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2010 IEEE International Conference on Intelligent Computing and Intelligent Systems最新文献

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Assistant detection of skewed data streams classification in cloud security 云安全中偏斜数据流分类的辅助检测
2010 IEEE International Conference on Intelligent Computing and Intelligent Systems Pub Date : 2010-12-06 DOI: 10.1109/ICICISYS.2010.5658721
Qun Song, Jun Zhang, Qian Chi
{"title":"Assistant detection of skewed data streams classification in cloud security","authors":"Qun Song, Jun Zhang, Qian Chi","doi":"10.1109/ICICISYS.2010.5658721","DOIUrl":"https://doi.org/10.1109/ICICISYS.2010.5658721","url":null,"abstract":"Data stream in the cloud is characterized by imbalanced distribution and concept drift. To solve the problem of classification of skewed and concept drift data stream in cloud security, we present an one-class classifier dynamic ensemble method which aims at separating virus data, reducing the amount of data analyzed in clouds, improving the efficiency of intrusion detection in cloud security and assisting detection of virus. The proposed method is based on using K-means algorithm to adjust data distribution, makes use of interval estimation combined with AUC value to check concept drift and updates classifiers and dynamically allocates weights. Experimental results illustrate that the proposed method can achieve good classification performance on synthetic dataset and effectively separate most of the virus samples on KDDCUP'99 dataset.","PeriodicalId":339711,"journal":{"name":"2010 IEEE International Conference on Intelligent Computing and Intelligent Systems","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123063168","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}
引用次数: 9
A novel stream-weight method for the multi-stream speech recognition system 多流语音识别系统的一种新的流权方法
2010 IEEE International Conference on Intelligent Computing and Intelligent Systems Pub Date : 2010-12-06 DOI: 10.1109/ICICISYS.2010.5658488
Hongyu Guo, Xiaoqun Zhao, Hongmiao Guo
{"title":"A novel stream-weight method for the multi-stream speech recognition system","authors":"Hongyu Guo, Xiaoqun Zhao, Hongmiao Guo","doi":"10.1109/ICICISYS.2010.5658488","DOIUrl":"https://doi.org/10.1109/ICICISYS.2010.5658488","url":null,"abstract":"A multi-stream speech recognition system is based on the combination of multiple complementary feature streams. Utilizing the fusion scheme of multi-stream, better performance was achieved in speech recognition system. The stream-weight method plays a very important role in the fusion collaborative scheme. The stream weights should be selected to be proportional to the feature stream reliability and informativeness. The posterior probability estimate is a measure of reliability, and the classification error is a measure of informativeness. The larger separation between class distributions in a given stream implies better discriminative power. The intra-class distances are an estimate of the class variance. The inter- and intra-class distances are combined to yield and estimate of the misclassification error for each stream. An unsupervised stream weight estimation method for multi-stream speech recognition system based on the computation of intra-and inter-class distances in each stream is proposed here. Experiments are conducted using Chinese Academy of Science speech database. Applying the new stream-weigh algorithm, we achieve better fusion performance compared with some traditional fusion methods, and the word error rate was decreased by 6%.","PeriodicalId":339711,"journal":{"name":"2010 IEEE International Conference on Intelligent Computing and Intelligent Systems","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123066415","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}
引用次数: 0
A forward secure threshold signature scheme based on bilinear pairing 一种基于双线性对的前向安全门限签名方案
2010 IEEE International Conference on Intelligent Computing and Intelligent Systems Pub Date : 2010-12-06 DOI: 10.1109/ICICISYS.2010.5658612
Sun Hua, Guo Li, W. Aimin
{"title":"A forward secure threshold signature scheme based on bilinear pairing","authors":"Sun Hua, Guo Li, W. Aimin","doi":"10.1109/ICICISYS.2010.5658612","DOIUrl":"https://doi.org/10.1109/ICICISYS.2010.5658612","url":null,"abstract":"Threshold signature is an important digital signature, but many of proposed schemes suffer from conspiracy attack. This paper proposes a forward secure threshold signature based on bilinear pairing. It divides signature course into several periods, the public key is fixed, while the private key is updated by one-way function in each period. So this scheme has the characters of forward security. It can not only satisfy the properties of the threshold signature, but also withstand the conspiracy attack. The security of the scheme is also analyzed and it shows that the proposed scheme is secure and effective.","PeriodicalId":339711,"journal":{"name":"2010 IEEE International Conference on Intelligent Computing and Intelligent Systems","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124841044","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}
引用次数: 3
Improved chaos-genetic algorithm optimization method on back analysis of earth temperature field in coal mine 煤矿地温场反分析的改进混沌遗传算法优化方法
