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Research on Mongolian lexical analyzer based on NFA 基于非词法的蒙古语词汇分析器研究
2010 IEEE International Conference on Intelligent Computing and Intelligent Systems Pub Date : 2010-12-06 DOI: 10.1109/ICICISYS.2010.5658760
S. Loglo, Sarula, Hua Shabao
{"title":"Research on Mongolian lexical analyzer based on NFA","authors":"S. Loglo, Sarula, Hua Shabao","doi":"10.1109/ICICISYS.2010.5658760","DOIUrl":"https://doi.org/10.1109/ICICISYS.2010.5658760","url":null,"abstract":"Mongolian is an adhesive language. Its word-formation and configuration is built through the stem is connected to different suffixes. In theory, Mongolian vocabulary is unlimited, so the dictionary can not encompass all of the words and their numerous morphological changes. Development of independent, efficient lexical analyzing software to identify and generate the words and their morphological changes is needed. In this paper, we have introduced a Mongolian lexical analyzer, which has used dictionaries and NFA-based methods to greatly improve the speed of analyzing. After used in the modern Mongolian parsing software, we found that compare with the simple dictionary or rules-based algorithm it improves the speed by nearly two orders of magnitudes.","PeriodicalId":339711,"journal":{"name":"2010 IEEE International Conference on Intelligent Computing and Intelligent Systems","volume":"68 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":"130896894","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
A bacterial foraging global optimization algorithm based on the particle swarm optimization 基于粒子群优化的细菌觅食全局优化算法
2010 IEEE International Conference on Intelligent Computing and Intelligent Systems Pub Date : 2010-12-06 DOI: 10.1109/ICICISYS.2010.5658828
Liu Xiaolong, Liu Rongjun, Yang Ping
{"title":"A bacterial foraging global optimization algorithm based on the particle swarm optimization","authors":"Liu Xiaolong, Liu Rongjun, Yang Ping","doi":"10.1109/ICICISYS.2010.5658828","DOIUrl":"https://doi.org/10.1109/ICICISYS.2010.5658828","url":null,"abstract":"In this paper, a new hybrid algorithm is introduced to improve the efficiency, accuracy and overcome the drawbacks of weak ability to perceive the environment and vulnerable to perception of local extreme in the optimization process of bacterial foraging optimization (BFO) algorithm. In the new algorithm, the idea of particle swarm optimization (PSO) is merged into the chemotaxis of bacterial foraging optimization algorithms and elimination probability is proposed in elimination-dispersion according to the energy of bacteria. In order to compare the performance of this new hybrid algorithm with BFO and PSO, some typical high dimensional complex functions was proposed to test these three bionic algorithms. The results show that the new algorithm has a better searching speed an obvious improvement in accuracy. This algorithm is suitable to solve the complex functions optimization.","PeriodicalId":339711,"journal":{"name":"2010 IEEE International Conference on Intelligent Computing and Intelligent Systems","volume":"51 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":"130452538","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: 25
Feature selection based file type identification algorithm 基于特征选择的文件类型识别算法
2010 IEEE International Conference on Intelligent Computing and Intelligent Systems Pub Date : 2010-12-06 DOI: 10.1109/ICICISYS.2010.5658559
D. Cao, Junyong Luo, Meijuan Yin, Huijie Yang
{"title":"Feature selection based file type identification algorithm","authors":"D. Cao, Junyong Luo, Meijuan Yin, Huijie Yang","doi":"10.1109/ICICISYS.2010.5658559","DOIUrl":"https://doi.org/10.1109/ICICISYS.2010.5658559","url":null,"abstract":"Identifying the true type of an arbitrary file is very important in information security. Methods based on file extensions or magic numbers can be easily spoofed, while a more reliable way is based on analyzing the file's binary content. We propose an algorithm to generate models for each file type based on analyzing the binary contents of a set of known input files by using n-gram analysis and design a novel feature selection evaluation function for extracting signatures from the models, then using the signatures to recognize the true type of unknown files. Our aim is not to use the structure and key words of any specific file types as this allows the approach to be applied to general file types. Experiments show that the proposed approach is promising especially when the feature selection evaluation function is applied.","PeriodicalId":339711,"journal":{"name":"2010 IEEE International Conference on Intelligent Computing and Intelligent Systems","volume":"26 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":"127919685","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: 17
Study on damaged region segmentation model of image 图像损伤区域分割模型的研究
2010 IEEE International Conference on Intelligent Computing and Intelligent Systems Pub Date : 2010-12-06 DOI: 10.1109/ICICISYS.2010.5658284
Huaming Liu, Yun Chen, Xuehui Bi
