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21st IFAc World congress on Automatic control - Meeting Societal challenges

作者： Zhang, Jianhua Liu, Xiuling Oslo Metropolitan Univ Dept Comp Sci N-0166 Oslo Norway East China Univ Sci & Technol Sch Informat Sci & Engn Shanghai 200237 Peoples R China

In this paper we developed a modified Hidden Markov Model (HMM) to analyze the raw nanopore experimental data. Traditionally, prior to further analysis the measured nanopore data must be pre-filtered, but the filtering usually distorts the waveform of the blockage current, especially for rapid translocations and bumping blockages. The HMM is known to be robust with respect to strong noise and thus suitable for processing the raw nanopore data, but its performance is susceptible to the setting of initial parameters. To overcome this problem, we use the fuzzy c-means (FcM) algorithm to initialize the HMM parameters in this work. Then we use the Viterbi training algorithm to optimize the HMM. Finally, both the simulated and experimental data analysis results are presented to show the effectiveness of the proposed method for detection of the nanopore current blockage events in analytical chemistry. copyright (c) 2020 The Authors.

关键词： Nanopore Time series analysis Hidden Markov model Viterbi algorithm fuzzy c-means clustering algorithm

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Research on Emergency Material Optimization Scheduling and Importance Decision in cluster Supply chain 32

Research on Emergency Material Optimization Scheduling and I...

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第32届中国控制与决策会议

作者： Xue Hong Wang Yuan Liao Zhifeng Xu Ruidi Xu Zhuoran School of Computer and Information Engineering Beijing Technology and Business University

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158563

In order to solve the problem of optimal scheduling and reasonable allocation of limited materials in a short time after a natural disaster,a clustering supply chain emergency material distribution priority decision algorithm based on density clustering algorithm is *** clustering as a factor indicator to determine the priority level of emergency material distribution in each supply chain in the clustered supply *** on the material importance,timeliness,and gap index factors,a fuzzy c-means clustering algorithm for supply chain emergency material demand importance decision algorithm is proposed to classify a variety of emergency materials required in disaster areas when an emergency occurs.,And decide the importance of each type of emergency *** results of simulation experiments verify the feasibility of the emergency supply materials scheduling and importance decision-making method for the clustered supply *** decision results guarantee the optimal scheduling and allocation of limited supplies in the shortest possible *** transportation programs provide theoretical support.

关键词： Emergency material distribution priority decision Emergency material demand importance decision Density clustering algorithm fuzzy c-means clustering algorithm cluster supply chain

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Effective convolution method for privacy preserving in cloud over big data using map reduce framework

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IET SOFTWARE 2019年第3期13卷 187-194页

作者： Sudhakar, Katherapaka Farquad, M. A. H. Narshimha, G. Univ JNTUH Hyderabad Telangana India Islamic Univ Madinah Fac Comp & Informat Syst Medina Saudi Arabia JNTUH Karimnagar Telangana India

cloud computing is used to connect several number of remote servers through Internet to accumulate and recover large data anywhere and anytime. As of the conventional privacy defending process, there is a possibility for malevolent assault on the sensitive information accumulated in the cloud. In this research, the authors have proposed a competent large data convert among privacy defending by Hadoop map reduce in the cloud. The procedure exploits fuzzy c-means clustering (FcM) algorithm grouping the data. For dimensionality reduction, map reduce framework will be used. In evaluation module, the recommended technique performed with the aid of K-nearest neighbour (KNN) classification algorithm in this phase using KNN technique to check the convolution process based on the threshold value, which is improving the utility of the privacy data. The consequence acquired illustrates that authors' proposed scheme has enhanced the clustering exactness and also accomplishes the effectual convolution procedure to improve the privacy. From the experimental results, the proposed research achieved an effective clustering accuracy 76.07% and the existing K-means approach gets the clustering accuracy of 73.5% which is minimum value when compared to the proposed researches. The suggested technique is implemented in JAVA with cloud Sim platform.

关键词： data privacy pattern clustering Big Data parallel processing fuzzy set theory cloud computing security of data pattern classification nearest neighbour methods sensitive information Hadoop map dimensionality reduction evaluation module recommended technique KNN technique convolution process privacy data clustering exactness effectual convolution procedure effective clustering accuracy cloud Sim platform effective convolution method big data remote servers conventional privacy defending process malevolent assault FcM algorithm K-means approach cloud computing privacy preserving map reduce framework Internet fuzzy c-means clustering algorithm K-nearest neighbour classification algorithm threshold value JAVA

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A time-use activity-pattern recognition model for activity-based travel demand modeling

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TRANSPORTATION 2019年第4期46卷 1369-1394页

作者： Hafezi, Mohammad Hesam Liu, Lei Millward, Hugh Dalhousie Univ Dept Civil & Resource Engn Fac Engn 1360 Barrington St Halifax NS B3H 4R2 Canada St Marys Univ Sch Environm Dept Geog & Environm Studies 923 Robie St Halifax NS B3H 3C3 Canada

