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arXiv 2022年

作者： Bialik, Kamil Kowalczyk, Marcin Blachut, Krzysztof Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Al. Mickiewicza 30 Krakow30-059 Poland

This paper proposes the use of an event camera as a component of a vision system that enables counting of fast-moving objects – in this case, falling corn grains. These type of cameras transmit information about the change in brightness of individual pixels and are characterised by low latency, no motion blur, correct operation in different lighting conditions, as well as very low power consumption. The proposed counting algorithm processes events in real time. The operation of the solution was demonstrated on a stand consisting of a chute with a vibrating feeder, which allowed the number of grains falling to be adjusted. The objective of the control system with a PID controller was to maintain a constant average number of falling objects. The proposed solution was subjected to a series of tests to determine the correctness of the developed method operation. On their basis, the validity of using an event camera to count small, fast-moving objects and the associated wide range of potential industrial applications can be confirmed. © 2022, CC BY.

关键词： Cameras

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TASF: Terrestrial-Aerial Synergistic Framework for a New Generation of Intelligent Transportation

TASF: Terrestrial-Aerial Synergistic Framework for a New Gen...

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International Conference on Intelligent Transportation

作者： Cong Wang Dujun Nie Ruijun Zhang Siyu Teng Long Chen State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Hong Kong Baptist University Kowloon Hong Kong China WAYTOUS Inc. Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems and the State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Terrestrial-Aerial Synergistic Framework (TASF) represents a novel approach to intelligent transportation, integrating terrestrial and aerial vehicles to enhance urban transportation efficiency and capacity. In this paper, we offer an in-depth analysis of TASF, emphasizing its innovative applications in urban transportation, parcel delivery, and emergency response services. TASF incorporates a five-layer architecture comprising perception, communication, planning, control, and interaction. With the five-layer architecture, TASF can achieve effective collaboration among terrestrial and aerial vehicles, encompassing two principal features: terrestrial-aerial synergistic parallel perception and multi-agent parallel management. Parallel perception results from fusing Bird's Eye View (BEV) representations from Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs), whereas decentralized multi-agent management employs foundational models to enhance decision-making capabilities. Additionally, we consider challenges and potential solutions related to TASF, including safety, reliability, energy efficiency, management and integration. TASF marks a paradigm-shifting leap in the realm of intelligent transportation systems, providing a scalable and efficacious solution tailored to the dynamic requirements of urban mobility and emergency services.

关键词： Foundation models Urban areas Decision making Transportation Emergency services Land vehicles Energy efficiency Planning Reliability Vehicle dynamics

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Optimization of Simulation Parameters of Input Shaper Based on Genetic Algorithm 2

Optimization of Simulation Parameters of Input Shaper Based ...

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2nd IEEE International Conference on Advanced Technologies in Intelligent control, Environment, Computing and Communication Engineering, ICATIECE 2022

作者： Yi, Tao Pei, Quanshen Li, Deying Wei, Shugong Jia, Danping Zhang, Hualiang Hunan University of Information Changsha China School of Information Science and Engineering Shenyang University of Technology Shenyang China College of Physics and Electronic Engineering Guangxi Normal University for Nationalities Chongzuo China Chinese Academy of Sciences Shenyang Institute of Automation Key Laboratory of Networked Control Systems Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China

ISBN: (纸本)9781665493963

In the motion control system, input shaper is often used to suppress the residual oscillation of high-precision positioning system, but the selection of input shaper parameters is difficult. In view of the difficulty of selecting input shaper parameters, this paper proposes to use genetic algorithm to select the parameters of input shaper. Firstly, the input shaper is designed in the light of the three-loop mathematical model of motor;Secondly, because the genetic algorithm directly optimizes the output of the motor and the algorithm can reduce the influence of the deviation of generator parameters on the parameter selection of the input shaper to a certain extent, the parameters of the input shaper are selected by the genetic algorithm to optimize the output of the motor;In the finish, a comparative experiment between a system with input shaper and a system with simple PID control is carried out. The results show that the optimized system can obtain higher robustness and smaller tracking deviation. © 2022 IEEE.

关键词： Genetic algorithms

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An Improved Method for Small Target Recognition Based on Faster RCNN 17th

An Improved Method for Small Target Recognition Based on Fas...

