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Prospects on mangrove knowledge services in the smart era: From plant atlas to knowledge graphs

Science China Earth Sciences 2025年第1期68卷 111-127页

作者： Zhi-Wei HOU Wenlong JING Cheng-Zhi QIN Ji YANG Qing XIA Xiaoling YIN Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical information System Guangdong Open Laboratory of Geospatial Information Technology and Application Guangzhou Institute of Geography Guangdong Academy of Sciences Southern Marine Science and engineering Guangdong Laboratory (Guangzhou) State Key Laboratory of Resources and Environmental information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of Sciences College of Resources and Environment University of Chinese Academy of Sciences school of Traffic & Transportation engineering Changsha University of Science and Technology

Mangroves are crucial to the ecological security of the Earth and human well-being. Their management, conservation, and restoration are of great importance and necessitate the support of spatio-temporal information and multidisciplinary knowledge such as biology and ecology. Traditional knowledge services such as plant atlas provide illustrated textual knowledge of mangroves. However, this kind of service is oriented to information retrieval and is incapable of effectively mining and utilizing fragmented knowledge from multi-source heterogeneous data, facing the problem of “massive data, rare knowledge”. Knowledge graphs are capable of extracting, organizing, and fusing the knowledge contained in massive data into semantic networks that can be understood and computed by computers. They provide a solution for the realization of intelligent knowledge services. Focusing on the urgent need for mangrove knowledge acquisition, formal representation, and intelligent services, this paper proposes a research prospect on mangrove knowledge graphs and knowledge services. We first analyze the similarities and differences between various domain-specific concepts of Tupu. On this basis, we define the mangrove knowledge graph as a large-scale knowledge base that integrates multi-disciplinary knowledge and spatio-temporal information with mangrove ecosystems as the core. Then, we propose a research framework for mangrove knowledge services that can realize the transformation from multi-modal data to intelligent knowledge services, including multiple research levels such as ubiquitous data sensing and aggregation, knowledge organization and graph construction, and intelligent mangrove knowledge services. Subsequently, the methods and workflow for constructing mangrove knowledge graphs are introduced. Finally, we discuss the challenges and possible future directions of mangrove knowledge services in the smart era, including the construction of a mangrove knowledge system that integra

关键词： Mangrove Tupu Knowledge graph Multi-modal Knowledge service

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Investigation on the interaction of dispersed and elongated bubbles based on laser-induced fluorescence

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Optoelectronics Letters 2025年

作者： XUE Ting SHI Ruini LIU Jinshun WANG Haixia WU Yan school of Electrical and information engineering， Tianjin University

The purpose of this paper is to investigate the interaction between dispersed and elongated bubbles in horizontal slug flow utilizing the laser-induced fluorescence method. A Segmentation method based on the fuzzy C-mean (FCM) algorithm is proposed to effectively separate elongated bubbles from the liquid phase， and an extreme value extraction method is developed to calculate the number of dispersed bubbles in front of the nose of elongated bubbles. Moreover， the velocity offsets of elongated bubbles with and without dispersed bubbles are calculated separately based on contour extraction. In addition， the effect of dispersed bubbles on the fluctuating offsets of the nose tip position and velocity of elongated bubbles are statistically investigated under different flow velocities. The experimental results show that the increase of the gas-liquid flow velocity exacerbates the radial deviation of the nose tip and the fluctuation of axial velocity.

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Deep learning-based software engineering: progress,challenges, and opportunities

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Science China(information Sciences) 2025年第1期68卷 57-144页

