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Degradation-Decoupling Vision Enhancement for Intelligent Underwater Robot Vision Perception system

IEEE Internet of Things Journal 2025年第11期12卷 17880-17895页

作者： Zhou, Jingchun Liu, Chunjiang Long, Bing Zhang, Dehuan Jiang, Qiuping Muhammad, Ghulam Dalian Maritime University College of Information Science and Technology Dalian116026 China Sichuan Police College Intelligent Policing Key Laboratory of Sichuan Province Sichuan Luzhou64600 China Ningbo University School of Information Science and Engineering Zhejiang Ningbo315211 China King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh Saudi Arabia

Underwater robots rely on high-quality visual data for precise monitoring and manipulation, yet complex underwater environments often degrade image quality through color distortion, texture blurring, and detail loss. Existing enhancement methods partially address these issues, but fail to effectively decouple nonlinear relationships among degradation factors, leading to inconsistent performance. To address these challenges, we propose a degradation-content decoupling-based underwater image enhancement network (DCDN). The framework integrates a super-fusion cascade module for dynamic feature weighting, reducing artifacts, and combines multi-channel color space transformation with texture-guided correction to decouple and optimize degradation factors. This approach improves color fidelity and texture detail restoration by refining color information and adapting local textures. Experiments on public datasets demonstrate that DCDN outperforms existing methods in various underwater scenarios. This work enhances the visual capabilities of underwater robots, supporting intelligent transportation applications such as marine logistics and underwater inspections. © 2014 IEEE.

关键词： Robot vision

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Joint Content Caching and Request Routing for User-Centric Many-Objective Metaverse Services

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IEEE Transactions on Network science and engineering 2025年第3期12卷 1911-1925页

作者： Hu, Zhaoming Fang, Chao Wang, Zhuwei Chen, Jining Tseng, Shu-Ming Dong, Mianxiong Beijing University of Technology College of Computer Science Beijing100021 China Beijing University of Technology School of Information Science and Technology Beijing100021 China Guangxi Information Center Guangxi Key Laboratory of Digital Infrastructure Nanning530000 China National Taipei University of Technology Department of Electronic Engineering Taipei106 Taiwan Muroran Institute of Technology Department of Sciences and Informatics Muroran050-0071 Japan

Metaverse, as a revolutionary technology that changes the way of human interaction, brings new challenges to content delivery services due to the extensive data transmission and personalized service requirements. To ensure a personalized user experiences while improving the utilization of heterogeneous network resources, a user-centric many-objective metaverse content delivery framework is proposed to optimize content delivery through user attention awareness. This framework addresses two key subproblems in metaverse content delivery by investigating user-centric many-objective cooperative content caching and deep reinforcement learning (DRL)-based request routing. The user-centric many-objective cooperative content caching is proposed to dynamically combine three basic preference prediction results to predict user preferences and control network resource allocation, which can simultaneously optimize prediction precision, delay, offloaded traffic, and load balancing. In DRL-based request routing, the reward function is designed to enable the optimization of multiple objectives. The multi-objective DRL routing algorithm is employed to continuously observe network states and make adaptive routing decisions in response to user requests. In the simulation, a movie dataset is employed to simulate user requests and support user attention awareness. The results show that the proposed content delivery framework outperforms existing basic prediction algorithms and other content delivery algorithms on four evaluation indicators. © 2013 IEEE.

关键词： Deep reinforcement learning

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MiT-Unet: Mixed Transformer Unet for Transmission Line Segmentation in UAV Images

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IEEE Transactions on Power Delivery 2025年第3期40卷 1279-1288页

作者： Wei, Ning Chen, Jianwei Sun, Shuifa The Hubei Key Laboratory of Intelligent Vision-Based Monitoring for Hydro electric Engineering College of Computer and Information Technology China Three Gorges University Yichang443002 China The Engineering Research Center of Mobile Health Management System Ministry of Education Hangzhou Normal University China

The segmentation of power lines in drone images is one of the challenging tasks in the field of computer vision. Although power lines share the same difficulties with tiny object segmentation, occupying only a very small proportion of pixel areas in the images, the greater challenge is that they also have a very large visual perspective field. Therefore, the results obtained by traditional convolutional neural network-based segmentation methods are still unsatisfactory. To tackle these problems, we propose MiT-Unet (Mixed Transformer Unet), which has an analogous multi-level convolutional neural network structure similar to Unet for encoding and decoding transmission line detailed features. Meanwhile, dealing with excessive scales, we employ an Efficient Self-Attention-based module to enhance the global features of straight lines. Furthermore, in the level transition of this architecture, we propose simple yet efficient upsampling and downsampling modules, TLFM and TLFE, to fuse the global attention feature of power lines. These modules effectively extract and preserve the vulnerable features of power lines during the level transition process. Experimental results demonstrate that our proposed method achieves state-of-the-art performance in transmission line segmentation on the public TTPLA dataset and PLDU dataset. Moreover, the computational efficiency of the proposed model makes it potentially deployable on mobile platforms. The testing code has been released at: https://***/xiachengs/MiT-Unet. ©1986-2012 IEEE.

