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A Time-Efficient Path Navigating Landmine Detection Robot

A Time-Efficient Path Navigating Landmine Detection Robot

2024 IEEE International Conference on Intelligent Signal Processing and Effective communication Technologies, INSPECT 2024

作者： Bhattacharjee, Rituraj Mukherjee, Subhradip Hardik Gururaj Rajpurohit, Manish Manipal Institute of Technology Manipal Academy of Higher Education Instrumentation & Control Engineering Department Manipal India MVJ College of Engineering Electronics and communication Department Bangalore India

ISBN: (纸本)9798350379525

The goal of a landmine detection robot is to map the remaining area on a visual map with millimeter precision after covering as much ground as it can show landmines. The prototype land mine detection robot model described in this study offers a visual interface for mapping landmines, modifying PIDs, and aligning cameras. It is also reasonably powerful, accurate, and easy to operate with other sensors. The manual, semi-auto, and auto modes of controlling the differential drive robot are emphasized. Image processing determines the exact location of the robot and provides live reckoning feedback to its dead reckoning servo control. A beat metal detector is a kind of quiet sensor used to locate landmines. The overall objective of the system is to provide the user with something robust, affordable, and easily comprehensible for the user. © 2024 IEEE.

关键词： Landmine detection

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Recent technology Advancements in Smart City Management:A Review

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Computers, Materials & Continua 2024年第12期81卷 3617-3663页

作者： Chiranjeevi Karri JoséJ.M.Machado Joao Manuel R.S.Tavares Deepak Kumar Jain Suresh Dannana Santosh Kumar Gottapu Amir H.Gandomi Department of Mechanical Engineering Faculty of EngineeringUniversity of PortoPorto4200-465Portugal Department of Electronics and Communication Engineering Andhra University College of EngineeringAndhra UniversityVisakhaptnam530003Andhra PradeshIndia Laboratory of lntelligent Control and Optimization for Industrial Equipment of Ministry of Education School of Artificial IntelligenceDalian University of TechnologyDalian116024China Department of Electronics and Communication Engineering GMR Institute of TechnologyRajamVizianagaram532127Andhra PradeshIndia Department of Civil Engineering Gayatri Vidya Parishad College of Engineering(GVPCE)Visakhapatnam530048Andhra PradeshIndia Faculty of Engineering&Information Technology University of Technology SydneyUltimoNSW 2007Australia University Research and Innovation Center(EKIK) óbuda UniversityBudapest1034Hungary

The rapid population growth,insecure lifestyle,wastage of natural resources,indiscipline behavior of human beings,urgency in the medical field,security of patient information,agricultural-related problems,and automation requirements in industries are the reasons for invention of *** cities aim to address these challenges through the integration of technology,data,and innovative *** a smart city involves integrating advanced technologies and data-driven solutions to enhance urban living,improve resource efficiency,and create sustainable *** review presents five of the most critical technologies for smart and/or safe cities,addressing pertinent topics such as intelligent traffic management systems,information and communications technology,blockchain technology,re-identification,and the Internet of *** challenges,observations,and remarks of each technology in solving problems are discussed,and the dependency effects on the technologies’performance are also *** deep learning models for various applications are *** models performance,their dependency on dataset size,type,hyper-parameters,and the non-availability of labels or ground truth are discussed.

关键词： Smart cities intelligent traffic management systems internet of things deep learning blockchain technology

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Multi-scale network for single image deblurring based on ensemble learning module

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Multimedia Tools and Applications 2025年第11期84卷 9045-9064页

作者： Wu, WenBo Pan, Yun Su, Na Wang, JingTao Wu, ShaoChuan Xu, ZeKun Yu, YouJian Liu, YaPeng State Key Laboratory of Media Convergence and Communication Communication University of China Beijing100024 China School of Computer and Cyberspace Security Communication University of China Beijing100024 China School of Electronics and Information Engineering Harbin Institute of Technology Harbin150080 China College of Computer and Control Engineering Northeast Forestry University Harbin150040 China School of Computer and Information Engineering Tianjin Chengjian University Tianjin300384 China School of Computer Science and Technology Anhui University Hefei230601 China

In this paper, we have identified two primary issues with current multi-scale image deblurring methods. On the one hand, the blurring scale is ignored. On the other hand, the context information of images is not fully utilized, and the spatial detail information of images is lost. To better solve the above problems, on top of that, this paper proposes a multi-scale network for single image deblurring. The designed network contains two parts: 1) Ensemble Learning Model: The Weak Learning Model in this module connects multiple weak learners in series to increase network depth. Moreover, through multi-scale feature ensemble module, the output features and intermediate features of different weak learner are integrated, and finally strong learner is used to enhance the features with semantic information at different scales;2) Spatial Feature Enhancement Module: This module can preserve spatial detail information of images and compensate for the disadvantages of losing image spatial details due to continuous downsampling in the Ensemble Learning Model structure. Our proposed deblurring model has been extensively evaluated on several benchmark datasets, and the model achieves superior performance compared to the most advanced deblurring methods. In particular, the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) values are 33.57 dB and 0.965 on the GoPro dataset, respectively, with PSNR and SSIM improving by 0.49 dB and 0.002 over NAFNet, and 0.65 dB and 0.004 over Restormer. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Learning systems

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Interval Consensus of Uncertain Positive Multiagent Systems with External Disturbances and measurement Noises 41

Interval Consensus of Uncertain Positive Multiagent Systems ...