作者： Xiangping CHEN Xing HU Yuan HUANG He JIANG Weixing JI Yanjie JIANG Yanyan JIANG Bo LIU Hui LIU Xiaochen LI Xiaoli LIAN Guozhu MENG Xin PENG Hailong SUN Lin SHI Bo WANG Chong WANG Jiayi WANG Tiantian WANG Jifeng XUAN Xin XIA Yibiao YANG Yixin YANG Li ZHANG Yuming ZHOU Lu ZHANG school of Journalism and Communication Sun Yat-sen University school of Software Technology Zhejiang University school of Software engineering Sun Yat-sen University school of Software Dalian University of Technology school of Computer Science and Technology Beijing Institute of Technology Key Laboratory of High Confidence Software Technologies (Peking University) Ministry of EducationSchool of Computer Science Peking University State Key Laboratory for Novel Software Technology Nanjing University school of Computer Science and engineering Beihang University Institute of information engineering Chinese Academy of Sciences school of Computer Science Fudan University State Key Laboratory of Complex & Critical Software Environment (CCSE) School of Software Beihang University school of Computer and information Technology Beijing Jiaotong University school of Computer Science and Technology Harbin Institute of Technology school of Computer Science Wuhan University Huawei Technologies

Researchers have recently achieved significant advances in deep learning techniques, which in turn has substantially advanced other research disciplines, such as natural language processing, image processing, speech recognition, and software engineering. Various deep learning techniques have been successfully employed to facilitate software engineering tasks, including code generation, software refactoring, and fault localization. Many studies have also been presented in top conferences and journals, demonstrating the applications of deep learning techniques in resolving various software engineering tasks. However,although several surveys have provided overall pictures of the application of deep learning techniques in software engineering,they focus more on learning techniques, that is, what kind of deep learning techniques are employed and how deep models are trained or fine-tuned for software engineering tasks. We still lack surveys explaining the advances of subareas in software engineering driven by deep learning techniques, as well as challenges and opportunities in each subarea. To this end, in this study, we present the first task-oriented survey on deep learning-based software engineering. It covers twelve major software engineering subareas significantly impacted by deep learning techniques. Such subareas spread out through the whole lifecycle of software development and maintenance, including requirements engineering, software development, testing, maintenance, and developer collaboration. As we believe that deep learning may provide an opportunity to revolutionize the whole discipline of software engineering, providing one survey covering as many subareas as possible in software engineering can help future research push forward the frontier of deep learning-based software engineering more systematically. For each of the selected subareas,we highlight the major advances achieved by applying deep learning techniques with pointers to the available datasets i

关键词： deep learning software engineering software benchmark software artifact representation survey

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Lightweight Underwater Target Detection Using YOLOv8 with Multi-Scale Cross-Channel Attention

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Computers, Materials & Continua 2025年第1期82卷 713-727页

作者： Xueyan Ding Xiyu Chen Jiaxin Wang Jianxin Zhang school of Computer Science and engineering Dalian Minzu UniversityDalian116600China Research Center of Multimodal information Perception and Intelligent Processing Dalian Minzu UniversityDalian116600China

Underwater target detection is extensively applied in domains such as underwater search and rescue,environmental monitoring,and marine resource *** is crucial in enabling autonomous underwater robot operations and promoting ocean ***,low imaging quality,harsh underwater environments,and obscured objects considerably increase the difficulty of detecting underwater targets,making it difficult for current detection methods to achieve optimal *** order to enhance underwater object perception and improve target detection precision,we propose a lightweight underwater target detection method using You Only Look Once(YOLO)v8 with multi-scale cross-channel attention(MSCCA),named *** the proposed multiscale cross-channel attention module,multi-scale attention(MSA)augments the variety of attentional perception by extracting information from innately diverse sensory *** cross-channel strategy utilizes RepVGGbased channel shuffling(RCS)and one-shot aggregation(OSA)to rearrange feature map channels according to specific *** aggregates all features only once in the final feature mapping,resulting in the extraction of more comprehensive and valuable feature *** experimental results show that the proposed YOLOv8-UOD achieves a mAP50 of 95.67%and FLOPs of 23.8 G on the Underwater Robot Picking Contest 2017(URPC2017)dataset,outperforming other methods in terms of detection precision and computational cost-efficiency.

