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International Conference on Guidance, Navigation and control, ICGNC 2022

作者： Zhang, Yujie Zheng, Hongwei Zhang, Zheng Yu, Jianglong Hua, Yongzhao Dong, Xiwang Ren, Zhang Institute of Artificial Intelligence Beihang University Beijing100191 China School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control Laboratory Beihang University Beijing100191 China Beijing Academy of Blockchain and Edge Computing Beijing100086 China

ISBN: (纸本)9789811966125

In the multi-sensor multi-target tracking (MSMTT) problem, the matching of the measurements and the targets will generate a huge computational burden, resulting in an unsatisfactory real-time performance of the maneuvering targets tracking. In order to reduce the computational burden, this paper proposes a self-organizing feature map (SOM) neural network based Gaussian mixture probability hypothesis density algorithm (SOM-GMPHD). Firstly, a distributed filtering MSMTT algorithm based on SOM neural network is proposed. The distributed SOM-GMPHD algorithm (DSOM-GMPHD) has two fusion steps. Secondly, to further reduce the computational complexity, a centralized SOM-GMPHD algorithm (CSOM-GMPHD) with only one-step fusion is proposed. The computational complexity analysis of the existing MSMTT algorithms (DGMPHD and CGMPHD) and the proposed SOM-GMPHD algorithms are carried out in this paper. Finally, the effect of the proposed algorithms is evaluated in the simulation experiment. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Self organizing maps

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Non-line-of-Sight Recognition Algorithm Based on Deep Learning

Non-line-of-Sight Recognition Algorithm Based on Deep Learni...

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International Conference on Internet of Things, Communication and Intelligent Technology, IoTCIT 2022

作者： Zhang, Chengjun Wang, Hongmei Wang, Zhiwei Wang, Faguang Min, Minghui Li, Shiyin School of Information and Control Engineering China University of Mining and Technology Jiangsu Xuzhou221116 China Xuzhou First People’s Hospital Xuzhou221116 China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education and School of Cyber Science and Engineering Wuhan University Wuhan430072 China

ISBN: (纸本)9789819904150

With the development of the information age, large indoor places such as hospitals, mines and factories have higher and higher requirements for indoor positioning. GPS and other common outdoor positioning methods cannot meet the requirements of accurate indoor navigation. Ultra-wideband is widely used in indoor places because of its strong anti-interference and high accuracy. Accurate identification of errors in UWB indoor non-visual range helps to improve the data for positioning. Therefore, this study combined with convolutional neural network to solve the problem of low recognition rate of non-line-of-sight signals in indoor scenes, and proposed an improved convolutional neural network based on empirical mode decomposition (EMD), wavelet domain denoising (WD) and improved CNN) fusion model EMD-WD-CNN. First, the channel impulse response signal of UWB is denoised based on the EMD-WD algorithm, and then the denoised signal is used as the input of the CNN model for training. The comparison based on similar algorithms shows that the improved CNN model can significantly improve the line-of-sight and NLOS recognition rate of different media in indoor environment. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Empirical mode decomposition

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Event-Triggered Distributed Multisensor Multitarget Tracking with GM-PHD Filter

Event-Triggered Distributed Multisensor Multitarget Tracking...

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International Conference on Guidance, Navigation and control, ICGNC 2022

作者： Qi, Jialin Zheng, Zhang Dong, Jin Hong, Jiang Xiwang, Dong Yu, Jianglong Zhang, Ren School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control Laboratory Beihang University Beijing100191 China Institute of Artificial Intelligence Beihang University Beijing100191 China Beijing Academy of Blockchain and Edge Computing Beijing100086 China

ISBN: (纸本)9789811966125

This paper focuses on a distributed event-triggered Gaussian mixture probability hypothesis density (GM-PHD) filtering algorithm. In this algorithm, sensors transmit their Gaussian components to neighbors basing on the event-triggered conditions. Then Gaussian components are fused for certain times by matrix reformulation method. This algorithm can reduce data transmission and energy consumption with guaranteeing the estimation performance. Simulation experiment verifies the performance of this algorithm. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Matrix algebra

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Adaptive Neural Tracking control for Nonlinear Servo System with Unknown Time-Varying Parameters and Uncertainties 42

Adaptive Neural Tracking Control for Nonlinear Servo System ...