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第41届中国控制会议

作者： Ying Chu Han Wu Li Chai Engineering Research Center of Metallurgical Automation and Measurement Technology Wuhan University of Science and Technology College of Control Science and Engineering Zhejiang University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In this paper,an interval distributed consensus algorithm is proposed for uncertain positive multiagent systems with external disturbances and measurement *** system states are *** the upper bound and lower bound of the external disturbances and measurement noises,distributed interval observers are designed to estimate the bounds of the unmeasurable *** utilizing the estimated states,a distributed algorithm is proposed to make the positive multiagent systems achieve interval *** conditions for interval consensus are derived for both undirected topologies and directed *** feedback matrices are obtained by solving linear programming *** studies are given to illustrate the effectiveness of the proposed algorithm.

关键词： Positive multiagent system interval observer state estimation external disturbance measurement noise

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Super-resolution reconstruction of remote sensing images based on dimension permutation and asymmetric feature fusion

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Signal, Image and Video Processing 2025年第9期19卷 1-9页

作者： Liu, Jie Wu, You Tian, Ming School of Measurement and Control Technology and Communication Engineering Harbin University of Science and Technology Harbin China China Telecom Heilongjiang Branch Harbin China

Remote sensing images have wide application prospects in transportation, agriculture and environmental monitoring. However, different regions of remote sensing images have the characteristics of different size, which tend to be affected by noise, leading to artifacts, thus, accurately recovering terrain texture details is challenging. This paper proposes a super-resolution reconstruction method of remote sensing images, SwinDSR, based on dimension permutation and asymmetric feature fusion. A parallel architecture combining self-attention mechanism and depthwise separable convolution is employed to achieve global and local feature fusion. To capture broader contextual information, the single-scale convolution in the reconstruction network is improved to multi-scale asymmetric convolutions, enhancing the network’s adaptability to features at different scales. Additionally, a random shuffle module is introduced, along with Sinc filters to truncate high-frequency components in the image, simulating ringing and overshoot artifacts. This constructs a multi-factor degradation network that effectively suppresses artifact generation during remote sensing image reconstruction. In addition to the reconstruction of high-quality remote sensing images, it is potential to achieve the lightweight of SwinDSR model.

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Evolution analysis of two-layer time-varying trust Hegselmann-Krause models with opinion leaders 41

Evolution analysis of two-layer time-varying trust Hegselman...

引用

第41届中国控制会议

作者： Yajing Wang Jingwen Yi Li Chai Engineering Research Center of Metallurgical Automation and Measurement Technology Wuhan University of Science and Technology College of Control Science and Engineering Zhejiang University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

As a typical state-dependent model which considers psychological factors,bounded confidence model is becoming a popular tool in opinion evolution *** this paper,a novel two-layer time-varying trust Hegselmann-Krause model(twolayer THK model) is proposed by describing the "Similarity Effect" in *** two-layer THK model consists of leaderlayer and *** in leader-layer are agents with reputation and more power to influence *** in follower-layer are agents with sensitivity,which can change their trust to others according to opinion *** analyze the convergence of the proposed model and simulate phenomena of opinion polarization in *** reveal the following facts:Reputation of leaders does not have obvious effects on opinion *** sensitivity and proper confidence radius are promotive to opinion polarization.

关键词： Hegselmann-Krause model time-varying trust opinion leader opinion polarization

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Wrapper-based feature selection for general data set: the quantum sand cat swarm optimization

Wrapper-based feature selection for general data set: the qu...

引用

Computer, Vision and Intelligent technology (ICCVIT), International Conference on

作者： Jin Yue Ying Guo Hongyuan Gao Dalian Institute of Measurement and Control Technology Dalian China College of Information and Communication Engineering Harbin Engineering University Harbin China

ISBN: (数字)9798331540043

ISBN: (纸本)9798331540050

The existing wrapper feature selection methods have problems such as the difficulty of balancing a relatively high classification accuracy rate with a low feature selection rate, and there are problems such as long time consumption. In response to these issues, this paper combines quantum computing theory and sand cat swarm optimization to design quantum sand cat swarm optimization and combined with K-nearest neighbor method for feature selection. The optimal solution of quantum sand cat swarm optimization is set as the selected optimal feature subset. Simulation experiments are executed on 6 UCI data sets to compare with 4 classical swarm intelligence algorithms. The simulation outcomes show that the wrapper feature selection on the ground of quantum sand cat swarm optimization achieves lower feature selection rate , faster time and higher classification accuracy than the comparison method. It is a high precision and practical wrapper feature selection method.