This study develops a new comprehensive pattern recognition modeling framework that leverages activity data to derive clusters of homogeneous daily activity patterns, for use in activity-based travel demand modeling. The pattern recognition model is applied to time use data from the large Halifax STAR household travel diary survey. Several machine learning techniques not previously employed in travel behavior analysis are used within the pattern recognition modeling framework. Pattern complexity of activity sequences in the dataset was recognized using the FcM algorithm, and resulted in identification of twelve unique clusters of homogeneous daily activity patterns. We then analysed inter-dependencies in each identified cluster and characterized the cluster memberships through their socio-demographic attributes using the cART classifier. Based on the socio-demographic characteristics of individuals we were able to correctly identify which cluster individuals belonged to, and also predict various information related to their activities, such as start time, duration, travel distance, and travel mode, for use in activitybased travel demand modeling. To execute the pattern recognition model, the 24-h activity patterns are split into 288 three dimensional 5 min intervals. Each interval includes information on activity types, duration, start time, location, and travel mode if applicable. Results from aggregated statistical evaluation and Kolmogorov-Smirnov tests indicate that there is heterogeneous diversity among identified clusters in terms of temporal distribution, and substantial differences in a variety of socio-demographic variables. The homogeneous clusters identified in this study may be used to more accurately predict the scheduling behavior of specific population groups in activity-based modeling, and hence to improve prediction of the times and locations of their travel demands. Finally, the results of this study are expected to be implemented within the activi

关键词： Activity-based model Time-use Machine learning fuzzy c-means clustering algorithm Activity pattern recognition

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Impact of surface machining complexity on energy consumption and efficiency in cNc milling

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INTERNATIONAL JOURNAL OF ADVANcED MANUFAcTURING TEcHNOLOGY 2019年第9-12期102卷 2891-2905页

作者： Zhao, Junhua Li, Li Wang, Yue Sutherland, John W. Southwest Univ Coll Engn & Technol Chongqing 400715 Peoples R China Purdue Univ Environm & Ecol Engn W Lafayette IN 47906 USA

Energy consumption of machining systems has been a great concern of many manufacturing enterprises. It is pointed out that complex properties of sculptured surface have important influence on cNc machining process where energy consumption and machining efficiency are treated as two evaluation indicators of machining system performance. This paper studies the impact of Surface Machining complexity (SMc) on energy consumption and efficiency in cNc machining. By analyzing critical factors that influence machining power and efficiency, a pentagon model that refers to the workpiece, equipment, cutter, goal, and process is provided. Based on the pentagon model, a model for calculating SMc, which reflects the difficulty level of cNc machining, is developed. Furthermore, a detailed process of the solution using fuzzy c-means clustering algorithm is introduced with a case study. Finally, the impact of SMc on energy consumption of machining system is discussed via a group of experiments. The experiments verified the effectiveness of the proposed method and present the increased trend between surface machining complexity and energy consumption, in particular considering the effect of surface curvature on machining energy consumption.

关键词： Energy consumption Surface machining complexity (SMc) Machining efficiency Surface partitioning fuzzy c-means clustering algorithm

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The product quality risk assessment of e-commerce by machine learning algorithm on spark in big data environment

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JOURNAL OF INTELLIGENT & fuzzy SYSTEMS 2019年第4期37卷 4705-4715页

作者： Liu, Yi Lu, Jiahuan Mao, Feng Tong, Kaidi Hangzhou Dianzi Univ Management Sch Hangzhou Zhejiang Peoples R China Zhejing Inst Informatizat Dev Hangzhou Zhejiang Peoples R China

In order to pre-warning the product quality risk of the e-commerce platform, this paper studies the machine learning algorithm for the products quality risk assessment, which propose the fuzzy c-means clustering algorithm for the feature extraction and the cost Sensitive Leaning (cSL)-Naive Bayesian algorithm to construct the assessment model for E-commerce product quality risk form the massive and unbalanced data. The experimental results show that the Machine Learning algorithm based on Spark has better scalability and superiority in the large-scale data environment, which can accurately identify e-commerce product quality risk.

关键词： E-commerce product quality risk assessment fuzzy c-means clustering algorithm cost sensitive leaning Naive Bayesian algorithm

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fuzzy clustering approach for brain tumor tissue segmentation in magnetic resonance images

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SOFT cOMPUTING 2019年第20期23卷 10105-10117页

作者： Rodriguez-Mendez, Ivan A. Urena, Raquel Herrera-Viedma, Enrique Univ Granada Dept Comp Sci & Artificial Intelligence Granada Spain Univ Autonoma Chiriqui UNACHI Dept Comp Sci David Panama De Montfort Univ Inst Artificial Intelligence Leicester Leics England

The early and accurate detection of brain tumors is key to improve the quality of life and the survival of cancer patients. Magnetic resonance imaging (MRI) is a widely used imaging technique to assess these tumors, but the large amount of data produced by MRI prevents manual segmentation in a reasonable time, limiting the use of precise quantitative measurements in the clinical practice. consequently, automatic and reliable segmentation methods are required. However, the large spatial and structural variability among brain tumors make automatic segmentation a challenging problem. In this contribution, we present a new model of segmentation of brain magnetic resonance images. In order to obtain the region of interest, we propose a hybrid approach that carries out both fuzzy c-mean algorithm and multiobjective optimization taking into account both compactness and separation in the clusters with the purpose of improving the cluster center detection and speed up the convergence time. This new segmentation approach is a key component of the proposed magnetic resonance image-based classification system for brain tumors. Experimental results are presented to demonstrate the effectiveness and efficiency of the proposed approach using the DIcOM MRI database.

