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13th International Conference on Intelligent Control and Information Processing, ICICIP 2025

作者： Zhang, Zhiwei Zhuang, Yan Liang, Zhida Kong, Shuai Zhu, Shengyi School of Control Science and Engineering Dalian University of Technology Dalian China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Special Equipment Safety Testing Technology of Zhejiang Province Zhejiang Academy of Special Equipment Science Hangzhou China

ISBN: (纸本)9798331516147

High-current out-of-phase closed bus is an essential link in the transmission of electric energy and is widely used in thermal power stations, hydropower stations and nuclear power plants. Although these busbars have the advantages of excellent dustproof effect and strong magnetic shielding, they are susceptible to insulator aging and damage in long-term operation, posing a potential safety risk to the power grid. Due to the enclosed space inside the bus and the complexity of external maintenance tasks, manual inspection has problems such as inefficiency and low safety. In order to solve these problems, this paper proposes an improved detection algorithm based on Yolov7 for insulator defect detection in hydropower station scenarios. Because of the particularity of Yolov7 network structure and application scenarios, this paper proposes a Channel Feature Enhancement module to enhance the channel representation ability of the network. In addition, an Edge Feature Enhancement module is introduced to improve the boundary detection of insulators by enhancing the contrast between the target and the background. Considering the limitations of existing studies in specific scenarios and the scarcity of dataset, this paper constructs and accurately annotates an insulator defect detection dataset applied to hydropower station scenarios. Experimental results show that the improved-Yolov7-tiny algorithm in this paper achieves a balance between accuracy and inference speed. © 2025 IEEE.

关键词： Magnetic shielding

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UMIENet: Underwater image enhancement based on multi-degradation knowledge integration

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Optics and Lasers in Engineering 2025年 193卷

作者： Lv, Pin Zha, Fusheng Wang, Xiangji Li, Rongchao Li, Mantian Wang, Pengfei Guo, Wei Sun, Lining State key Laboratory of Robotics and System Harbin Institute of Technology Harbin150001 China Lanzhou University of Technology Lanzhou China Institute of Intelligent Manufacturing Technology Shenzhen Polytechnic University Shenzhen China

Due to the absorption and scattering effects of water on light, underwater images generally suffer from multiple degradations such as blur, color cast, and non-uniform illumination, which severely affect image quality and visual processing tasks. Therefore, underwater image enhancement (UIE) has gained widespread application in various marine exploration tasks. While supervised learning-based methods currently dominate this field, existing methods have two main problems. The limited availability of real paired images and the incompleteness of the degradation types of synthetic datasets restricts the model training performance, and most UIE models are designed for specific types of degradation, lacking systematic processing of multiple underwater degradations. These problems lead to poor model performance. In this work, we construct an Underwater Multi-Degradation Knowledge Integration dataset, called UMDKI, it models multiple degradation factors including blur, color cast, and non-uniform illumination by incorporating a revised image formation model and point light mathematical modeling. Besides, we propose an Underwater Multi-degradation Image Enhancement Network, called UMIENet, it integrates the advantages of various traditional methods and achieves collaborative enhancement of multiple degradations. Extensive experiments demonstrate that the proposed UMIENet achieves excellent performance on multiple benchmarks and shows good effectiveness in real underwater vision tasks. © 2025 Elsevier Ltd

关键词： Photointerpretation

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Universal Fan-In/Fan-Out Solution for Few-Mode Multi-Core Fibers Using Multi-Mode Fibers

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Journal of Lightwave technology 2025年第11期43卷 5498-5505页

作者： Hua, Xuchen Du, Haoze Liu, Zhuyixiao Zhang, Fengming Yang, Chen Zhao, Zhiyong Tang, Ming Yangtze Optical Fibre and Cable Joint Stock Limited Company State Key Laboratory of Optical Fiber and Cable Manufacture Technology Hubei Wuhan China National Engineering Laboratory for Next Generation Internet Access System School of Optical and Electronic Information Hubei Wuhan430074 China Optics Valley Laboratory Hubei Wuhan430074 China

