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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Guangyu Xiong Lulu Niu Yanfu Tian Jiehan Zhou Babak Mohajeri Timo R. Nyberg Silk Road Business School Sanya University(USY) China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing The Cloud Computing Center Chinese Academy of Sciences Dongguan China School of Articial Intelligence University of Chinese Academy of Sciences Beijing China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China University of Oulu Oulu Finland Aalto University Espoo Finland

ISBN: (数字)9781728151694

ISBN: (纸本)9781728151700

In this paper, we developed a path planning system for smart cars for teaching electronic engineering or computer science, which consists of the interactive platform for smart cars development and path planning. Designed by Visual C++, the interactive platform can call Matlab engine, allows users to choose path optimization algorithms such as genetic or A-star(A*) algorithm for different tasks and control smart cars through serial ports. The simulation and practice demonstrate that our interactive platform can help learners to plan paths and control intelligent vehicles without specially designing a user interface.

关键词： Education Path planning Automobiles Task analysis Optimization Programming profession Matlab

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Performance Evaluation of Faults in a Photovoltaic Array Based on V-I and V-P Characteristic Curve

Performance Evaluation of Faults in a Photovoltaic Array Bas...

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International Conference on Measuring Technology and Mechatronics Automation, ICMTMA

作者： Ehtisham Lodhi Wang Lina Yang Pu M. Yaqoob Javed Zeeshan Lodhi Jiao Zhijie Usman Javed School of Automation Science and Electrical Engineering Beihang University Beijing China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Department of Electrical and Computer Engineering COMSATS University Islamabad Lahore Pakistan Department of Electrical and Computer Engineering COMSATS University Islamabad Abbottabad Pakistan The Guo China-US photonics Lab Changchun Institute of Optics Fine Mechanics and Physics Chinese Academy of Sciences Changchun China

ISBN: (数字)9781728170817

ISBN: (纸本)9781728170824

The prediction and forecasting of faults occurring in photovoltaic (PV) system is one of the important aspect to escalate the reliability, output power generation, proficiency, lifetime and effectiveness of overall system. In this article, the behavior of PV array is studied under various faulty circumstances and new approach has offered for the better performance assessment of PV array. This approach is established on the analysis of peculiarities displayed by voltage power (V-P) and voltage-current (V-I) characteristics curves of PV array under different faulty conditions. The proposed technique can be used to identify and classify the five common faults in PV system. These progressive faults (includes open circuit, line-to-line, partial shading, degradation and bridging faults) have been occurred at the basic components of PV cell and at the connection point between PV modules. The characteristic curves under normal and faulty conditions have been compared by utilizing Matlab/Simulink environment. Hence, the suggested method can be utilized to determine the important fact about the health PV array.

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Chaotic system optimal tracking using data-based synchronous method with unknown dynamics and disturbances

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Chinese Physics B 2017年第3期26卷 268-275页

作者：宋睿卓魏庆来 School of Automation and Electrical Engineering University of Science and Technology Beijing The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

We develop an optimal tracking control method for chaotic system with unknown dynamics and disturbances. The method allows the optimal cost function and the corresponding tracking control to update synchronously. According to the tracking error and the reference dynamics, the augmented system is constructed. Then the optimal tracking control problem is defined. The policy iteration （PI） is introduced to solve the rain-max optimization problem. The off-policy adaptive dynamic programming （ADP） algorithm is then proposed to find the solution of the tracking Hamilton-Jacobi- Isaacs （HJI） equation online only using measured data and without any knowledge about the system dynamics. Critic neural network （CNN）, action neural network （ANN）, and disturbance neural network （DNN） are used to approximate the cost function, control, and disturbance. The weights of these networks compose the augmented weight matrix, and the uniformly ultimately bounded （UUB） of which is proven. The convergence of the tracking error system is also proven. Two examples are given to show the effectiveness of the proposed synchronous solution method for the chaotic system tracking problem.