关键词： Quantum computing Accuracy High dimensional data Quantum mechanics Nearest neighbor methods Feature extraction Robustness Classification algorithms Particle swarm optimization Optimization

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Fast Consensus of Discrete Multi-Agent Systems with Delay

Fast Consensus of Discrete Multi-Agent Systems with Delay

引用

2023 China Automation Congress, CAC 2023

作者： Huang, Mengya Yi, Jing-Wen Chai, Li Wuhan University of Science and Technology Engineering Research Center of Metallurgical Automation and Measurement Technology Wuhan430081 China College of Control Science and Engineering State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310027 China

ISBN: (纸本)9798350303759

This paper studied the fast consensus of discrete multi-agent systems (MASs) with time delay. A consensus protocol incorporating memory information is formulated to accelerate consensus. The necessary and sufficient conditions to reach average consensus are provided. Furthermore, an optimal design of the fast consensus for MASs with small delay is given, and the explicit solutions of the fastest convergence speed and corresponding optimal control gains are deduced. Finally, the theoretical findings are validated through numerical simulations, underscoring the effectiveness of the nronosed annroach. © 2023 IEEE.

关键词： Multi agent systems

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Satellite Power System Anomaly Detection based on Spatio-Temporal Uncertainty Fusion

Satellite Power System Anomaly Detection based on Spatio-Tem...

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2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2023

作者： Wang, Zhipeng Gao, Yanjiao Pang, Jingyue Song, Yuchen Peng, Yu Harbin Institute of Technology School of Electronics and Information Engineering Harbin China Harbin University of Science and Technology School of Measurement and Control Technology and Communication Engineering Harbin China Chongqing Technology and Business University School of Artificial Intelligence Chongqing China

ISBN: (纸本)9798350337754

Satellites have been widely used for military and civil applications. The power system is critical for the satellite's operating safety. However, satellite power system anomaly monitoring is faced with challenges like large amounts of data, insufficient anomaly samples, and complex operating conditions. To address these challenging issues, this paper proposes a data-driven anomaly detection method that fuses the time-related historical data and spatial-associated sub-system monitor data. Firstly, the probabilistic prediction model is used to establish the prediction model based on time and space The corresponding prediction uncertainties of the temporal and spatial models are quantitatively represented. Then, the stochastic distribution model is introduced to fuse the two uncertainties to obtain the predicted mean value and fusion uncertainty of the current power system state. The mean value is taken as the expected result of the state, and the fused uncertainty interval is taken as the threshold of abnormal judgment to realize the detection of the abnormal condition. The telemetry data of the actual satellite power system are used for experimental and comparative analysis. The results show that the false negative rate of anomaly detection results can be effectively reduced, and the detection accuracy can be improved through the fusion of spatio-temporal uncertainty. © 2023 IEEE.

关键词： Anomaly detection

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A Repetitive Motion Obstacle Avoidance Planning Scheme for Omnidirectional Mobile Manipulators with Noise Suppression Capability

A Repetitive Motion Obstacle Avoidance Planning Scheme for O...

引用

2023 China Automation Congress, CAC 2023

作者： Shi-Jun, Tang Yun-Feng, Hu Zhong-Bo, Sun Chong, Zhang Yu-Zhe, Fei Xing-Tian, Xiao Jilin University College of Communication Engineering Jilin University State Key Laboratory of Automotive Simulation and Control Changchun China Changchun University of Technology Intelligent Rehabilitation Engineering Technology Laboratory Department of Control Engineering China

ISBN: (纸本)9798350303759

Obstacle avoidance is essential for an omnidirectional mobile robotic arm (OMRA) to accomplish a given task in a complex environment. To make OMRA accomplish repetitive motion, obstacle avoidance, and trajectory tracking tasks in complex dynamic environments with the presence of obstacles and external disturbances, a new noise-suppressed repetitive motion obstacle avoidance (NSRMOA) scheme is proposed. As established previously many OMRA motion planning assumes that the simulation is noise free. However, noise interference may prevent OMRA from accomplishing repetitive motion, obstacle avoidance, and trajectory tracking tasks, which may lead to task failure. The proposed the NSRMOA scheme with intrinsic noise suppression can solve external noise interference, so that the OMRA can accomplish the repetitive motion, obstacle avoidance and trajectory tracking tasks in the presence of external noise interference. Finally, the optimal solution of the NSRMOA scheme can be effectively obtained by using recurrent neural network (RNN). The effectiveness and accuracy of the scheme are verified by examples and comparisons. © 2023 IEEE.

关键词： Robotic arms

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