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Corrections to “STAT: Spatial-Temporal Attention Mechanism for Video Captioning”

IEEE Transactions on Multimedia 2020年第3期22卷 830-830页

作者： Chenggang Yan Yunbin Tu Xingzheng Wang Yongbing Zhang Xinhong Hao Yongdong Zhang Qionghai Dai School of Information Science and Technology University of Science and Technology of China Hefei China Institute of Information and Control Hangzhou Dianzi University Hangzhou China College of Mechatronics and Control Engineering Shenzhen University Shenzhen China Graduate School at Shenzhen Tsinghua University Shenzhen China Science and Technology on Mechatronic Dynamic Control Laboratory Beijing Institute of Technology Beijing China Department of Automation Tsinghua University Beijing China

Presents corrections to affiliations in the above named paper.

关键词： Feature extraction Convolutional neural networks Benchmark testing Streaming media Semantics

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Reinforcement Learning-Based Predictive control for Autonomous Electrified Vehicles

Reinforcement Learning-Based Predictive Control for Autonomo...

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2018 IEEE Intelligent Vehicles Symposium, IV 2018

作者： Liu, Teng Yang, Chao Hu, Chuanzheng Wang, Hong Li, Li Cao, Dongpu Wang, Fei-Yue State Key Laboratory of Automotive Simulation and Control Jilin University China Vehicle Intelligence Pioneers Inc. Qingdao Shandong266109 China Jiangsu XCMG Construction Machinery Research Institute Ltd. Xuzhou Jiangsu221004 China Mechanical and Mechatronics Engineering Department Waterloo University N2L 3G1 Canada Faculty of Department of Automation Tsinghua University China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781538644522

This paper proposes a learning-based predictive control technique for self-driving hybrid electric vehicle (HEV). This approach is a hierarchical framework. The higher-level is a human-like driver model, which is applied to predict accelerations in the car following situation to replicate a human driver's demonstrations. The lower-level is a reinforcement learning (RL)-based controller, which enforces the battery and fuel consumption constraints to improve energy efficiency of HEV. In addition, we present induced matrix norm (IMN) to handle cases that the training data cannot provide sufficient information on how to operate in current driving situation. Simulation results illustrate that the proposed method can reproduce human driver's driving style and promote fuel economy. © 2018 IEEE.

关键词： Reinforcement learning

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End-to-End Driving Activities and Secondary Tasks Recognition Using Deep Convolutional Neural Network and Transfer Learning

End-to-End Driving Activities and Secondary Tasks Recognitio...

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IEEE Intelligent Vehicles Symposium

作者： Yang Xing Jianlin Tang Hong Liu Chen Lv Dongpu Cao Efstathios Velenis Fei-Yue Wang Advanced Vehicle Engineering Centre Cranfield University JIANGSU XCMG CONSTRUCTION MACHINERY RESEARCH INSTITUTE LTD School of Mechanical and Mechatronics Engineering University of Waterloo State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences

ISBN: (纸本)9781538644539

Drivers' decision and their corresponding behaviors are important aspects that can affect the driving safety, and it is necessary to understand the driver behaviors in real-time. In this study, an end-to-end driving-related tasks recognition system is proposed. Specifically, seven common driving activities are identified, which are normal driving, right mirror checking, rear mirror checking, left mirror checking, using in-vehicle video device, texting, and answering mobile phone. Among these, the first four activities are regarded as normal driving tasks, while the rest three are divided into distraction group. The images are collected using a consumer range camera, namely, Kinect. In total, five drivers are involved in the naturalistic data collection. Before training the identification model, the raw images are first segmented using a Gaussian mixture model (GMM) to extract the driver region from the background. Then, a pre-trained deep convolutional neural network (CNN) model is trained to classify the behaviors, which directly takes the processed RGB images as the input and outputs the identified label. In this work, the AlexNet is selected as the pre-trained CNN model. Then, to reduce the training cost, the transfer learning mechanism is applied to the CNN model. An average of 79% detection accuracy is achieved for the seven driving tasks. The proposed integration model can be used as a low-cost driver distraction and dangerous tasks recognition modeL.

