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  • 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

    ABSTRACT

    Machine Learning Prediction of Tissue Strength and Local Rupture Risk in Ascending Thoracic Aortic Aneurysms

    Xuehuan He1, Stephane Avril2, Jia Lu1,*

    Molecular & Cellular Biomechanics, Vol.16, Suppl.2, pp. 50-52, 2019, DOI:10.32604/mcb.2019.07390

    Abstract A Multi-layer Perceptron (MLP) neural network model [1] is developed to predict the strength of ascending thoracic aortic aneurysm (ATAA) tissues using tension-strain data and assess local rupture risk. The data were collected through in vitro inflation tests on ATAA samples from 12 patients who underwent surgical intervention [2]. An inverse stress analysis was performed to compute the wall tension at Gauss points. Some of these Gauss points are at or near sites where the samples eventually ruptured, while others are at locations where the tissue remained intact. A total of 27,648 tension- strain curves,… More >

  • Open Access

    ABSTRACT

    Automatic Segmentation Methods Based on Machine Learning for Intracoronary Optical Coherence Tomography Image

    Caining Zhang1, Xiaoya Guo2, Dalin Tang1,3,*, David Molony4, Chun Yang3, Habib Samady4, Jie Zheng5, Gary S. Mintz6, Akiko Maehara6, Mitsuaki Matsumura6, Don P. Giddens4,7

    Molecular & Cellular Biomechanics, Vol.16, Suppl.1, pp. 79-80, 2019, DOI:10.32604/mcb.2019.05747

    Abstract Cardiovascular diseases are closely associated with sudden rupture of atherosclerotic plaques. Previous image modalities such as magnetic resonance imaging (MRI) and intravascular ultrasound (IVUS) were unable to identify vulnerable plaques due to their limited resolution. Optical coherence tomography (OCT) is an advanced intravascular imaging technique developed in recent years which has high resolution approximately 10 microns and could provide more accurate morphology of coronary plaque. In particular, it is now possible to identify plaques with fibrous cap thickness <65 μm, an accepted threshold value for vulnerable plaques. However, the current segmentation of OCT images are… 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 >

  • Open Access

    ABSTRACT

    Using Machine Learning Methods in The Simulation of Heat Transfer and Fluid Flow: a Brief Review

    Minshan Li1,3, Dongchuan Mo2,3*, Shushen Lyu2,3*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.22, No.3, pp. 165-165, 2019, DOI:10.32604/icces.2019.05510

    Abstract In the past few years, machine learning algorithms and models have shown great power in the emerging field of data mining and artificial intelligence, attracting a great deal of attention. Given specific learning task and training data set, a machine learning model can improve automatically through training and can help people make decisions and predictions. To date, a lot of advanced machine learning algorithms and theories have been proposed and developed, including random forest, support vector machine, artificial neural network, deep learning and so on. Well-chosen and well-trained machine learning model is proved to have… More >

  • Open Access

    ABSTRACT

    Prediction Models Generation by Machine Learning for Structural Materials Performance by Utilizing the Mi System

    Satoshi Minamoto*, Takuya Kadohira, Kaita Ito, Makoto Watanabe, Masahiko Demura

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.22, No.2, pp. 136-136, 2019, DOI:10.32604/icces.2019.05447

    Abstract The Materials Integration (MI) System is a domestically developed system in the “Cross-ministerial Strategic Innovation Promotion Program” to analyze structural materials performance. The performance on structural materials having complicated inputs/outputs would be solved with the combination of different scientific programs or data from experiment. One of the merits of constructing a combined model (here we call workflow) is that calculations are performed and the data would be stored in the system automatically.
    Furthermore, we developed a web application (“MIREA”: MI REgression Analyzer) that enables us to build high versatile prediction models based on machine learning… More >

  • Open Access

    ABSTRACT

    Machine Learning Prediction of Creep Rupture Time for Steels

    Masahiko Demura1,*, Junya Sakurai1,2, Masayoshi Yamazaki1, Junya Inoue1,2

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.22, No.2, pp. 123-123, 2019, DOI:10.32604/icces.2019.05303

    Abstract Creep is a complicated and time-dependent phenomenon, which is affected by the initial state and the degradation of microstructures. It is thus considered that the information about the microstructure is essential to predict the creep rupture time. On the other hand, there is a strong, practical need for the prediction without the investigation of microstructures nor the disclosure of the detailed process that should control the initial microstructures. In this study, we examined how modern machine learning technique can help to predict the creep rupture time in heat-resistant ferrite-type steels without the direct information about… More >

  • Open Access

    ARTICLE

    A Self-Organizing Memory Neural Network for Aerosol Concentration Prediction

    Qiang Liu1,*, Yanyun Zou2,3, Xiaodong Liu4

    CMES-Computer Modeling in Engineering & Sciences, Vol.119, No.3, pp. 617-637, 2019, DOI:10.32604/cmes.2019.06272

    Abstract Haze-fog, which is an atmospheric aerosol caused by natural or man-made factors, seriously affects the physical and mental health of human beings. PM2.5 (a particulate matter whose diameter is smaller than or equal to 2.5 microns) is the chief culprit causing aerosol. To forecast the condition of PM2.5, this paper adopts the related the meteorological data and air pollutes data to predict the concentration of PM2.5. Since the meteorological data and air pollutes data are typical time series data, it is reasonable to adopt a machine learning method called Single Hidden-Layer Long Short-Term Memory Neural Network (SSHL-LSTMNN)… More >

  • Open Access

    ARTICLE

    A Survey on Machine Learning Algorithms in Little-Labeled Data for Motor Imagery-Based Brain-Computer Interfaces

    Yuxi Jia1, Feng Li1,2, Fei Wang1,2,*, Yan Gui1,2,3

    Journal of Information Hiding and Privacy Protection, Vol.1, No.1, pp. 11-21, 2019, DOI:10.32604/jihpp.2019.05979

    Abstract The Brain-Computer Interfaces (BCIs) had been proposed and used in therapeutics for decades. However, the need of time-consuming calibration phase and the lack of robustness, which are caused by little-labeled data, are restricting the advance and application of BCI, especially for the BCI based on motor imagery (MI). In this paper, we reviewed the recent development in the machine learning algorithm used in the MI-based BCI, which may provide potential solutions for addressing the issue. We classified these algorithms into two categories, namely, and enhancing the representation and expanding the training set. Specifically, these methods More >

  • Open Access

    ARTICLE

    Crack Detection and Localization on Wind Turbine Blade Using Machine Learning Algorithms: A Data Mining Approach

    A. Joshuva1, V. Sugumaran2

    Structural Durability & Health Monitoring, Vol.13, No.2, pp. 181-203, 2019, DOI:10.32604/sdhm.2019.00287

    Abstract Wind turbine blades are generally manufactured using fiber type material because of their cost effectiveness and light weight property however, blade get damaged due to wind gusts, bad weather conditions, unpredictable aerodynamic forces, lightning strikes and gravitational loads which causes crack on the surface of wind turbine blade. It is very much essential to identify the damage on blade before it crashes catastrophically which might possibly destroy the complete wind turbine. In this paper, a fifteen tree classification based machine learning algorithms were modelled for identifying and detecting the crack on wind turbine blades. The More >

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