2010 IEEE International Conference on Intelligent Computing and Intelligent Systems Pub Date : 2010-12-06 DOI: 10.1109/ICICISYS.2010.5658718
Shilong Wang
{"title":"Improved chaos-genetic algorithm optimization method on back analysis of earth temperature field in coal mine","authors":"Shilong Wang","doi":"10.1109/ICICISYS.2010.5658718","DOIUrl":"https://doi.org/10.1109/ICICISYS.2010.5658718","url":null,"abstract":"Aiming at complexity of hydrogeological conditions of coal mine, this paper brought forward the improved chaos-genetic optimization method based on the chaos optimization method and genetic algorithm method. Using the optimization method, this paper have carried out the back analysis of earth temperature field of coal mine, then predicted the earth temperature field using the parameters of the back analysis, which provided a scientific basis for control of heat-harm. Through engineering example, the result showed that the improved chaos and genetic optimization method on the back analysis of earth temperature field in coal mine was feasible.","PeriodicalId":339711,"journal":{"name":"2010 IEEE International Conference on Intelligent Computing and Intelligent Systems","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126077641","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}
引用次数: 0
FPGA Implementation of efficient FFT algorithm based on complex sequence 基于复杂序列的高效FFT算法的FPGA实现
2010 IEEE International Conference on Intelligent Computing and Intelligent Systems Pub Date : 2010-12-06 DOI: 10.1109/ICICISYS.2010.5658418
Zou Wen, Qiu Zhongpan, Song Zhijun
{"title":"FPGA Implementation of efficient FFT algorithm based on complex sequence","authors":"Zou Wen, Qiu Zhongpan, Song Zhijun","doi":"10.1109/ICICISYS.2010.5658418","DOIUrl":"https://doi.org/10.1109/ICICISYS.2010.5658418","url":null,"abstract":"Fast Fourier Transform (FFT) is an efficient algorithm to compute the Discrete Fourier Transform (DFT). In many applications the input data are purely real-time, and efficient FFT can satisfy the situation. FFT algorithm based on complex sequence is an improved algorithm of primary FFT. This paper studies how efficient FFT algorithm is implemented on the basis of Field Programmable Gate Array (FPGA). When processing the same sequence length of data such as data of voltage or image, the algorithm put forward in this paper can save half time that is used for the amount of calculation in theory. The simulation indicates that the calculation can reach equivalent precision and the system performs satisfactorily. The method can applied well in many real-time systems and image processing.","PeriodicalId":339711,"journal":{"name":"2010 IEEE International Conference on Intelligent Computing and Intelligent Systems","volume":"515 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123426524","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}
引用次数: 5
Application of single agent Q-learning for light exploration 单智能体q -学习在光探测中的应用
2010 IEEE International Conference on Intelligent Computing and Intelligent Systems Pub Date : 2010-12-06 DOI: 10.1109/ICICISYS.2010.5658569
D. Ray, A. K. Mandal, S. Mazumder, S. Mukhopadhay
{"title":"Application of single agent Q-learning for light exploration","authors":"D. Ray, A. K. Mandal, S. Mazumder, S. Mukhopadhay","doi":"10.1109/ICICISYS.2010.5658569","DOIUrl":"https://doi.org/10.1109/ICICISYS.2010.5658569","url":null,"abstract":"Machine learning refers to systematic design and development of algorithms that allows computers to evolve behaviors based on some realistic data (online or offline). Q-learning, a sub-part of the reinforcement learning is being used world wide for easy learning of mobile robots. Light exploration is one of the important issues for developing green robots. This paper describes the work carried out for light exploration by a robot using single-agent based Q-learning. Here a single agent is taking care of all the tasks for learning. ARBIB III, an indigenous behaviour-based robot has been used to implement the Q-learning algorithm for light exploration. The system uses one light sensor and two touch (press) sensors for exploration. It has been found that the algorithm has good applicability for robot learning.","PeriodicalId":339711,"journal":{"name":"2010 IEEE International Conference on Intelligent Computing and Intelligent Systems","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123741016","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}
引用次数: 4
Texture image recognition based on bispectrum slice and BP neural network ensembles 基于双谱切片和BP神经网络集成的纹理图像识别
2010 IEEE International Conference on Intelligent Computing and Intelligent Systems Pub Date : 2010-12-06 DOI: 10.1109/ICICISYS.2010.5658582
Zhengjian Ding, Yasheng Yu
