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2017 International Conference on Education and Management Innovation(EMI2017)

作者： Gongrang Zhang Wei Hu School of Management Hefei University of Technology Anhui Transport Interconnection Management Center School of Computer & InformationHefei University of Technology

The original k-means algorithm is sensitive to the selection of the initial clustering center and unstable in the network intrusion detection application based on data mining. In this paper, the optimization ability of particle swarm optimization(PSO) was used to solve the problem that k-means algorithm is sensitive to the selection of the initial clustering center. Different global optimal solutions were obtained by PSO algorithm and used as the basis of choosing the initial clustering center of k-means clustering algorithm. Based on this idea, the k-means algorithm was improved and a network intrusion detection model was established. Experimental results show that the improved k-means clustering algorithm based on PSO has better clustering effect than original k-means clustering algorithm, and can detect more intrusion behaviors in network intrusion detection.

关键词： Network intrusion detection k-means clustering algorithm Particle swarm optimization algorithm Initial clustering center

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Unsupervised Satellite Image Segmentation using a Bivariate Beta type-II Mixture Model

Unsupervised Satellite Image Segmentation using a Bivariate ...

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IEEE International Conference on Computational Cybernetics

作者： Taher Ben Arab Mourad Zribi Afif Masmoudi Laboratoire d'Informatique Signal et Image de la Cote d'Opale (LISIC-EA 4491) ULCO 50 Rue Ferdinand Buisson BP 719 62228 Calais Cedex France. Laboratoire de Probabilités et Statistique Faculté des Sciences de Sfax Université de Sfax. B. P. 1171 Sfax Laboratoire d'Informatique Signal et Image de la C?te d'Opale (LISIC-EA 4491) ULCO 50 Rue Ferdinand Buisson BP 719 62228 Calais Cedex France. Laboratoire de Probabilités et Statistique Faculté des Sciences de Sfax Université de Sfax. B. P. 1171 Sfax Tunisie.

ISBN: (纸本)9781479900626

This paper presents an unsupervised satellite color image segmentation approach based on Bivariate Beta type-II. Such a method could be considered as original since it uses a k-means clustering algorithm in order to initialize the image class number. Moreover, it exploits a Bivariate Beta type-II for statistical distributions applying it for each class. Satellite image exploitation requires the use of different approaches, especially those based on the unsupervised statistical segmentation principle. Such approaches necessitate the definition of several parameters such as image class number, class variables estimation and mixture distributions. The use of statistical image attributes has allowed us to get convincing results, provided that we ensure under the condition of having an initialization step with appropriate statistical distributions. Bivariate Beta type-II associated with a k-means clustering algorithm and Expectation-Maximization (EM) algorithm could be adapted to such a problem. For each image class, Bivariate Beta type-II attributes a specific distribution type according to different parameters. Different adapted algorithms (namely k-means clustering algorithm, EM algorithm and Bivariate Beta type-II algorithm) are then applied to the satellite image segmentation problem. The efficiency of those combined algorithms is validated with the Mean Squared Errors (MSE), Signal to Noise Ratio (SNR) and Maximum Distance (MD).

关键词： Bivariate Beta type-II Expectation Maximization algorithm k-means clustering algorithm Mean Squared Errors Unsupervised color image segmentation

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Prediction Study on Anti-Slide Control of Railway Vehicle Based on RBF Neural Networks

Prediction Study on Anti-Slide Control of Railway Vehicle Ba...

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2012 International Conference on Solid State Devices and Materials Science(SSDMS 2012)

作者： Lijun Yang Jimin Zhang College of Automotive Engineering Tongji University Railway &Urban Mass Transit Research Institute Tongji University

While railway vehicle braking, Anti-slide control system will detect operating status of each wheel-sets e.g. speed difference and deceleration etc. Once the detected value on some wheel-set is over pre-defined threshold, brake effort on such wheel-set will be adjusted automatically to avoid blocking. Such method takes effect on guarantee safety operation of vehicle and avoid wheel-set flatness, however it cannot adapt itself to the rail adhesion variation. While wheel-sets slide, the operating status is chaotic time series with certain law, and can be predicted with the law and experiment data in certain time. The predicted values can be used as the input reference signals of vehicle anti-slide control system, to judge and control the slide status of wheel-sets. In this article, the RBF neural networks is taken to predict wheel-set slide status in multi-step with weight vector adjusted based on online self-adaptive algorithm, and the center & normalizing parameters of active function of the hidden unit of RBF neural networks' hidden layer computed with k-means clustering algorithm. With multi-step prediction simulation, the predicted signal with appropriate precision can be used by anti-slide system to trace actively and adjust wheel-set slide tendency, so as to adapt to wheel-rail adhesion variation and reduce the risk of wheel-set blocking.