With the development of AI, the capacity demand for interconnection between computing component is increasing. As a solution has been vigorously developed in the past half of century, optical interconnection has been put forward with higher demands. Few-mode multi-core fiber (FM-MCF), a key technology within spatial division multiplexing (SDM), has consistently pushed the boundaries of single optical fiber communication capacity records. However, the non-standardized fiber design of FM-MCF poses a significant challenge in terms of multiplexing and demultiplexing. The difficulty lies in the preservation of high-order modes and mode field adaptation. To address these challenges, we propose a universal fiber-core multiplexing/demultiplexing solution for FM-MCF, leveraging multi-mode optical fiber (MMF) as a versatile bridging medium. The simulation and experimental results demonstrate excellent insertion loss and high-order mode retention capabilities. This solution offers compatibility with a wide range of FM-MCF designs, making it highly adaptable for various research applications. Furthermore, by using commercially available MMF, the solution reduces both cost and complexity, presenting a practical and scalable approach to advancing FM-MCF technology as a next-generation transmission medium for AI-driven computing systems. © 1983-2012 IEEE.

关键词： Optical interconnects

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Comprehensive Assessment of AIRS, TSHS, and VASS Temperature Profile Products in the Arctic Land Region

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Advances in Atmospheric Sciences 2025年 1-14页

作者： Fan, Rongfeng Zhang, Lei Chen, Junming Wei, Ting Huang, Wentao Tian, Biao Ding, Minghu State Key Laboratory of Disaster Weather Science and Technology Chinese Academy of Meteorological Sciences Beijing China China Meteorological Administration Xiong’an Atmospheric Boundary Layer Key Laboratory Xiong’an New Area China MNR Key Laboratory for Polar Science Polar Research Institute of China Shanghai China Key Laboratory of Polar Atmosphere-ocean-ice System for Weather and Climate Ministry of Education Shanghai China

The Atmospheric Infrared Sounder (AIRS) on the Aqua satellite, along with the MWTS/MWHS Synergy (TSHS) sounding system and Atmospheric Vertical Sounder system (VASS) on the Fengyun-3D (FY-3D) satellite, provide high-quality data for studying Arctic temperature change. The generalized cold bias of AIRS is confirmed through horizontal comparisons with Arctic land radiosonde stations. VASS corrects the warm bias of TSHS by incorporating the Hyperspectral Infrared Atmospheric Sounder-I (HIRAS-I). Vertical comparisons demonstrate that AIRS, TSHS, and VASS offer excellent temperature detection from the top of the boundary layer to the lower stratosphere (800–100 hPa). However, the overestimation and errors of stratospheric temperatures by TSHS and VASS increase with altitude (pressures below 60 hPa). Specifically, the warm bias trends at 0.06 K hPa−¹, reaching 2.87 K and 2.92 K at 10 hPa. Similarly, RMSE values trend at 0.05 K hPa⁻¹, reaching 3.62 K and 3.69 K at 10 hPa. The low correlation (R ⩾ 0.65) of TSHS near 250 hPa in summer is significantly improved in VASS (R ⩾ 0.78) after adding HIRAS-I. The high vertical resolution due to infrared hyperspectral resolution facilitates the detection of complex temperature junctions. The retrieval error of AIRS in the boundary layer increases with cloudiness, while VASS combines microwave and infrared channel data to reduce the impact of cloud cover. Assessing the Arctic applicability of these three satellite temperature profile products will facilitate their widespread use in the Arctic region, enhance accurate climate change monitoring, and further reveal the mechanisms of Arctic warming. © Institute of Atmospheric Physics/Chinese Academy of Sciences, and Science Press 2025.

