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Multi-branch low-light enhancement algorithm based on spatial transformation

Multimedia Tools and Applications 2025年第18期84卷 19647-19667页

作者： Wang, Wenlu Sun, Ying Zou, Chunlong Tang, Dalai Fang, Zifan Tao, Bo Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Wuhan430081 China School of Computer Information Management Inner Mongolia University of Finance and Economics Hohhot010051 China College of Mechanical Engineering Hubei University of Automotive Technology Shiyan442000 China Research Center for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan430081 China Precision Manufacturing Research Institute Wuhan University of Science and Technology Wuhan430081 China Hubei Key Laboratory of Hydroelectric Design & Maintenance China Three Gorges University Yichang443002 China

Many state-of-the-art low-light image enhancement techniques now suffer from issues like color distortion, detail blurring, and the halo effect, hindering their ability to produce visual effects. This paper presents a multi-branch low-light enhancement algorithm based on spatial transformation to significantly improve the visual impact of images under low-light settings, overcoming the limitations of existing approaches. The process involves dividing the initial low-light picture into three distinct layers. Bootstrap filtering and Contrast Limited Adaptive Histogram Equalization are used to process the main feature layer. Primary feature extraction is used to extract features for the compensation layer, which is then integrated with Contrast Limited Adaptive Histogram Equalization and MSRCR. The auxiliary layer utilizes spatial transformation to apply gamma correction to the V-channel and performs color space transformation and additional processing. Ultimately, these three layers are combined and refined to create the final improved image. Based on the experimental results, the proposed method outperforms the standard MSRCR algorithm by 52.47%, 71.58%, and 12.10%, respectively, on the assessment metrics AG, SD, and IE. LOE declines by 22.3%. The processed image presents a good visual quality, effectively enhancing the image brightness while retaining a large amount of information and improving the image details. This paper proposes an effective method to deal with the problem of low illumination image enhancement, which provides a strong reference for future research and application in related fields. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image enhancement

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A Two-Stage Robust Scheduling Strategy for Multi-Energy Microgrid Considering Multiple Uncertainties 8

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

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8th International Conference on Power Energy Systems and Applications, ICoPESA 2024

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

ISBN: (纸本)9798350351668

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. © 2024 IEEE.

关键词： Demand response

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Radio Signal Modulation Pattern Recognition Based on Time-Frequency Adaptive Decomposition and Hybrid Neural Network

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IEEE Internet of Things Journal 2025年

作者： Dai, Shaogang Xu, Jianwei Zhou, Huaji Hangzhou Dianzi University School of communication engineering Hangzhou310018 China Science and Technology on Communication Information Security Control Laboratory Jiaxing314000 China

Automatic modulation classification of electromagnetic signals has an important role in the field of signal processing. The current methods for identifying the modulation pattern of radio signals mainly used end-to-end neural network models, which suffered from the problems such as network redundancy and non-interpretability. In order to solve the above problems, this paper proposes a radio signal modulation pattern recognition method based on time-frequency adaptive decomposition and hybrid neural network. The discrete wavelet decomposition is used to decompose the original radio signals into adaptive high and low frequencies. Then feed the resulting high and low frequency portions into a multi-channel hybrid neural network (MHNN) for training to obtain the classification results. In the discrete wavelet decomposition process, the number of decomposition layers and the decomposition threshold are determined adaptively according to the difference of SNRs. In the design of multi-path hybrid neural network structure, the signal characteristics of high and low frequency parts are considered comprehensively. The network channels with different convolutional kernel scales and different layers are constructed to reduce the number of model parameters and training time while ensuring the classification accuracy. Using RadioML2016.10a dataset for experiments, the recognition accuracy of MHNN is prior to the end-to-end deep learning methods, which can achieve the accuracy of 92.30% and the F1 of 91.84% when SNR=2dB. It shows that the proposed radio signal modulation pattern recognition method based on the time-frequency adaptive decomposition and hybrid neural network can balance the accuracy and lightweight. Moreover, it has certain interpretability for the original input data, distinguishing the main body and details of the signal by dividing the signal into high and low frequencies, which provides a new idea for the interpretability of deep convolutional neural networks. ©

关键词： Signal modulation

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Interpretable DRL-Based Maneuver Decision of UCAV Dogfight

Interpretable DRL-Based Maneuver Decision of UCAV Dogfight

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Han, Haoran Cheng, Jian Lv, Maolong School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China Air Traffic Control and Navigation College Air Force Engineering University Xi'an710051 China College of Aeronautics Engineering Air Force Engineering University Xi'an710038 China

ISBN: (纸本)9781665410205

This paper proposes a three-layer unmanned combat aerial vehicle (UCAV) dogfight frame where Deep reinforcement learning (DRL) is responsible for high-level maneuver decision. A four-channel low-level control law is firstly constructed, followed by a library containing eight basic flight maneuvers (BFMs). Double deep Q network (DDQN) is applied for BFM selection in UCAV dogfight, where the opponent strategy during the training process is constructed with DT. Our simulation result shows that, the agent can achieve a win rate of 85.75% against the DT strategy, and positive results when facing various unseen opponents. Based on the proposed frame, interpretability of the DRL-based dogfight is significantly improved. The agent performs yo-yo to adjust its turn rate and gain higher maneuverability. Emergence of 'Dive and Chase' behavior also indicates the agent can generate a novel tactic that utilizes the drawback of its opponent. © 2024 IEEE.