关键词： Railway vehicle Brake anti-slide control RBF neural network k-means clustering algorithm Multi-step prediction

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PRkAR2A-derived circular RNAs promote the malignant transformation of colitis and distinguish patients with colitis-associated colorectal cancer

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CLINICAL AND TRANSLATIONAL MEDICINE 2022年第2期12卷 e683页

作者： Wan, Daiwei Wang, Sentai Xu, Zhihua Zan, Xinquan Liu, Fei Han, Ye Jiang, Min Wu, Airong Zhi, Qiaoming Soochow Univ Dept Gen Surg Affiliated Hosp 1 Suzhou 215006 Peoples R China Soochow Univ Dept Gastroenterol Affiliated Hosp 1 Suzhou 215006 Peoples R China Soochow Univ Dept Oncol Affiliated Hosp 1 Suzhou 215006 Peoples R China

Background Emerging studies have proved that colonic inflammation caused by refractory inflammatory bowel disease (IBD) can initiate the colitis-associated cancer (CAC), but the transition from inflammation to carcinoma is still largely unknown. Methods In this study, mouse colitis and CAC models were established, and the RNA-seq by circRNA microarray was employed to identify the differentially expressed circRNAs and mRNAs in different comparisons (DSS vs. NC and AOM/DSS vs. DSS). The bioinformatics analyses were used to search the common characteristics in mouse colitis and CAC. Results The k-means clustering algorithm packaged these differential expressed circRNAs into subgroup analysis, and the data strongly implied that mmu_circ_0001109 closely correlated to the pro-inflammatory signals, while mmu_circ_0001845 was significantly associated with the Wnt signalling pathway. Our subsequent data in vivo and in vitro confirmed that mmu_circ_0001109 could exacerbate the colitis by up-regulating the Jak-STAT3 and NF-kappa B signalling pathways, and mmu_circ_0001845 promoted the CAC transformation through the Wnt signalling pathway. By RNA blasting between mice and humans, the human RTEL1- and PRkAR2A-derived circRNAs, which might be considered as homeotic circRNAs of mmu_circ_0001109 and mmu_circ_0001845, respectively, were identified. The clinical data revealed that RTEL1-derived circRNAs had no clinical significance in human IBD and CAC. However, three PRkAR2A-derived circRNAs, which had the high RNA similarities to mmu_circ_0001845, were remarkably up-regulated in CAC tissue samples and promoted the transition from colitis to CAC. Conclusions Our results suggested that these human PRkAR2A-derived circRNAs could be novel candidates for distinguishing CAC patients and predicted the prognosis of CAC.

关键词： circular RNA colitis-associated colorectal cancer PRkAR2A RNA sequencing k-means clustering algorithm

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Effective Multipath Mitigation Methods for RTk in Urban Environments

Effective Multipath Mitigation Methods for RTK in Urban Envi...

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第九届中国卫星导航学术年会

作者： kai-qin Lin Zhong-liang Deng Lu Yin Beijing University of Posts and Telecommunications

RTk is one of the most precise positioning technologies,which has been widely used in many ***,in an urban area,GNSS receiver easily receive multipath signals caused by signal reflection and diffraction by both line-of-sight(LOS) and non-line-of-sight(NLOS) *** signals degrade the phase observation quality and makes it difficult to detect integer ambiguities in RTk *** paper proposes two methods to mitigate multipath *** the first method,a least square equation is constructed by using the relationship between the classical satellite elevation angle noise model and the signal-to-noise ratio(SNR) noise *** least square residual is used to determine NLOS and LOS satellites based on the k-means clustering *** second method mitigates LOS multipath signal based on the consistency check between prediction residuals and prediction covariance of Extended kalman *** experiences show that the proposed tow methods can able mitigate multipath effect and increase the availability of integer ambiguity resolution.

关键词： RTk positionings multipath effect least square residual k-means clustering algorithm

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A kM-Net Model with Weight Initialization by k-means for Images Classification

A KM-Net Model with Weight Initialization by k-Means for Ima...

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第十五届中国体视学与图像分析学术会议

作者： LIN Liu MA Miao School of Computer Science Shaanxi Normal University Key Laboratory of Modern Teaching Technology Ministry of Education

Convolutional neural networks(CNNs) have been successfully applied in the fields of image *** the training cost is increasing rapidly with the appearances of various large CNN ***,it is challenging to balance the classification accuracy and the convergence speed of CNN models on a particular *** this paper,a kM-Net model with the convolutional kernels in the first layer initialized by k-means clustering algorithm is *** results compared with other methods on the most widely-used image datasets(SVHN、ASL) show that the proposed kM-Net model could improve the classification accuracy,while accelerating the convergence of the training process.