关键词： Driver Behavior Religious Missions Transfer (Psychology) Automobile driving driving safety end-to-end Mirrors Drivers Neural network

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PID2018 benchmark challenge: Model-based feedforward compensator with a conditional integrator ??

arXiv

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arXiv 2018年

作者： Yuan, Jie Ates, Abdullah Dehghan, Sina Zhao, Yang Fei, Shumin Chen, YangQuan School of Automation Southeast University Nanjing210096 China Engineering Faculty Computer Engineering Department Inonu University Malatya44280 Turkey Mechatronics Embedded Systems and Automation Lab University of California MercedCA95340 United States School of Science and Engineering Shandong University Jinan250061 China Key Laboratory of Measurement and Control of CSE Ministry of Education School of Automation Southeast University Nanjing210096 China

Since proportional-integral-derivative (PID) controllers absolutely dominate the control engineering, numbers of different control structures and theories have been developed to enhance the effciency of PID controllers. Thus, it is essential and inspiring to operate different PID control strategies to the PID2018 Benchmark Challenge. In this paper, a novel control strategy is designed for this refrigeration system, where a feedforward compensator and a conditional integrator are utilized to compensate the disturbances and remove the steady-state error in the benchmark problem, respectively. The simulation results given in the benchmark problem show the straightforward effectiveness of the proposed control structure compared with the existing control methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Disturbance rejection

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Siamese-ResNet: Implementing Loop Closure Detection based on Siamese Network

Siamese-ResNet: Implementing Loop Closure Detection based on...

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2018 IEEE Intelligent Vehicles Symposium, IV 2018

作者： Qiu, Kai Ai, Yunfeng Tian, Bin Wang, Bin Cao, Dongpu School of Aritificial Intelligence University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Cloud Computing Center Chinese Academy of Sciences Dongguan523808 China School of Software Engineering University of Science and Technology of China HeFei230026 China Department of Mechanical and Mechatronics Engineering University of Waterloo WaterlooONN2L 3G1 Canada

ISBN: (纸本)9781538644522

Deep learning has made significant breakthroughs in the tasks of image classification, detection, segmentation, etc. However, the application of deep learning in robotics is still scarce. SLAM is a fundamental problem in robotics and loop closure detection is an important part of SLAM. This paper attempts to use supervised learning methods to solve the loop closure detection problem in vision SLAM. We proposed Siamese-ResNet network, which combines Siamese network with ResNet to detect loop closure. To show the effectiveness of Siamese-ResNet, we evaluate Siamese-ResNet and FabMap2.0 on several open published datasets, like TUM SLAM dataset and FabMap SLAM dataset. Compared with FabMap2.0, Siamese-ResNet shows higher accuracy, better robustness and shorter time-consuming. © 2018 IEEE.

关键词： Robotics

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Design of Output Feedback Nonlinear Model Predictive control for Inverted Pendulum on Cart

Design of Output Feedback Nonlinear Model Predictive Control...

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International Conference on control, automation and Systems

作者： Petchakrit Pinyopawasutthi David Banjerdpongchai Yasuaki Oishi Intelligent Control Automation of Process Systems (iCAPS) Research Unit Department of Electrical Engineering Chulalongkorn University 254 Phayathai Road Pathumwan Bangkok Thailand Department of Mechatronics Nazan University Yamazatocho 18 Showa-ku Nagoya Japan

ISBN: (纸本)9781538670804;9788993215168

This paper presents designing of output feedback nonlinear model predictive control (NMPC) for nonlinear systems. NMPC employs the state feedback and updates the control input at each sampling step. The control design of NMPC is formulated as optimal control which requires the information of states of the process. However, in practice, we can measure only some states of the process. This paper aims to use the state observer to estimate the unknown states and use them as feedback for NMPC. We apply output feedback NMPC to inverted pendulum on cart. Numerical results show the response of output feedback NMPC asymptotically converges to that of state feedback NMPC.

关键词： Optimal control Nonlinear systems Nonlinear model predictive control State observer Inverted pendulum on cart

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Stability Analysis of Nonlinear Positive Singular Systems with Delays

Stability Analysis of Nonlinear Positive Singular Systems wi...

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SICE Annual Conference

作者： Yukang Cui Jun Shen College of Mechatronics and Control Engineering Shenzhen University Shenzhen China College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing China

This paper is devoted to the stability analysis of positive nonlinear singular systems with time-varying delays via Takagi-Sugeno fuzzy modeling. We introduce an auxiliary system to exploit the positivity criterion for the singular fuzzy time-delay system. Then, a sufficient condition of α-exponential stability for positive fuzzy singular systems is established, which depends on a given scalar 0 < α < 1 and the bound of time-varying delays. Finally, a numerical simulation is utilized to illustrate the effectiveness of our result.

关键词： T-S fuzzy systems Singular systems Positive nonlinear systems Time-delay systems

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Visual Place Recognition in Long-term and Large-scale Environment based on CNN Feature

Visual Place Recognition in Long-term and Large-scale Enviro...