关键词： Deep reinforcement learning

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User Cooperative Receiving Scheme of Misaligned OAM Signals for UAV Emergency communications 14

User Cooperative Receiving Scheme of Misaligned OAM Signals ...

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14th IEEE International Conference on Electronics Information and Emergency communication, ICEIEC 2024

作者： Zhou, Yanguo Chen, Ruirui Xu, Keyue Feng, Rui Ding, Jicun Lin, Jinyang China Academy of Electronics and Information Technology National Engineering Laboratory for Risk Perception and Prevention Beijing China China University of Mining and Technology School of Information and Control Engineering Xuzhou China North University of China State Key Laboratory of Dynamic Measurement Technology Taiyuan China Xuzhou First Peoples Hospital Xuzhou China

ISBN: (纸本)9798350361889

For the massive users in post-disaster scenario, unmanned aerial vehicle (UAV) can rapidly establish emergency communications, and orbital angular momentum (OAM) can significantly improve spectrum efficiency (SE). Due to hollow divergence and alignment requirement, it is hard for users to receive OAM signals by using antenna-limited devices. For UAV emergency communications with line-of-sight link, we propose the user cooperative receiving (UCR) scheme, which can realize 2 OAM mode multiplexing communications for users at long distance. Two cooperative users (CUs) are chosen, and the waist radius is adjusted to guarantee the maximum intensity at two CUs. The OAM beam steering is designed to eliminate the inter-mode interference of misaligned OAM signals caused by the antenna misalignment. Simulation results demonstrate that the proposed UCR scheme can obtain higher SE than the traditional scheme due to the effective reception of misaligned OAM signals for UAV emergency communications. © 2024 IEEE.

关键词： Antennas

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Acceleration of Formation control Based on Hessian Matrix of Artificial Potential Field 42

Acceleration of Formation Control Based on Hessian Matrix of...

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42nd Chinese control Conference, CCC 2023

作者： Han, Haoran Xi, Zhilong Lv, Maolong Cheng, Jian School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China Air Traffic Control and Navigation College Air Force Engineering University Xian710051 China

ISBN: (纸本)9789887581543

The formation control of multi-agent system has become a hotspot in recent years. One of the most commonly used methods is to use artificial potential field (APF) to generate the attractive and repulsive force to organize the agents into a low-energy and stable formation. In this paper, we regard the APF as the difference in the desired position, and introduce the derivative of the gradient of the APF with respect to time, which is simplified as the Hessian matrix of the APF, into the formation control from the perspective of trajectory tracking. The energy-based Lyapunov function is selected to prove the stability and collision avoidance ability of the proposed control law. Moreover, we show that the maneuverability of the multi-agent system is enormously improved with the additional Hessian matrix term. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Multi agent systems

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Multi-Pedestrian Tracking by Decoupling Confidence Weighting and Hierarchical Appearance Embedding Updates

Multi-Pedestrian Tracking by Decoupling Confidence Weighting...

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communication technology and Information technology (ICCTIT), International Conference on

作者： Guopei Zeng Luping Wang School of Electronics and Communication Engineering Sun Yat-sen University Shenzhen China Zhejiang Aerospace Runbo Measurement and Control Technology Co. Ltd Zhejiang China

ISBN: (数字)9798331528973

ISBN: (纸本)9798331528980

The advent of powerful object detectors has led to the widespread adoption of the “tracking-by-detection” paradigm in multi-object tracking (MOT). However, challenges such as association errors during occlusions continue to hinder tracking performance. To address this issue, we propose three key improvements. First, we leverage the confidence scores generated by a detector with decoupled heads to weight the cost matrix. Second, we introduce a height-constrained intersection-over-union (IoU) distance metric. Finally, we classify the appearance features of objects in the current frame into two levels—high and low—based on their respective confidence scores. These features are then matched and updated against stored appearance embeddings from previous frames. Extensive comparative and ablation experiments conducted on the MOT17 dataset demonstrate that the proposed method significantly improves tracking performance in multi-object tracking scenarios.

