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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Tang, Zongbing Yang, Dan Qu, Junsuo School of Communication and Information Engineering Xi'an University of Posts and Telecommunications China School of Automation Xi'an Key Laboratory of Advanced Control and Intelligent Process Xi'an Robertic Intelligent Systems International Science and Technology Cooperation Base Xi'an University of Posts and Telecommunications Shaanxi Xi'an China

ISBN: (纸本)9781450396899

With the development of deep learning, small object detection has a significant role in application fields such as smart factories and remote sensing images. In order to address the problem of difficult and low accuracy detection of small objects due to small pixel scale and little feature information. In this paper, we present a path aggregation network with residual characteristic RPANet on YOLOv3 algorithm, which can twice use the feature information of the backbone network to enhance the small object feature information, and also offer a positional convolution attention mechanism module PCAM to thoroughly learn and extract the small object feature information as well as reduce the unnecessary feature information in the background, so as to further enhance the detection capability of the model for small objects. The experimental results demonstrate that the improved YOLOv3 algorithm is more effective for small object detection. © 2022 ACM.

关键词： Convolution

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Path Planning for Mars Rovers Using Constraint-Based Approach on Path Length and Turning Radius

SSRN

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SSRN 2023年

作者： Dai, Juan Li, Jianfeng Zhu, Shengtao Su, Zhong Zhu, Cui Beijing Key Laboratory of High Dynamic Navigation Technology Beijing Information Science & Technology University Beijing100192 China Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing100192 China School of Automation Beijing Information Science & Technology University Beijing100192 China School of Information and Communication Engineering Beijing Information Science & Technology University Beijing100101 China

An improved dynamic window approach is proposed for local path planning of the Mars rover based on its constraint characteristics and kinematic model. The dynamic window algorithm is commonly utilized in intelligent rover obstacle avoidance and unmanned driving due to its simple principle and easy implementation. The proposed approach can effectively satisfy the dynamic obstacle avoidance requirements of the Mars rover. However, the traditional dynamic window method may suffer from local motion dead zones and longplanned paths. To resolve the issue of dynamic obstacle avoidance for the Mars rover, a length limitation vector is proposed to alleviate the problem of motion dead zone and enable dynamic obstacle recognition. Moreover, a new dynamic window space is generated by incorporating a turning radius constraint to accommodate the Mars rover’s large turning radius. The evaluation function is optimized to achieve a safe, efficient, and smooth path plan for the Mars rover. © 2023, The Authors. All rights reserved.

关键词： Motion planning

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Task Offloading and Primary Node Selection in Blockchain, Digital Twin, and MEC Enabled Internet of Vehicles

Task Offloading and Primary Node Selection in Blockchain, Di...

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IEEE Conference on Vehicular technology (VTC)

作者： Jianbo Du Huifang Fang Shulei Li Pengfei Du Ziwen Kong Jiawen Kang Dusit Niyato Shaanxi Key Laboratory of Information Communication Network and Security School of Communications and Information Engineering Xi’an University of Posts and Telecommunications Xi’an China School of Communications Engineering Xidian University Xi’an China Tianyuan Ruixin Communication Technology Co. LTD. Xi’an China Engineering Research Center of Intelligent Airground Integrated Vehicle and Traffic Control Ministry of Education Xihua University Chengdu China School of Automation Guangdong University of Technology China School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

In this paper, we investigate the safe task offloading and primary node selection in blockchain, digital twin (DT) and Multi-access Edge Computing (MEC) enabled Internet of Vehicles (IoV). Edge servers centers provide computing power for task processing for Mobile Vehicles (MVs), while blockchain can provide security guarantees for MVs during task offloading. Based on the above system, we propose a joint optimization scheme for vehicle task offloading decision and the Practical Byzantine Fault Tolerance (PBFT) consensus process. Due to the large number of optimization variables and constraints, the problem becomes more complex. Traditional convex optimization and dynamic programming methods are difficult to effectively solve this problem. To address this issue, we propose a deep reinforcement learning based algorithm that utilizes Proximal Policy Optimization (PPO). The experimental results show that the algorithm proposed in this paper outperforms the benchmark algorithm in terms of convergence and other aspects.

关键词： Fault tolerance Vehicular and wireless technologies Heuristic algorithms Simulation Fault tolerant systems Blockchains Digital twins Servers Optimization Internet of Vehicles

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A Modulation Recognition Algorithm for Analog and Digital Mixed Signals Based on Multidimensional Features 6

A Modulation Recognition Algorithm for Analog and Digital Mi...

