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12th International Conference on CYBER Technology in Automation, Control, and intelligent Systems, CYBER 2022

作者： Zhang, Xianmiao Liu, Jie Liu, Jiying Wang, Yuzhe Zhang, Mian Xiang, Hongbiao School of Mechanical Engineering Tianjin University of Technology Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control Tianjin300384 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin300384 China Hong Kong Baptist University United International College Division of Culture and Creativity Beijing Normal University Guangdong Zhuhai519087 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

Droplet actuation simplifies the handling of various reagents or samples and can be applied to a wide range of fields, including chemistry, biology, biomedical, and others. This paper presents a programmable droplet control system based on a magnetoelastic membrane and electromagnetic pillar array. Different magnetic blocks with different magnetization directions were designed on the silicone rubber membrane, and the magnetoelastic membrane deformed under the magnetic field generated by the array of electromagnetic pillars. By combining the gravitational forces of the droplet and the deformation of the magnetic membranes, the motion of the droplet can be controlled. Furthermore, the surface of membranes was ablated with a laser machine to impart superhydrophobic properties. The simulation results show that with the different magnetic fields, the droplet can move lengthwise, widthwise, and diagonally in the horizontal plane, and multiple droplets can be merged and mixed. In contrast to the traditional droplet control method, the droplet programmable movement control system utilizing superhydrophobic magnetoelastic membranes and an electromagnetic pillar array has better stationarity, flexibility and does not affect the basic properties of the droplets. © 2022 IEEE.

关键词： Silicones

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design of Three-Dimensional B-C-N Structures Via Tailoring Gnrs and Bnnrs

SSRN

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SSRN 2023年

作者： Ma, Mengdong Zhu, Li Xiong, Mei Li, Baozhong Wu, Yingju Ying, Pan Zhao, Zhisheng He, Julong Kang, Zhenhui Macau University of Science and Technology Taipa999078 China State Key Laboratory of Metastable Materials Science and Technology Yanshan University Qinhuangdao066004 China National-Local Joint Engineering Laboratory of Intelligent Manufacturing Oriented Automobile Die & Mould Tianjin University of Technology and Education Tianjin300222 China National Key Laboratory of Advanced Casting Technologies MIIT Key Laboratory of Advanced Metallic and Intermetallic Materials Technology Engineering Research Center of Materials Behavior and Design Ministry of Education Nanjing University of Science and Technology Nanjing210094 China South China University of Technology China

A series of porous 3D Cx(BN)y crystal structures with full sp2 hybridization are obtained by polymerizing edge-functionalized graphene nanoribbons (GNRs) and boron nitride nanoribbons(BNNRs). Herein, the crystal structure and properties of Cx(BN)y are systematically and comprehensively investigated by first-principles calculations, deriving that it is feasible to achieve performance tuning by controlling the nanoribbon width. Thermodynamic stability, these structures have energy advantages with respect to the previously proposed B-C-N structures, and the thermodynamic stability of the structures deteriorates with the increase of the carbon content. Electrical properties, the bandgap value is tunable between 0.1-1.1eV, and the bandgap value increases with the width of BNNRs at the same width of GNRs. Mechanical properties, these structures are mechanically stable, and exhibit unexpected superstretching properties, with tensile lengths up to 2.4 times the original length, bulk modulus tunable between 137-167GPa, shear modulus tunable between 81-115GPa, and the incompressibility of the structure decreases as the nanoribbon width increases. © 2023, The Authors. All rights reserved.

关键词： Energy gap

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A DQN-Based Approach for Online Service Placement in Mobile Edge computing 16th

A DQN-Based Approach for Online Service Placement in Mobile ...

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16th EAI International Conference on Collaborative computing: Networking, Applications, and Worksharing, CollaborateCom 2020

作者： Jie, Xiaogan Liu, Tong Gao, Honghao Cao, Chenhong Wang, Peng Tong, Weiqin School of Computer Engineering and Science Shanghai University Shanghai China Shanghai Key Laboratory of Data Science Shanghai China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China Shanghai Engineering Research Center of Intelligent Computing System Shanghai China

ISBN: (纸本)9783030675394

Due to the development of 5G networks, computation intensive applications on mobile devices have emerged, such as augmented reality and video stream analysis. Mobile edge computing is put forward as a new computing paradigm, to meet the low-latency requirements of applications, by moving services from the cloud to the network edge like base stations. Due to the limited storage space and computing capacity of an edge server, service placement is an important issue, determining which services are deployed at edge to serve corresponding tasks. The problem becomes particularly complicated, with considering the stochastic arrivals of tasks, the additional latency incurred by service migration, and the time spent for waiting in queues for processing at edge. Benefiting from reinforcement learning, we propose a deep Q network based approach, by formulating service placement as a Markov decision process. Real-time service placement strategies are output, to minimize the total latency of arrived tasks in a long term. Extensive simulation results demonstrate that our approach works effectively. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Reinforcement learning

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Augmented Reality and Quick Response Code Technology in Engineering Drawing Course 17

Augmented Reality and Quick Response Code Technology in Engi...

