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

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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Efficient prediction of CU depth and PU mode for fast HEVC encoding using statistical analysis

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2016年 38卷 474-486页

作者： Liu, Zhaoyi Lin, Ting-Lan Chou, Chi-Chan Beijing Inst Technol Sch Informat & Elect 5 South Zhongguancun St Beijing 100081 Peoples R China Chung Yuan Christian Univ Dept Elect Engn 200 Zhongbei Rd Zhongli City 320 Taoyuan County Taiwan

High Efficiency Video Coding (HEVC) adopts complex quadtree-structured CU (coding unit) and PU (prediction unit) modes. Thus, the search process for optimal modes causes high computational complexity in HEVC. To solve this problem, a fast coding algorithm is proposed. Thirteen neighboring CTUs (Coding Tree Unit) are divided into three classes with the k-means method, and are selectively used as the reference CTUs to calculate the predicted CU depth levels for the current CTU. Additionally, for every CU depth, we skip some rarely used PU modes to reduce the complexity of the PU selection algorithm. Experimental results show that compared with the HEVC reference software, our algorithm can achieve 56.71% time saving with Low Delay Configuration profile and 59.76% with Random Access Configuration profile, whereas values of BDBR (Bjontegaard Delta Bit Rate) are only 1.0517% and 0.9918%, respectively. Compared with five state-of-the-art algorithms, the proposed algorithm has significant encoding time reduction with good bitrate performances. (C) 2016 Elsevier Inc. All rights reserved.

关键词： HEVC (High Efficiency Video Coding) CU (Coding Unit) size decision PU (Prediction Unit) mode decision Statistical analysis k-means clustering algorithm

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A computational approach for optimizing vehicles' interior noise and vibration

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INTERNATIONAL JOURNAL OF AUTOMOTIVE AND MECHANICAL ENGINEERING 2017年第4期14卷 4690-4703页

作者： Al-Dhahebi, Adel Mohammed Junoh, Ahmad kadri Mohamed, Zamri Muhamad, Wan Zuki Azman Wan Univ Malaysia Perlis Inst Engn Math Arau Perlis 02600 Malaysia Univ Malaysia Pahang Fac Mech Engn Pekan Pahang 26600 Malaysia

This paper proposes a Genetic algorithm (GA) to optimise vehicles' interior noise and vibration caused by powertrain, tire-road surface interaction and type of car. Toward this end, an experimental design was carried out to obtain the noise and vibration data of three local compact-sized cars at stationary and running conditions and varying engine speeds. The acquired data were analysed to obtain sound quality parameters such as loudness and sharpness, sound pressure level and vibration exposures in the interior cabin. Besides that, a k-means clustering algorithm was utilised to cluster the noise and vibration to determine the comfort level in the vehicle's interior cabin. The overall findings indicate that the comfort level is influenced by the types of road surface, powertrain and vehicle design. The results also indicate that the proposed GA approach is reliable and can be utilised by automotive researchers to identify the optimal Noise, Vibration and Harshness (NVH) values for vehicle refinement and noise control.

关键词： k-means clustering algorithm Genetic algorithm Interior Vehicle Noise and Vibration Optimization Sound Quality Structure Vibrations Experimental Designs of NVH

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Real-time crash prediction on urban expressways: identification of key variables and a hybrid support vector machine model

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IET INTELLIGENT TRANSPORT SYSTEMS 2016年第5期10卷 331-337页

作者： Sun, Jie Sun, Jian Tongji Univ Key Lab Rd & Traff Engn Minist Educ 4800 Caoan Rd Shanghai 201804 Peoples R China Jiangsu Prov Collaborat Innovat Ctr Modern Urban SiPaiLou 2 Nanjing 210096 Jiangsu Peoples R China

