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2011 3rd IEEE International Conference on Information Management and Engineering(ICIME 2011)

作者： Li Lei Pattern Recognition and Intelligence System, College of Automation, Nanjing University of Post and Telecommunications Nanjing, China Hang Xin Pattern Recognition and Intelligence System, College of Automation, Nanjing University of Post and Telecommunications Nanjing, China

This paper presents a new method to preprocess the datasets, and an efficient way for assigning data points to clusters. The improved method avoids k-means algorithm sensitive to the initial centroids and instability, and it avoids the large number of iterations, saving the running time. Experimental results show that the improved method can effectively improve the speed of clustering and accuracy, reducing the computational complexity of the k-means.

关键词： k-means clustering algorithm initial centroids iterations computational complexity data mining

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Error Prediction method of Electronic Transformers Based on RBF Neural Network

Error Prediction method of Electronic Transformers Based on ...

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13th IEEE International Conference on Electronic Measurement & Instruments (ICEMI)

作者： Wu Qiao Xu Xin Yang Shihai Lu Shufeng Chen Gang Jiangsu Elect Power Co Elect Power Res Inst Nanjing 211103 Jiangsu Peoples R China Southeast Univ Sch Elect Engn Nanjing 210096 Jiangsu Peoples R China

ISBN: (纸本)9781509050352

With the gradual application of electronic transformers in the intelligent substation, the influence of the typical disturbing source on the electronic transformers is gradually to the industry's attention. In order to ensure the accuracy and reliability of electronic transformers, it is necessary to determine the error of the electronic transformers accurately through the study of the influence factors of the measurement error of the electronic transformers. In this regard, this paper proposes a transformer error prediction method which combines RBF neural network prediction with k-means clustering algorithm. First, the historical data are classified by k-means clustering. Then, train the prediction model with the historical data clustered. Take the influencing factors such as temperature, load capacity and magnetic field as the independent variables and the error as the prediction target to establish the prediction model. The prediction model is suitable for the analysis of a large number of data and can accurately predict the error. After using this model to analyze the long-term experimental data of Dongshan substation in 2016, the relative error of the error result is around 5%, which is relatively and verifies the validity of the model.

关键词： Electronic transformers k-means clustering algorithm RBF neural network long-term experimental data

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Research on Intrusion Detection Based on Feature Extraction of Autoencoder and the Improved k-means algorithm

Research on Intrusion Detection Based on Feature Extraction ...

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10th International Symposium on Computational Intelligence and Design (ISCID)

作者： Wang, Xingang Wang, Linlin Qilu Univ Technol Sch Informat Jinan Shandong Peoples R China

ISBN: (纸本)9781538636749

Nowadays, intrusion detection is a technology to effectively avoid a number of risks of network intrusion. The k-means algorithm is widely used in intrusion detection. But, the algorithm has some shortcomings, such as random selection of k value, sensitive selection of initial cluster centers, and low accuracy in clustering high-dimensional data. In order to make up for these shortcomings of the k-means algorithm, this paper proposes an AE-kmeans architecture which combines an autoencoder with the improved k-means algorithm. The AE-kmeans architecture realizes the dimension reduction and feature extraction of these original data by introducing an autoencoder, and uses the improved k-means algorithm to cluster these processed data. These improvements of the improved k-means algorithm mainly include two aspects: the first aspect, a new method is introduced to select initial cluster centers;the second aspect, the algorithm calculates the weight of each attribute with the coefficient of variation, and then uses these weights in Euclidean distance formula, resulting in a weighted Euclidean distance formula. Finally, the AE-kmeans architecture uses kDD CUP99 data set for intrusion detection simulation experiments. The Experimental results show that the AE-kmeans architecture not only enhances the ability to deal with high-dimensional data, but also improves the detection rate and reduces the error-detection rate compared with k-means algorithm.

关键词： intrusion detection k-means clustering algorithm autoencoder AE-kmeans architecture feature extraction weighted Euclidean distance formula

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Hybrid clustering-Time Series-Bayesian Neural Network Short-Term Load Forecasting Method 48

Hybrid Clustering-Time Series-Bayesian Neural Network Short-...