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17th IEEE International Conference on Mechatronics and Automation, ICMA 2020

作者： Wang, Xiaofei Chen, Chenfei Li, Zhaotong Tianjin University of Technology Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin300384 China

ISBN: (纸本)9781728164151

Augmented reality (AR) technology is a cutting technology. It can enhance the perception of reality and provide more information of the real world. AR technology is used in many fields, like medical, industry, and education. In this paper, AR is introduced into engineering drawing course. Engineering drawing course is difficult to learn as it is a knowledge that students rarely contact with. To have a close contact with the models and the views, a convenient AR application (AR app) is proposed in the paper. With the help of the AR app, virtual models and real views are combined well. The models can be moved and rotated by touching the screen. Some videos of model assembly and disassembly can be obtained when scanning the quick response code (QR code). Thus, it is convenient for students to use mobile phone to study engineering drawing effectively. The interactive teaching environment enhances students' spatial thinking and cultivates their sense of innovation. © 2020 IEEE.

关键词： Augmented reality

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Structural model updating based on metamodel using modal frequencies

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Frattura ed Integrita Strutturale 2021年第58期15卷 114-127页

作者： Wang, Jutao Liu, Zhenzhong Xue, Lijun Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China China

Modal frequencies are often used in structural model updating, based on the finite element model, while the metamodel approach is often used in the respective optimization process. In this work, the Kriging model is adopted as the metamodel. First, the influence of different correlation functions of Kriging model is inspected, followed by the investigation of the approximate capability of Kriging model by inspecting the approximate accuracy of nonlinear functions. Next, a model updating procedure is proposed, based on the Kriging model, constructed using samples generated via the method of Optimal Latin Hypercube. Finally, a typical frame structure is taken as a case study, demonstrating the feasibility and efficiency of the proposed approach. The results show that, the Kriging model can match the target functions very well, while the finite element model can achieve accurate frequencies and can reliably predict the frequencies after model updating. © 2021, Gruppo Italiano Frattura. All rights reserved.

关键词： Interpolation

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Analysis of Anchor-Based and Anchor-Free Object Detection Methods Based on Deep Learning 17

Analysis of Anchor-Based and Anchor-Free Object Detection Me...

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17th IEEE International Conference on Mechatronics and Automation, ICMA 2020

作者： Liu, Shujian Zhou, Haibo Li, Chenming Wang, Shuo Tianjin University of Technology Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin300384 China

ISBN: (纸本)9781728164151

As one of the core mission of computer vision, object detection has been widely applied with the rapid development of computer technology, especially in the fields of face detection, behavior detection, auto driving, and intelligent monitoring. Aiming at the shortcomings of the traditional object detection, such as low detection accuracy, low efficiency and poor robustness, this article summarizes deep learning-based detectors of two kinds of modules: Anchor-Based and Anchor-Free. The performance of each detector is compared and analyzed in this paper as well. In addition, we summarize the development of object detection's key technologies from the aspect of improvement of Backbone, the optimization of NMS, imbalance of positive and negative samples' solution etc. Finally, the development trend of object detection is discussed from the prospect of lightweight detection model, weakly supervision detection and small object detection etc. © 2020 IEEE.

关键词： Object detection

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An Efficient and Truthful Online Incentive Mechanism for a Social Crowdsensing Network 16th

An Efficient and Truthful Online Incentive Mechanism for a S...

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16th EAI International Conference on Collaborative computing: Networking, Applications, and Worksharing, CollaborateCom 2020

作者： Fang, Lu Liu, Tong Gao, Honghao Cao, Chenhong Li, Weimin Tong, Weiqin School of Computer Engineering and Science Shanghai University Shanghai China Shanghai Key Laboratory of Data Science Shanghai China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China Shanghai Engineering Research Center of Intelligent Computing System Shanghai China

