Syed Muhammad Saqlain Shah^1,*, 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 >

Maryam Monazzah¹, Sattar Tahmasebi Enferadi^1,*, 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 >

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 >

G.Ananthakrishna¹

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 >

Yanpeng Wu¹, Xiaoqi Peng^1,*, 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 >

Jingcheng Chen¹, Zhili Zhou^1,2,*, 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 >

Zhizheng Zhang¹, Jing Feng^1,*, 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 >

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 >

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 >

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 >