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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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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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2018 IEEE Intelligent Vehicles Symposium, IV 2018

作者： Zhu, Jianliang Ai, Yunfeng Tian, Bin Cao, Dongpu Scherer, Sebastian School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100190 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 Department of Mechanical and Mechatronics Engineering University of Waterloo WaterlooONN2L 3G1 Canada Robotics Institute Carnegie Mellon University PittsburghPA15213 United States

ISBN: (纸本)9781538644522

关键词： Hamming distance

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Evaluation of Human Postural System Dynamical Behavior via Developed Statokinesigram Trajectory

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IFAC-PapersOnLine 2017年第1期50卷 15062-15067页

作者： Barbolyas, B. Suttova, K. Vachalek, J. Belavy, C. Hucko, B. Dedik, L. Institute of automation measurement and applied informatics Faculty of Mechanical Engineering STU in Bratislava Nam. slobody 17 Bratislava812 31 Slovakia Laboratory of motor control Institute of Normal and Pathological Physiology Slovak Academy of Sciences Sienkiewiczova 1 Bratislava813 71 Slovakia Institute of applied mechanics and mechatronics Faculty of Mechanical Engineering STU in Bratislava Nam. Slobody 17 Bratislava812 31 Slovakia

Presented study deals with the formulation of a model of the postural system behavior in the form of transfer function derived from Development Statokinesigram Trajectory (DST). As compared with statokinesigram, a DST shows a minimal signs of chaotic behavior while it takes anteroposterior and mediolateral postural sway direction into account together. Proposed method provides an alternative approach to established methodology based on the Center of Pressure (CoP) in evaluating the autoregulation control process in humans. For demonstration of DST application, CoP signals of young and healthy participants without and with bilateral vibration stimulus located on Achilles tendons were registered. Data analysis involves DST construction and numerical parameter estimation of the proposed model. Quadratic criterion showed that there is a disproportion between actual and mean DST time profile that was proposed as a sensitive parameter of posture control quality in all tested subjects in quiet stance condition. © 2017

关键词： Quality control Tendons Biomedical systems Biosignals Center of pressure interpretation Model formulation Postural system

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Diagnosing interdental decays in mouth radiography images using Kernel Fuzzy C means segmentation and cascade object detector

Diagnosing interdental decays in mouth radiography images us...

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International Conference on Electrical and Electronics engineering, ELECO

作者： Navid Khalili Dizaji Tufan Kumbasar Department of Mechatronics Engineering Istanbul Technical University Istanbul Turkey Department of Control and Automation Engineering Istanbul Technical University Istanbul Turkey

Mouth radiography is one of the common ways of diagnosing tooth decays. Especially for interdental decays which are hard to be examined by naked eyes. In this paper, we present a method for diagnosing internal decay in real word mouth radiography images, which have been gathered in Tabirz Sina dental clinic. Firstly, we will use Kernel Fuzzy C-Means (KFCM) algorithm, which is modifying the objective function in the fuzzy C-means algorithm using a kernel-induced distance metric, as an image segmentation method. Then, the processed images are labelled with decay and are then employed to a cascade object detector for diagnosing purposes. In order to show the efficiency of the employed method the performance is tested on testing mouth radiography image data set. The results indicate that this method composed of KFCM and cascade object detector structures is successful in detecting interdental decays.

关键词： Detectors Kernel Image segmentation Diagnostic radiography Teeth Training

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Reinforcement Learning-Based Predictive control for Autonomous Electrified Vehicles

Reinforcement Learning-Based Predictive Control for Autonomo...

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IEEE Symposium on Intelligent Vehicle

作者： Teng Liu Chao Yang Chuanzheng Hu Hong Wang Li Li Dongpu Cao Fei-Yue Wang Vehicle Intelligence Pioneers Inc. Qingdao Shandong China Jiangsu XCMG Construction Machinery Research Institute Ltd. Xuzhou Jiangsu China Mechanical and Mechatronics Engineering Department Waterloo University 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 Beijing China

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.

