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• OAI收割 [27]

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浏览/检索结果: 共27条，第1-10条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择题名升序题名降序提交时间升序提交时间降序作者升序作者降序发表日期升序发表日期降序 A numerical approach to the simulation of landslide motion by considering rock disintegration 期刊论文  OAI收割 COMPUTERS AND GEOTECHNICS, 2024, 卷号: 173, 页码: 12 作者:     |  收藏  |  浏览/下载：25/0  |  提交时间：2024/08/19 Landslide motion  Rock disintegration  Energy transfer  Dynamic behavior transition  Numerical simulation   A Visual Pathway into Central Complex for High-Frequency Motion-Defined Bars in Drosophila 期刊论文  OAI收割 JOURNAL OF NEUROSCIENCE, 2023, 卷号: 43, 期号: 26, 页码: 4821-4836 作者:  Duan, Wenlan;  Zhang, Yihao   |  收藏  |  浏览/下载：15/0  |  提交时间：2023/10/09 central complex  Drosophila  motion-defined bar  ring neurons  visual behavior   Automated Student Classroom Behaviors' Perception and Identification Using Motion Sensors 期刊论文  OAI收割 BIOENGINEERING-BASEL, 2023, 卷号: 10, 期号: 2 作者:  Wang, Hongmin;  Gao, Chi;  Fu, Hong;  Ma, Christina Zong-Hao;  Wang, Quan   |  收藏  |  浏览/下载：28/0  |  提交时间：2023/03/22 intelligent system  deep learning  classroom behavior  motion identification   Exploring Coherent Motion Patterns via Structured Trajectory Learning for Crowd Mood Modeling 期刊论文  OAI收割 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2017, 卷号: 27, 期号: 3, 页码: 635-648 作者:  Zhang, Yanhao;  Qin, Lei;  Ji, Rongrong;  Zhao, Sicheng;  Huang, Qingming   |  收藏  |  浏览/下载：35/0  |  提交时间：2019/12/12 Coherent motion pattern  crowd behavior analysis  emotional motion feature  structured trajectory learning (STL)   Drivers' Visual Behavior-Guided RRT Motion Planner for Autonomous On-Road Driving 期刊论文  OAI收割 SENSORS, 2016, 卷号: 16, 期号: 1, 页码: 1-19 作者:     |  收藏  |  浏览/下载：34/0  |  提交时间：2017/11/06 Motion Planning  Autonomous Vehicle  Drivers' Visual Behavior  Rrt (Rapidly-exploring Random Tree)  On-road Driving   Contribution of correlated noise and selective decoding to choice probability measurements in extrastriate visual cortex 期刊论文  OAI收割 ELIFE, 2014, 卷号: 3, 期号: 1, 页码: e02670-e02670 Gu, Y; Angelaki, DE; DeAngelis, GC 收藏  |  浏览/下载：49/0  |  提交时间：2014/12/15 DECISION-RELATED ACTIVITY  SELF-MOTION PERCEPTION  SENSORY NEURONS  AREA MT  HEADING PERCEPTION  MSTD  REPRESENTATION  BEHAVIOR  DISCRIMINATION  POPULATIONS   Structure and Properties of Fe3O4/Polystyrene Nanocomposites 期刊论文  OAI收割 高等学校化学学报 chemical journal of chinese universities-chinese, 2014, 卷号: 35, 期号: 11, 页码: 2472-2480 Zhang,Zhijie; Tang,Tao 收藏  |  浏览/下载：35/0  |  提交时间：2015/10/22 CARBON NANOTUBES  MAGNETIC-PROPERTIES  NANOPARTICLES  VISCOSITY  PERFORMANCE  SURFACTANT  DISPERSION  DIFFUSION  BEHAVIOR  MOTION   Non-Gaussian statistics for the motion of self-propelled Janus particles: Experiment versus theory 期刊论文  OAI收割 PHYSICAL REVIEW E, 2013, 卷号: 88, 期号: 3, 页码: 032304/1-032304/11 作者:  Zheng X(郑旭);  ten Hagen B;  Kaiser A;  Wu ML;  Cui HH 收藏  |  浏览/下载：34/0  |  提交时间：2013/10/23 Janus particles  Self-propulsion  Brownian motion  Non-Gaussian behavior  Microfluidic   Distinct Acute Zones for Visual Stimuli in Different Visual Tasks in Drosophila 期刊论文  OAI收割 PLOS ONE, 2013, 卷号: 8, 期号: 4, 页码: -e61313 作者:  Guo, AK 收藏  |  浏览/下载：31/0  |  提交时间：2013/06/04 FLIGHT ORIENTATION  MOTION DETECTION  FRUIT-FLY  MELANOGASTER  PERIPHERY  ATTENTION  BEHAVIOR  VISION  EYES  SENSITIVITY   Approach for detecting crowd panic behavior based on fluid kinematic features and entropy (EI CONFERENCE) 会议论文  OAI收割 International Workshop on Advanced Computational Intelligence and Intelligent Informatics, IWACIII 2011, November 19, 2011 - November 23, 2011, Suzhou, China 作者:  Li Y.;  Li Y.;  Li Y. 收藏  |  浏览/下载：32/0  |  提交时间：2013/03/25 Crowd panic behavior detection is an important task in video analysis and event recognition  whose purpose is to detect when the panic behavior happened and alarming the abnormal event timely. In this paper  the crowd is regard as a fluid  and the crowd motion is described by four fluid kinematic features (divergence  vorticity  gradient tensor invariant and rotation tensor invariant). To discriminate the panic event from normal crowd behavior  an information entropy is calculated as a high level feature based on the fluid kinematic features. Experimental results show that the entropy raised dramatically once a panic event happened.