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2014 10th International Conference on Natural Computation (ICNC)最新文献

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The indoor wireless location technology research based on WiFi 基于 WiFi 的室内无线定位技术研究
2014 10th International Conference on Natural Computation (ICNC) Pub Date : 2014-12-08 DOI: 10.1109/ICNC.2014.6975984
Yuying Hou, Guoyue Sum, Binwen Fan
{"title":"The indoor wireless location technology research based on WiFi","authors":"Yuying Hou, Guoyue Sum, Binwen Fan","doi":"10.1109/ICNC.2014.6975984","DOIUrl":"https://doi.org/10.1109/ICNC.2014.6975984","url":null,"abstract":"The main research content of this article is based on fingerprint method of AP selection and location estimation algorithm. We introduce RANSAC algorithm used in image processing art to AP selection in the online stage for external detection. It can filter to remove the APs impacted by environmental variation, not only reduces the amount of calculation but also improves the positioning accuracy. Aiming at the disadvantages of traditional Bayesian algorithm and KNN algorithm, we improve the two kinds of algorithms. Based on traditional Bayesian algorithm, we adopt the concept of a regional division. Classification based on the traditional KNN algorithm is introduced into cluster and the cluster partition, allows a reference point to be assigned to multiple clusters, using different fingerprint in different clusters. Finally we adopt a new method of dynamic union combined with the above two kinds of improved algorithm. Based on the above research, the average error of our positioning system is 1.63 meters, the minimum error is 0.76 meters.","PeriodicalId":208779,"journal":{"name":"2014 10th International Conference on Natural Computation (ICNC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130917872","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
Optimization on clustering method of the liquid drop fingerprint 液滴指纹聚类方法的优化
2014 10th International Conference on Natural Computation (ICNC) Pub Date : 2014-12-08 DOI: 10.1109/ICNC.2014.6975923
Q. Song, M. Qiao, Shihui Zhang
{"title":"Optimization on clustering method of the liquid drop fingerprint","authors":"Q. Song, M. Qiao, Shihui Zhang","doi":"10.1109/ICNC.2014.6975923","DOIUrl":"https://doi.org/10.1109/ICNC.2014.6975923","url":null,"abstract":"In order to effectively reduce the time complexity of clustering algorithm, a new method based on multiple linear regression is put forward to reduce the eigenvector dimensions of the liquid drop fingerprint. After feature extraction with waveform analysis method applied on 38 kinds of liquid samples, optimization is carried out to decrease the 10 characteristic values to 8 values, which is then used in subsequent hierarchical clustering and dynamic clustering. Based on the first dynamic clustering results, comprehensive analysis is applied and dynamic clustering method is used once more. Experimental results show that the recognition ratio of the liquid drop fingerprint can be ensured, together with the reduced computational complexity and excellent clustering accuracy. Compared with hierarchical clustering method, the iterative dynamic clustering method is more effective in liquid identification, with its accuracy up to 100% among selected samples.","PeriodicalId":208779,"journal":{"name":"2014 10th International Conference on Natural Computation (ICNC)","volume":"153 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133864316","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 novel method for image clustering 一种新的图像聚类方法
2014 10th International Conference on Natural Computation (ICNC) Pub Date : 2014-12-08 DOI: 10.1109/ICNC.2014.6975912
Zhongtang Zhao, Q. Ma
{"title":"A novel method for image clustering","authors":"Zhongtang Zhao, Q. Ma","doi":"10.1109/ICNC.2014.6975912","DOIUrl":"https://doi.org/10.1109/ICNC.2014.6975912","url":null,"abstract":"Image clustering has been attracting mounting focus on widely used fields, such as data compression, information retrieval, character recognition and so on, due to the emerging applications of various web-based and mobile-based image retrieval and services. To study this, based on Voronoi diagram, we propose a novel image clustering algorithm to effective discovery of image clusters in this paper. More specifically, based on Voronoi diagrams at first, a number of irregular grids are built across the whole plane. Furthermore, leveraging the good property of “the nearest neighbor” for the Voronoi diagrams, various irregular grids of plane are assigned by the points to different clusters. On the one hand, based on the density of grid points, it automatically adjusts the final suitable number of clustering; on the other hand, according to the changes of the centroids, it tunes the positions for the Voronoi's seeds. At last, the Voronoi cells finally become the result of clustering process. The empirical experiment results show that our proposed method not only can cluster image dataset effectively, but also can achieve the comparative performance with X-means algorithm and K-means algorithm. Moreover, our proposed method can outperform the effectiveness for both DBSCAN and OPTICS algorithms, which are classic density-based clustering algorithms towards larger-scale real-world applications.","PeriodicalId":208779,"journal":{"name":"2014 10th International Conference on Natural Computation (ICNC)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132995720","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