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17th Chinese Intelligent systems Conference, CISC 2021

作者： Liu, Qun-po Wang, Qi-jing Hanajima, Naohiko Su, Bo Department of Robotics Engineering Henan Polytechnic University 2001 Century Avenue Jiaozuo Henan454003 China Henan International Joint Laboratory of Direct Drive and Control of Intelligent Equipment 2001 Century Avenue Jiaozuo Henan454003 China College of Information and Systems Muroran Institute of Technology 27-1 Mizumoto-cho Muroran-shi Hokkaido050-8585 Japan

ISBN: (纸本)9789811663239

As one of the most challenging tasks in the field of deep learning, target detection has achieved remarkable results. The existing algorithms have achieved excellent results in the detection of large and medium-sized targets, but the detection accuracy of small targets is not ideal because of their small size and weak features. A small target recognition method based on improved Faster_Rcnn that aims at the problem of low accuracy in small target detection is proposed in this paper. Since the ROI Pooling method in Faster Rcnn might cause quantization errors in the operation process, resulting in inaccurate positioning and other problems during detection, an improved ROI Align method is adopted to eliminate the quantization errors. The cell public data set is used for the experiment, the results show that the average recognition accuracy of the recognition model with improved algorithm is 92.11%, which is an increase of 7%. The method also obtains good effect on NWPU VHR-10 data set, which indicates that it has good universality. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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A Multi-Model Fusion Approach for Transformer Fault Detection

A Multi-Model Fusion Approach for Transformer Fault Detectio...

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Information Technology,Big Data and Artificial Intelligence (ICIBA), International Conference on IEEE International Conference on

作者： Ke Zhou Wei Zhang Ligang Li Shaoming Pan Chengwei Huang Guangxi Power Grid Equipment Monitoring and Diagnosis Technology Innovation Center Guangxi Key Laboratory of Intelligent Control and Maintenance of Power Equipment Electric Power Research Institute of Guangxi Power Grid CO. LTD. Nanning State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences China Power Supply Bureau of Guangxi Hezhou Power Grid Co. Ltd.

ISBN: (数字)9798350363609

ISBN: (纸本)9798350363616

Addressing the weak generalization capability and suboptimal prediction performance of single mechanistic and data-driven models, this paper proposes a transformer fault detection method based on the fusion of mechanism and data models. Firstly, vibration and current data are collected on-site to train the SVM vibration model and the Adaboost current model. Secondly, current simulation data is obtained using laboratory simulation software. This simulation data, combined with the current data collected under the same conditions on-site, is used as input to train the GBDT error compensation model. Finally, these three models are used as inputs to the GRNN, completing the overall model design. This fusion of mechanistic and data-driven models not only addresses the parameter mismatch issue of the mechanistic model under complex working conditions but also deeply mines time-series data. Experimental results show that The mean squared error of the fusion model is 0.002 lower than that of the data model, and the coefficient of determination is improved by 5.07% compared to the mechanistic model, enhancing the overall predictive accuracy of the model.

关键词： Vibrations Support vector machines Training Accuracy Biological system modeling Fault detection Predictive models Transformers Prediction algorithms Data models

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Polytope Volume Monitoring Problem: Formulation and Solution via Parametric Linear Program Based control Barrier Function

arXiv

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arXiv 2025年

作者： Wu, Shizhen Dong, Jinyang Fang, Xu Sun, Ning Fang, Yongchun Institute of Robotics and Automatic Information System College of Artificial Intelligence Nankai University Tianjin300353 China The Institute of Intelligence Technology and Robotic Systems Shenzhen Research Institute of Nankai University Shenzhen518083 China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian116024 China

Motivated by the latest research on feasible space monitoring of multiple control barrier functions (CBFs) as well as polytopic collision avoidance, this paper studies the Polytope Volume Monitoring (PVM) problem, whose goal is to design a control law for inputs of nonlinear systems to prevent the volume of some state-dependent polytope from decreasing to zero. Recent studies have explored the idea of applying Chebyshev ball method in optimization theory to solve the case study of PVM;however, the underlying difficulties caused by nonsmoothness have not been addressed. This paper continues the study on this topic, where our main contribution is to establish the relationship between nonsmooth CBF and parametric optimization theory through directional derivatives for the first time, so as to solve PVM problems more conveniently. In detail, inspired by Chebyshev ball approach, a parametric linear program (PLP) based nonsmooth barrier function candidate is established for PVM, and then, sufficient conditions for it to be a nonsmooth CBF are proposed, based on which a quadratic program (QP) based safety filter with guaranteed feasibility is proposed to address PVM problems. Finally, a numerical simulation example is given to show the efficiency of the proposed safety filter. © 2025, CC BY.

