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  • Open Access


    Human Behavior Classification Using Geometrical Features of Skeleton and Support Vector Machines

    Syed Muhammad Saqlain Shah1,*, Tahir Afzal Malik2, Robina khatoon1, Syed Saqlain Hassan3, Faiz Ali Shah4

    CMC-Computers, Materials & Continua, Vol.61, No.2, pp. 535-553, 2019, DOI:10.32604/cmc.2019.07948

    Abstract Classification of human actions under video surveillance is gaining a lot of attention from computer vision researchers. In this paper, we have presented methodology to recognize human behavior in thin crowd which may be very helpful in surveillance. Research have mostly focused the problem of human detection in thin crowd, overall behavior of the crowd and actions of individuals in video sequences. Vision based Human behavior modeling is a complex task as it involves human detection, tracking, classifying normal and abnormal behavior. The proposed methodology takes input video and applies Gaussian based segmentation technique followed by post processing through presenting… More >

  • Open Access


    Changes in Anatomical Features and Protein Pattern of Sunflower Partially Resistant and Susceptible Lines During Infection By Virulence Factors of Sclerotinia Sclerotiorum

    Maryam Monazzah1, Sattar Tahmasebi Enferadi1,*, Zohre Rabiei1 and Alessandro Mattiello2

    Phyton-International Journal of Experimental Botany, Vol.88, No.2, pp. 149-159, 2019, DOI:10.32604/phyton.2019.05053

    Abstract Helianthus annuus L. as an oil seed crop is widely grown throughout the world. One of the most destructive diseases of sunflower is stem rot caused by Sclerotinia sclerotiorum. Oxalic acid is the major virulence factor of this necrotrophic pathogen. It is important to further investigate plant responses to this non-specific toxin. Therefore, in the present study, we compared the patterns of total soluble proteins and xylem morphology of partially resistant and susceptible sunflower lines after treatment with Sclerotinia culture filtrate. The basal stems of both lines were treated with 40 mM oxalic acid (pH 3.7) of fungus culture filtrate… More >

  • Open Access


    Vibration Based Tool Insert Health Monitoring Using Decision Tree and Fuzzy Logic

    Kundur Shantisagar, R. Jegadeeshwaran*, G. Sakthivel, T. M. Alamelu Manghai

    Structural Durability & Health Monitoring, Vol.13, No.3, pp. 303-316, 2019, DOI:10.32604/sdhm.2019.00355

    Abstract The productivity and quality in the turning process can be improved by utilizing the predicted performance of the cutting tools. This research incorporates condition monitoring of a non-carbide tool insert using vibration analysis along with machine learning and fuzzy logic approach. A non-carbide tool insert is considered for the process of cutting operation in a semi-automatic lathe, where the condition of tool is monitored using vibration characteristics. The vibration signals for conditions such as heathy, damaged, thermal and flank were acquired with the help of piezoelectric transducer and data acquisition system. The descriptive statistical features were extracted from the acquired… More >

  • Open Access


    A Dynamical Approach to the Spatio-temporal Features of the Portevin-Le Chatelier Effect


    CMES-Computer Modeling in Engineering & Sciences, Vol.7, No.3, pp. 233-240, 2005, DOI:10.3970/cmes.2005.007.233

    Abstract We show that the extended Ananthakrishna's model exhibits all the features of the Portevin - Le Chatelier effect including the three types of bands. The model reproduces the recently observed crossover from a low dimensional chaotic state at low and medium strain rates to a high dimensional power law state of stress drops at high strain rates. The dynamics of crossover is elucidated through a study of the Lyapunov spectrum. More >

  • Open Access


    Correlation Analysis of Control Parameters of Flotation Process

    Yanpeng Wu1, Xiaoqi Peng1,*, Nur Mohammad2

    Journal on Internet of Things, Vol.1, No.2, pp. 63-69, 2019, DOI:10.32604/jiot.2019.06111