An evolutionary genetic neural networks for problems without prior knowledge 无先验知识问题的进化遗传神经网络
2014 10th International Conference on Natural Computation (ICNC) Pub Date : 2014-12-08 DOI: 10.1109/ICNC.2014.6975800
H. U. Ha, Jong-Kook Kim
{"title":"An evolutionary genetic neural networks for problems without prior knowledge","authors":"H. U. Ha, Jong-Kook Kim","doi":"10.1109/ICNC.2014.6975800","DOIUrl":"https://doi.org/10.1109/ICNC.2014.6975800","url":null,"abstract":"Many problems are now being solved using a version of a neural network (NN). These NN are usually constructed using genetic neural networks (GNNs) for optimizing variables in the NN using a fixed structure or neural evolution (NE) to optimize the structure of the NN using fixed values for the variables in the NN. Thus, previous methods need experienced knowledge of the problem such that either the structure or variables are known to construct a meaningful NN. This paper presents a method called leap evolution adopted neural network (LEANN) that optimizes the NN without prior knowledge such as the values of the variables and the structure of the NN for a given problem. Our method in this paper finds an optimal structure and variables of the NN successfully for the XOR gate problem.","PeriodicalId":208779,"journal":{"name":"2014 10th International Conference on Natural Computation (ICNC)","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134623943","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
Further experiments in biocomputational structural analysis of malware 恶意软件生物计算结构分析的进一步实验
2014 10th International Conference on Natural Computation (ICNC) Pub Date : 2014-12-08 DOI: 10.1109/ICNC.2014.6975904
Vijay Naidu, A. Narayanan
{"title":"Further experiments in biocomputational structural analysis of malware","authors":"Vijay Naidu, A. Narayanan","doi":"10.1109/ICNC.2014.6975904","DOIUrl":"https://doi.org/10.1109/ICNC.2014.6975904","url":null,"abstract":"Initial work on structural analysis of malware using the nature-inspired technique of projecting malware signatures into the amino acid/protein domain was promising in a number of ways, including the demonstration of potential links with real-world pathogen proteins. That initial work was necessarily speculative and limited by a number of experimental factors. The aim of the research reported here is to address some of these limitations and to repeat, with malware code and signatures that can be assured as genuine, the experiments previously reported but with enhancements and improvements. Intriguingly, the outcome is the same: for some reason that is not yet known, matching artificial malware code consensuses after multiple alignment against protein databases returns a high proportion of naturally occurring viral proteins.","PeriodicalId":208779,"journal":{"name":"2014 10th International Conference on Natural Computation (ICNC)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132263817","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: 13
Boosting variable selection algorithm for linear regression models 线性回归模型的增强变量选择算法
2014 10th International Conference on Natural Computation (ICNC) Pub Date : 2014-12-08 DOI: 10.1109/ICNC.2014.6975934
Chunxia Zhang, Guanwei Wang
{"title":"Boosting variable selection algorithm for linear regression models","authors":"Chunxia Zhang, Guanwei Wang","doi":"10.1109/ICNC.2014.6975934","DOIUrl":"https://doi.org/10.1109/ICNC.2014.6975934","url":null,"abstract":"With respect to variable selection for linear regression models, this paper proposes a novel boosting learning method based on genetic algorithm. Its main idea is as follows: each training example is first assigned to a weight and genetic algorithm is adopted as the base learning algorithm of boosting. Then, the training set associated with a weight distribution is taken as the input of genetic algorithm to do variable selection. Subsequently, the weight distribution is updated according to the quality of the previous variable selection results. Through repeating the above steps for multiple times, the results are then fused via a weighted combination rule. The performance of the proposed method is investigated on several simulated data sets. The experimental results show that boosting can significantly improve the variable selection performance of a genetic algorithm and can accurately identify the relevant variables.","PeriodicalId":208779,"journal":{"name":"2014 10th International Conference on Natural Computation (ICNC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125762115","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 hybrid artificial fish-school optimization algorithm for solving the quadratic assignment problem 求解二次分配问题的混合人工鱼群优化算法
2014 10th International Conference on Natural Computation (ICNC) Pub Date : 2014-12-08 DOI: 10.1109/ICNC.2014.6975994
L. Yi, Qiwei Yang
{"title":"A hybrid artificial fish-school optimization algorithm for solving the quadratic assignment problem","authors":"L. Yi, Qiwei Yang","doi":"10.1109/ICNC.2014.6975994","DOIUrl":"https://doi.org/10.1109/ICNC.2014.6975994","url":null,"abstract":"The quadratic assignment problem (QAP) is a classic combinatorial optimization problem, which is of the NP-hard nature. In this paper, a hybrid artificial fish school optimization algorithm (HAFSOA) is proposed. In HAFSOA, the heuristic information is used in constructing some better initial individuals and its search ability of the global optimal solution is improved by a combination of the modified fish school optimization and differential evolution. In addition, by taking different visual distances for three behaviors: preying, clustering and following, the convergence speed of the proposed HAFSOA is speeded up. Many QAP experimental results show that the proposed HAFSOA can solve QAP better.","PeriodicalId":208779,"journal":{"name":"2014 10th International Conference on Natural Computation (ICNC)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125119195","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
