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  • Open Access


    Improved Anomaly Detection in Surveillance Videos with Multiple Probabilistic Models Inference

    Zhen Xu1, Xiaoqian Zeng1, Genlin Ji1,*, Bo Sheng2

    Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1703-1717, 2022, DOI:10.32604/iasc.2022.016919

    Abstract Anomaly detection in surveillance videos is an extremely challenging task due to the ambiguous definitions for abnormality. In a complex surveillance scenario, the kinds of abnormal events are numerous and might co-exist, including such as appearance and motion anomaly of objects, long-term abnormal activities, etc. Traditional video anomaly detection methods cannot detect all these kinds of abnormal events. Hence, we utilize multiple probabilistic models inference to detect as many different kinds of abnormal events as possible. To depict realistic events in a scene, the parameters of our methods are tailored to the characteristics of video sequences of practical surveillance scenarios.… More >

  • Open Access


    Applying Probabilistic Model Checking to Path Planning in an Intelligent Transportation System Using Mobility Trajectories and Their Statistical Data

    Honghao Gao1, 2, 5, Wanqiu Huang1, 4, Xiaoxian Yang3

    Intelligent Automation & Soft Computing, Vol.25, No.3, pp. 547-559, 2019, DOI:10.31209/2019.100000110

    Abstract Path planning is an important topic of research in modern intelligent traffic systems (ITSs). Traditional path planning methods aim to identify the shortest path and recommend this path to the user. However, the shortest path is not always optimal, especially in emergency rescue scenarios. Thus, complex and changeable factors, such as traffic congestion, road construction and traffic accidents, should be considered when planning paths. To address this consideration, the maximum passing probability of a road is considered the optimal condition for path recommendation. In this paper, the traffic network is abstracted as a directed graph. Probabilistic data on traffic flow… More >

  • Open Access


    Comparative Variance and Multiple Imputation Used for Missing Values in Land Price DataSet

    Longqing Zhang1, Liping Bai1,*, Xinwei Zhang2, Yanghong Zhang2, Feng Sun2, Changcheng Chen2

    CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1175-1187, 2019, DOI:10.32604/cmc.2019.06075

    Abstract Based on the two-dimensional relation table, this paper studies the missing values in the sample data of land price of Shunde District of Foshan City. GeoDa software was used to eliminate the insignificant factors by stepwise regression analysis; NORM software was adopted to construct the multiple imputation models; EM algorithm and the augmentation algorithm were applied to fit multiple linear regression equations to construct five different filling datasets. Statistical analysis is performed on the imputation data set in order to calculate the mean and variance of each data set, and the weight is determined according to the differences. Finally, comprehensive… More >

  • Open Access


    Probabilistic Modeling of Material Variability in Fatigue Crack Growth

    G. Renaud1, M. Liao1

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.1, No.2, pp. 87-92, 2007, DOI:10.3970/icces.2007.001.087

    Abstract This paper presents a probabilistic crack growth model developed for the Holistic Structural Integrity Process (HOLSIP) framework. Statistical data, obtained from testing and fractographic analyses of 2024-T3 test coupons, were used to derive the fatigue crack growth material variability. Results showed the relative impact of material variability in the short and long crack regimes. Monte Carlo simulations showed good agreement between analytical life distributions and test results. More >

  • Open Access


    Influence of Scale Specific Features on the Progressive Damage of Woven Ceramic Matrix Composites (CMCs)

    K. C. Liu1, S. M. Arnold2

    CMC-Computers, Materials & Continua, Vol.35, No.1, pp. 35-65, 2013, DOI:10.3970/cmc.2013.035.035

    Abstract It is well known that failure of a material is a locally driven event. In the case of ceramic matrix composites (CMCs), significant variations in the microstructure of the composite exist and their significance on both deformation and life response need to be assessed. Examples of these variations include changes in the fiber tow shape, tow shifting/nesting and voids within and between tows. In the present work, the influence of many of these scale specific architectural features of woven ceramic composite are examined stochastically at both the macroscale (woven repeating unit cell (RUC)) and structural scale (idealized using multiple RUCs).… More >

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