model to predict the effective porosity of the reservoirs. The results shows that the porosity and the developed zone of fracture can be predicted in combination of three attributes of seismic and well logging data, moreover, the result is comparatively consistent well with the actually measured porosity and the well performance in study area.
{"title":"An Application of the BP Neural Network to Carbonate Karst Reservoirs Prediction","authors":"Yixin Yu, Jinchuan Zhang, Zhijun Jin","doi":"10.1109/ISCID.2011.135","DOIUrl":"https://doi.org/10.1109/ISCID.2011.135","url":null,"abstract":"Effective porosity is one of the most important parameters in reservoir predication, especially in the carbonate karst reservoirs. In contrast to the calculated results by conventional statistical models, the BP neural network model can predict the porosity of reservoir more accurately because of its high nonlinear mapping ability and very strong abilities of self-adaptation and self-study. In this article, the author unified the different sampling interval of seismic and well logging responses by the mathematical method. Then discussed the correlation of them by the multiple linear regression. On that basis, the authors established the BP neural network model to predict the effective porosity of the reservoirs. The results shows that the porosity and the developed zone of fracture can be predicted in combination of three attributes of seismic and well logging data, moreover, the result is comparatively consistent well with the actually measured porosity and the well performance in study area.","PeriodicalId":224504,"journal":{"name":"2011 Fourth International Symposium on Computational Intelligence and Design","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131729228","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}
Logistics distribution supports the economy development in urban agglomeration. This paper analyses the factors impacted on vehicle routing schemes in distribution of urban agglomeration in a new point of view. The alternative routes which are prepared for private car drivers to choice are generated based on anticipation regret. The method which was presented in this paper is approved to be more accord with reality compared to traditional algorithms. The paper introduces the alternative routes of the drivers impacting on vehicle routing schemes in distribution and developing of urban agglomeration.
{"title":"Generation of Alternative Routes to Private Cars Impacted on Vehicle Routing Schemes in Logistics Distribution of Urban Agglomeration","authors":"Xinquan Liu","doi":"10.1109/ISCID.2011.187","DOIUrl":"https://doi.org/10.1109/ISCID.2011.187","url":null,"abstract":"Logistics distribution supports the economy development in urban agglomeration. This paper analyses the factors impacted on vehicle routing schemes in distribution of urban agglomeration in a new point of view. The alternative routes which are prepared for private car drivers to choice are generated based on anticipation regret. The method which was presented in this paper is approved to be more accord with reality compared to traditional algorithms. The paper introduces the alternative routes of the drivers impacting on vehicle routing schemes in distribution and developing of urban agglomeration.","PeriodicalId":224504,"journal":{"name":"2011 Fourth International Symposium on Computational Intelligence and Design","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122974361","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}
Threshold selection is extremely important in wavelet transform for image denoising. The threshold selection problem can be viewed as continuous optimization problem. Recently, Particle Swarm Optimization was introduced to solve this problem, but its effectiveness is destroyed by the premature convergence. In order to overcome this drawback and obtain satisfactory effect, this paper proposes a modified chaos Particle Swarm Optimization algorithm for threshold selection, then adopts the optimal threshold achieved and a non-negative garrote function to process wavelet decomposed coefficients. When the premature convergence occurs, chaos search strategy will come into effect to help particles jump out of local optimization, and seek global optimization. Experimental results reveal the encouraging effectiveness of the proposed algorithm.
{"title":"A Novel Wavelet Threshold Optimization Via PSO for Image Denoising","authors":"Xuejie Wang, Yi Liu, Yanjun Li","doi":"10.1109/ISCID.2011.95","DOIUrl":"https://doi.org/10.1109/ISCID.2011.95","url":null,"abstract":"Threshold selection is extremely important in wavelet transform for image denoising. The threshold selection problem can be viewed as continuous optimization problem. Recently, Particle Swarm Optimization was introduced to solve this problem, but its effectiveness is destroyed by the premature convergence. In order to overcome this drawback and obtain satisfactory effect, this paper proposes a modified chaos Particle Swarm Optimization algorithm for threshold selection, then adopts the optimal threshold achieved and a non-negative garrote function to process wavelet decomposed coefficients. When the premature convergence occurs, chaos search strategy will come into effect to help particles jump out of local optimization, and seek global optimization. Experimental results reveal the encouraging effectiveness of the proposed algorithm.","PeriodicalId":224504,"journal":{"name":"2011 Fourth International Symposium on Computational Intelligence and Design","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124529403","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 paper takes National Topographic Database by assuming vector diagram water system with 1:250,000 scale as a data source. A reasonable and efficient coding method for river network is presented, which can solve the coding problem of converged river network, bifurcate river network, crossed river network and water of lake and reservoir. The coding method can reflect the topology of river network and can locate any stream segment of river network directly and operate the topology of river network effectively. At the last, this coding method is applied to parts of stream segments in Taihu Lake basin. As the results, the coding method is made by the paper can solve the coding problem of complicated river network, the upstream-downstream relationship can be easily identified through the coding of stream segment, the self-replicating coding has excellent expansibility and high efficiency, and can be handled easily by the computer.
{"title":"Coding Method and Application for Complicated River Network Based on Surveyed River Network","authors":"Xuelian Chen, Feng Jin","doi":"10.1109/ISCID.2011.113","DOIUrl":"https://doi.org/10.1109/ISCID.2011.113","url":null,"abstract":"The paper takes National Topographic Database by assuming vector diagram water system with 1:250,000 scale as a data source. A reasonable and efficient coding method for river network is presented, which can solve the coding problem of converged river network, bifurcate river network, crossed river network and water of lake and reservoir. The coding method can reflect the topology of river network and can locate any stream segment of river network directly and operate the topology of river network effectively. At the last, this coding method is applied to parts of stream segments in Taihu Lake basin. As the results, the coding method is made by the paper can solve the coding problem of complicated river network, the upstream-downstream relationship can be easily identified through the coding of stream segment, the self-replicating coding has excellent expansibility and high efficiency, and can be handled easily by the computer.","PeriodicalId":224504,"journal":{"name":"2011 Fourth International Symposium on Computational Intelligence and Design","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127906675","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：