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48th North American Power Symposium (NAPS)

作者： Ghofrani, M. Carson, D. Ghayekhloo, M. Univ Washington Sch Sci Technol Engn & Math STEM Bothell WA 98011 USA Qazvin Islamic Azad Univ Dept Elect & Comp Engn Qazvin Iran

ISBN: (纸本)9781509032709

This paper offers a hybrid short-term load forecasting (STLF) model using a Bayesian neural network (BNN) with a pre-processing stage consisting of a k-means clustering algorithm and time series analysis. The data clusters are time series analyzed to provide the most accurate data sets for each hour of the day. The final forecast is provided from the BNN output. California load data is used to determine the accuracy and processing speed of the proposed method. Additionally a comparison between BNN and other intelligent algorithms is provided using the same pre-processing stage to further gauge performance benchmarks.

关键词： Bayesian neural network k-means clustering algorithm short-term load forecasting

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Towards an EEG-based brain-computer interface for online robot control

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MULTIMEDIA TOOLS AND APPLICATIONS 2016年第13期75卷 7999-8017页

作者： Li, Yantao Zhou, Gang Graham, Daniel Holtzhauer, Andrew Southwest Univ Coll Comp & Informat Sci Chongqing 400715 Peoples R China Coll William & Mary Dept Comp Sci Williamsburg VA 23187 USA Mitre Corp Mclean VA USA

According to New York Times, 5.6 million people in the United States are paralyzed to some degree. Motivated by requirements of these paralyzed patients in controlling assisted-devices that support their mobility, we present a novel EEG-based BCI system, which is composed of an Emotive EPOC neuroheadset, a laptop and a Lego Mindstorms NXT robot in this paper. We provide online learning algorithms that consist of k-means clustering and principal component analysis to classify the signals from the headset into corresponding action commands. Moreover, we also discuss how to integrate the Emotiv EPOC headset into the system, and how to integrate the LEGO robot. Finally, we evaluate the proposed online learning algorithms of our BCI system in terms of precision, recall, and the F-measure, and our results show that the algorithms can accurately classify the subjects' thoughts into corresponding action commands.

关键词： EEG BCI system k-means clustering algorithm Principal component analysis

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An approach to evaluate resource utilization in energy management systems

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ENERGY SOURCES PART B-ECONOMICS PLANNING AND POLICY 2016年第9期11卷 855-860页

作者： Haeri, Abdorrahman Rezaie, kamran Iran Univ Sci & Technol Sch Ind Engn POB 16846-13114 Tehran Iran Univ Tehran Coll Engn Sch Ind Engn Tehran Iran

Organizations implement management systems to achieve better performance in different functions. One important area of management systems is "energy management systems". ISO 50001: 2011 proposed a systematic framework for operation of an effective energy management system (EnMS). Resources should be used efficiently to obtain suitable performance. Thus, it is useful to evaluate resource utilization by considering the importance and efficiency of resources in processes. In this study, major functions of an EnMS are identified in accordance with ISO 50001. Then, for each function, processes and resources are specified and classified. Later, data are gathered, and appropriate measures are calculated. Finally, data analysis techniques are applied to propose strategic decisions for improvement. In addition, an oil refinery is considered as a case study.

关键词： Business process energy management system ISO 50001:2011 k-means clustering algorithm resource utilization

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An Efficient Predictive Model for Probability of Genetic Diseases Transmission

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INTERNATIONAL JOURNAL OF ENGINEERING 2017年第8期30卷 1152-1159页

作者： Hamidi, H. Qaribpour, F. KN Toosi Univ Technol Dept Ind Engn Informat Technol Grp Tehran Iran

In this article, a new combined approach of a decision tree and clustering is presented to predict the transmission of genetic diseases. In this article, the performance of these algorithms is compared for more accurate prediction of disease transmission under the same condition and based on a series of measures like the positive predictive value, negative predictive value, accuracy, sensitivity and specificity. The results show that support vector machine algorithm outperformed the other two simple algorithms and the neural network and genetic algorithms offered better prediction at the end, while the proposed combined approach is developed using different parameters and outperformed the simple methods.