关键词： Chebyshev filters

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Low-Power Heterogeneous Networking Method Based on NB-IoT and WSN

Low-Power Heterogeneous Networking Method Based on NB-IoT an...

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International Conference on Communication Engineering and Technology (ICCET)

作者： Mo Guan Liangliang Qi Xi Jin Suoling Guan Changqing Xia Chi Xu Shenyang University of Technology Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Chinese Academy of Sciences Shenyang Institute of Automation Shenyang China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China

Digital oilfields generally adopt the multi-hop technology to expand the network scale, and the data generated depends on the large-scale wireless sensor network (WSN) for transmission. However, multi-hop communication will lead to uneven distribution of network energy consumption and premature death of nodes. To prolong the network lifetime of digital oilfields, we extended the narrowband internet of things (NB-IoT) module on some WSN nodes deployed on site to form heterogeneous nodes. Through the heterogeneity of NB-IoT and WSN, the nodes could transmit as little data as possible from other nodes, fundamentally reducing the node energy consumption. Furthermore, an optimization method was proposed that could simultaneously optimize the network lifetime and node expansion cost. The experimental results revealed that the proposed algorithm could greatly prolong the network lifetime while only introducing a small number of nodes.

关键词： Wireless sensor networks Energy consumption Costs Optimization methods Spread spectrum communication Internet of Things Narrowband

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Research on Target Detection Method Based on Improved YOLOv5 19th

Research on Target Detection Method Based on Improved YOLOv5

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19th International Conference on Web Information systems and Applications, WISA 2022

作者： Wang, Rongzhuang Liu, Jun Zhang, Hualiang School of Computer Science and Technology Shenyang University of Chemical Technology Shenyang110142 China Liaoning Key Laboratory of Intelligent Technology for Chemical Process Industry Shenyang110142 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China

ISBN: (纸本)9783031203084

At present, various target detection algorithms are used in detection and classification. It is a problem to improve the accuracy and speed of target detection by using deep learning and neural network model to train the parameters of the data set. Aiming at the problems of insufficient detection, inaccurate detection of complex background and loss of small target feature information in yolov5 target detection, an improved method combining hole convolution, moving exponential average and retraining of positive samples is proposed in this paper. This method effectively solves the problems of complex background, occlusion and incomplete feature extraction, reduces the loss rate of target features, and improves the detection accuracy of each category. The improved yolov5 target detection algorithm in this paper can not only use multi feature fusion and accelerate positive sample matching, but also use cavity convolution to increase receptive field, improve the detection effect of small targets and reduce the occurrence of missed detection and false detection. Experiments show that the improved yolov5 algorithm has better detection rate and lower loss rate than many existing target detection algorithms. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolution

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Trajectory planning of a manipulator based on the DDPG algorithm

Trajectory planning of a manipulator based on the DDPG algor...

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2022 International Workshop on Automation, control, and Communication Engineering, IWACCE 2022

作者： Jing, Xiangyu Zang, Chuanzhi Liu, Yuqi Liu, Yu Xia, Changqing Shenyang University of Technology No. 111 Shenliao West Road Economic and Technological Development Zone Shenyang110870 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China

ISBN: (纸本)9781510660915

Trajectory planning is one of the key technologies in the manipulator motion system, and the effectiveness of the manipulator as a whole is directly influenced by this technology. Thus, accurate and effective trajectory planning is crucial. In this study, a reinforcement learning system is used to plan a manipulator's trajectory. A trajectory planning of a manipulator based on the deep deterministic policy gradient (DDPG) method, which is easier to converge on than the Actor-Critic (AC) approach, is proposed in order to address the issue that the AC algorithm is difficult to converge on. A guiding reward function is provided in the reward function area to address the issue that the manipulator can't contact the target rapidly due to blind exploration. This can help the manipulator touch the target more quickly. In this paper, a twodegree of freedom manipulator is selected as the experimental object. The obstacle avoidance reward function based on the AC algorithm, the obstacle avoidance reward function based on the DDPG algorithm, and the obstacle avoidance reward function and guiding reward function based on the DDPG algorithm are compared. The findings demonstrate that the DDPG method converges easier than the AC algorithm, and that the directed reward function can help the manipulator avoid obstacles. © 2022 SPIE. All rights reserved.

关键词： Manipulators

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Fast-moving object counting with an event camera

TechRxiv

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TechRxiv 2022年

关键词： Cameras

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