关键词： Image segmentation

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MLPFormer: MLP-integrated transformer for colorectal histopathology whole slide image segmentation

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Neural Computing and Applications 2025年第6期37卷 4651-4661页

作者： Wang, Yuxuan Li, Dan Li, Xuechen Guo, Yan Zuo, Yanfei Shen, Linlin Department of Computer Science Wenzhou-Kean University Wenzhou China School of Electronics and Information Engineering Wuyi University Jiangmen China Department of Histo Pathology Shanghai Histo Pathology Diagnostic Center Shanghai China College of Computer Sicence and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

Colorectal intraepithelial neoplasia is a precancerous lesion of colorectal cancer, which is mainly diagnosed using pathological images. According to the characteristics of lesions, precancerous lesions can be classified into four different grades, i.e., background, normal, low and high level. However, due to the continuity of lesion development, tissue in different stages has high similarity. Recently, visual transformers have achieved impressive results in different visual tasks, due to their capacity of capturing long-range dependencies. However, due to computational cost, transformer cannot well extract detailed feature, which is important for lesion grading. To accurately segment pathological tissues with high similarity, in this work, we embed the multi-head MLP mixer module in the transformer network to extract cell and edge information. Furthermore, we propose a hierarchical MLP decoder to facilitate multi-scale feature fusion. The proposed transformer, namely MLPFormer, achieves remarkable results against competitive baselines on the Histo-CRC Biopsy dataset. The experimental results demonstrate that MLPFormer significantly outperforms the competitive baselines, i.e., a 3% dice improvement is achieved over the SegFormer series. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Image segmentation

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A trajectory-level robot skill learning method based on fuzzy systems

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science China(information sciences) 2025年第5期68卷 38-52页

作者： Chao ZENG Zhen DENG Boyang TI Yongsheng GAO Department of Computer Science University of Liverpool School of Mechanical Engineering and Automation Fuzhou University State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences State Key Laboratory of Robotics and System Harbin Institute of Technology

Learning from demonstration(LfD) allows for the effective transfer of human manipulation skills to a robot by building a model that represents these skills based on a limited number of demonstrated ***,a skilllearning model that can comprehensively satisfy multiple requirements,such as computational complexity,modeling accuracy,trajectory smoothness,and robustness,is still ***,this work aims to provide such a model by employing fuzzy ***,we introduce an LfD model named Takagi-Sugeno-Kang fuzzy system-based movement primitives(TSKFMPs),which exploits the advantages of the fuzzy theory for effective robotic imitation learning of human *** work formulates the TSK fuzzy system and gradient descent(GD) as imitation learning models,leveraging recent advancements in GD-based optimization for fuzzy *** study takes a two-step strategy.(ⅰ) The input-output relationships of the model are established using TSK fuzzy systems based on demonstration *** this way,the skill is encoded by the model parameter in the latent space.(ⅱ) GD is used to optimize the model parameter to increase the modeling accuracy and trajectory *** further explain how learned trajectories are adapted to new task scenarios through local *** conduct multiple tests using an open dataset to validate our method,and the results demonstrate performance comparable with those of other ***,we implement it in a real-world case study.

关键词： robot skill learning movement primitives TSK fuzzy systems gradient descent robotics

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Channel Emulation for Dynamic Channel Models with Programmable Turntable in OTA Testing

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IEEE Antennas and Wireless Propagation Letters 2025年第5期24卷 1099-1103页

作者： Wang, Haomin Li, Yong Li, Chunhui Guo, Yuhang Zhang, Xiang Beijing University of Posts and Telecommunications Key Laboratory of Universal Wireless Communications Beijing100876 China Southeast University National Mobile Communications Research Laboratory School of Information Science and Engineering Nanjing210096 China China Academy of Information and Communications Technology Beijing100191 China

Dynamic channel models are under extensive investigation to provide a more accurate modeling of varying interaction between wireless transceivers and propagation environments. On the other hand, the existing channel emulation methods for over-the-air (OTA) testing were designed for spatially-static channel models. In this letter, we propose a novel method for dynamic channel emulation by utilizing the ability of the programmable turntable to adjust the orientation of the device-under-test (DUT) during OTA testing. Thanks to the additional degree-of-freedom provided by the programmable turntable, more favorable angular positions between the DUT and the OTA probe antennas can be dynamically obtained to cater for the instantaneous channel multipath directions. Simulation results demonstrate that the proposed method can significantly improve the accuracy of dynamic channel emulation, which exhibits strong feasibility even under the practical constraint of turntable rotation speed. © 2002-2011 IEEE.

