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Cyber-Physical-Social systems for Smart City: An Implementation Based on intelligent Loop

IFAC-PapersOnLine 2020年第5期53卷 501-506页

作者： Gang Xiong Xiaoyu Chen Nan Shuo Yisheng Lv Fenghua Zhu Tianci Qu Peijun Ye State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing 100049 China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing The Cloud Computing Center Chinese Academy of Sciences Dongguan 523808 China Graduate School of System Design Tokyo Metropolitan University Tokyo 191-0065 Japan

Cyber-Physical-Social systems (CPSS) provides a novel perspective for constructing “Smart City”, which is also known as the Human-Machine-Things-System (HMTS), focusing on the fusion of ternary space: social network of human society, network of machines and the Internet of things. In this paper, we propose a specific implementation framework of CPSS for Smart City based on intelligent loops, including basic modeling and interactive fusion, state perception and cognition, and adaptive learning. On this basis, an overall architecture of the CPSS platform is designed, which is applied in the urban transportation management in Hangzhou. The application results demonstrate that the intelligent loop could optimize the control and management strategies for actual urban transportation.

关键词： CPSS intelligent Loop Human-Cyber-Physical Ternary Space

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Multi-Convolution Neural Networks-Based Deep Learning Model for Emotion Understanding

Multi-Convolution Neural Networks-Based Deep Learning Model ...

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第37届中国控制会议

作者： Luefeng Chen Min Wu Wanjuan Su Kaoru Hirota School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Tokyo Institute of Technology

Multi-convolution neural networks-based deep learning model in combination with multimodal data for emotion understanding is proposed, in which facial expression and body gesture are used to achieve emotional states recognition for emotion understanding. It aims to understand coexistence multimodal information in human-robot interaction by using multi-convolution neural networks, where multilayer convolutions are connected in series and multiple networks are executed in parallel. Moreover,when optimizing the weights of deep neural network by traditional method, it is easy to fall into poor local optimal. To address this problem, a hybrid genetic algorithm with stochastic gradient descent is developed, which has the capacity of inherent implicit parallelism and better global optimization of genetic algorithm so that it can adaptively find the better weights of the *** in order to speed up the convergence of the proposal, the weights optimized by stochastic gradient descent will be taken as a chromosome of genetic algorithms initial population, and it also can be used as a priori knowledge. To verify the effectiveness of the proposal, experiments on benchmark database of spontaneous emotion expressions are developed, and experimental results show that the proposal outperforms the state-of-the-art methods. Meanwhile, the preliminary application experiments are also carried out and the results indicate that the proposal can be extended to human-robot interaction.

关键词： Deep learning emotion understanding convolution neural network facial expression body gesture

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Bat Algorithm Optimizer for Drilling Trajectory Designing under Wellbore Stability Constraints

Bat Algorithm Optimizer for Drilling Trajectory Designing un...

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第37届中国控制会议

作者： Jiafeng Xu Xin Chen School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China University of Geosciences

In the process of deep geological resources exploration, there are often some complex stratigraphic situations. A threedimension drilling trajectory is designed by using the geological environment data. The trajectory should be sure that the target position is reachable. And it can avoid the area prone to be in an accident. In order to solve this problem, this paper discusses a three-dimensional wellbore trajectory design optimization model suitable for shale gas horizontal mining. Considering the wellbore stability and dogleg severity as constraints, the minimum drilling length is taken as the objective function. Depending on the Mohr-Coulomb criterion, the range of azimuth angle and inclination angle of safety trajectory can be determined. It can be seen as a constraint on the wellbore derrick segment. As for the optimization algorithm, bat algorithm（BA） optimizer combined with a penalty function method is applied to this optimization model. Finally, the case study shows that the BA performs well in the wellbore optimization design. It obtains the trade-off result between the safe and efficient drilling demands. And, it provides a basic model idea for the real-time trajectory optimization while drilling.

关键词： Drilling Trajectory Designing Wellbore Stability Bat Algorithm

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A Detection System of Tool Parameter Using Machine Vision

A Detection System of Tool Parameter Using Machine Vision

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第37届中国控制会议

作者： Hantao Wang Hong Wang School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China University of Geosciences

This paper suggests a novel technique for the tool parameter measurement based on machine vision. Tool images are captured by using a machine vision system and the outer contour image of the cutter is obtained by using the machine vision technology. The HALCON image processing library is used as the development platform to build the tool parameters test system. Several algorithms including image segmentation, edge extraction and fitting ellipse determination is used for image *** tool parameters such as external diameter and contour angle of tool edge can be obtained after rebuilding the contour of tool *** proposed scheme is shown to be reliable and effective for the automated tool parameter measurement.

关键词： Machine vision image processing tool parameter

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Back Cover: PD-L1 Aptamer-Functionalized Metal–Organic Framework Nanoparticles for Robust Photo-Immunotherapy against Cancer with Enhanced Safety (Angew. Chem. Int. Ed. 5/2023)

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Angewandte Chemie International Edition 2023年第5期62卷

作者： Dr. Jingfang Zhang Dr. Wenzhe Li Dr. Yafei Qi Dr. Guorong Wang Prof. Lele Li Prof. Zhengyu Jin Prof. Jie Tian Prof. Yang Du Beijing Key Laboratory for Bioengineering and Sensing Technology School of Chemistry and Biological Engineering University of Science and Technology Beijing Beijing 100083 China CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety and CAS Center for Excellence in Nanoscience National Center for Nanoscience and Technology Beijing 100190 China State Key Laboratory of Natural and Biomimetic Drugs School of Pharmaceutical Sciences Peking University Beijing 100191 China CAS Key Laboratory of Molecular Imaging Beijing Key Laboratory of Molecular Imaging the State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China Department of Radiology Peking Union Medical College Hospital Peking Union Medical College and Chinese Academy of Medical Sciences Beijing 100730 China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Medicine Beihang University Beijing 100191 China