关键词： AIRS Arctic temperature TSHS VASS

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Spatiotemporal characteristics of soil moisture memory: an integrated analysis using multiple metrics and datasets

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Climate Dynamics 2025年第5期63卷 1-15页

作者： Wei, Jiangfeng Song, Renjie State Key Laboratory of Climate System Prediction and Risk Management/Key Laboratory of Meteorological Disaster Ministry of Education/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters Nanjing University of Information Science and Technology Nanjing 210044 China School of Atmospheric Sciences Nanjing University of Information Science and Technology Nanjing 210044 China

The ability of soil moisture to memorize past climatic anomalies—known as soil moisture memory (SMM)—is a potential factor influencing weather and climate predictions. However, our understanding of its spatiotemporal characteristics remains inadequate. In this study, we compared the boreal warm season (April–September) surface and root zone SMM across five gridded global datasets using metrics of lagged autocorrelation time scale, average duration of dry/wet anomalies, and stored precipitation fraction. Research revealed significant spatial heterogeneity in SMM, with generally higher SMM in drier regions. The SMMs from Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) and the stored precipitation fraction method diverge more from those of other datasets and metrics. Globally, SMM tends to be greater during El Niño years compared to La Niña years, which is linked to the generally drier climate conditions typical of El Niño phases. Comparison with in situ data reveals that the gridded datasets are generally consistent with observations in terms of soil moisture variability and memory. This study provides a reference for the spatiotemporal features of SMM, contributing to model evaluation and climate prediction. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： ERA5 GLDAS GLEAM MERRA-2 Soil moisture memory Spatiotemporal variation

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An Adaptive Feedforward Optimization Mechanism for Improving Industrial Robots Trajectory Tracking Performance through Iterative Learning

An Adaptive Feedforward Optimization Mechanism for Improving...

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robotics, Control and Automation (ICRCA), International Conference on

作者： Chengzhi Wang Sikai Zhao Tianjiao Zheng Hegao Cai Jie Zhao Yanhe Zhu State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China

ISBN: (数字)9798331544577

ISBN: (纸本)9798331544584

This paper presents an adaptive feedforward optimization method based on iterative learning control mechanism, improving trajectory tracking accuracy from trial to trail. Conventional feedforward control researches focus on the precise prediction and compensation of torques, either by means of dynamic modelling and parameter identification or by data-driven approaches. However, there exists unpredictable time-delay for torques to work. The proposed method takes the previously less-concerned velocity feedforward part of the servo control system into consideration, and a self-adaptive fine-tuning mechanism is designed and established. The error measurement and feedforward optimization processes are integrated into an iterative learning framework to obtain efficiency and precision. The effectiveness of proposed method is validated by physical experiments on an EFORT ER15 industrial robot, and an averagely 90.71% error reduction is witnessed.

关键词： Accuracy Torque Parameter estimation Trajectory tracking Service robots Predictive models Industrial robots Feedforward systems Servosystems Tuning

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Improving the ductility of laser directed energy deposited borated stainless steel by increasing laser power

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Journal of Manufacturing Processes 2025年 145卷 494-507页

作者： Huang, Sheng Li, Qingyu Zhang, Xiaoyu Cui, Bin Li, Dichen Zhang, Anfeng Miao, Kai State Key Laboratory for Manufacturing Systems Engineering School of Mechanical Engineering Xi'an Jiaotong University Xi'an710049 China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu610213 China Department of Mechanical and Electrical Engineering Yuncheng University Yuncheng044000 China Co. Ltd. Chengdu610091 China

Borated stainless steels (BSSs) are extensively applied in the nuclear energy industry due to boron's excellent thermal neutron absorption capacity, but large and brittle borides formed during casting result in a low ductility. Here, a BSS was produced by laser directed energy deposition, and an investigation was conducted to analyze the impact of laser power on both the microstructure and mechanical properties of the deposited material. Fibrous borides with relatively low volume fraction in the overlapping zone resulted in higher ductility, while lamellar borides with relatively large volume fraction in the non-overlapping zone led to higher strength. As the laser power increased from 180 W to 330 W, the size of fibrous and lamellar eutectic borides and austenite increased significantly, the width of the soft overlapping zone increased by 2.27 times, and the elongation increased by 2.03 times, although yield strength and ultimate tensile strength decreased by 46.3 % and 31.7 %, respectively. As a result, these findings confirmed the feasibility of significantly improving the ductility of the as-deposited BSS by increasing the laser power. The work here provides a way to tailoring the mechanical properties of BSS produced by laser additive manufacturing. © 2025 The Society of Manufacturing Engineers