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42nd Chinese control Conference, CCC 2023

作者： Zhang, Faxiang Gao, Guanbin Na, Jing Wu, Jiande Kunming University of Science and Technology Faculty of Mechanical and Electrical Engineering Yunnan International Joint Laboratory of Intelligent Control and Application of Advanced Equipment Kunming650500 China Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing Kunming650500 China

ISBN: (纸本)9789887581543

This paper solves the neural network (NN) tracking control problem for uncertain nonlinear servo system with time-varying parameters and uncertainties. To address the uncertain nonlinear function with unknown time-varying parameters (i.e., unknown nonlinear spatiotemporal function), the time-varying parameter extraction method is used to separate the time-varying parameters from uncertain nonlinear spatiotemporal function, which yields an unknown state-based boundary function. By the tools of NN and adaptive technology, an adaptive neural tracking controller is designed, which guarantees the uniformly ultimately bounded (UUB) performance of the resulting closed-loop system. The effectiveness of the designed method is verified by simulations. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Servomechanisms

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Distributed Time-Varying Optimization for a Class of Second-Order Multi-agent Systems with Disturbance

Distributed Time-Varying Optimization for a Class of Seco...

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International Conference on Guidance, Navigation and control, ICGNC 2022

作者： Su, Piaoyi Zheng, Hongwei Yu, Jianglong Hua, Yongzhao Dong, Xiwang Ren, Zhang School of Automation Science and Electronic Engineering Science and Technology on Aircraft Control Laboratory Beihang University Beijing100191 China Beijing Academy of Blockchain and Edge Computing Beijing100086 China Institute of Artificial Intelligence Beihang University Beijing100191 China

ISBN: (纸本)9789811966125

This paper investigates the distributed convex time-varying optimization issue for multi-agent systems described by second-order with local disturbances produced by exosystems. Assuming each agent possessing a local cost function, the objective of this paper is to realize zero optimal tracking error considering the influences of extra disturbances and time-varying cost function simultaneously. Firstly, construst a local observer for each agent to obtain the accurate value of disturbance. Then, a distributed optimization algorithm is proposed to consensus optimal realization term. Thirdly, the system stability is confirmed by some Lyapunov-based analysis method. At last, in order to demonstrate the effectiveness of the proposed algorithm, a numerical simulation is constructed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cost functions

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An Obstacle Avoidance Path Planning Algorithm for Manipulator in Deep Crate Grasping

An Obstacle Avoidance Path Planning Algorithm for Manipulato...

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2023 China Automation Congress, CAC 2023

作者： Yao, Zhuobin Gan, Yahui Han, Zao Gao, Huan Li, Ang School of Automation Southeast University Key Laboratory of Measurement and Control of Systems of Engineering Ministry of Education NanJing China Intel Joint Research Institute on Intelligent Edge Computing Nanjing YingQi Intelligent Technology Co. Ltd. NanJing China

ISBN: (纸本)9798350303759

The deep crate grasping problem is a major challenge for manipulator in industrial applications. In order to solve the issue of deep crate grasping and ensure the path quality of the algorithm, this paper proposes a new hybrid obstacle avoidance planning algorithm based on goal-biased rapidly exploring random tree and improved artificial potential field (RRT-APF). Initially, the algorithm strategizes an efficient trajectory for the working element within the operational area. Subsequently, it dissects this trajectory through sampling, creating a sequence of sub-target points. These points serve as the basis for obstacle avoidance planning within the manipulator's configuration space, mitigating planning complexities. Moreover, the RRT algorithm is employed to address the challenge of local optima within the artificial potential field method. The stochastic nature of the RRT algorithm plays a crucial role in enabling the APF algorithm to break free from local optima. Ultimately, the efficacy of the algorithm is demonstrated through the planning of a path for obstacle avoidance on the DOBOT CR5 manipulator using MATLAB. © 2023 IEEE.

关键词： Trees (mathematics)

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A Decentralized Vehicle-to-Vehicle Energy Trading System Based on Efficient Sharding Services 21

A Decentralized Vehicle-to-Vehicle Energy Trading System Bas...

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big Data and Cloud computing, 16th IEEE International Conference on Social computing and Networking and 13th International Conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Meng, Kun Sun, Lijun Chen, Xiao Wu, Haiqin Li, Shuaiyong Qingdao University of Science and Technology College of Information Science and Technology Shandong China University of Leicester School of Computing and Mathematical Sciences Leicester United Kingdom Software Engineering Institute Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing China

ISBN: (纸本)9798350329223

Vehicle-to-vehicle (V2V) energy trading stands as a significant technology, allowing electric vehicles (EVs) to share energy. This balances energy demand and supply, reducing pressure on the power grid. However, two substantial challenges require attention: 1) Creating a standardized protocol universally adoptable by EV manufacturers and energy trading participants to establish a common secure and reliable platform for real-time energy trading between EVs. 2) Determining a fair and transparent pricing and transaction management mechanism, accounting for factors such as energy demand and supply, location, and user preferences. To tackle these hurdles, this paper proposes a decentralized V2V energy trading system (VETS) based on a novel high-performance sharding blockchain framework. VETS incorporates a standard energy trading management protocol, achieving trustless and transparent transaction processing. It employs a blockchain sharding and parallel architecture for high transaction throughput, catering to a large number of EVs. Furthermore, an efficient consensus algorithm is developed to minimize service delays and adapt to real-time transaction demands. A practical testbed operationalizes VETS, facilitating experimental analysis within a real-world cloud environment. © 2023 IEEE.

