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A novel GSP auction mechanism for ranking Bitcoin transactions in blockchain mining

DECISION SUPPORT systems 2019年 124卷 113094-113094页

作者： Li, Juanjuan Yuan, Yong Wang, Fei-Yue Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Shandong Peoples R China

Bitcoin is gaining ground in recent years. In the Bitcoin system, miners provide computing power to confirm transactions and mine blocks in pursuit of transaction fees, while users compete by bidding transaction fees for faster confirmation. This process is in essence analogous to online ad auctions, where advertisers bid for more prominent ad slots. Therefore, inspired by the Generalized Second Price (GSP) mechanism dominantly used in online ad auctions, we propose to adopt the GSP auction model in the Bitcoin transaction confirmation game. Also, we use weighted fees as the new ranking basis, which can be calculated by user-submitted fees, transaction size, quality scores and virtual fees accumulated from the waiting time. We show that the formulated static GSP transaction fee auction with complete information has a unique Pure Strategy Nash Equilibrium. Then, we discuss the impacts of quality scores and virtual fees on users' equilibrium fee decisions and payoffs. Finally, computational experiments are designed to validate our theoretical models and analysis. Our research findings indicate that this novel GSP mechanism is superior to the currently adopted GFP mechanism, and can help users save fees. Besides, quality scores and virtual fees are also proven to be effective on reducing users' paid fees. Moreover, the design of virtual fees allows all transactions to be processed more efficiently in a uniform pipeline, and the interests of transactions with and without associated fees are taken into consideration.

关键词： Blockchain Generalized Second Price auction Transaction fee Quality score Virtual fee

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controlling a Robotic Hip Exoskeleton With Noncontact Capacitive Sensors

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2019年第5期24卷 2227-2235页

作者： Crea, Simona Manca, Silvia Parri, Andrea Zheng, Enhao Mai, Jingeng Lova, Raffaele Molino Vitiello, Nicola Wang, Qining Scuola Super Sant Anna BioRobot Inst I-56127 Pisa Italy IRCCS Fdn Don Carlo Gnocchi I-20148 Milan Italy Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100864 Peoples R China Peking Univ Coll Engn Robot Res Grp Beijing 100871 Peoples R China Peking Univ Beijing Engn Res Ctr Intelligent Rehabil Engn Beijing 100871 Peoples R China Peking Univ Beijing Innovat Ctr Engn Sci & Adv Technol Beijing 100871 Peoples R China

For partial lower-limb exoskeletons, an accurate real-time estimation of the gait phase is paramount to provide timely and well-tailored assistance during gait. To this end, dedicated wearable sensors separated from the exoskeletons mechanical structure may be preferable because they are typically isolated from movement artifacts that often result from the transient dynamics of the physical human-robot interaction. Moreover, wearable sensors that do not require time-consuming calibration procedures are more easily acceptable by users. In this paper, a robotic hip orthosis was controlled using capacitive sensors placed in orthopedic cuffs on the shanks. The capacitive signals are zeroed after donning the cuffs and do not require any further calibration. The capacitive-sensing-based controller was designed to perform online estimation of the gait cycle phase via adaptive oscillators, and to provide a phase-locked assistive torque. Two experimental activities were carried out to validate the effectiveness of the proposed control strategy. Experiments conducted with seven healthy subjects walking on a treadmill at different speeds demonstrated that the controller can estimate the gait phase with an average error of 4%, while also providing hip flexion assistance. Moreover, experiments carried out with four healthy subjects showed that the capacitive-sensing-based controller could reduce the metabolic expenditure of subjects compared to the unassisted condition (mean +/- SEM, - 3.2% +/- 1.1).

关键词： Capacitive sensing gait phase estimation hip exoskeleton wearable robotics

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The ParallelEye Dataset: A Large Collection of Virtual Images for Traffic Vision Research

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IEEE TRANSACTIONS ON intelligent TRANSPORTATION systems 2019年第6期20卷 2072-2084页

作者： Li, Xuan Wang, Kunfeng Tian, Yonglin Yan, Lan Deng, Fang Wang, Fei-Yue Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266000 Shandong Peoples R China Univ Sci & Technol China Dept Automat Hefei 230027 Anhui Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Natl Univ Def Technol Res Ctr Computat Expt & Parallel Syst Technol Changsha 410073 Hunan Peoples R China