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6th IEEE International Conference on Computer and communication Systems, ICCCS 2021

作者： Bin, Jang Wei, Wu Xilin, Wang Lei, Shen 36th Research Institute of China Electronics Technology Group Corporation Science and Technology on Communication Information Security Control Laboratory Jiaxing China School of Communication Engineering Hangzhou Dianzi University Hangzhou China

ISBN: (纸本)9780738126043

Aiming at the problem of mixed signal recognition, this paper proposes a modulation recognition algorithm based on multidimensional features of all digital receiver. Use the features of demodulated signal, such as constellation quadrant distribution, frequency tracking distribution distance and baseband data distribution distance, realized the recognition of A/D mixed signal in dense environment. Theoretical analysis and simulation results show that this algorithm has better performance than the widely used modulation recognition algorithm based on cyclic spectrum and higher-order spectrum. It has fewer requirements for prior information and synchronization, and it is easy to be implemented. © 2021 IEEE.

关键词： Modulation

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Multi-step Ahead Time Series Forecasting Based on the Improved Process Neural Networks 9th

Multi-step Ahead Time Series Forecasting Based on the Improv...

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9th International Conference on Computer engineering and Networks, CENet2019

作者： Shao, Haijian Hu, Chunlong Deng, Xing Jiang, Dengbiao School of Computer Science and Engineering Jiangsu University of Science and Technology JiangsuZhenjiang China Key Laboratory of Measurement and Control for CSE School of Automation Ministry of Education Southeast University Nanjing China

ISBN: (纸本)9789811537523

This paper proposes a multi-step ahead time series forecasting based on the improved process neural network. The intelligent algorithm particle swarm optimization (PSO) is used to overcome the potential disadvantages of the neural network, such as slow convergence speed and derivative local minima. Firstly, the theoretical analysis of the PSO is given to optimize the multilayer perceptron (MLP) neural network architecture. Secondly, the theoretical analysis and processing flow about the MLP architecture optimization is given. Thirdly, the performance criteria are applied to verify the performance of the proposed approach. Finally, the experimental evaluation based on a typically chaotic time series with rich spectrum information is utilized to demonstrate that the proposed approach has comparative results and superior on forecasting accuracy comparing to the traditional methods. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Time series analysis

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Distributed Forward Stimulated Brillouin Scattering measurement on Polarization-Maintaining fiber with 80cm Spatial Resolution

Distributed Forward Stimulated Brillouin Scattering Measurem...

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International Conference on Optical communications and Networks (ICOCN)

作者： Wenping Xie Qiwen Liang Yongchun Liang Tianfu Li Hongying Zhang Yongkang Dong Electric Power Research Institute of Guangdong Power Grid Co. Ltd. Guangzhou China Heilongjiang Provincial Key Laboratory of Quantum Control School of Measurement and Communication Engineering Harbin University of Science and Technology Harbin China National Key Laboratory of Science and Technology on Tunable Laser Harbin Institute of Technology Harbin China

This article presents a novel distributed forward stimulated Brillouin scattering measurement scheme on polarization maintaining fiber (PM fiber) with a spatial resolution of 0.8 m named polarization separation assisted opto-mechanical time-domain analysis (PS-OMTDA). The activation pulse is injected into the fast axis and the probe pulse and probe wave is injected into the slow one, therefore, the acoustic wave is excited upon the fast axis but affected the probe wave on the slow axis. The backward Brillouin scattering of an activation pulse is effectively suppressed by isolating the acoustic activation and probing process. A spatial resolution of 80 cm is experimentally demonstrated over a PM fiber with a length of 34 m.

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Gated Fusion and Dual Attention Network for Road Extraction from Satellite Imagery

Gated Fusion and Dual Attention Network for Road Extraction ...

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International Conference on Image, Vision and Computing (ICIVC)

作者： Haijuan Wang Lin Bai Bo Ji Jiaming Shen Ying Jin Fei Ye Chunlin Zhu school of electronics and control engineering Chang'an University Xi'an China Zhejiang Aerospace Runbo Measurement and Control Technology Co. Ltd. Hangzhou China Xi'an Aeronautics Computing Technique Research Institute Xi'an China

Automatic recognition and extraction of roads from high-resolution satellite images is a crucial task in remote sensing and computer vision. With the continuous development of remote sensing technology, more ground object information is contained in images, making it more challenging to extract roads due to increased interference. This paper proposes a gated fusion and dual attention network with an encoder-decoder structure to address this problem. The full-stage gated fusion module in skip connection selectively fuses feature maps of different scales using the Gated Fusion (GFF) unit, which increases the receptive field of the network and improves the accuracy of road extraction. The context extraction and dual attention module introduce rich global information and simultaneously weights features from both spatial and channel dimensions. This improves the semantic segmentation problem caused by focusing only on local information in current road extraction models. Experimental results on two public datasets show that GFDANet can effectively extract roads in complex scenes.