关键词： Ductility

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Swarm-Based Dynamic Coverage of Multi-ASV systems in the Presence of Measurement Noises

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IEEE Transactions on Vehicular technology 2025年第5期74卷 7237-7250页

作者： Liu, Lu Jiao, Shijian Han, Bing Li, Tieshan Peng, Zhouhua Dalian Maritime University School of Marine Electrical Engineering Dalian116026 China Dalian Key Laboratory of Swarm Control and Electrical Technology for Intelligent Ships Dalian116026 China State Key Laboratory of Maritime Technology and Safety Dalian116026 China Shanghai Ship and Shipping Research Institute National Engineering Research Center of Ship & Shipping Control System Shanghai200135 China University of Electronic Science and Technology of China School of Automation Engineering Chengdu611731 China

This paper is concerned with the dynamic coverage of multiple autonomous surface vehicles (ASVs) in the presence of static and dynamic obstacles, limited communication distance, and measurement noises. A modular control method including planning, guidance, and control is proposed. At the planning level, a swarm-based dynamic coverage strategy is developed to achieve the coverage of a given domain. Both the collision avoidance and connectivity preservation issues are solved by using artificial potential fields. At the kinematic level, novel adaptive line-of-sight guidance laws are presented based on cascade extended state observers (CESOs), such that accurate guidance signals are given by compensating for time-varying sideslip angle and suppressing measurement noises. At the kinetic level, two CESOs are developed to estimate the internal model uncertainties and external environmental disturbance, and antidisturbance kinetic control laws are designed subsequently. The proposed CESOs overcome the noises sensitivity problem of classical observers, and thus facilitate the practical implementation under measurement noises. The input-to-state stability of the closed-loop system is confirmed by the Lyapunov theory and cascade stability theory. Simulation and hardware-in-the-loop experiment results are provided to demonstrate the effectiveness of the proposed swarm-based dynamic coverage method. © 1967-2012 IEEE.

关键词： Velocity measurement

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Impact of net-zero emissions on atmospheric CO_(2) concentration in China: Ideal simulations based on the GEOS-Chem model

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Science China Earth Sciences 2025年第3期68卷 867-881页

作者： Jingye TAN Jun WANG Huiqin MAO Hengmao WANG Zhiqiang LIU Meirong WANG Ran YAN Xunmei WANG Fei JIANG Frontiers Science Center for Critical Earth Material Cycling International Institute for Earth System ScienceNanjing UniversityNanjing 210023China Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural ResourcesSchool of Geography and Ocean ScienceNanjing UniversityNanjing 210023China Satellite Environmental Application Center Ministry of Environmental ProtectionBeijing 100094China CMA Key Open Laboratory of Transforming Climate Resources to Economy Chongqing Institute of Meteorological SciencesChongqing 401147China State Key Laboratory of Climate System Prediction and Risk Management Key Laboratory of Meteorological DisasterMinistry of EducationCollaborative Innovation Center on Forecast and Evaluation of Meteorological DisastersNanjing University of Information Science and TechnologyNanjing 210044China Xizang Field Station for Scientific Observation and Research on Atmospheric Water Cycle in Mêdog Xigazêand Mêdog National Climate ObservatoryXizang Meteorological ServiceLhasa 850000China