Link prediction via nonnegative matrix factorization enhanced by blocks information 基于块信息增强的非负矩阵分解的链路预测
2014 10th International Conference on Natural Computation (ICNC) Pub Date : 2014-12-08 DOI: 10.1109/ICNC.2014.6975944
Qian Yang, Enming Dong, Zheng Xie
{"title":"Link prediction via nonnegative matrix factorization enhanced by blocks information","authors":"Qian Yang, Enming Dong, Zheng Xie","doi":"10.1109/ICNC.2014.6975944","DOIUrl":"https://doi.org/10.1109/ICNC.2014.6975944","url":null,"abstract":"Low rank matrices approximations which have been used in networks link prediction are usually global optimal methods and use little local information. However, links are more likely to be found within dense blocks. It is also found that the block structure represents the local feature of matrices because entities in the same block have similar values. So we combines link prediction method by convex nonnegative matrix factorization with block detection to predict potential links using both of global and local information. A probabilistic latent variable model is presented by us and the experiments show that our method gives better prediction accuracy than original method alone (For example, AUC=0.861991 is higher 10% on Karate club network with 5% missing links.).","PeriodicalId":208779,"journal":{"name":"2014 10th International Conference on Natural Computation (ICNC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130339025","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: 12
Two-dimensional short-range microwave holography algorithm and convolutional gridding 二维近程微波全息算法与卷积网格
2014 10th International Conference on Natural Computation (ICNC) Pub Date : 2014-12-08 DOI: 10.1109/ICNC.2014.6975972
B. Lao, Jun-yi Wang, Jin-qing Wang, Jie-lin Fu, T. An, Hong-bing Qiu
{"title":"Two-dimensional short-range microwave holography algorithm and convolutional gridding","authors":"B. Lao, Jun-yi Wang, Jin-qing Wang, Jie-lin Fu, T. An, Hong-bing Qiu","doi":"10.1109/ICNC.2014.6975972","DOIUrl":"https://doi.org/10.1109/ICNC.2014.6975972","url":null,"abstract":"A new-style two-dimensional short-range microwave holography imaging technique is introduced to compensate the inadequateness of short-range millimeter-wave holography imaging. In this method, the scattered data collected includes not only back-scattered but also forward-scattered data, and the incident field obtained by simulation or measurement, can further improve the imaging quality and resolution. A simulation in FEKO about the algorithm is given to provide reference for practical applications. In this paper, a new technique has been proposed to reduce the alias by using convolution function gridding the microwave holographic data which are irregularly data. To choose a proper convolution function, the performance of alias rejection of different convolution functions has been analyzed, validate the spheroidal function is the most rejection alias convolution function. To evaluate the proposed technique, the convolution gridding used to interpolate the irregularly holographic data onto a rectangular gird is examined in detail, validate the convolution gridding can be reduced aliasing. The convolution grid algorithm will be used in the actual imaging system to process the irregular data.","PeriodicalId":208779,"journal":{"name":"2014 10th International Conference on Natural Computation (ICNC)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130383133","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
Topic mining for call centers based on LDA 基于LDA的呼叫中心主题挖掘
2014 10th International Conference on Natural Computation (ICNC) Pub Date : 2014-12-08 DOI: 10.1109/ICNC.2014.6975947
Wenming Guo, Tianlang Deng
{"title":"Topic mining for call centers based on LDA","authors":"Wenming Guo, Tianlang Deng","doi":"10.1109/ICNC.2014.6975947","DOIUrl":"https://doi.org/10.1109/ICNC.2014.6975947","url":null,"abstract":"Latent Dirichlet Allocation, which is a non-supervised learning method, can be used for topic detection, automatic text categorization, keyword extraction and so on. It only focuses on the text itself, not considering other external correlation properties. External association property refers to some structured attributes that correspondence with the text data, for example, a paper usually has several properties like authors, publishing time etc. A telephone call usually has several properties like caller number, call time etc. To iron out flaws; we propose an improved model A-LDA based LDA. We use data sets from telephone call centers (a kind of data centers in rapid growth) to experiment on topic detection. The topic results show that A-LDA with introduce of external correlation properties, compared with the traditional LDA, is decreased in perplexity value and has better generalization performance. At the same time, we can obtain the topic that external attributes contained.","PeriodicalId":208779,"journal":{"name":"2014 10th International Conference on Natural Computation (ICNC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127310091","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
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