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  • Open Access

    ARTICLE

    Automated and Precise Event Detection Method for Big Data in Biomedical Imaging with Support Vector Machine

    Lufeng Yuan, Erlin Yao, Guangming Tan

    Computer Systems Science and Engineering, Vol.33, No.2, pp. 105-113, 2018, DOI:10.32604/csse.2018.33.105

    Abstract This paper proposes a machine learning based method which can detect certain events automatically and precisely in biomedical imaging. We detect one important and not well-defined event, which is called flash, in fluorescence images of Escherichia coli. Given a time series of images, first we propose a scheme to transform the event detection on region of interest (ROI) in images to a classification problem. Then with supervised human labeling data, we develop a feature selection technique to utilize support vector machine (SVM) to solve this classification problem. To reduce the time in training SVM model,… More >

  • Open Access

    ARTICLE

    The Machine Learning Based Finite Element Analysis on Road Engineering of Built-in Carbon Fiber Heating Wire

    Yuhua Penga, Dingyue Chena, Lihao Chenb, Jiayu Yub, Mengjie Baoa

    Intelligent Automation & Soft Computing, Vol.24, No.3, pp. 531-539, 2018, DOI:10.31209/2018.100000020

    Abstract For the study of the effect of deicing with carbon fiber heating wire in the bridge pavement structure, through built-in carbon fiber heating wire in the bridge pavement structure, experimental studies were carried out indoor on the effects of thermal conductivity in different embedding positions, layout spacing and the installs power of carbon fiber heating wire. With indoor laboratory test data as the basic parameters, using ABAQUS finite element software simulation, an analysis was carried out of the degree that the surface temperature of the heating wire, the thermal physical parameters of asphalt concrete, and More >

  • Open Access

    EDITORIAL

    Special Issue on Machine Learning and Data Mining for Cyber-Physical Systems

    Zheng Xu, Zhiguo Yan

    Intelligent Automation & Soft Computing, Vol.24, No.3, pp. 517-518, 2018, DOI:10.31209/2018.100000018

    Abstract This article has no abstract. More >

  • Open Access

    ARTICLE

    Machine Learning Based Resource Allocation of Cloud Computing in Auction

    Jixian Zhang1, Ning Xie1, Xuejie Zhang1, Kun Yue1, Weidong Li2,*, Deepesh Kumar3

    CMC-Computers, Materials & Continua, Vol.56, No.1, pp. 123-135, 2018, DOI:10.3970/cmc.2018.03728

    Abstract Resource allocation in auctions is a challenging problem for cloud computing. However, the resource allocation problem is NP-hard and cannot be solved in polynomial time. The existing studies mainly use approximate algorithms such as PTAS or heuristic algorithms to determine a feasible solution; however, these algorithms have the disadvantages of low computational efficiency or low allocate accuracy. In this paper, we use the classification of machine learning to model and analyze the multi-dimensional cloud resource allocation problem and propose two resource allocation prediction algorithms based on linear and logistic regressions. By learning a small-scale training More >

  • Open Access

    ARTICLE

    Data Mining and Machine Learning Methods Applied to 3 A Numerical Clinching Model

    Marco Götz1,*, Ferenc Leichsenring1, Thomas Kropp2, Peter Müller2, Tobias Falk2, Wolfgang Graf1, Michael Kaliske1, Welf-Guntram Drossel2

    CMES-Computer Modeling in Engineering & Sciences, Vol.117, No.3, pp. 387-423, 2018, DOI:10.31614/cmes.2018.04112

    Abstract Numerical mechanical models used for design of structures and processes are very complex and high-dimensionally parametrised. The understanding of the model characteristics is of interest for engineering tasks and subsequently for an efficient design. Multiple analysis methods are known and available to gain insight into existing models. In this contribution, selected methods from various fields are applied to a real world mechanical engineering example of a currently developed clinching process. The selection of introduced methods comprises techniques of machine learning and data mining, in which the utilization is aiming at a decreased numerical effort. The More >

  • Open Access

    ARTICLE

    Machine Learning Models of Plastic Flow Based on Representation Theory

    R. E. Jones1,*, J. A. Templeton1, C. M. Sanders1, J. T. Ostien1

    CMES-Computer Modeling in Engineering & Sciences, Vol.117, No.3, pp. 309-342, 2018, DOI:10.31614/cmes.2018.04285