关键词： Acceleration Hybrid electric vehicles Trajectory Hidden Markov models Predictive control Energy efficiency

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The Gateway Anomaly Detection And Diagnosis In WSNs 28

The Gateway Anomaly Detection And Diagnosis In WSNs

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第28届中国控制与决策会议

作者： Feng Zhen Fu JingQi Su Wei School of Mechatronic Engineering and Automation Shanghai University College of Mechatronics and Control Engineering Hubei Normal University

ISBN: (纸本)9781467397155

The gateway plays the essential role in wireless sensor networks,which is critical component in terms of sending and receiving *** abnormal of gateway will have a great influence on data transmission and reduce the stability and reliability of *** research described in the paper origins from equipment monitoring system based on the wireless sensor network system,which is deployed at Cold Rolling and Continuous Annealing line(CRCAL) in steel *** communication data of gateway state were analyzed with statistic method in cluster using the diagnosis *** with the theory of multivariate statistical process control(MSPC),we propose a method of gateway anomaly detection based on Principal Component Analysis(PCA).Finally,the method is proved to be reliable and effective based on the actual data from field monitoring network system.

关键词： Wireless sensor networks(WSN) Gateway Anomaly detection Anomaly diagnosis Principal Component Analysis(PCA)

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Containment control of Multiagent Systems: A DOBC Approach

Containment Control of Multiagent Systems: A DOBC Approach

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IEEE International Conference on automation Science and engineering

作者： Bo Xiao Tao Han Qian Tang Tao Jin College of Mechatronics and Control Engineering Hubei Normal University Huangshi China Department of Automation Wuhan University Wuhan China

The containment control problem of multi-agent systems with exogenous disturbances is investigated in this paper, where the disturbances are generated by an exogenous system. Consequently, a disturbance observer-based control (DOBC) approach is used to estimate the exogenous disturbances, and a distributed containment control protocol are proposed via the state feedback control. Moreover, using the tools from the algebraic graph theory and Lyapunov stability theory, sufficient conditions are obtained to guarantee the containment control of multi-agent systems with exogenous disturbances can be achieved by the DOBC approach. Finally, a numerical simulation is presented to illustrate the effectiveness of the theoretical results.

关键词： DOBC sufficient conditions obtained

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CPSS-based Signal Forwarding Method at Relays for Full-duplex Cooperative Vehicular Networks

CPSS-based Signal Forwarding Method at Relays for Full-duple...

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2018 IEEE Intelligent Vehicles Symposium, IV 2018

作者： Han, Shuangshuang Ma, Houxue Wang, Xiao Liu, Hong Cao, Dongpu Wang, Fei-Yue State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Qingdao Academy of Intelligent Industries Shandong266000 China Qingdao Huituo Intelligent Machine Company Qingdao China JIANGSU XCMG CONSTRUCTION MACHINERY RESEARCH INSTITUTE LTD Xuzhou Jiangsu221004 China Mechanical and Mechatronics Engineering University of Waterloo 200 University Avenue Wes WaterlooONN2L 3G1 Canada Research Center for Military Computational Experiments and Parallel Systems Technology National University of Defense Technology Changsha410073 China

ISBN: (纸本)9781538644522

With increasing popularity of Internet of Vehicles (IoV), concerns for reliable and low complexity communication techniques are proposed due to the requirements of signal reliability and transmission delay for vehicles. Meanwhile, the explosive and pervasive use of social network applications further adds drivers' social relationships and behavioural characteristics into it, and makes it a cyber-physical-social system (CPSS). This paper proposes and analyzes an improved forward scheme for full deplex cooperative vehicular networks in terms of its CPSS features. The proposed CPSS-based forwarding (CPSS-F) strategy forwards a soft estimate of the received signal based on social historic data at the relay node (vehicle/infrastructure) to the destination node (vehicle/infrastructure), which achieves improved reliability than the two conventional strategies in cooperative networks, i.e., amplify-and-forward (AF) and decode-and-forward (DF). Furthermore, the proposed CPSS-F relay achieves performance gains and complexity reduction compared to the conventional AF and DF. Experimental results further confirm the advantages of the proposed CPSS-F for cooperative vehicular networks. The proposed CPSS-F approach is easily extended to other cooperative vehicular social networks, for example, multi-way, half-duplex, or large-antenna networks. © 2018 IEEE.

关键词： Complex networks

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