The traffic safety on expressways is crucial for the efficient operation of the expressway system, and there is a close relationship between traffic states and crashes on expressways, and the occurrence of crashes may be influenced by the interaction of different combinations of traffic states upstream and downstream of the crash location. Based on the crash data and the corresponding traffic flow detector data collected on expressways in Shanghai, this study proposes a hybrid model combining a support vector machine (SVM) model with a k-means clustering algorithm to predict the likelihood of crashes. The random forest (RF) model is employed to select the important and significant variables for model construction from the data of the traffic flow 5-10 min before the crash occurred. Then, the cross-validation and transferability of different models (SVM model without variable selection, SVM model with variable selection, and hybrid SVM model with variable selection) are determined using 577 crashes and 5794 matched non-crash events. The results show that the crash prediction model along with the four most important variables selected using the RF model can obtain a satisfactory prediction performance for crashes. With the combination of the clustering algorithm and SVM model, the accuracy of the crash prediction model can be as high as 78.0%. Moreover, the results of the transferability of the three different models imply that the variable selection and clustering algorithm both have an advantage for crash prediction.

关键词： road accidents road safety roads support vector machines learning (artificial intelligence) pattern clustering traffic engineering computing road traffic RF model hybrid SVM model random forest model k-means clustering algorithm Shanghai traffic flow detector data traffic safety hybrid support vector machine model urban expressways real-time crash prediction

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Application research of improved k-means algorithm in network intrusion detection

Application research of improved K-means algorithm in networ...

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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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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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Image Segmentation using Firefly algorithm

Image Segmentation using Firefly Algorithm

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International Conference on Information Technology (InCITe) - The Next Generation IT Summit on the Theme - Internet of Things - Connect Your Worlds

作者： Sharma, Akash Sehgal, Smriti Amity Univ Dept CSE Noida India

ISBN: (纸本)9781509026128

Image segmentation is an important step in the domain of image processing in which we segment the image into several parts which carry certain type of information for the user. Image segmentation is very difficult step in the processing of the image which aims at extracting the information from image. clustering is used to segment the image. clustering algorithms are part of data mining algorithm that groups the data into various number of given clusters. All the data points in one cluster have similar properties based on which they are clustered i.e. each cluster has minimum difference between its points and maximum difference from other cluster data points. The proposed algorithm uses k-mean algorithm and firefly to cluster image pixels into k cluster for segmentation. Since k-mean clustering algorithm is gets trapped in local optima it is optimized using firefly algorithm. Swarm intelligence based algorithms forms the basis of the firefly algorithm which has several application and used to solve optimization problems. Firefly algorithm has been applied in many research and optimization areas. Firefly algorithm and its hybridized version have been used to solve various problems successfully. To apply firefly algorithm to wide areas of problem the firefly algorithm must be modified or integrated with other algorithms. Presently metaheuristic nature of algorithm plays an important role and current optimization algorithm include this nature and are very efficient in solving NP-hard problems.

关键词： k-means clustering algorithm Firefly Optimization Image Segmentation Correlation

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An Efficient Lane Markings Detection and Tracking Method based on Vanishing Point Constraints 35

An Efficient Lane Markings Detection and Tracking Method bas...

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第35届中国控制会议

作者： HOU Changzheng HOU Jin YU Chaochao School of Information Science and Technology Southwest Jiaotong University

ISBN: (纸本)9781509009107

This paper proposes an efficient lane markings detection and tracking method,which utilizes line segments as feature information combined with vanishing point *** method can be highlighted in four items as ***,the region of interest(ROI) of road image is determined,and then the edge information is extracted by Canny edge ***,edge image is scanned to calculate the orientation of edge-linking pixels and we filter out noise edges which have abnormal ***,line segments detected by Progressive Probabilistic Hough Transform(PPHT) are applied to analyze the structural information of lanes and the interferential line segments are eliminated under vanishing point ***,kmeans clustering algorithm is used to classify and fit the closest two lane ***,a kalman filter is utilized for lane markings *** experimental results demonstrate the good accuracy and robustness of our method in various complex environment.

关键词： Vanishing Point Constraint Noise Edges Progressive Probabilistic Hough Transform k-means clustering algorithm kalman Filter

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