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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

Secure beamforming and deployment design for rate-splitting multiple access-based UAV communications

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Science China(information Sciences) 2025年第1期68卷 342-356页

作者： Bing WANG Xiaofeng TAO Shujun HAN Huici WU Kai YANG Zhu HAN National engineering research center of Mobile Network Technologies Beijing University of Posts and Telecommunications Peng Cheng Laboratory Department of Electrical and Computer engineering University of Houston

Rate-splitting multiple access(RSMA) has recently gained attention as a potential robust multiple access(MA)scheme for upcoming wireless networks. Given its ability to efficiently utilize wireless resources and design interference management strategies, it can be applied to unmanned aerial vehicle(UAV) networks to provide convenient services for large-scale access ground users. However, due to the line-of-sight(LoS) broadcast nature of UAV transmission, information is susceptible to eavesdropping in RSMA-based UAV networks. Moreover, the superposition of signals at the receiver in such networks becomes complicated. To cope with the challenge, we propose a two-user multi-input single-output(MISO) RSMA-based UAV secure transmission framework in downlink communication networks. In a passive eavesdropping scenario, our goal is to maximize the sum secrecy rate by optimizing the transmit beamforming and deployment location of the UAV-base station(UAV-BS),while considering quality-of-service(QoS) constraints, maximum transmit power, and flight space limitations. To address the non-convexity of the proposed problem, the optimization problem is first decoupled into two subproblems. Then, the successive convex approximation(SCA) method is employed to solve each subproblem using different propositions. In addition, an alternating optimization(AO)-based location RSMA(L-RSMA) beamforming algorithm is developed to implement joint optimization to obtain the suboptimal solution. Numerical results demonstrate that(1) the proposed L-RSMA scheme yields a28.97% higher sum secrecy rate than the baseline L-space division multiple access(SDMA) scheme;(2) the proposed L-RSMA scheme improves the security performance by 42.61% compared to the L-non-orthogonal multiple access(NOMA) scheme.

关键词： rate-splitting multiple access UAV communication precoding optimization deployment location physical layer security

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A robust statistical prediction model for late-summer heavy precipitation days in North China

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Science China Earth Sciences 2025年第1期68卷 158-171页

作者： Shunli JIANG Tingting HAN Xin ZHOU Huijun WANG Zhicong YIN Xiaolei SONG Collaborative Innovation center on Forecast and Evaluation of Meteorological Disasters/Key Laboratory of Meteorological Disaster Ministry of Education Nanjing University for Information Science and Technology Nansen-Zhu International research Centre Institute of Atmospheric Physics Chinese Academy of Sciences Southern Marine Science and engineering Guangdong Laboratory (Zhuhai)

Recently, heavy precipitation(HP) events have occurred frequently in North China(NC), causing devastating economic losses and human fatalities. However, the short-term climate prediction of HP is quite limited. Combining year-to-year increment(DY) method and sliding correlations, we developed a robust seasonal prediction model for late-summer HP days(HPDs) in NC during 1982–2022, utilizing three independent predictors—February sea surface temperature(SST) in the Indian Ocean(SST＿IO), February snow depth over North Asia(SDE＿NA), and May melted snow depth in NC(MSDE＿NC). The SST＿IO anomalies affect NC's precipitation through the Pacific-Japan pattern. The SDE＿NA anomalies are associated with East Asian anomalous anticyclone by southeastern propagation of Rossby wave at Eurasia. The MSDE＿NC anomalies are followed by vertical motion and moisture anomalies in situ and thereby cause precipitation anomalies. This prediction model can well simulate the variations of the HPDs, with a correlation coefficient(CC) of 0.81(0.65) between the observed and predicted HPDs＿DY(HPDs＿anomaly). The percentage with the same sign for 15 extreme HPDs＿anomaly years(PSSE) is 100%.Moreover, in the cross-validation test during 1982–2022, the PSSE for HPDs＿anomaly is as high as 100%, along with a low rootmean-square error of 1.14. For independent hindcasts during 2013–2022, the CC between the observed and predicted HPDs＿DY(HPDs＿anomaly) is 0.93(0.83), together with high Nash-Sutcliffe efficiency(0.82) and agreement index(0.89). Specifically, the predictions are broadly consistent with the observations for 2015, 2016, 2017, 2021, and 2022, reflecting excellent performance of this prediction model of HPDs in NC.

关键词： Heavy precipitation at North China Year-to-year increment approach Robust seasonal prediction Sea surface temperature Snow depth

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Boosting cationic and anionic redox activity of Li-rich layered oxide cathodes via Li/Ni disordered regulation

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Journal of Energy Chemistry 2025年第1期100卷 533-543页

作者： Zewen Liu Zhen Wu Hao Wang Xudong Zhang Yuanzhen Chen Yongning Liu Shengwu Guo Shenghua Chen Yanli Nan Yan Liu State Key Laboratory for Mechanical Behavior of Materials Xi'an Jiaotong University School of Materials Science and engineering Xi'an University of Architecture and Technology Hefei Advanced Computing center Operation Management Corp Ltd Network information center Xi'an Jiaotong University