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A Two-Stage Robust Scheduling Strategy for Multi-Energy Microgrid Considering Multiple Uncertainties

A Two-Stage Robust Scheduling Strategy for Multi-Energy Micr...

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Power Energy Systems and Applications (ICoPESA), International Conference on

作者： Sen Zhang Weihao Hu Xilin Cao Yincheng Zhao Rundong Liu Haojie Ma Wei Zhan Mingxuan Li Sichuan Provincial Key Lab of Power System Wide Area Measurement and Control School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China School of Management and Economics Beijing Institute of Technology Beijing China SPIC Southwest Energy Research Institute Chengdu China

ISBN: (数字)9798350351668

ISBN: (纸本)9798350351675

To achieve the economic operation of a multi-energy micro grid under multiple uncertain environments, this study introduces a novel two-stage robust scheduling approach, encompassing both day-ahead planning and real-time adjustments. First, the multi-energy micro grid day-ahead economic dispatch model is constructed based on the prediction scenarios, and integrated demand response is considered to increase the flexibility of the system. Afterwards, the strategy is adjusted in the real-time phase, considering the uncertainty of energy production and consumption. The Column-and-Constraint Generation algorithm is employed to resolve the above two-stage issues effectively, and the optimal economic operation strategy of the MEMG is obtained. Finally, a case study demonstrates the economy and robustness of the proposed methodology.

关键词： Uncertainty Costs Energy resolution Production Microgrids Predictive models Prediction algorithms Real-time systems Robustness Planning

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An Adaptive Power Allocation Scheme for Mini-UAV Tracking with Distributed Radar in Dense Urban Environment 10

An Adaptive Power Allocation Scheme for Mini-UAV Tracking wi...

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10th International Conference on control, Automation and Information Sciences, ICCAIS 2021

作者： Cao, Ziyi Yuan, Ye Yi, Wei Chen, Linxiu University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9781665440295

In this paper, a new power allocation scheme of the distributed radar system is proposed for mini-UAV tracking tasks in urban environments, considering the influence of the building occlusion on the probability of detection. Firstly, the probability of detection of each radar node is derived according to the estimated position of the target and the visibility of each radar node. Then, according to the probability of detection, the adaptive cost function concerning the transmit power and tracking accuracy is designed and used to the formulation of the optimization problem. Simulation results show that the proposed power allocation scheme can improve the target tracking accuracy in urban environments compared to the conventional method. © 2021 IEEE.

关键词： Cost functions

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Mental arithmetic task load recognition using EEG signal and Bayesian optimized K-nearest neighbor

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International Journal of Information technology (Singapore) 2021年第6期13卷 2363-2369页

作者： Sharma, Lakhan Dev Chhabra, Himanshu Chauhan, Urvashi Saraswat, Ritesh Kumar Sunkaria, Ramesh Kumar School of Electronics Engineering VIT-AP University Amaravati India Electronics and Communication Engineering M.L.V. Textile and Engineering College Bhilwara India Instrumentation and Control Engineering Division Netaji Subhas University of Technology Delhi India Electronics and Communication Engineering Dr. B. R. Ambedkar National Institute of Technology Jalandhar India

Cognitive load recognition during mental arithmetic activity facilitates to observe and identify the brain’s response towards stress stimulus. As a result, an efficient mental load characterization approach using electroencephalogram (EEG) signal and Bayesian optimized K-Nearest Neighbor (BO-KNN) has been proposed in this work. The study has been conducted on a recorded EEG dataset of 30 healthy subjects who were exposed to an arithmetic questioner. To obtain artifacts free EEG signal, the Savitzky–Golay filtering approach has been utilized. Further, the decomposition of the extracted EEG signal has been carried out using stationary wavelength transform. In this work, the entropy based feature extraction has been performed followed by F-score based feature selection. Top 40 features having the highest precedence have been used for classification using BO-KNN. The rigorous experimental analysis has been performed to analyze the effectiveness of the proposed method over other state-of-the-art methods and it shows that the classification accuracy is substantially improved. © 2021, Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： Bayesian optimized K-nearest neighbor (BO-KNN) Cognitive task EEG Stationary wavelet transformation

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