Dataset plays an essential role in the training and testing of traffic vision algorithms. However, the collection and annotation of images from the real world is time-consuming, lab.r-intensive, and error-prone. Therefore, more and more researchers have begun to explore the virtual dataset, to overcome the disadvantages of real datasets. In this paper, we propose a systematic method to construct large-scale artificial scenes and collect a new virtual dataset (named "ParallelEye") for the traffic vision research. The Unity3D rendering software is used to simulate environmental changes in the artificial scenes and generate ground-truth lab.ls automatically, including semantic/instance segmentation, object bounding boxes, and so on. In addition, we utilize ParallelEye in combination with real datasets to conduct experiments. The experimental results show the inclusion of virtual data helps to enhance the per-class accuracy in object detection and semantic segmentation. Meanwhile, it is also illustrated that the virtual data with controllab.e imaging conditions can be used to design evaluation experiments flexibly.

关键词： Traffic vision complex environments parallel vision artificial scenes ParallelEye virtual dataset

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Traffic Flow Prediction with Parallel Data 21

Traffic Flow Prediction with Parallel Data

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21st IEEE International Conference on intelligent Transportation systems (ITSC)

作者： Chen, Yuanyuan Lv, Yisheng Wang, Xiao Wang, Fei-Yue Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Qingdao Qingdao 266109 Shandong Peoples R China

ISBN: (纸本)9781728103235

Traffic prediction is an elemental function of intelligent Transportation systems, and accurate and timely prediction is of great significance to both traffic management agencies and individual drivers. With the development of deep learning and big data, deep neural networks (DNN) achieve superior performances in traffic prediction. Developing DNN prediction models needs large scale and diverse data, however, it is costly to collect large volume of accurate traffic data. In this paper, we propose to use small volume of real traffic data and large volume of synthetic traffic data to developing traffic prediction models. The evolving of parallel system paradigm for traffic prediction and the algorithm to incrementally train traffic data generation models and traffic prediction models are presented. We use an improved generative adversarial networks to generate traffic data, and a stacked long short-term memory model for traffic prediction. Experimental results on a real traffic dataset demonstrate that our method can significantly improve the performance of traffic flow prediction.

关键词： Data models Predictive models Generators Gallium nitride Logic gates Transportation Computer architecture

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Depth control of a Bioinspired Robotic Dolphin Based on Sliding-Mode Fuzzy control Method

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2018年第3期65卷 2429-2438页

作者： Yu, Junzhi Liu, Jincun Wu, Zhengxing Fang, Hao Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Beijing Inst Technol Beijing Adv Innovat Ctr Intelligent Robots & Syst Beijing 100081 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

This paper presents a hybrid controller that combines sliding-mode control (SMC) with a fuzzy strategy to regulate the vertical displacement of a bioinspired robotic dolphin. The structure of the robot and a simplified mathematical model for depth control are described. An SMC based on the line-of-sight guidance law and the slidingmode observer is developed to overcome systematic uncertainties and environmental disturbances. The Lyapunov stability theory is utilized to analyze the stability and convergence properties of the closed-loop system. Depth control in the physical robot is realized by utilizing a fuzzy logic controller, which is represented as the mapping of the input propulsion forces/ moments and output control parameters. Numerical and experimental results show that the proposed control strategy successfully steers the robot toward and along the desired depth.

关键词： Bioinspired robotic dolphin depth control fuzzy logic sliding-mode control (SMC)

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End-to-End Autonomous Driving: An Angle Branched Network Approach

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2019年第12期68卷 11599-11610页

作者： Wang, Qing Chen, Long Tian, Bin Tian, Wei Li, Lingxi Cao, Dongpu Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou 510245 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266111 Shandong Peoples R China Chinese Acad Sci Cloud Comp Ctr Dongguan 523808 Peoples R China Karlsruhe Inst Technol Inst Measurement & Control Syst D-76131 Karlsruhe Germany Indiana Univ Purdue Univ Purdue Sch Engn & Technol Dept Elect & Comp Engn Indianapolis IN 46202 USA Univ Waterloo Dept Mech & Mechatron Engn Waterloo ON N2L 3G1 Canada

Imitation learning for the end-to-end autonomous driving has drawn renewed attention from academic communities. Current methods either only use images as the input, which will yield ambiguities when a vehicle approaches an intersection, or use additional command information to navigate the vehicle but inefficiently. Focusing on making the vehicle automatically drive along the given path, we propose a new and effective navigation command called as subgoal anglewhich does not require human participation and is calculated by the current position and subgoal of the ego-vehicle. Thus, the subgoal angle contains more information than the navigation command represented as a one-hot vector. Additionally, we propose a model architecture called as angle branched network that makes predictions based on the subgoal angle. In this network, the subgoal angle is not only used for extracting useful features but also for guiding the appropriate prediction layer to make predictions for both the steer angle and the throttle status (which controls the acceleration). Experiments are conducted in a three-dimensional urban simulator. Both quantitive and qualitive results show the effectiveness of the navigation command and the angle branched network. Moreover, the performance can be further boosted by integrating both semantic and depth information into the driving model. Especially by using the depth information, collisions with vehicles and pedestrians can be reduced.