{"title":"Study on damaged region segmentation model of image","authors":"Huaming Liu, Yun Chen, Xuehui Bi","doi":"10.1109/ICICISYS.2010.5658284","DOIUrl":"https://doi.org/10.1109/ICICISYS.2010.5658284","url":null,"abstract":"After studying the damaged region classification of Thangka image, and in light of the specific damaged situation, as for damaged region can be segmented accurately and the advantages of different image segmentation algorithms can be full played, it is proposed a damaged region segmentation model of image. Model integrates different image segmentation algorithms, the system can segment damaged regions using different algorithms according to the feature of the image damaged regions, so it can avoid looking for “universal” algorithms for segmenting image damaged regions. For the image segmentation results, it is needed to evaluate through by segmentation evaluation, at the same time, considering the error segmented region condition, here subjective evaluation is introduced in the model. The system selects algorithm, and the results of the evaluation and so on, these features are stored in the information database of image segmentation, decision-making module analysis and learning continually, and will optimize all types of segmentation algorithm in the information database. Decision-making module can make use of the historical information to guide the image damaged region segmentation, increase system segmentation efficiency and accuracy.","PeriodicalId":339711,"journal":{"name":"2010 IEEE International Conference on Intelligent Computing and Intelligent Systems","volume":"30 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":"127983669","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
Application of Gaussian Mixture Model Genetic Algorithm in data stream clustering analysis 高斯混合模型遗传算法在数据流聚类分析中的应用
2010 IEEE International Conference on Intelligent Computing and Intelligent Systems Pub Date : 2010-12-06 DOI: 10.1109/ICICISYS.2010.5658322
M. Gao, Chan Tai-hua, Xiang-xiang Gao
{"title":"Application of Gaussian Mixture Model Genetic Algorithm in data stream clustering analysis","authors":"M. Gao, Chan Tai-hua, Xiang-xiang Gao","doi":"10.1109/ICICISYS.2010.5658322","DOIUrl":"https://doi.org/10.1109/ICICISYS.2010.5658322","url":null,"abstract":"Data stream is infinite data and quick stream speed, so traditional clustering algorithm can not be applied to data stream clustering directly. As an efficient tool for data analysis, Gaussian mixture model has been widely applied in the fields of signal and information processing. We can use Gaussian mixture model (GMM) simulate arbitrary clustering graphics. There are two critical problems for the clustering analysis technology to select the appropriate value of number of clusters and partition overlapping clusters. Base on an extending method of Gaussian mixture modeling, a new feature mining method named Gaussian Mixture Model with Genetic Algorithms is proposed in this paper. This method is use a probability density based data stream clustering which requires only the newly arrived data, not the entire historical data, and also can choose optimal estimation clusters number value. The algorithm can determine the number of Gaussian clusters and the parameters of each Gaussian through random split and merge operation of Genetic Algorithms. We can get the accurate information each attribute characteristic describe. So that can make an effective date stream mining.","PeriodicalId":339711,"journal":{"name":"2010 IEEE International Conference on Intelligent Computing and Intelligent Systems","volume":"15 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131673035","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: 6
An iterative algorithm used for node position derivation in Wireless Sensor Networks 一种用于无线传感器网络中节点位置推导的迭代算法
2010 IEEE International Conference on Intelligent Computing and Intelligent Systems Pub Date : 2010-12-06 DOI: 10.1109/ICICISYS.2010.5658615
Q. Shi, Jianping Zhang, Jiujun Cheng
{"title":"An iterative algorithm used for node position derivation in Wireless Sensor Networks","authors":"Q. Shi, Jianping Zhang, Jiujun Cheng","doi":"10.1109/ICICISYS.2010.5658615","DOIUrl":"https://doi.org/10.1109/ICICISYS.2010.5658615","url":null,"abstract":"A node position derivation algorithm called IR for WSNs is developed in this paper. IR is an iterative algorithm based on node-beacon distance information. It is assumed that each sensor node can connect with several beacons by one-hop or multihop and estimate the node-beacon distances, and then, the algorithm is executed in a distributed mode. Simulation is provided to compare our proposed algorithm with two representative position derivation algorithms, Min-max and Lateration, in terms of some evaluation parameters. The simulation shows that our proposed algorithm can be a utilitarian node position derivation algorithm for WSNs.","PeriodicalId":339711,"journal":{"name":"2010 IEEE International Conference on Intelligent Computing and Intelligent Systems","volume":"49 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":"129219569","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 novel soft sensor modelling method based on kernel PLS 一种新的基于核PLS的软测量建模方法
2010 IEEE International Conference on Intelligent Computing and Intelligent Systems Pub Date : 2010-12-06 DOI: 10.1109/ICICISYS.2010.5658683
Xi Zhang, Weijian Huang, Yaqing Zhu, Shihe Chen