关键词： convolutional neural network LeNet-5 k-means clustering algorithm

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Photovoltaic Power Prediction Model Based on Weather Forecast

Photovoltaic Power Prediction Model Based on Weather Forecas...

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Sustainable Power and Energy Conference (iSPEC)

作者： Wei Huang Chao Zhang Xinhe Zhang Junxia Meng Xiaohua Liu Bo Yuan China Electric Power Research Institute Beijing China Xinjiang Electric Power Company Wulumuqi China

ISBN: (纸本)9781728149318

In order to improve the accuracy of PV power generation forecasting, this paper proposes a photovoltaic power generation prediction model combined with weather forecast. In this paper, the k-means clustering algorithm is used to classify historical photovoltaic power generation data, and the correlation analysis method is used to determine the input variables, which reduces the dimension of the input variables and simplifies the calculation process. Then we propose a photovoltaic power generation prediction model based on long-short memory neural network (LSTM) combined with Attention mechanism. After verification using real data, the power prediction model proposed in this paper has higher accuracy and better application range. It can select the appropriate model for power prediction according to different weather conditions.

关键词： power prediction attention mechanism LSTM neural network k-means clustering algorithm Input variables Solar photovoltaics k-means algorithm computational procedure Weather Correlation analysis Power (Psychology) Energy sources Power production weather condition

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A Method for Coherency Identification Based on Singular Value Decomposition

A Method for Coherency Identification Based on Singular Valu...

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IEEE Power and Energy Society General Meeting

作者： Qiaomu Zhu Jinfu Chen Xianzhong Duan Xin Sun Yinhong Li Dongyuan Shi State Key Laboratory of Advanced Electromagnetic Engineering and Technology Huazhong University of Science and Technology

ISBN: (纸本)9781509041695

A novel method to identify coherent generator groups based on singular value decomposition (SVD) in power system is presented in this paper. Via the SVD algorithm, the high-dimensional real-time rotor-angle data of generators is projected into low-dimensional subspace, and the key characteristic coefficients indicating the information of coherency is obtained. Then, the coherency identification is realized by running k-means clustering algorithm on the coefficients. The dimensionality reduction for clustering algorithm by SVD speeds up the coherency identification. Using the real-time rotor-angle data, the proposed method has the potential to be applied to measured data to implement coherency identification. The effectiveness of this method is demonstrated by the simulation results on IEEE 39-bus system.

关键词： Coherency identification k-means clustering algorithm Real-time rotor angles Singular value decomposition (SVD) Singular value decomposition Swine vesicular disease clustering algorithms k-means algorithm dimensionality reduction angular data Authentication dynamos power systems

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Research on glass classification and identification based on heritage samples

Research on glass classification and identification based on...

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作者： Zhiyuan Liu Institute of electric power South China University of Technology

The study of ancient glass has been intensified in archaeological circles today,but its susceptibility to weathering can affect archaeologists' ability to classify *** order to classify glass artefacts,this paper establishes a 'subclass classification model based on the sum of the absolute values of relative changes' and uses the k-means clustering algorithm to classify the *** order to identify different classes of glass objects,the chemical composition of the glass is extracted from the centroids of the different subclasses,and the Euclidean distance between the class centres is solved for,and the class represented by the class centre with the lowest Euclidean distance is taken as the class corresponding to the unknown object.

关键词： k-means clustering algorithm Euclidean distance glass artefacts

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Research on Falling Behavior Monitoring of Three-axis accelerometer

Research on Falling Behavior Monitoring of Three-axis accele...

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第32届中国过程控制会议(CPCC2021)

作者： Gao Fei Zhang Lu Wang Yaxin ZhangHongyuan School of Electronic Information Engineering Inner Mongolia University

Falling behavior recognition in the elderly is an emerging research direction in the field of pattern *** and foreign researchers have introduced the elderly fall behavior monitoring technologies such as acoustic detection technology,but these monitoring technologies are greatly affected by external factors and are not conducive to practical *** this paper,the three-axis accelerometer is used to identify the fall behavior of the elderly,and the k-means clustering algorithm is *** algorithm is used to identify the static state and the walking state of the ***,this paper studies the threshold judgment algorithm to identify the elderly ***,an intelligent self-rescue crutch is designed based on the threshold judgment *** experiment shows that the algorithm used in this paper has a high accuracy in the detection of the behavior and posture of the elderly,and it can effectively identify the fall behavior of the elderly through data,which has important reference significance for the research in this direction.

关键词： Fall Three-axis accelerometer k-means clustering algorithm

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