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anomalous behavior REVISITED:DYNAMIC RESPONSE OF ELASTIC-PLASTIC STRUCTURES

Acta Mechanica Solida Sinica 1998年第4期11卷 283-294页

作者： Xi, F Yang, JL Li, ZL Beijing Univ Aeronaut & Astronaut Solids Mech Res Ctr Beijing 100083 Peoples R China

Based on the minimum principle of acceleration in the elastic-plastic continua under finite def ormation, the dynamic response of an elastic-perfectly plastic pin-ended beam subjected to rectangular impulse loading is studied with the help of a numerical approach. The calculated results once again show the anomalous behavior of the beam during its response process, which was previously found in [1]. By carefully analyzing the instantaneous distribution of the bending moment, the membrane force, the curvature and displacement during the response process, it is concluded that the interactive effect between the geometry and materials nonlinearities of the structure is the key reason for leading to the anomalous behavior. This will be helpful for clarifying some misunderstandings in explaining the problem before.

关键词： finite deformation minimum principle of acceleration dynamic elastic-plastic response bending moment distribution anomalous behavior

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anomalous behavior Analysis of Some Yield Criteria

Anomalous Behavior Analysis of Some Yield Criteria

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中国力学学会学术大会'2009

作者： Huh Hoon Park Sung-Ho School of Mechanical Aerospace and Systems EngineeringKAISTDaedoek Science TownGwahangno 335DaejeonRepunlic of Korea305-701 Automotive Steel Research Center POSCO699Gumbo-dongGwangyang-siJeonnamRepublic of Korea545-090

The main purpose of the present work is to investigate the anomalous behavior of different yield *** experimental work has shown that the equibiaxial flow stress of some metals is larger than the uniaxial flow stress ... 详细信息

关键词： Yield criteria anomalous behavior equibiaxial yield stress anisotropy

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anomalous Situations Recognition in Surveillance Images Using Deep Learning

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Computers, Materials & Continua 2023年第7期76卷 1103-1125页

作者： Qurat-ul-Ain Arshad Mudassar Raza Wazir Zada Khan Ayesha Siddiqa Abdul Muiz Muhammad Attique Khan Usman Tariq Taerang Kim Jae-Hyuk Cha Department of Computer Science COMSATS University IslamabadWah Campus47040Pakistan Deptartment of Computer Science University of WahWah Cantt47040Pakistan Deptartment of Computer Science HITEC UniversityTaxila47080Pakistan Management Information System Department College of Business AdministrationPrince Sattam Bin Abdulaziz UniversityAl-Kharj 16278Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea

anomalous situations in surveillance videos or images that may result in security issues,such as disasters,accidents,crime,violence,or terrorism,can be identified through video anomaly ***,differentiat-ing anomalous situations from normal can be challenging due to variations in human activity in complex environments such as train stations,busy sporting fields,airports,shopping areas,military bases,care centers,*** learning models’learning capability is leveraged to identify abnormal situations with improved *** work proposes a deep learning architecture called anomalous Situation Recognition Network(ASRNet)for deep feature extraction to improve the detection accuracy of various anomalous image *** proposed framework has five *** the first step,pretraining of the proposed architecture is performed on the CIFAR-100 *** the second step,the proposed pre-trained model and Inception V3 architecture are used for feature extraction by utilizing the suspicious activity recognition *** the third step,serial feature fusion is performed,and then the Dragonfly algorithm is utilized for feature optimization in the fourth ***,using optimized features,various Support Vector Machine(SVM)and K-Nearest Neighbor(KNN)based classification models are utilized to detect anomalous *** proposed framework is validated on the suspicious activity dataset by varying the number of optimized features from 100 to *** results show that the proposed method is effective in detecting anomalous situations and achieves the highest accuracy of 99.24%using cubic SVM.

关键词： Anomaly detection anomalous events anomalous behavior anomalous objects violence detection deep learning

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