关键词： Probes

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Long-term spatiotemporal variations of ammonia in the Yangtze River Delta region of China and its driving factors

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Journal of Environmental sciences 2025年第4期150卷 202-217页

作者： Jingkai Xue Chengzhi Xing Qihua Li Shanshan Wang Qihou Hu Yizhi Zhu Ting Liu Chengxin Zhang Cheng Liu School of Environmental Science and Optoelectronic Technology University of Science and Technology of ChinaHefei 230026China Key Lab of Environmental Optics&Technology Anhui Institute of Optics and Fine MechanicsHefei Institutes of Physical ScienceChinese Academy of SciencesHefei 230031China Department of Precision Machinery and Precision Instrumentation University of Science and Technology of ChinaHefei 230026China Anhui Institute of Optics and Fine Mechanics Hefei Institutes of Physical ScienceChinese Academy of SciencesHefei 230031China Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes University of Science and Technology of ChinaHefei 230026China Institute of Physical Science and Information Technology Anhui UniversityHefei 230601China Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention(LAP3) Department of Environmental Science and EngineeringFudan UniversityShanghai 200438China School of Earth and Space Sciences University of Science and Technology of ChinaHefei 230026China School of Environmental Science and Engineering Suzhou University of Science and TechnologySuzhou 215009China

This study focuses on the spatiotemporal distribution,urban-rural variations,and driving factors of ammonia Vertical Column Densities(VCDs)in China’s Yangtze River Delta region(YRD)from 2008 to *** data from the Infrared Atmospheric Sounding Interfer-ometer(IASI),Generalized Additive Models(GAM),and the GEOS-Chem chemical transport model,we observed a significant increase of NH_(3)VCDs in the YRD between 2014 and *** spatial distribution analysis revealed higher NH_(3)concentrations in the northern part of the YRD region,primarily due to lower precipitation,alkaline soil,and intensive agricul-tural ***_(3)VCDs in the YRD region increased significantly(65.18%)from 2008 to *** highest growth rate occurs in the summer,with an annual average growth rate of 7.2%during the period from 2014 to *** emissions dominated NH_(3)VCDs during spring and summer,with high concentrations primarily located in the agricultural areas adjacent to densely populated urban *** within several large urban areas have been discovered to exhibit relatively stable variations in NH_(3)*** rise in NH_(3)VCDs within the YRD region was primarily driven by the reduction of acidic gases like SO_(2),as emphasized by GAM modeling and sensitivity tests using the GEOS-Chem *** concentration changes of acidic gases contribute to over 80%of the interannual variations in NH_(3)*** emphasizes the crucial role of environmental policies targeting the reduction of these acidic *** emission control is urgent tomitigate environmental hazards and secondary particulate matter,especially in the northern YRD.

关键词： Yangtze River Delta Ammonia Spatiotemporal distribution Driving factors

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Improving atmospheric pressure vertical correction model using Gaussian function

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Geodesy and Geodynamics 2025年第1期16卷 67-74页

作者： Baoshuang Zhang Junyu Li Lilong Liu Yibin Yao Liangke Huang Chao Ren Hongchang He Tengxu Zhang Yuxin Wang College of Geomatics and Geoinformation Guilin University of TechnologyGuilin 541004China Guangxi Key Laboratory of Spatial Information and Geomatics Guilin 541004China School of Geodesy and Geomatics Wuhan UniversityWuhan 430079China Schoolof Resources and Environmental Science and Engineering Hubei University of Science and TechnologyXianning 437100China

The Zenith Hydrostatic Delay(ZHD)is essential for high-precision Global Navigation Satellite system(GNSS)and Very Long Baseline Interferometry(VLBI)data *** estimation of ZHD relies on in situ atmospheric pressure,which is primarily variable in the vertical *** atmospheric pressure is either site-specific or has limited spatial coverage,necessitating vertical corrections for broader *** study introduces a model that uses a Gaussian function for the vertical correction of atmospheric pressure when in situ meteorological observations are *** with the fifth-generation European Centre for Medium-Range Weather Forecasts reanalysis(ERA5)reveals an average Bias and RMS for the new model of 0.31 h Pa and 2.96 h Pa,*** corresponds to improvements of 37.5%and 80.3%in terms of RMS compared to two commonly used models(T0and Tvmodels)that require in situ meteorological observations,*** validation with radiosonde data shows an average Bias and RMS of 1.85 h Pa and 4.87 h Pa,corresponding to the improvement of 42.8%and 71.1%in RMS compared with T0and Tv models,*** accuracies are sufficient for calculating ZHD to an accuracy of 1 mm by performing atmospheric pressure vertical *** new model can correct atmospheric pressure from meteorological stations or numerical weather forecasts to different heights of the troposphere.