Elevated atmospheric carbon dioxide(CO_(2)) concentrations have caused global climate change such as global warming and more frequent climate extremes. Countries worldwide have proposed carbon neutrality strategies to curb the rising CO_(2) concentrations. To investigate the impact of China's carbon neutrality goal on atmospheric CO_(2) concentrations, we conducted a series of ideal simulations from 2015 to 2019 using a global 3D chemistry transport model, Goddard Earth Observing system Chemistry(GEOS-Chem). Compared with the column-averaged dry-air mole fraction of atmospheric CO_(2) (XCO_(2) ) from Orbiting Carbon Observatory-2(OCO-2) and surface CO_(2) measurements in Obs Pack, we find that GEOS-Chem effectively reproduces the spatiotemporal variability of CO_(2) . The model exhibits a root mean square error(RMSE) of 1.51 ppm(R^(2)=0.89) for OCO-2 XCO_(2) in China and 2.65 ppm(R^(2)=0.75) for surface CO_(2) concentrations at the WLG station. Further, compared to 2.83 ppm yr^(-1)in the control experiment, we suggest that net-zero CO_(2) emissions in China decelerate the increasing trends of XCO_(2) to 1.81 ppm yr^(-1),making a decrease of approximately 35.89%. Meanwhile, the seasonal cycle amplitude(SCA) of XCO_(2) is moderately reduced from 7.39±0.81 to 6.75±0.70 ppm, representing a relative reduction of 9.91%. Spatially, net-zero CO_(2) emissions induce a more significant decrease in XCO_(2) trends over northern and southern China, while their impact on SCA is more evident in northern and northeastern China. Moreover, ideal experiments demonstrate that zero fossil CO_(2) emissions lead to a greater attenuation of the linear trends of XCO_(2) by 40.81%, while the absence of terrestrial CO_(2) sinks largely diminishes the SCA by 16.61%. Additionally,trends and SCA in surface CO_(2) concentrations exhibit almost identical decreasing responses to net-zero CO_(2) emissions but display greater sensitivities compared to XCO_(2) . Overall, our study underscores the pote

关键词： XCO_(2) GEOS-Chem Carbon neutrality Net-zero emissions Surface CO_(2)concentrations

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Synthesized colonoscopy dataset from high-fidelity virtual colon with abnormal simulation

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Computers in Biology and Medicine 2025年 186卷 109672-109672页

作者： He, Dongdong Liu, Ziteng Yin, Xunhai Liu, Hao Gao, Wenpeng Fu, Yili School of Life Science and Technology Harbin Institute of Technology Harbin150080 China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150080 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Department of Gastroenterology The First Affiliated Hospital of Harbin Medical University Harbin150001 China

With the advent of the deep learning-based colonoscopy system, the need for a vast amount of high-quality colonoscopy image datasets for training is crucial. However, the generalization ability of deep learning models is challenged by the limited availability of colonoscopy images due to regulatory restrictions and privacy concerns. In this paper, we propose a method for rendering high-fidelity 3D colon models and synthesizing diversified colonoscopy images with abnormalities such as polyps, bleeding, and ulcers, which can be used to train deep learning models. The geometric model of the colon is derived from CT images. We employed dedicated surface mesh deformation to mimic the shapes of polyps and ulcers and applied texture mapping techniques to generate realistic, lifelike appearances. The generated polyp models were then attached to the inner surface of the colon model, while the ulcers were created directly on the inner surface of the colon model. To realistically model blood behavior, we developed a simulation of the blood diffusion process on the colon's inner surface and colored vertices in the traversed region to reflect blood flow. Ultimately, we generated a comprehensive dataset comprising high-fidelity rendered colonoscopy images with the abnormalities. To validate the effectiveness of the synthesized colonoscopy dataset, we trained state-of-the-art deep learning models on it and other publicly available datasets and assessed the performance of these models in abnormal classification, detection, and segmentation. Notably, the models trained on the synthesized dataset exhibit an enhanced performance in the aforementioned tasks, as evident from the results. © 2025 Elsevier Ltd

关键词： Digital elevation model

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