    Abstract The dosage of gold-antimony flotation process of 5 main drugs, including Copper Sulfate, Lead Nitrate, Yellow Medicine, No. 2 Oil, Black Medicine, with corresponding visual features of foam images, including Stability, Gray Scale, Mean R, Mean G, Mean B, Mean Average, Dimension and Degree Variance, were recorded. Parameter correlation analysis showed that the correlation among Copper Sulfate, Yellow Medicine, Black Medicine, as well as the correlation among Gray Scale, Mean R, Mean G, Mean B, is strong, and the correlation among Dimension, Gray Scale, Mean R, Mean G, Mean B, as well as the correlation between Stability and each dosing… More >

  • Open Access


    Instance Retrieval Using Region of Interest Based CNN Features

    Jingcheng Chen1, Zhili Zhou1,2,*, Zhaoqing Pan1, Ching-nung Yang3

    Journal of New Media, Vol.1, No.2, pp. 87-99, 2019, DOI:10.32604/jnm.2019.06582

    Abstract Recently, image representations derived by convolutional neural networks (CNN) have achieved promising performance for instance retrieval, and they outperform the traditional hand-crafted image features. However, most of existing CNN-based features are proposed to describe the entire images, and thus they are less robust to background clutter. This paper proposes a region of interest (RoI)-based deep convolutional representation for instance retrieval. It first detects the region of interests (RoIs) from an image, and then extracts a set of RoI-based CNN features from the fully-connected layer of CNN. The proposed RoI-based CNN feature describes the patterns of the detected RoIs, so that… More >

  • Open Access


    Ground-Based Cloud Recognition Based on Dense_SIFT Features

    Zhizheng Zhang1, Jing Feng1,*, Jun Yan2, Xiaolei Wang1, Xiaocun Shu1

    Journal of New Media, Vol.1, No.1, pp. 1-9, 2019, DOI:10.32604/jnm.2019.05937

    Abstract Clouds play an important role in modulating radiation processes and climate changes in the Earth's atmosphere. Currently, measurement of meteorological elements such as temperature, air pressure, humidity, and wind has been automated. However, the cloud's automatic identification technology is still not perfect. Thus, this paper presents an approach that extracts dense scale-invariant feature transform (Dense_SIFT) as the local features of four typical cloud images. The extracted cloud features are then clustered by K-means algorithm, and the bag-of-words (BoW) model is used to describe each ground-based cloud image. Finally, support vector machine (SVM) is used for classification and recognition. Based on… More >

  • Open Access


    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 models are built based on… More >

  • Open Access


    Time Series Analysis for Vibration-Based Structural Health Monitoring: A Review

    Kong Fah Tee 1,*

    Structural Durability & Health Monitoring, Vol.12, No.3, pp. 129-147, 2018, DOI: 10.3970/sdhm.2018.04316

    Abstract Structural health monitoring (SHM) is a vast, interdisciplinary research field whose literature spans several decades with focusing on condition assessment of different types of structures including aerospace, mechanical and civil structures. The need for quantitative global damage detection methods that can be applied to complex structures has led to vibration-based inspection. Statistical time series methods for SHM form an important and rapidly evolving category within the broader vibration-based methods. In the literature on the structural damage detection, many time series-based methods have been proposed. When a considered time series model approximates the vibration response of a structure and model coefficients… More >

  • Open Access


    Use of Discrete Wavelet Features and Support Vector Machine for Fault Diagnosis of Face Milling Tool

    C. K. Madhusudana1, N. Gangadhar1, Hemantha Kumar, Kumar,*,1, S. Narendranath1

    Structural Durability & Health Monitoring, Vol.12, No.2, pp. 111-127, 2018, DOI: 10.3970/sdhm.2018.01262

    Abstract This paper presents the fault diagnosis of face milling tool based on machine learning approach. While machining, spindle vibration signals in feed direction under healthy and faulty conditions of the milling tool are acquired. A set of discrete wavelet features is extracted from the vibration signals using discrete wavelet transform (DWT) technique. The decision tree technique is used to select significant features out of all extracted wavelet features. C-support vector classification (C-SVC) and ν-support vector classification (ν-SVC) models with different kernel functions of support vector machine (SVM) are used to study and classify the tool condition based on selected features.… More >

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