关键词： Magnetic resonance images segmentation Tumor tissue detection Oncologic fuzzy c-means clustering algorithm cluster compelling information Region of interest extraction Optimization fuzzy compactness and separation Genetic algorithm

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A fuzzy clustering based color -coded diagram for effective illustration of blood perfusion parameters in contrast -enhanced ultrasound videos

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cOMPUTER METHODS AND PROGRAMS IN BIOMEDIcINE 2020年 190卷 105233-105233页

作者： Zhuang, Zhemin Fan, Guangwen Yuan, Ye Raj, Alex Noel Joseph Qiu, Shunmin Shantou Univ Dept Elect Engn Shantou Guangdong Peoples R China Shantou Univ Guangdong Prov Key Lab Digital Signal & Image Pro Shantou Guangdong Peoples R China Shantou Univ Imaging Dept Hosp 1 Med Coll Shantou Guangdong Peoples R China

Background and Objective Early identification and diagnosis of tumors are of great significance to improve the survival rate of patients. Amongst other techniques, contrast-enhanced ultrasound is an important means to help doctors diagnose tumors. Due to the advantages of high efficiency, accuracy and objectivity, more and more computer-aided methods are used in medical diagnosis. Here we propose, a color-coded diagram based on quantitative blood perfusion parameters for contrast-enhanced ultrasound video. The method realizes the static description of the dynamic blood perfusion process in contrast-enhanced ultrasound videos and reveal the blood perfusion characteristics of all regions of the tissue providing assistance to the doctors in their clinical *** For effective illustration of the blood perfusion through tissues, we propose (a) an improved block matching algorithm to eliminate the image distortions caused by breathing; (b) compute the time-grayscale intensity curve for each pixel to obtain four different quantitative blood perfusion parameters; and finally (c) employ the fuzzy c-means clustering algorithm to cluster the blood perfusion parameters, where each parameter is associated with a particular color. Thus based on the correspondence between the pixel and the blood perfusion parameters, all the pixels are color-coded to obtain the color-coded *** To the best of our knowledge, the proposed technique is one-of-its-kind to color code the contrast-enhanced ultrasound videos using blood perfusion parameters in order to understand the hemodynamic characteristics of the benign and malignant lesion. In our experiments, various contrast-enhanced ultrasound videos corresponding to several real-world cases were color-coded and the results of the experiments illustrated that the proposed color-coded diagrams are consistent with the diagnosis presented by the *** The experimental results suggested that the proposed metho

关键词： color-coded diagram Video stabilization Time-grayscale intensity curve Blood perfusion parameters fuzzy c-means clustering algorithm

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Multi-objective optimization of cFRP drilling parameters with a hybrid method integrating the ANN, NSGA-II and fuzzy c-means

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cOMPOSITE STRUcTURES 2020年第0期235卷 111803-000页

作者： Wang, Qian Jia, Xiaoliang Northwestern Polytech Univ Sch Mech Engn 127 West Youyi Rd Xian 710072 Shaanxi Peoples R China

A full factorial experiment is performed for the conventional dry drilling of cFRP with spindle speed, feed rate and point angle as drilling parameters, response variables are thrust force and exit-delamination. Artificial neural network (ANN) is developed to express thrust force and delamination factor as a function of drilling parameters. Multi-objective optimization of drilling parameters is accomplished based on Non-dominated Sorting Genetic algorithm (NSGA-II) with thrust force, delamination factor and material removal rate as optimization objectives, delamination factor also serves as a constraint. The Pareto front of drilling response variables determined by NSGA-II consists of a large number of non-dominated solutions. In order to facilitate the experimental verification of optimization results, fuzzy c-means clustering algorithm is used to narrow down the solutions on the front to several representative ones. conformation tests are conducted and results show that the representative solutions can give satisfactory performance with achieving a trade-off among thrust force, exit-delamination and material removal rate.

关键词： carbon fiber reinforced polymer Multi-objective optimization Artificial neural network (ANN) Non-dominated Sorting Genetic algorithm (NSGA-II) fuzzy c-means clustering algorithm

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Automatic Analysis of Large-scale Nanopore Data Using Hidden Markov Models

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IFAc-PapersOnLine 2020年第2期53卷 16759-16766页

作者： Jianhua Zhang Xiuling Liu Department of Computer Science Oslo Metropolitan University 0166 Oslo Norway School of Information Science and Engineering East China University of Science and Technology Shanghai 200237 P.R. China

关键词： Nanopore Time series analysis Hidden Markov model Viterbi algorithm fuzzy c-means clustering algorithm

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