Jiaming Liang’s article entitled “Filtering enhanced tomographic PIV reconstruction based on deep neural networks” has been accepted for oral presentation by the committee of the International Symposium on High-Fidelity Computational Methods & Applications 2019, which will be held in Shanghai during December 14-16, 2019. The symposium is to enhance deployment and applications of the high-fidelity methods in complex industrial fluid flows. (For more information, please refer to https://www.ishfcma.org/ ).
ccxu
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ZMART is the abbreviation for the ZJU Micro-Aerial Robotics Team, majorly for the International Aerial Robotics Competition in the Asia-Pacific Venue. ZMART won the Best System Design Award (2015 in Beihang University) and the First Prize Award (2016 in Beihang University). 2018年8月27日，经国际空中机器人大赛（IARC）委员会评定，浙江大学代表队ZMART以综合评分第一、比赛成绩第一，获得IARC第七代任务世界冠军，赢得2万美元比赛奖金。浙江大学成为继斯坦福大学（1995），卡耐基梅隆大学（1997），柏林工大（2000），佐治亚理工（2008），麻省理工（2009），清华大学（2013）之后IARC第七个世界冠军得主。自此第七代任务结束，IARC比赛进入第八代任务。更多信息，请见页面：ZMART made a record in the IARC history Team Structure for 2017 总体组：王宏达、翁一桢、邱炜、万旭东、郭磊、叶鸿凯 感知组：朱疆成、崔粲、茹祥宇、朱均、汪哲培、王琦 ZMART featured in media 2016, ZMART Featured in Media ZMART Performance video 2016 IARC Official Results 2016 IARC Performance Collection – ZMART ZMART 2017 Trailer – We need you Award 2015 – the Best System Design Award 最佳系统设计 2016 – the First Prize (the Asia-Pacific Venue) 亚太赛区冠军 2017 – the First Prize (the Asia-Pacific Venue) 亚太赛区冠军 Competition 2016 the First Prize in the Asia-Pacific Venue 指导老师：许超、张宇 队员：王宏达、翁一桢、叶波、茹祥宇、朱均、万旭东、朱疆成、崔粲、邱炜、郭磊 Technical Progress: Visual Odometry, Boundary Detection (SVM), Dynamic Approaching (DP), Reinforcement Learning Platform: DJI M100 Task Computer: Intel NUC i5 Navigation: DJI Guidance / Hokuyo UTM-30LX Vision: Bluefox 2015 Best System Design Award in the Asia-Pacific Venue 指导老师：许超、王伟、张宇 队员：崔粲、叶长春、王宏达、翁一桢、叶波、邱炜、朱疆成、茹祥宇、黄永斌 Technical Progress: Visual Tracking to Moving Target Platform: X650 Carbon Flight Controller: Pixhawk Propulsion: T-Motor Power: ACE Navigation: Hokuyo URG-04LX / PX4-Flow Vision: Bluefox 2014 指导老师：许超 队员：秦通、翁一桢、娄常绪、黄夏楠、刘昊俣、王钟雷、陈乙宽、叶长春、朱疆成、韩滔 Technical Progress: 3D printing, ROS Platform: X650 Carbon Flight Controller: DJI Wookong Task Computer: Intel NUC i5 Propulsion: HLY / HobbyWing Power: ACE Navigation: Hokuyo UTM-30LX / Ultrosonic Sensor Vision: USB camera 2012 指导老师：许超 队员：崔粲、朱疆成、邱炜、俞中杰、王文龙、韩滔、张泉泉 Technical Progress: Flight Control, SLAM, Auto-exploration, Visual Tracking Platform: X650 Task Computer: ARM cortex A9 Flight Computer: Yutu Propulsion: 新西达（XXD） / （好盈）HobbyWing Navigation: Hokuyo UTM-30LX / Ultrosonic Sensor Power: […]
Approach one: Variational optical flowIntroductionIn this project, we propose a novel optical flow formulation for estimating two-dimensional velocity fields from an image sequence depicting the evolution of a passive scalar transported by a fluid flow. This motion estimator relies on a stochastic representation of the flow allowing to incorporate naturally a notion of uncertainty in the flow measurement. The Eulerian fluid flow velocity field is decomposed into two components: a large-scale motion field and a small-scale uncertainty component. We define the small-scale component as a random field. Subsequently, the data term of the optical flow formulation is based on a stochastic transport equation, derived from the formalism under location uncertainty proposed in Mémin (2014) and Resseguier et al. (2017a). In addition, a specific regularization term built from the assumption of constant kinetic energy involves the very same diffusion tensor as the one appearing in the data transport term. Opposite to the classical motion estimators, this enables us to devise an optical flow method dedicated to fluid flows in which the regularization parameter has now a clear physical interpretation and can be easily estimated. Experimental evaluations are presented on both synthetic and real-world image sequences. Results and comparisons indicate a very good performance of the proposed formulation for turbulent flow motion estimation.
IntroductionThis project mainly studies on the observation and motion parameters reconstruction of flapping flight, the stability analysis of insects hovering, and the flapping mechanisms design for the PIV experiments, aiming to improve the existing hull reconstruction and pose estimation algorithms, propose the analysis method of insect hovering under the active control under varying flapping frequency, and design flapping mechanisms with multiple freedoms for PIV experiments to implement more fined motion. As for hull reconstruction and pose estimation, the project reconstruct the hull of insect under the assumption that the insect body is rigid and its section is elliptical with the data of body radius, centerline and the wing outline. As for the experimental flapping motion system design, this project analyzes the design principles and keeps the Reynolds number and Strouhal number same in the real and experimental environment respectively, and describes how to the design bee-like and dragonfly-like flapping mechanisms and how the mechanisms are driven.Paper1.Y. Huang, J. Liang and C. Xu. Sability of the flapping-wing vehicle near hovering under active control by varying flapping frequency [C]. The Chinese Congress of Automation 2017, Jinan, Shandong, China, October 21-22, 2017.