关键词： Genetic Disease Transmitting Genetic Diseases k-means clustering algorithm Data Mining Decision Trees

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Railway accidents analysis based on the improved algorithm of the maximal information coefficient

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INTELLIGENT DATA ANALYSIS 2016年第3期20卷 597-613页

作者： Shao, Fubo Li, keping Xu, Xiaoming Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing Peoples R China Beijing Jiaotong Univ Beijing Lab Urban Rail Transit Beijing Peoples R China

The maximal information coefficient (MIC), a measure of dependence for two-variable relationships, can be used to discover the relationships between two variables in big data. This paper proposes a new mathematical program model for calculating the value of MIC. A corresponding efficient algorithm is designed to solve the model in big data environment. In order to illustrate the validity of the proposed algorithm, the proposed algorithm is applied into the analysis of railway accidents data. Experimental results show that the proposed algorithm could find important relationships between two variables from big data. And some factors influencing accidents are identified from many factors. In addition, compared with the algorithm proposed by Reshef et al. in 2011, the proposed algorithm has lower time complexity and needs less computation time. Hence the proposed algorithm is more suitable for big data environment.

关键词： Maximal information coefficient k-means clustering algorithm big data railway accidents analysis

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Fuzzy time series forecasting based on fuzzy logical relationships and similarity measures

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INFORMATION SCIENCES 2016年 327卷 272-287页

作者： Cheng, Shou-Hsiung Chen, Shyi-Ming Jian, Wen-Shan Chien Kuo Technol Univ Dept Informat Management Changhua Taiwan Chien Kuo Technol Univ Dept Kinesiol Hlth Leisure Studies Changhua Taiwan Natl Taiwan Univ Sci & Technol Dept Comp Sci & Informat Engn Taipei Taiwan

In this paper, we propose a new fuzzy time series forecasting method for forecasting the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) based on fuzzy time series, fuzzy logical relationships, particle swarm optimization techniques, the k-means clustering algorithm, and similarity measures between the subscript of the fuzzy set of the fuzzified historical testing datum on the previous trading day and the subscripts of the fuzzy sets appearing in the current states of the fuzzy logical relationships in the chosen fuzzy logical relationship group. The particle swarm optimization techniques are used to get the optimal partition of the intervals in the universe of discourse. The k-means clustering algorithm is used to cluster the subscripts of the fuzzy sets of the current states of the fuzzy logical relationships to get the cluster center of each cluster and to divide the constructed fuzzy logical relationships into fuzzy logical relationship groups. The experimental results show that the proposed fuzzy forecasting method gets higher forecasting accuracy rates than the existing methods. The advantages of the proposed fuzzy forecasting method is that it uses the particle swarm optimization techniques to get the optimal partition of the intervals in the universe of discourse and uses the k-means clustering algorithm to cluster the subscripts of the fuzzy sets of the current states of the fuzzy logical relationships to get the cluster center of each cluster and to divide the constructed fuzzy logical relationships into fuzzy logical relationship groups for increasing the forecasting accuracy rates. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Fuzzy logical relationships Fuzzy time series k-means clustering algorithm Particle swarm optimization Similarity measures

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Image classification based on improved VLAD

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MULTIMEDIA TOOLS AND APPLICATIONS 2016年第10期75卷 5533-5555页

作者： Long, Xianzhong Lu, Hongtao Peng, Yong Wang, Xianzhong Feng, Shaokun Nanjing Univ Posts & Telecommun Sch Software Sch Comp Sci & Technol Nanjing 210023 Jiangsu Peoples R China Shanghai Jiao Tong Univ Dept Comp Sci & Engn Key Lab Shanghai Educ Commiss Intelligent Interac Shanghai 200240 Peoples R China

Recently, a coding scheme called vector of locally aggregated descriptors (VLAD) has got tremendous successes in large scale image retrieval due to its efficiency of compact representation. VLAD employs only the nearest neighbor visual word in dictionary to aggregate each descriptor feature. It has fast retrieval speed and high retrieval accuracy under small dictionary size. In this paper, we give three improved VLAD variations for image classification: first, similar to the bag of words (BoW) model, we count the number of descriptors belonging to each cluster center and add it to VLAD;second, in order to expand the impact of residuals, squared residuals are taken into account;thirdly, in contrast with one nearest neighbor visual word, we try to look for two nearest neighbor visual words for aggregating each descriptor. Experimental results on UIUC Sports Event, Corel 10 and 15 Scenes datasets show that the proposed methods outperform some state-of-the-art coding schemes in terms of the classification accuracy and computation speed.

关键词： Image classification Scale-invariant feature transform Vector of locally ggregated descriptors k-means clustering algorithm

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