关键词： Deep learning underwater target detection attention mechanism

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A coordinated planning strategy of energy storage allocation and line upgrading to increase photovoltaic consumption with lowest planning cost

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Science China Technological Sciences 2025年

作者： Bo ZHANG Yuqi JI Lu ZHANG Wei TANG Xiaohui ZHANG Liang WANG Haoran DING Hanchen MAO College of information and Electrical engineering China Agricultural University State Grid Heilongjiang Electric Power Research Institute State Grid Heilongjiang Electric Power Company Limited school of Electrical and information engineering Zhengzhou University of Light Industry

Random integration of massive distributed photovoltaic (PV) generation poses serious challenges to distribution networks. Voltage violations, line overloads, increased peak–valley differences, and power-flow reversals can occur at different locations, times, and severities. Traditional planning methods such as energy storage (ES) allocation and upgrading of lines may result in poor economics and low equipment utilization. This study proposes a distribution-network planning strategy that coordinates three planning mechanisms:ES allocation to substations and to feeders, and line upgrading. The proposed strategy achieved the lowest annual cost, considering the distribution network problems at different locations, times, and severities. A net load curve description method was first proposed considering economic planning and the uncertain distribution of loads and PVs. Second, a multistage optimization model for ES allocation and line upgrading was established based on the net load curves. Finally, an improved IEEE33 node system was used in the case study. The planning results show that the proposed coordinated planning strategy can address the problems caused by the random integration of massive distributed PVs, improve equipment utilization, and obtain the lowest total annual planning cost.

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BER performance analysis of non-Hermitian symmetry OFDM-VLC systems with ADC quantization noise

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Optoelectronics Letters 2025年

作者： Wang Zhongpeng Ai Caihua Zhang Lijuan school of information and Electronic engineering， Zhejiang University of Science and Technology

Quantization noise caused by analog-to-digital converter (ADC) gives rise to the reliability performance degradation of communication systems. In this letter， a quantized non-hermitian Symmetry (NHS) orthogonal frequency-division multiplexing-based visible light communication (OFDM-VLC) system is presented. In order to analyze the effect of the resolution of ADC on NHS OFDM-VLC， a quantized mathematical model of NHS OFDM-VLC is established. Based on the proposed quantized model， a closed-form bit rate error (BER) Expression is derived. The theoretical analysis and simulation results both confirm the effectiveness of the obtained BER formula in high-resolution ADC. In addition， channel coding is helpful in compensating for the BER performance loss due to the utilization of lower resolution ADC.

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Grid-Connected/Islanded Switching Control Strategy for Photovoltaic Storage Hybrid Inverters Based on Modified Chimpanzee Optimization Algorithm

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Energy engineering 2025年第1期122卷 265-284页

作者： Chao Zhou NarisuWang Fuyin Ni Wenchao Zhang school of Electrical and information engineering Jiangsu University of TechnologyChangzhou213001China Jiangsu Key Laboratory of Power Transmission&Distribution Equipment Technology Jiangsu University of TechnologyChangzhou213001China

Uneven power distribution,transient voltage,and frequency deviations are observed in the photovoltaic storage hybrid inverter during the switching between grid-connected and island *** response to these issues,this paper proposes a grid-connected/island switching control strategy for photovoltaic storage hybrid inverters based on the modified chimpanzee optimization *** proposed strategy incorporates coupling compensation and power differentiation elements based on the traditional droop ***,it combines the angular frequency and voltage amplitude adjustments provided by the phase-locked loop-free pre-synchronization control *** pre-synchronization is achieved by regulating the virtual current to zero and aligning the photovoltaic storage hybrid inverter with the grid ***,two novel operators,learning and emotional behaviors are introduced to enhance the optimization precision of the chimpanzee *** operators ensure high-precision and high-reliability optimization of the droop control parameters for photovoltaic storage hybrid inverters.A Simulink model was constructed for simulation analysis,which validated the optimized control strategy’s ability to evenly distribute power under load *** strategy effectively mitigated transient voltage and current surges during mode ***,seamless and efficient switching between gridconnected and island modes was achieved for the photovoltaic storage hybrid *** enhanced energy utilization efficiency,in turn,offers robust technical support for grid stability.