关键词： End-to-end driving imitation learning navigation command

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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 lab.led data. However, collecting and annotating images from the real world is too demanding in terms of lab.r 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 availab.e 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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Path Planning System for Smart Cars Used in Education

Path Planning System for Smart Cars Used in Education

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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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A Two-level Adaptive Target Recognition and Tracking Method Based on Vision for Multi-robot System

A Two-level Adaptive Target Recognition and Tracking Method ...

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IEEE International Conference on Robotics and Biomimetics

作者： Liang Ren Zhiqiang Cao Min Tan Peng Zhao Xuechao Chen State Key Laboratory of Management and Control for Complex Systems University of Chinese Academy of Sciences Beijing China The 21st Research Institute of China Electronics Technology Group Corporation Shanghai China Beijing Advanced Innovation Center for Intelligent Robots and Systems BIT Beijing China

The vision-based target recognition and tracking have received much attention in the field of robotics. Existing methods mainly focus on the vision perception of individual robot with a single view, however, the performance is susceptible to illumination and occlusion. Multi-robot collab.rative perception provides a potential solution to deal with the limitation of single-view observation, however, the challenging of environmental adaptability for multi-robot collab.rative decision still remains unsolved. To solve this problem, this paper proposes a two-level adaptive target recognition and tracking method based on vision for multi-robot system. The problem of multi-robot target recognition and tracking is solved under a two-level framework, which contains the features fusion level of individual robot and the cooperation level of multi-robot system. In the first level, the features measuring results that influence the visual perception of individual robot are fused, while the second level combines the voting of each robot together to determine the target for multi-robot system. Both the features measuring weights and robots voting weights are adaptively updated according to their evaluation, which lead to a beneficial result where the features and robots with higher accuracy play major roles in the first and second levels, respectively. Therefore, a good adaptability to the environments can be guaranteed. The experimental results show that the proposed approach can realize the coordination of multi-robot system in target recognition and tracking with an effective performance.

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Simultaneous Observation of Hybrid states for Cyber-Physical systems: A Case Study of Electric Vehicle Powertrain

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IEEE TRANSACTIONS ON CYBERNETICS 2018年第8期48卷 2357-2367页

作者： Lv, Chen Liu, Yahui Hu, Xiaosong Guo, Hongyan Cao, Dongpu Wang, Fei-Yue Cranfield Univ Adv Vehicle Engn Ctr Cranfield MK43 0AL Beds England Qingdao Huituo Intelligent Machine Co Qingdao 266112 Peoples R China Tsinghua Univ State Key Lab Automot Safety & Energy Beijing 100084 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

As a typical cyber-physical system (CPS), electrified vehicle becomes a hot research topic due to its high efficiency and low emissions. In order to develop advanced electric powertrains, accurate estimations of the unmeasurable hybrid states, including discrete backlash nonlinearity and continuous half-shaft torque, are of great importance. In this paper, a novel estimation algorithm for simultaneously identifying the backlash position and half-shaft torque of an electric powertrain is proposed using a hybrid system approach. System models, including the electric powertrain and vehicle dynamics models, are established considering the drivetrain backlash and flexibility, and also calibrated and validated using vehicle road testing data. Based on the developed system models, the powertrain behavior is represented using hybrid automata according to the piecewise affine property of the backlash dynamics. A hybrid-state observer, which is comprised of a discrete-state observer and a continuous-state observer, is designed for the simultaneous estimation of the backlash position and half-shaft torque. In order to guarantee the stability and reachability, the convergence property of the proposed observer is investigated. The proposed observer are validated under highly dynamical transitions of vehicle states. The validation results demonstrates the feasibility and effectiveness of the proposed hybrid-state observer.

关键词： Backlash nonlinearity cyber-physical system (CPS) electric powertrain hybrid states simultaneous observation

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