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Copyright of Command Control & Simulation / Zhihui Kongzhi yu Fangzhen is the property of Command Control & Simulation Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Marine battlefield environment is the important condition that restricts the effectiveness of maritime weapons and the conduct of military operations. Accurate assessment of environmental impact is the "soft power" for improving maritime combat capability and battlefield construction. Firstly, this paper briefly introduces the marine battlefield environment and its impact assessment. Then, the assessment methods and modeling techniques of battlefield environment are summarized into four categories: dynamic simulation-based assessment method, decision science-based assessment method, data science-based assessment method and uncertain artificial intelligence-based assessment methods, and the above methods are described in detail. Finally, the different methods are compared and the application prospect is analyzed. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Preventive Veterinary Medicine / Zhongguo Yufang Shouyi Xuebao is the property of Chinese Journal of Preventive Veterinary Medicine Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Shanghai Physical Education Institute / Shanghai Tiyu Xueyuan Xuebao is the property of Shanghai Physical Education Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The complex nonlinear relation between water supply and water demand can not be simulated by the traditional risk loss assessment methods. A risk loss model of water supply and water demand is developed by copula function for the first time. First, marginal probability density functions of water supply and water demand are simulated by difference and theorem of existence of primitive function; Second, the parameters of copula functions are estimated by nonlinear optimization; Third, goodness of fit test and non-parametric test are made; and Clayton copula function is chosen for simulating joint distribution of water supply and water demand; Finally, risk loss between water supply and water demand is defined via a double integral. The results show that the expected risk loss of 2020 is about 1159.7 billion RMB without consideration of transferred water and reclaimed water and that is about 37.7 billion RMB after using reclaimed water and South-to-North transferred water, decreased by 96.7%. [ABSTRACT FROM AUTHOR]

Traditional texture analysis methods mark global similarity only by related attribution of each face area. For the issue that global information is represented by local information which causes bad feature extracting, weighting estimation for texture analysis (WETA) based on Fisher discriminative analysis (FDA) is proposed. Firstly, face images are divided into some non-overlapping local patches with same sizes after texture coding by using local binary patterns (LBP) or local phase quantization (LPQ). The solution is given by the most discriminative axis within a similarity space using Fisher discriminative analysis and weight optimization after extracting coordinate axes with the most discrimination. Finally, the efficiency of proposed method is verified by experiments conducted on the FERET and on the FEI face databases. The experiments indicate that the proposed method brings a better recognition performance in comparison to other weighting methods proposed in the literature. [ABSTRACT FROM AUTHOR]