关键词： Blockchain

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Towards Instance Segmentation-Based Litter Collection with Multi-Rotor Aerial Vehicle

Towards Instance Segmentation-Based Litter Collection with M...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Filip Zoric Antonio Franchi Matko Orsag Zdenko Kovacic Chiara Gabellieri Laboratory for Robotics and Intelligent Control Systems (LARICS) at the Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Robotics and Mechatronics Group from the University of Twente Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

This paper presents a novel aerial robotics application of instance segmentation-based floating litter collection with a multi-rotor aerial vehicle (MRAV). In the scope of the paper, we present a review of the available datasets for litter detection and segmentation. The reviewed datasets are used to train a Mask-RCNN neural network for instance segmentation. The neural network is off-board deployed on an edge computing device and used for litter position estimation. Based on the estimated litter position, we plan a path based on a quadratic Bezier curve for the litter pickup. We compare different trajectory generation methods for the object pickup. The system is verified in a laboratory environment. Eventually, we present practical considerations and improvements necessary to enable autonomous litter collection with MRAV.

关键词： Instance segmentation Reviews Neural networks Laboratories Estimation Trajectory Aircraft

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Wi-Wheat+:Contact-free wheat moisture sensing with commodity WiFi based on entropy

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Digital Communications and Networks 2023年第3期9卷 698-709页

作者： Weidong Yang Erbo Shen Xuyu Wang Shiwen Mao Yuehong Gong Pengming Hu College of Information Science and Engineering Henan University of TechnologyZhengzhou450001China Henan Key Laboratory of Grain Photoelectric Detection and Control Zhengzhou450001China College of International Education Kaifeng UniversityKaifengHenan475004China Knight Foundation School of Computing&Information Sciences Florida International UniversityMiamiFL33199USA Department of Electrical and Computer Engineering Auburn UniversityAuburnAL36849-5201USA

In this paper,we propose a contact-free wheat moisture monitoring system,termed Wi-Wheatþ,to address the several limitations of the existing grain moisture detection technologies,such as time-consuming process,expensive equipment,low accuracy,and difficulty in real-time *** proposed system is based on Commodity WiFi and is easy to *** WiFi CSI data,this paper proposes a feature extraction method based on multi-scale and multi-channel *** feasibility and stability of the system are validated through experiments in both Line-Of-Sight(LOS)and Non-Line-Of-Sight(NLOS)scenarios,where ten types of wheat moisture content are tested using multi-class Support Vector Machine(SVM).Compared with the Wi-Wheat system proposed in our prior work,Wi-Wheatþhas higher efficiency,requiring only a simple training process,and can sense more wheat moisture content levels.

关键词： Channel state information(CSI) WiFi Multi-scale entropy Multi-class support vector machine(SVM) Radio frequency(RF)sensing

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Procedural Generation of Synthetic Dataset for Robotic Applications in Sweet Pepper Cultivation 5

Procedural Generation of Synthetic Dataset for Robotic Appli...

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5th IEEE International Conference on Smart Systems and Technologies, SST 2022

作者： Vuletić, Jelena Polić, Marsela Orsag, Matko University of Zagreb Faculty of EE&C Zagreb Croatia University of Zagreb LARICS Laboratory for Robotics and Intelligent Control Systems Faculty of Electrical Engineering and Computing Zagreb10000 Croatia

ISBN: (数字)9781665482158

ISBN: (纸本)9781665482158

In this paper, a deep learning based object detection model is developed for robotic applications in structured agricultural cultivation of sweet peppers (Capsicum annuum). We propose a realistic simulation based method for procedural generation of a labeled sweet pepper dataset. Realistic 3D models of fruit, leaves, and other relevant plant body parts are generated through photogrammetry, enabling generation of a large and relevant dataset. The dataset is labeled for both object detection and semantic segmentation by scene rendering in multiple layers and using artificial depth information. Smart augmentation techniques are deployed in procedural generation, as well as label filtering techniques for more realistic dataset labels. A MobileNet-SSD based deep learning model is trained through transfer learning on the generated dataset. The trained model is tested both in simulated environment, as well as on a relevant real-world setup. © 2022 IEEE.

关键词： Object detection

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