ISBN: (纸本)9783030675363

Crowdsening plays an important role in spatiotemporal data collection by leveraging ubiquitous smart devices equipped with sensors. Considering rational and strategic device users, designing a truthful incentive mechanism is a crucial issue. Moreover, another key challenge is that there may not exist adequate participating users in reality. To encourage more users to participate, the social relationship among them can be leveraged, as users may be significantly influenced by their social friends. In this paper, we assume recruited users to diffuse uncompleted sensing tasks to their friends, and propose an efficient and truthful online incentive mechanism for a such social crowdsensing network. Specially, we model the time-varying social influence of a user by extending two metrics of node centrality used in social networks. In order to maximize the accumulated social welfare achieved by the network, we design a user selection algorithm and a payment determination algorithm respectively, in which payments given to participants not only depend on data qualities but also related with social influences. We theoretically prove that our mechanism achieves properties of computational efficiency, individual rationality, and truthfulness. Extensive simulations are conducted, and the results show the superiority of our mechanism. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Computational efficiency

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Lightweight Task-Oriented Semantic Communication Empowered by Large-Scale AI Models

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IEEE Transactions on Vehicular Technology 2025年

作者： Liu, Chuanhong Guo, Caili Yang, Yang Chen, Mingzhe Quek, Tony Q. S. Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL United States Singapore University of Technology and Design Dept. of Information Systems Technology and Design 487372 Singapore

Recent studies have focused on leveraging large-scale artificial intelligence (LAI) models to improve semantic representation and compression capabilities. However, the substantial computational demands of LAI models pose significant challenges for real-time communication scenarios. To address this, this paper proposes utilizing knowledge distillation (KD) techniques to extract and condense knowledge from LAI models, effectively reducing model complexity and computation latency. Nevertheless, the inherent complexity of LAI models leads to prolonged inference times during distillation, while their lack of channel awareness compromises the distillation performance. These limitations make standard KD methods unsuitable for task-oriented semantic communication scenarios. To address these issues, we propose a fast distillation method featuring a pre-stored compression mechanism that eliminates the need for repetitive inference, significantly improving efficiency. Furthermore, a channel adaptive module is incorporated to dynamically adjust the transmitted semantic information based on varying channel conditions, enhancing communication reliability and adaptability. In addition, an information bottleneck-based loss function is derived to guide the fast distillation process. Simulation results verify that the proposed scheme outperform baselines in term of task accuracy, model size, computation latency, and training data requirements. © 1967-2012 IEEE.

关键词： NP-hard

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Efficient Hierarchical Exploration with An Active Subgoal Generation Strategy

Efficient Hierarchical Exploration with An Active Subgoal Ge...

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IEEE International Conference on Robotics and Biomimetics

作者： Xin Xu Guoyu Zuo Jiangeng Li Gao Huang Faculty of Information Technology Beijing University of Technology Beijing China Beijing Key Laboratory of Computing Intelligence and Intelligent Systems Beijing China Faculty of Information Beijing University of Technology Beijing China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing China

ISBN: (纸本)9781665481106

Goal-conditioned hierarchical reinforcement learning (HRL) is a promising approach in long-horizon and complex tasks. It has been successfully used by decomposing tasks via subgoals. However, the existing goal-conditioned HRL methods often suffer from training inefficiency in the long-horizon tasks with sparse external rewards. For this problem, this paper proposes a novel framework that can train high-level policy efficiently with an effective sub goal generation method. The key component of the framework is an active subgoal generation strategy which consists of (a) an efficient high-level policy learning mechanism and (b) two measures of novelty and coverage for filtering effective subgoals. We also introduce the HER mechanism to the framework to solve the problem of sparse reward in the environment. Building upon the proposed framework, this paper develops an active hierarchical exploration strategy with an efficient subgoal generation method. We test our method on a variety of continuous control tasks. Experimental results demonstrate that the strategy outperforms the state-of-the-art HRL approaches.

关键词： Training Learning systems Representation learning Filtering Biomimetics Buildings Reinforcement learning

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Trajectory Optimization and Control Method Based on Multistage Nonlinear Model Predictive Control

Trajectory Optimization and Control Method Based on Multista...

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Artificial Intelligence, Networking and Information Technology (AINIT), International Seminar on

作者： Qingchun Zheng Zhi Peng Peihao Zhu Ran Zhai Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control Tianjin University of Technology Tianjin China School of Mechanical Engineering Tianjin University of Technology Tianjin China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin China

To improve the trajectory optimization efficiency of robot manipulators, this paper proposes a novel approach for trajectory optimization and control of manipulators based on multistage nonlinear model predictive control (msNLMPC). This approach is realized by using a neural state space model as the prediction model of nonlinear model predictive control (NLMPC). The simulation results show that the actual trajectory of the manipulator in our proposed scheme coincides with the desired trajectory. The manipulator achieves the control task of following the optimal trajectory. Our proposed method improves the trajectory optimization efficiency of the robot manipulator.

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