关键词： Photovoltaic storage hybrid inverters modified chimpanzee optimization algorithm droop control seamless switching

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Dynamic event-triggered data-driven iterative learning bipartite tracking control for nonlinear MASs with prescribed performance

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Science China(information Sciences) 2025年第1期68卷 292-305页

作者： Tao SHI Wei-Wei CHE school of Mechatronic engineering and Automation Shanghai University State Key Laboratory of Synthetical Automation for Process Industries Northeastern University College of information Science and engineering Northeastern University

This article proposes a distributed dynamic event-triggered data-driven iterative learning control(DET-DDILC)scheme under a predefined performance to tackle the bipartite tracking control problem for multiagent systems(MASs). An improved dynamic linearization technique is utilized to convert the nonlinear MASs into an iterative linear data model. First,a peer-to-peer mapping function is introduced to map the constrained distributed system output homeomorphism to an unconstrained one. In addition, a DET mechanism based on a time-iteration-varying function is devised to conserve network communication resources. Based on the unconstrained transformation and the designed DET mechanism, the DET-DDILC algorithm is devised to ensure that the bipartite tracking performance of MASs can be within the preset range. Finally, the effectiveness and feasibility of the designed control scheme are demonstrated via a simulation case by a comparison.

关键词： bipartite tracking control prescribed performance event-triggered iterative learning control multiagent systems

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Joint Estimation of SOH and RUL for Lithium-Ion Batteries Based on Improved Twin Support Vector Machineh

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Energy engineering 2025年第1期122卷 243-264页

作者： Liyao Yang HongyanMa Yingda Zhang Wei He school of Electrical and information engineering Beijing University of Civil Engineering and ArchitectureBeijing100044China Institute of Distributed Energy Storage Safety Big Data Beijing100044China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing100044China

Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management ***,due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance,direct measurement of SOH and RUL is *** address these issues,the Twin Support Vector Machine(TWSVM)method is proposed to predict SOH and ***,the constant current charging time of the lithium battery is extracted as a health indicator(HI),decomposed using Variational Modal Decomposition(VMD),and feature correlations are computed using Importance of Random Forest Features(RF)to maximize the extraction of critical factors influencing battery performance ***,to enhance the global search capability of the Convolution Optimization Algorithm(COA),improvements are made using Good Point Set theory and the Differential Evolution *** Improved Convolution Optimization Algorithm(ICOA)is employed to optimize TWSVM parameters for constructing SOH and RUL prediction ***,the proposed models are validated using NASA and CALCE lithium-ion battery *** results demonstrate that the proposed models achieve an RMSE not exceeding 0.007 and an MAPE not exceeding 0.0082 for SOH and RUL prediction,with a relative error in RUL prediction within the range of[-1.8%,2%].Compared to other models,the proposed model not only exhibits superior fitting capability but also demonstrates robust performance.

关键词： State of health remaining useful life variational modal decomposition random forest twin support vector machine convolutional optimization algorithm

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Cooperative output regulation of heterogeneous directed multi-agent systems: a fully distributed model-free reinforcement learning framework

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Science China(information Sciences) 2025年

作者： Xiongtao SHI Yanjie LI Chenglong DU Huiping LI Chaoyang CHEN Weihua GUI Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics School of Mechanical Engineering and Automation Harbin Institute of Technology (Shenzhen) school of Automation Central South University school of Marine Science and Technology Northwestern Polytechnical University school of information and Electrical engineering Hunan University of Science and Technology

In this paper, the cooperative output regulation (COR) problem of a class of unknown heterogeneous multi-agent systems (MASs) with directed graphs is studied via a model-free reinforcement learning (RL) based fully distributed eventtriggered control (ETC) strategy. First, we consider the scenario that the exosystem is accessible globally to all agents, an internal model-based augmented algebraic Riccati equation (AARE) is constructed, and its solution is learned by the proposed model-free RL algorithm via online input-output data. Further, for the scenario that the exosystem is accessible only to its adjacent followers, the distributed observers are designed for each agent to get the state of the exosystem, and an internal modelbased fully distributed adaptive ETC protocol is then synthesized to construct the corresponding AARE, and the feedback gain matrix is learned in a model-free fashion. The model-free RL-based control protocol proposed in this paper can not only remove the prior knowledge of agents’ dynamics, but also release the dependence on global information by the adaptive event-triggered mechanism (ETM) and the new graph-based Lyapunov function. Finally, simulation results are illustrated to show the feasibility and effectiveness of the proposed control scheme.

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