关键词： Atmospheric pressure Vertical correction Zenith hydrostatic delay(ZHD) Gauss function ERA5

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DIG: Improved DINO for Graffiti Detection

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IEEE Transactions on systems, Man, and Cybernetics: systems 2025年第5期55卷 3557-3569页

作者： Wang, Bingshu Mao, Qianchen Liu, Aifei Chen, Long Chen, C. L. Philip Northwestern Polytechnical University School of Software Xi’an710129 China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen518060 China University of Macao Department of Computer and Information Science China South China University of Technology School of Computer Science and Engineering Guangzhou510641 China

Graffiti detection is essential in historic building protection and urban neighborhood management. Graffiti detection has made significant progress in recent years based on the development of deep learning. However, small-scale graffiti, interference from the background, and the false detection of word parts in graffiti make it a challenging problem. This article proposes a Transformer-based high-precision graffiti detection method, namely DIG. Precisely, it consists of three modules: 1) Spatial query selection (SQS), scale-aware IoU loss (SIL);2) Denoising Task with binary contrastive denoising (BCDN);and 3) IoU-guided box denoising (IBD) modules. To detect small-scale graffiti, this paper proposes SIL to help the loss function to perceive small-scale graffiti and large-scale graffiti fairly. To reduce the false detection of word parts, this article presents the SQS module, which integrates spatial information into the query selection process of the Encoder to filter out falsely detected bounding boxes within the graffiti. To reduce the interference from the background, this article introduces a denoising task with BCDN and IBD modules, improving the model’s ability to distinguish graffiti from the background and accurately select appropriate bounding boxes. A large number of experimental results on the STORM dataset show that our method achieves state-of-the-art results with an AP50 of 87.9%. Moreover, DIG achieved competitive results on the FineFM dataset for mask detection. This indicates that DIG can also be conveniently transferred to detect other scenarios. © 2013 IEEE.

关键词： Historic preservation

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Dynamic Modeling and Control for an Offshore Semisubmersible Floating Wind Turbine

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IEEE Transactions on Automation science and engineering 2025年 22卷 12371-12382页

作者： Gong, Yingjie Yang, Qinmin Geng, Hua Meng, Wenchao Wang, Lin Zhejiang University State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Hangzhou310027 China Huzhou Institute Zhejiang University State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Hangzhou310027 China Tsinghua University Beijing National Research Center for Information Science and Technology Department of Automation Beijing100084 China Zhejiang Windey Company Ltd. Hangzhou310027 China

Floating wind turbines (FWTs) hold significant potential for the exploitation of offshore renewable energy resources. Nevertheless, prior to the construction of FWTs, it is imperative to tackle several critical challenges, especially the issue of performance degradation under combined wind and wave loads. This study initiates with the development of a simplified nonlinear dynamical model for a semi-submersible FWT. In particular, both the rotor dynamics and the finite rotations of the platform are considered in presented modeling approach, thereby effectively capturing the complex interplay between the platform, tower, nacelle, and rotor under combined wind and wave loads. Subsequently, based on the developed FWT model, a novel adaptive nonlinear pitch controller is formulated with the goal of striking a trade-off between regulating power generation and reducing platform motion. Notably, the proposed control strategy adopts a continuous control approach, strategically beneficial in circumventing the chattering phenomenon commonly associated with sliding mode control. Furthermore, the controller integrates an online approximator and a robust integral of the sign of the tracking error, facilitating real-time learning of system unknown dynamics while compensating for bounded disturbances. Finally, both the accuracy of the established nonlinear FWT model in predicting key dynamics and the superiority of the presented pitch controller are validated through comprehensive comparative studies. Note to Practitioners - This paper addresses the conflicting goals between power regulation and load mitigation for floating wind turbines (FWTs) to ensure the reliable operation of wind turbine systems. This remains an ongoing challenge due to the inherent complexity of existing FWT models, frequently resulting in controllers crafted using linearized representations that fail to accommodate real-world uncertainties effectively. Through the utilization of a simplified physical-based n

关键词： Renewable energy

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