    Abstract We use machine learning (ML) to infer stress and plastic flow rules using data from representative polycrystalline simulations. In particular, we use so-called deep (multilayer) neural networks (NN) to represent the two response functions. The ML process does not choose appropriate inputs or outputs, rather it is trained on selected inputs and output. Likewise, its discrimination of features is crucially connected to the chosen inputoutput map. Hence, we draw upon classical constitutive modeling to select inputs and enforce well-accepted symmetries and other properties. In the context of the results of numerous simulations, we discuss the More >

  • Open Access

    ARTICLE

    Research on Hybrid Model of Garlic Short-term Price Forecasting based on Big Data

    Baojia Wang1, Pingzeng Liu1,*, Zhang Chao1, Wang Junmei1, Weijie Chen1, Ning Cao2, Gregory M.P. O’Hare3, Fujiang Wen1

    CMC-Computers, Materials & Continua, Vol.57, No.2, pp. 283-296, 2018, DOI:10.32604/cmc.2018.03791

    Abstract Garlic prices fluctuate dramatically in recent years and it is very difficult to predict garlic prices. The autoregressive integrated moving average (ARIMA) model is currently the most important method for predicting garlic prices. However, the ARIMA model can only predict the linear part of the garlic prices, and cannot predict its nonlinear part. Therefore, it is urgent to adopt a method to analyze the nonlinear characteristics of garlic prices. After comparing the advantages and disadvantages of several major prediction models which used to forecast nonlinear time series, using support vector machine (SVM) model to predict… More >

  • Open Access

    ARTICLE

    Classifying Machine Learning Features Extracted from Vibration Signal with Logistic Model Tree to Monitor Automobile Tyre Pressure

    P. S. Anoop1, V. Sugumaran2

    Structural Durability & Health Monitoring, Vol.11, No.2, pp. 191-208, 2017, DOI:10.3970/sdhm.2017.011.191

    Abstract Tyre pressure monitoring system (TPMS) is compulsory in most countries like the United States and European Union. The existing systems depend on pressure sensors strapped on the tyre or on wheel speed sensor data. A difference in wheel speed would trigger an alarm based on the algorithm implemented. In this paper, machine learning approach is proposed as a new method to monitor tyre pressure by extracting the vertical vibrations from a wheel hub of a moving vehicle using an accelerometer. The obtained signals will be used to compute through statistical features and histogram features for More >

  • Open Access

    ARTICLE

    Feature-Based Vibration Monitoring of a Hydraulic Brake System Using Machine Learning

    T. M. Alamelu Manghai1, R. Jegadeeshwaran2

    Structural Durability & Health Monitoring, Vol.11, No.2, pp. 149-167, 2017, DOI:10.3970/sdhm.2017.011.149

    Abstract Hydraulic brakes in automobiles are an important control component used not only for the safety of the passenger but also for others moving on the road. Therefore, monitoring the condition of the brake components is inevitable. The brake elements can be monitored by studying the vibration characteristics obtained from the brake system using a proper signal processing technique through machine learning approaches. The vibration signals were captured using an accelerometer sensor under a various fault condition. The acquired vibration signals were processed for extracting meaningful information as features. The condition of the brake system can More >

  • Open Access

    ARTICLE

    Condition Monitoring of Roller Bearing by K-Star Classifier and K-Nearest Neighborhood Classifier Using Sound Signal.

    Rahul Kumar Sharma*1, V. Sugumaran1, Hemantha Kumar2, Amarnath M3

    Structural Durability & Health Monitoring, Vol.11, No.1, pp. 1-16, 2017, DOI:10.3970/sdhm.2017.012.001

    Abstract Most of the machineries in small or large scale industry have rotating element supported by bearings for rigid support and accurate movement. For proper functioning of machinery, condition monitoring of the bearing is very important. In present study sound signal is used to continuously monitor bearing health as sound signals of rotating machineries carry dynamic information of components. There are numerous studies in literature that are reporting superiority of vibration signal of bearing fault diagnosis. However, there are very few studies done using sound signal. The cost associated with condition monitoring using sound signal (Microphone)… More >

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