关键词： adaptive dynamic programming approximate dynamic programming chaotic system zero-sum

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RASNet: Segmentation for tracking surgical instruments in surgical videos using refined attention segmentation network

arXiv

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arXiv 2019年

作者： Ni, Zhen-Liang Bian, Gui-Bin Xie, Xiao-Liang Hou, Zeng-Guang Zhou, Xiao-Hu Zhou, Yan-Jie State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China CAS Center for Excellence in Brain Science and Intelligence Technology Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

Segmentation for tracking surgical instruments plays an important role in robot-assisted surgery. Segmentation of surgical instruments contributes to capturing accurate spatial information for tracking. In this paper, a novel network, Refined Attention Segmentation Network, is proposed to simultaneously segment surgical instruments and identify their categories. The U-shape network which is popular in segmentation is used. Different from previous work, an attention module is adopted to help the network focus on key regions, which can improve the segmentation accuracy. To solve the class imbalance problem, the weighted sum of the cross entropy loss and the logarithm of the Jaccard index is used as loss function. Furthermore, transfer learning is adopted in our network. The encoder is pre-trained on ImageNet. The dataset from the MICCAI EndoVis Challenge 2017 is used to evaluate our network. Based on this dataset, our network achieves state-of-the-art performance 94.65% mean Dice and 90.33% mean IOU. Copyright © 2019, The Authors. All rights reserved.

关键词： Surgical equipment

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Poster: Tancreator: A tangible tool for children to create augmented reality games

Poster: Tancreator: A tangible tool for children to create a...

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2018 Joint ACM International Conference on Pervasive and Ubiquitous Computing, UbiComp 2018 and 2018 ACM International Symposium on Wearable Computers, ISWC 2018

作者： Jin, Qiao Wang, Danli Sun, Fang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China School of Computer and Information Technology Liaoning Normal University Dalian China

ISBN: (纸本)9781450359665

In this paper, we present TanCreator, a tangible authoring tool which facilitates children to create games based on Augmented Reality (AR) and sensor technologies. Combining AR elements and sensors in games bridges virtual world with realistic surroundings closely together, providing more joyful and intuitive creating experiment for children. Children could boost their creativity via creating their own maze games in daily life with paper tokens and sensors. It is also great training for children's motor skills such as hand-eye coordination. © 2018 Copyright is held by the owner/author(s).

关键词： Augmented reality

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Object Recognition, Localization and Grasp Detection Using a Unified Deep Convolutional Neural Network with Multi-task Loss

Object Recognition, Localization and Grasp Detection Using a...

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IEEE International Conference on Robotics and Biomimetics

作者： Qun Jia Zhiqiang Cao Xionglei Zhao Lei Pang Yingying Yu Junzhi Yu State Key Laboratory of Management and Control for Complex Systems University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781728103785;9781728103778

Recognize an object and detect a good grasp in unstructured scenes is still a challenge. In this paper, the problem of detecting robotic grasps is expressed by a two-point representation in an unstructured scene with an RGB-D camera. A deep Convolutional Neural Network is designed to predict good grasps in real-time on GTX1080, with using region proposal techniques. A contribution of this work is our proposed network framework can perform classification, location and grasp detection simultaneously so that in a single step, it not only recognizes the category and bounding-box of the object, but also finds a good grasp line. Besides, in training process, we minimize a multi-task loss objective function of object classification, location and grasp detection in order to train the network end-to-end. Our experimental evaluation on a real robotic manipulator demonstrates that the robotic manipulator can fulfill the grasping task effectively.