关键词： measurement Costs Detectors communications technology Information technology

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A Review of Spiking Neural Network Research in the Field of Bearing Fault Diagnosis 1

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TEPEN International Workshop on Fault Diagnostics and Prognostics, TEPEN-IWFDP 2024

作者： Wang, Yusen Wang, Hongjun Xie, Long Ge, Henglin Zhou, Mingyang Chen, Tao Shi, Yuxing School of Mechanical and Electrical Engineering Beijing Information Science & Technology University Beijing100192 China Key Laboratory of Modern Measurement and Control Technology Ministry of Education BISTU Beijing100192 China Beijing International Science Cooperation Base of High-End Equipment Intelligent Perception and Control BISTU Beijing100192 China

ISBN: (数字)9783031694837

ISBN: (纸本)9783031694820

In view of the bearing fault diagnosis problem, in order to better deal with the temporal data, extract nonlinear features, enhance the model robustness and adaptability, and realize efficient distributed computing, the spiking neural network is introduced to improve the accuracy and efficiency in the process of bearing fault diagnosis. In recent years, after a series of studies, the spiking neural network has developed in supervised learning, unsupervised learning, semi-supervised learning, and some other derived methods, such as reinforcement learning and transfer learning, for its applicable scenarios with different advantages. From the perspective of learning principle, method advantages and practical applications, we introduced several spiking neural network for bearing fault diagnosis, discussed the advantages, disadvantages and applicable scenarios of each learning method, discussed the application prospect of spiking neural network for bearing fault diagnosis, and proposed the spiking neural network for bearing fault diagnosis in future research, so as to provide reference for subsequent research. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Self-supervised learning

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Throughput-oriented associated transaction assignment in sharding blockchains for IoT social data storage

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Digital communications and Networks 2022年第6期8卷 885-899页

作者： Liping Tao Yang Lu Xu Ding Yuqi Fan Jung Yoon Kim School of Computer Science and Information Engineering Hefei University of TechnologyHefei230009China Engineering Research Center of Safety Critical Industrial Measurement and Control Technology Ministry of EducationHefei University of TechnologyHefei230009China Institute of Industry&Equipment Technology Hefei University of TechnologyHefei230009China Key Laboratory of Knowledge Engineering with Big Data(Hefei University of Technology) Ministry of EducationHefei230009China Graduate School of Game Gachon UniversitySeongnam-Si461-701South Korea

Blockchain is a viable solution to provide data integrity for the enormous volume of 5G IoT social data, while we need to break through the throughput bottleneck of blockchain. Sharding is a promising technology to solve the problem of low throughput in blockchains. However, cross-shard communication hinders the effective improvement of blockchain throughput. Therefore, it is critical to reasonably allocate transactions to different shards to improve blockchain throughput. Existing research on blockchain sharding mainly focuses on shards formation, configuration, and consensus, while ignoring the negative impact of cross-shard communication on blockchain throughput. Aiming to maximize the throughput of transaction processing, we study how to allocate blockchain transactions to shards in this paper. We propose an Associated Transaction assignment algorithm based on Closest Fit (ATCF). ATCF classifies associated transactions into transaction groups which are then assigned to different shards in the non-ascending order of transaction group sizes periodically. Within each epoch, ATCF tries to select a shard that can handle all the transactions for each transaction group. If there are multiple such shards, ATCF selects the shard with the remaining processing capacity closest to the number of transactions in the transaction group. When no such shard exists, ATCF chooses the shard with the largest remaining processing capacity for the transaction group. The transaction groups that cannot be completely processed within the current epoch will be allocated in the subsequent epochs. We prove that ATCF is a 2-approximation algorithm for the associated transaction assignment problem. Simulation results show that ATCF can effectively improve the blockchain throughput and reduce the number of cross-shard transactions.

关键词： IoT social data Blockchain Shar ding Associated transactions Transaction assi gnment

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Hyperspectral Image Cross Scene Classification Combined Domain Adaptation with XGBoost

Hyperspectral Image Cross Scene Classification Combined Doma...

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2024 International Conference on measurement, communication, and Virtual Reality, MCVR 2024

作者： Liu, Chengyang Wu, Haibin Li, Jiarong Liu, Xinyu Yan, Siqi Wang, Aili Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application College of Measurement and Control Technology and Communication Engineering Harbin University of Science and Technology Harbin150080 China School of Electronics and Information Engineering Harbin Institute of Technology Harbin150001 China

ISBN: (纸本)9781510691773

To address the issues of domain discrepancies and limited labeled samples in hyperspectral image classification of cross scene, this paper proposes an ensemble learning framework that integrates eXtreme Gradient Boosting (XGBoost) with confidence domain adaptation strategies to improve classification performance. First, the Houston dataset from different scenes is input separately into a deep embedding model. The hyperspectral images are embedded into a low-dimensional space, ensuring that similar spectra are represented more closely in this space, thereby capturing the intrinsic structure and distribution of the hyperspectral image dataset. Subsequently, these samples are mapped into a discriminator model, where domain adaptation techniques are employed to reduce differences in feature distributions across different scenes. Additionally, confidence-based learning is used to identify and remove noisy samples in the Houston dataset, thereby enhancing the alignment of feature distributions among various scenes. Finally, through ensemble learning, a weighted voting strategy is employed to combine multiple base classifiers into a strong classification model. This approach better captures the complex features within the data, thereby reducing the bias of individual classifiers and enhancing the overall generalization ability and robustness of the model. Experimental results on the Houston dataset demonstrate that the proposed method outperforms popular domain adaptation learning models and traditional methods in terms of classification accuracy and robustness, showcasing its potential and advantages in practical applications. This result confirms the effectiveness of combining XGBoost and confidence domain adaptation strategies in handling hyperspectral image classification. © 2025 SPIE.

关键词： Photointerpretation

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