{"title":"A novel soft sensor modelling method based on kernel PLS","authors":"Xi Zhang, Weijian Huang, Yaqing Zhu, Shihe Chen","doi":"10.1109/ICICISYS.2010.5658683","DOIUrl":"https://doi.org/10.1109/ICICISYS.2010.5658683","url":null,"abstract":"A novel soft sensor modeling method based on kernel partial least squares (kernel PLS, KPLS) was proposed. Kernel PLS is a promising regression method for tackling nonlinear problems because it can efficiently compute regression coefficients in high-dimensional feature space by means of nonlinear kernel function. Application results to the real data in a fluid catalytic cracking unit (FCCU) process show that the proposed method can effectively capture nonlinear relationship among variables and have better estimation performance than PLS and other linear approaches.","PeriodicalId":339711,"journal":{"name":"2010 IEEE International Conference on Intelligent Computing and Intelligent Systems","volume":"56 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":"128774355","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
A shadow elimination method based on color and texture 一种基于颜色和纹理的阴影消除方法
2010 IEEE International Conference on Intelligent Computing and Intelligent Systems Pub Date : 2010-12-06 DOI: 10.1109/ICICISYS.2010.5658517
Yihua Zhou, Lina Sun, Jianbiao Zhang
{"title":"A shadow elimination method based on color and texture","authors":"Yihua Zhou, Lina Sun, Jianbiao Zhang","doi":"10.1109/ICICISYS.2010.5658517","DOIUrl":"https://doi.org/10.1109/ICICISYS.2010.5658517","url":null,"abstract":"In video surveillance system, it is a key factor that extracting moving objects accurately to obtain better monitoring effect. The shadows followed with moving objects may change the object shapes or distort the object. Therefore, it become difficult to extract moving objects accurately. In this paper, According to optical characteristics of shadow, a shadow elimination method based on HSV color space and texture local cross-entropy is proposed. Experiments show that our method is efficient in detecting and eliminating shadows.","PeriodicalId":339711,"journal":{"name":"2010 IEEE International Conference on Intelligent Computing and Intelligent Systems","volume":"5 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":"121401988","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
A voltage de-noising method based on the wavelet transform of asynchronous motor stator 基于小波变换的异步电动机定子电压去噪方法
2010 IEEE International Conference on Intelligent Computing and Intelligent Systems Pub Date : 2010-12-06 DOI: 10.1109/ICICISYS.2010.5658361
Haiyan Zhang, Zhi Liu, Tao Zhang
{"title":"A voltage de-noising method based on the wavelet transform of asynchronous motor stator","authors":"Haiyan Zhang, Zhi Liu, Tao Zhang","doi":"10.1109/ICICISYS.2010.5658361","DOIUrl":"https://doi.org/10.1109/ICICISYS.2010.5658361","url":null,"abstract":"At present, all kinds of asynchronous motor equipment has been widely used in the fields of industrial production. Monitoring motor voltage, current signal and de-noising accurately are important to improve the stability in the whole application field. In this paper, the principle about voltage de-noising of traditional asynchronous motor stator is briefly introduced. A voltage de-noising method based on the wavelet transform of asynchronous motor stator is presented. It well makes use of the advantages of traditional Fourier transform in signal analysis and then overcomes the deficiency of it. At last, it shows that the de-noising effect of this method by analyzing the simulation and verifying the error is good.","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":"121617454","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
An algorithm for communication community organizational structure analysis 通信社区组织结构分析的一种算法
2010 IEEE International Conference on Intelligent Computing and Intelligent Systems Pub Date : 2010-12-06 DOI: 10.1109/ICICISYS.2010.5658415
Ying Hou, Hao-xiang Shen, Lixiong Liu, Hai Huang
{"title":"An algorithm for communication community organizational structure analysis","authors":"Ying Hou, Hao-xiang Shen, Lixiong Liu, Hai Huang","doi":"10.1109/ICICISYS.2010.5658415","DOIUrl":"https://doi.org/10.1109/ICICISYS.2010.5658415","url":null,"abstract":"The purpose of communication community structure detection in a network is to cluster weighted complex network. By learning from traditional clustering algorithm, OPTICS, an algorithm is designed to detect communication community and analyze its structure. This algorithm considers the effect and detects communication community based on its communication intensity. The detection result is organized in multi distinguishing granular to provide hierarchical structural organization in the communication community. Experiments showed that this algorithm is effective in detecting communication community and analyzing organizational structure.","PeriodicalId":339711,"journal":{"name":"2010 IEEE International Conference on Intelligent Computing and Intelligent Systems","volume":"23 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":"123052869","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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