关键词： Feature extraction Task analysis Robot kinematics Object recognition Manipulators Grasping

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Autonomous RC-Car for Education Purpose in iSTEM Projects

Autonomous RC-Car for Education Purpose in iSTEM Projects

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2018 IEEE Intelligent Vehicles Symposium, IV 2018

作者： Feng, Weihang Pei, Yun Hai, Bin Huang, Wuling Gong, Xiaoyan Zhu, Fenghua Beijing NO.161 High School China Jilin University China Beijing Institute of Fashion Technology China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781538644522

Software simulation and real environment running parallel execution is a lately proposed method, which also provides full coverage and convenience to accomplish autonomous driving education purpose. This paper introduces a new high-school iSTEM program of autonomous vehicles education. This program uses a Scaled RC-Car platform with several sensors and Raspberry Pi embedded platform, to build an autonomous driving car in scaled indoor simulation environment. The RC-Car is capable of safely autonomous driving. Many existing algorithms are put together to provide the necessary functions of autonomous driving, such lane detection, obstacle detection, lane following, vehicle control etc. In this paper, we provide the details of this program, hardware and software components of the RC-Car, Deep learning end-to-end approaching of algorithm deployment, and future works. © 2018 IEEE.

关键词： Autonomous vehicles

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Development of a Tetris playing robot controlled by KNR 15

Development of a Tetris playing robot controlled by KNR

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15th IEEE International Conference on Mechatronics and Automation, ICMA 2018

作者： Wang, Ming Zhang, Yanlu Dong, Huifang Li, Xu Yu, Junzhi Department of Information and Electrical Engineering University of Shandong Jianzhu University Jinan250100 China Department of Intelligent Robotics State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781538660720

Robot plays an increasingly important role in daily life. This paper presents a vision-based wheeled robot for playing Tetris. How to control the robot is elaborated at length in this paper. The robot adopted a KNR controller as its core controller. With a CCD sensor, it identified colored blocks, lines and the destination. All algorithms are programed with LabVIEW. The experiments and the contest validated the effectiveness of the proposed vision-based wheeled robot system. © 2018 IEEE.

关键词： controllers

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Short-term Bus Passenger Flow Forecast Based on Deep Learning

Short-term Bus Passenger Flow Forecast Based on Deep Learnin...

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2018 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2018

作者： Li, Xiaoshuang Chen, Ziyang Zhu, Fenghua Chang, Wei Tan, Chang Xiong, Gang Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Institute of Automation Beijing China Public Security Bureau Traffic Police Detachment of Hefei Hefei China IFLYTEK CO.LTD Hefei China

ISBN: (纸本)9781728105512

The public transportation system is an essential part of the life of the citizens and it's the basis of intelligent transportation system(ITS). This paper tries to predict shortterm bus passenger flow by using deep learning approach that called SAE model and DBN model. The model training and evaluation were carried out using the credit card records of the Suzhou bus IC card. The experimental results show that the SAE and DBN models can reduce the prediction error by 9.51% and 10.48%, respectively, compared with the traditional method. The methods of deep learning show a good application prospect in the short-term bus passenger flow forecasting. © 2018 IEEE.

关键词： Buses

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A novel CNN model for fine-grained classification with large spatial variants

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Journal of Physics: Conference Series 2020年第1期1544卷

作者： Junpeng Wang Yanfeng Lu The State Key Laboratory of Management and Control for 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

Convolutional Neural Networks (CNN) have achieved great performance in many visual tasks. However, CNN models are sensitive to samples with large spatial variants, especially severe in fine-grained classification task. In this paper, we propose a novel CNN model called ST-BCNN to solve these problems. ST-BCNN contains two functional CNN modules: Spatial Transform Network (STN) and Bilinear CNN(BCNN). Firstly, STN module is used to select key region in input samples and get it spatially modified. Since the adoption of STN will cause an information loss phenomenon called boundary loss, we design a brand-new IOU loss method to solve it. We make a theoretical analysis of the IOU loss method. Secondly, to discover discriminative features for fine-grained classification task, BCNN module is applied. BCNN interacts CNN features from different channels to produce more discriminative bilinear features than fully connected features of CNN. ST-BCNN works by reducing irrelevant spatial states and producing fine-grained features. We evaluate our model on 3 public fine-grained classification datasets with large spatial variants: CUB200-2011, Fish100 and UAV43. Experiments show that the IOU loss method can reduce boundary loss and make STN module output spatial transformed image appropriately. Our proposed ST-BCNN model outperforms other advanced CNN models on all three datasets.

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