Lithium-rich layered oxides （LLOs） are increasingly recognized as promising cathode materials for nextgeneration high-energy-density lithium-ion batteries （LIBs）.However,they suffer from voltage decay and low initial Coulombic efficiency （ICE） due to severe structural degradation caused by irreversible O ***,we introduce a three-in-one strategy of increasing Ni and Mn content,along with Li/Ni disordering and TM–O covalency regulation to boost cationic and anionic redox activity simultaneously and thus enhance the electrochemical activity of *** target material,Li1.2Ni0.168Mn0.558Co0.074O2（L1）,exhibits an improved ICE of 87.2%and specific capacity of 293.2 mA h g-1and minimal voltage decay of less than 0.53 m V cycle-1over 300 cycles at 1C,compared to Li1.2Ni0.13Mn0.54Co0.13O2（Ls）(274.4 mA h g-1for initial capacity,73.8%for ICE and voltage decay of 0.84 mV/cycle over 300 cycles at 1C).Theoretical calculations reveal that the density of states （DOS） area near the Fermi energy level for L1 is larger than that of Ls,indicating higher anionic and cationic redox reactivity than ***,L1 exhibits increased O-vacancy formation energy due to higher Li/Ni disordering of 4.76%（quantified by X-ray diffraction Rietveld refinement） and enhanced TM–O covalency,making lattice O release more difficult and thus improving electrochemical *** increased Li/Ni disordering also leads to more Ni2+presence in the Li layer,which acts as a pillar during Li+de-embedding,improving structural *** research not only presents a viable approach to designing low-Co LLOs with enhanced capacity and ICE but also contributes significantly to the fundamental understanding of structural regulation in high-performance LIB cathodes.

关键词： Ice

IDSSCNN-XgBoost:Improved Dual-Stream Shallow Convolutional Neural Network Based on Extreme Gradient Boosting Algorithm for Micro Expression Recognition

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Computers, Materials & Continua 2025年第1期82卷 729-749页

作者： Adnan Ahmad Zhao Li Irfan Tariq Zhengran He School of information Science and engineering Southeast UniversityNanjing210016China

Micro-expressions(ME)recognition is a complex task that requires advanced techniques to extract informative features fromfacial *** deep neural networks(DNNs)with convolutional structures have been ***,unlike DNNs,shallow convolutional neural networks often outperform deeper models in mitigating overfitting,particularly with small ***,many of these methods rely on a single feature for recognition,resulting in an insufficient ability to extract highly effective *** address this limitation,in this paper,an Improved Dual-stream Shallow Convolutional Neural Network based on an Extreme Gradient Boosting Algorithm(IDSSCNN-XgBoost)is introduced for ME *** proposed method utilizes a dual-stream architecture where motion vectors(temporal features)are extracted using Optical Flow TV-L1 and amplify subtle changes(spatial features)via EulerianVideoMagnification(EVM).These features are processed by IDSSCNN,with an attention mechanism applied to refine the extracted effective *** outputs are then fused,concatenated,and classified using the XgBoost *** comprehensive approach significantly improves recognition accuracy by leveraging the strengths of both temporal and spatial information,supported by the robust classification power of *** proposed method is evaluated on three publicly available ME databases named Chinese Academy of Sciences Micro-expression Database(CASMEII),Spontaneous Micro-Expression Database(SMICHS),and Spontaneous Actions and Micro-Movements(SAMM).Experimental results indicate that the proposed model can achieve outstanding results compared to recent *** accuracy results are 79.01%,69.22%,and 68.99%on CASMEII,SMIC-HS,and SAMM,and the F1-score are 75.47%,68.91%,and 63.84%,*** proposed method has the advantage of operational efficiency and less computational time.

关键词： ME recognition dual stream shallow convolutional neural network euler video magnification TV-L1 XgBoost

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

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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

Substrate bias effects in p-channel GaN-on-Si transistors

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Science China(information Sciences) 2025年第1期68卷 404-405页

作者： Mengyao ZHAO Jie MA Lanlan YANG Chuanqi PAN Denggui WANG Jianjun ZHOU Sheng LI Jiaxing WEI Long ZHANG Siyang LIU Weifeng SUN National ASIC System engineering research center School of Integrated Circuits Southeast University National center of Technology Innovation for Electronic Design Automation Southeast University National Key Laboratory of Solid-State Microwave Devices and Circuits

The drain current （ID） of the n-channel GaN field-effecttransistor （n-FET） is influenced by the substrate-to-source bias voltage (VBS)（substrate bias effect）[1].Actually,nonzero VBSis also likely to occur in p-FET in real application,because the source electrode of p-FET is usually connected to high *** study reports the dependency between IDand VBSin p-FET for the first time,and analyzes the underlying mechanisms from the perspective of vertical electric field （EF） distribution.

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Fractal autoencoder with redundancy regularization for unsupervised feature selection

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Science China(information Sciences) 2025年

作者： Meiting SUN Fangyu LI Honggui HAN School of information Science and Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System engineering research center of Digital Community Ministry of Education Beijing Institute of Artificial Intelligence(Beijing)

Feature selection is a crucial step in data preprocessing because feature selection reduces the dimensionality of data by eliminating irrelevant and redundant features. Since manual labeling is expensive, unsupervised feature selection has received increasing attention in recent years. However, existing unsupervised feature selection methods tend to prioritize selecting highly correlated features over exploring feature diversity. Thus, a regularized fractal autoencoder (RFAE) method is proposed to select informative features in an unsupervised way. Specifically, the fractal autoencoder network extends autoencoders to construct a correspondence neural network and a selection neural network. The correspondence neural network exploits interfeature correlations and the selection neural network selects the informative features. A redundancy regularization strategy consists of a redundancy elimination regularization term based on the dependency between features and a sparse regularization term based on the group lasso. The redundancy regularization strategy eliminates feature subset redundancy and enhances network generalization ability. Extensive experimental results on six publicly available datasets show that the proposed RFAE outperforms the compared methods regarding clustering accuracy and classification accuracy. Moreover, the proposed RFAE achieves acceptable computation efficiency.

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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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