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Rücktitelbild: PD-L1 Aptamer-Functionalized Metal–Organic Framework Nanoparticles for Robust Photo-Immunotherapy against Cancer with Enhanced Safety (Angew. Chem. 5/2023)

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Angewandte Chemie 2023年第5期135卷

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Training and Testing Object Detectors With Virtual Images

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IEEE/CAA Journal of Automatica Sinica 2018年第2期5卷 539-546页

作者： Yonglin Tian Xuan Li Kunfeng Wang Fei-Yue Wang Department of Automation University of Science and Technology of China Hefei 230027 China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijirtg 100190 China School of Automation Beijing Institute of Technology Beijing 100081. China Qingdao Academy of Intelligent Industries Qingdao 266000 China Research Center for Computational Experiments and Parallel Systems Technology National University of Defense Technology Changsha 410073 China IEEE

In the area of computer vision, deep learning has produced a variety of state-of-the-art models that rely on massive labeled data. However, collecting and annotating images from the real world is too demanding in terms of labor and money investments, and is usually inflexible to build datasets with specific characteristics, such as small area of objects and high occlusion level. Under the framework of Parallel Vision, this paper presents a purposeful way to design artificial scenes and automatically generate virtual images with precise annotations.A virtual dataset named Parallel Eye is built, which can be used for several computer vision tasks. Then, by training the DPM(Deformable parts model) and Faster R-CNN detectors, we prove that the performance of models can be significantly improved by combining Parallel Eye with publicly available real-world datasets during the training phase. In addition, we investigate the potential of testing the trained models from a specific aspect using intentionally designed virtual datasets, in order to discover the flaws of trained models. From the experimental results, we conclude that our virtual dataset is viable to train and test the object detectors.

关键词： Index Terms--Deep learning object detection parallel vision virtual dataset

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Optimal Rescue Path for Maritime Air Crash Based on Probability Density Distribution and Bayesian Formula

Optimal Rescue Path for Maritime Air Crash Based on Probabil...

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第37届中国控制会议

作者： Jing Wang Ping Yu Zhen-Tao Liu State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang University Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

Optimal rescue path for maritime air crash based on probability density distribution and Bayesian formula is proposed,where probable crash area is determined through surface search at a high altitude,and then the minimum wreckage floating zone is obtained by probability density ***,we divide it into regular hexagons based on Honeycomb Model to identify the point search area that is endowed with priority by Bayesian ***,the optimal path is worked out by transferring optimization problem to be a Traveling Salesman Problem(TSP).In simulations,some spots are randomly chosen on a Google map in which we can find debris in ocean through surface search at high *** a point search route is obtained by using Annealing algorithm,which strips out 4%of time compared with current *** intelligent search and rescue framework based on reinforcement learning is *** future work,search and rescue work will be free from manpower and bad weather constraints.

关键词： Point search Hexagonal regions Priority Bayesian formula Traveling Salesman Problem Reinforcement Learning

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Research on a Thermostatic Heating control System for Optically Pumped Cesium Magnetometer

Research on a Thermostatic Heating Control System for Optica...

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第37届中国控制会议

作者： Shuting Hu Jian Ge Haobin Dong Liyue Xue Zhiwen Yuan Jun Zhu HaiYang Zhang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex System

Since the operating temperature of absorption chamber affects the sensitivity of the optically pumped cesium magnetometer（OPCM） directly, it is necessary to control the temperature precisely. In this paper, by using the positive temperature coefficient（PTC） and the integral separation proportional-integral-derivative（PID） algorithm, a high-precision thermostatic heating system is designed for OPCM. Firstly, the PTC heating device and the TSic506 temperature sensor are used to form a closed loop control system. Then, the three parameters of P, I and D are adjusted for different temperature, and the temperature control system is realized by STM32 microcontroller. Finally, the integral separation PID algorithm is used to eliminate overshoot. Experiments show that the effective temperature control ranges are 45?C5?C, the accuracy is less than ±0.2?C, and the system stability time is 300 s. It is obviously that the designed system has reference value and guidance significance for OPCM.

关键词： Thermostatic heating control system Integral separation PID algorithm Optically pumped cesium magnetometer

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Study of Underground River Tracing System Based on SINS

Study of Underground River Tracing System Based on SINS

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第37届中国控制会议

作者： Hongwei Li Zhihua Li Fan Li Zikun Yang Pei Gao Wenqi Zhou School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Contol and Intelligent Automation for Complex Systems

Underground rivers are underground flowing water that develop in carbonate areas or in water enrichment formations. At present, the underground river detection methods mainly include isotope tracer method, magnetotelluric method, and controllable source audio magnetotelluric method. In this paper, according to consulting a large amount of literature data, a new method of underground river tracing based on strapdown inertial navigation technology is proposed. The inertial device is installed on a floating carrier and the carrier is drifted with river water. The acceleration and angular velocity of the carrier are recorded, and the trajectory of the carrier is calculated by the attitude algorithm, then obtaining a concrete trace of the underground river. This method overcomes the disadvantages of the traditional measurement methods that are greatly affected by the environment and difficult to measure.

关键词： Underground river inertial navigation attitude solving algorithm

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