{"title":"Texture image recognition based on bispectrum slice and BP neural network ensembles","authors":"Zhengjian Ding, Yasheng Yu","doi":"10.1109/ICICISYS.2010.5658582","DOIUrl":"https://doi.org/10.1109/ICICISYS.2010.5658582","url":null,"abstract":"To obtain the spatial relationship between three or more pixels in the texture image, bispectrum is choosen to extract texture features of the image, and it contains amplitude information and phase information of the image. Due to some problems in neural network, such as unstable classifier design, configuration, training, the research based on the ensemble of neural networks appears. Compared with a single neural network, an ensemble of neural networks has better fault tolerance and generalisation ability. In this paper, bispectrum is used to extract texture features and the neural network ensembles are used to recognize the texture images. The experimental results demonstrate that the ensemble of BP neural networks can effectively improve correct recognition rate of texture images.","PeriodicalId":339711,"journal":{"name":"2010 IEEE International Conference on Intelligent Computing and Intelligent Systems","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116100639","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}
引用次数: 1
A video semantic object extraction method based on motion feature and visual attention 一种基于运动特征和视觉注意的视频语义对象提取方法
2010 IEEE International Conference on Intelligent Computing and Intelligent Systems Pub Date : 2010-12-06 DOI: 10.1109/ICICISYS.2010.5658686
Yihua Zhou, Yong-Lang Liu, Jianbiao Zhang
{"title":"A video semantic object extraction method based on motion feature and visual attention","authors":"Yihua Zhou, Yong-Lang Liu, Jianbiao Zhang","doi":"10.1109/ICICISYS.2010.5658686","DOIUrl":"https://doi.org/10.1109/ICICISYS.2010.5658686","url":null,"abstract":"In order to solve the semantic object extraction problem under complex background in video retrieval, a semantic object extraction method based on combination of motion features and visual attention is presented. First, moving object is extracted using motion features; then static object that has important semantic characteristic is extracted using visual attention; finally, a complete integration method is proposed to combine moving semantic object and static semantic object in the above. In this method, we take full advantages of motion feature of moving object and color and edge feature of static object. Experimental results show that this method has achieved remarkable results.","PeriodicalId":339711,"journal":{"name":"2010 IEEE International Conference on Intelligent Computing and Intelligent Systems","volume":"419 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116136810","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}
引用次数: 4
An ensemble learning algorithm based on Lasso selection 基于Lasso选择的集成学习算法
2010 IEEE International Conference on Intelligent Computing and Intelligent Systems Pub Date : 2010-12-06 DOI: 10.1109/ICICISYS.2010.5658515
K. Chen, Yang Jin
{"title":"An ensemble learning algorithm based on Lasso selection","authors":"K. Chen, Yang Jin","doi":"10.1109/ICICISYS.2010.5658515","DOIUrl":"https://doi.org/10.1109/ICICISYS.2010.5658515","url":null,"abstract":"Ensemble learning, especially selective ensemble learning is now becoming more and more popular in the field of machine learning. This paper introduces a new ensemble algorithm, named Lasso-Bagging Trees ensemble algorithm. This algorithm is in order to improve the whole learning ability, which is a combination of tree predictors and this method chooses and ensembles trees based on the shrinkage estimation of lasso technology. Compared with a series of other learning algorithms, it demonstrates better generalization ability and higher efficiency.","PeriodicalId":339711,"journal":{"name":"2010 IEEE International Conference on Intelligent Computing and Intelligent Systems","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116145978","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}
引用次数: 2
Notice of RetractionModeling and optimization in the compression modeling fuel process of straw and rice bran mixture 秸秆米糠混合燃料过程压缩建模与优化的注意事项
2010 IEEE International Conference on Intelligent Computing and Intelligent Systems Pub Date : 2010-12-06 DOI: 10.1109/ICICISYS.2010.5658473
Da-zhong Li, Wen-jie Zhu
{"title":"Notice of RetractionModeling and optimization in the compression modeling fuel process of straw and rice bran mixture","authors":"Da-zhong Li, Wen-jie Zhu","doi":"10.1109/ICICISYS.2010.5658473","DOIUrl":"https://doi.org/10.1109/ICICISYS.2010.5658473","url":null,"abstract":"In this paper, straw and rice bran is the object of the molding process. It analyzes the impact of straw and rice bran mixture ratio and temperature on the main indicators of forming fuel. Based on the LS-SVM, multi-objective optimization function and the model of the straw and rice bran mixed-compression molding process have been established. Also the model and molding process has been tested and optimization with the experimental data. The results show that the model prediction and experimental values of the average maximum relative error is 1.59%, with good simulation results. Meanwhile, when the proportion of rice straw is 55.9% and the molding temperature is about 129.07°C, the optimal value is very close to the experimental maximum value and the maximum relative error is 2.86%.","PeriodicalId":339711,"journal":{"name":"2010 IEEE International Conference on Intelligent Computing and Intelligent Systems","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122907463","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}
引用次数: 0
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