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IEEE Intelligent Vehicles Symposium

作者： Jianliang Zhu Yunfeng Ai Bin Tian Dongpu Cao Sebastian Scherer School of Artificial Intelligence University of Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Department of Mechanical & Mechatronics Engineering University of Waterloo Robotics Institute Carnegie Mellon University

ISBN: (纸本)9781538644539

With the universal application of camera in intelligent vehicles, visual place recognition has become a major problem in intelligent vehicle localization. The traditional solution is to make visual description of place images using hand-crafted feature for matching places, but this description method is not very good for extreme variability, especially for seasonal transformation. In this paper, we propose a new method based on convolutional neural network (CNN), by putting images into the pre-trained network model to get automatically learned image descriptors, and through some operations of pooling, fusion and binarization to optimize them, then the similarity result of place recognition is presented with the Hamming distance of the place sequence. In the experimental part, we compare our method with some state-of-the-art algorithms, FABMAP, ABLE-M and SeqSLAM, to illustrate its advantages. The experimental results show that our method based on CNN achieves better performance than other methods on the representative public datasets.

关键词： Hamming distance Intelligent vehicles binarization Recognition (Psychology) Visualization network model Cameras images

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End-to-End Driving Activities and Secondary Tasks Recognition Using Deep Convolutional Neural Network and Transfer Learning

End-to-End Driving Activities and Secondary Tasks Recognitio...

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2018 IEEE Intelligent Vehicles Symposium, IV 2018

作者： Xing, Yang Tang, Jianlin Liu, Hong Lv, Chen Cao, Dongpu Velenis, Efstathios Wang, Fei-Yue Advanced Vehicle Engineering Centre Cranfield University BedfordMK43 0AL United Kingdom Vehicle Intelligence Pioneers Ltd Qingdao China Jiangsu XCMG Construction Machinery Research Institute Ltd. No. 26 Tuolanshan Road Economic Development Zone Xuzhou Jiangsu221004 China School of Mechanical and Mechatronics Engineering University of Waterloo 200 University Avenue West WaterlooONN2L 3G1 Canada State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781538644522

Drivers' decision and their corresponding behaviors are important aspects that can affect the driving safety, and it is necessary to understand the driver behaviors in real-time. In this study, an end-to-end driving-related tasks recognition system is proposed. Specifically, seven common driving activities are identified, which are normal driving, right mirror checking, rear mirror checking, left mirror checking, using in-vehicle video device, texting, and answering mobile phone. Among these, the first four activities are regarded as normal driving tasks, while the rest three are divided into distraction group. The images are collected using a consumer range camera, namely, Kinect. In total, five drivers are involved in the naturalistic data collection. Before training the identification model, the raw images are first segmented using a Gaussian mixture model (GMM) to extract the driver region from the background. Then, a pre-trained deep convolutional neural network (CNN) model is trained to classify the behaviors, which directly takes the processed RGB images as the input and outputs the identified label. In this work, the AIexNet is selected as the pre-trained CNN model. Then, to reduce the training cost, the transfer learning mechanism is applied to the CNN model. An average of 79\% detection accuracy is achieved for the seven driving tasks. The proposed integration model can be used as a low-cost driver distraction and dangerous tasks recognition modeL. © 2018 IEEE.

关键词： Neural network models

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Design, implementation and control of dual axis self balancing inverted pendulum using reaction wheels 10

Design, implementation and control of dual axis self balanci...

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10th International Conference on Electrical and Electronics engineering, ELECO 2017

作者： Türkmen, Abdullah Korkut, M. Yusuf Erdem, Mustafa Gönül, Ömer Sezer, Volkan Mechatronics Graduate Program Istanbul Technical University Istanbul Turkey Energy Institute Istanbul Technical University Istanbul Turkey Dept. of Control and Automation Engineering Istanbul Technical University Istanbul Turkey

ISBN: (纸本)9786050107371

Inverted pendulum systems have an important place in practice for control problems due to their nonlinear structures. It is a very difficult task to control the orientation and speed of objects and robots which are free in three dimensions. These systems constitute the substructure of many advanced systems. Conventional actuators may not be used in such applications due to various limitations. Under these circumstances usage of reaction wheels come into prominence. Reaction wheel based systems are used for orienting, coordinating and balancing of spacecraft, transport systems, and other such systems. In this study, dual axis self-balancing reaction wheel based inverted pendulum system which is a basis for many complex systems has been designed, manufactured and controlled. © 2017 EMO (Turkish Chamber of Electrical Enginners).

关键词： Inverted pendulum

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