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Jointly Optimizing the Energy and Time for Multi-UAV 3-D Coverage of Terrestrial Regions

IEEE Transactions on Mobile Computing 2025年

作者： Gong, Hao Huang, Baoqi Jia, Bing Hao, Lifei Shi, Zhenwei Inner Mongolia University Inner Mongolia Key Laboratory of Wireless Networking and Mobile Computing Engineering Research Center of Ecological Big Data Ministry of Education College of Computer Science Hohhot010021 China Inner Mongolia University College of Computer Science Hohhot010021 China Beihang University Image Processing Center School of Astronautics Beijing100191 China

Multi-rotor unmanned aerial vehicles (UAVs) have been widely employed in various sensing tasks, e.g., environmental monitoring and disaster rescuing, many of which often require full coverage of terrestrial regions by UAVs. Efforts have been devoted to minimizing one of two objectives, i.e., energy consumptions and time costs of UAVs fulfilling such tasks, whereas it is still challenging to jointly optimize both objectives due to their complicated interdependent relationship. Therefore, this paper deals with the tasks of sensing terrestrial regions with multiple UAVs, and focuses on the three-dimensional (3-D) coverage problem by formulating a multi-objective optimization problem of jointly minimizing both objectives. Specifically, in order to optimize energy consumption effectively, an advanced closed-form energy consumption model for multi-rotor UAVs is developed based on a rigorous theoretical analysis by introducing the influences of torque and acceleration, which are often ignored by existing heuristic models. Moreover, considering the NP-hardness of the problem, an innovative swarm intelligence optimization framework is established by leveraging a multitasking learning pattern to exploit cross-task knowledge transfer and adopting an improved multi-objective salp swarm algorithm. Therein, two novel operators, i.e., a variable characteristic-guided hybrid solution initialization operator and a large-scale search-space-oriented multi-mechanism solution update operator, are designed to handle continuous, discrete and even high-dimensional variables involved. Real-world experiments validate the proposed energy model due to the reduction of power consumption estimation error by up to 59% compared to baselines, and besides, extensive simulations demonstrate that the proposed algorithm significantly outperforms the benchmarks in terms of both energy consumptions and time costs. © 2002-2012 IEEE.

关键词： Energy utilization

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Spatio-temporal Dynamics and Drivers of Ecological Quality in Yulin City Using the MRSEI Model

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Journal of Resources and Ecology 2025年第2期16卷 340-355页

作者： MU Weichen HE Zhilin CHEN Yanglong GAO Dongkai YUE Tianming QIN Fen College of Geographical Sciences Faculty of Geographical Science and EngineeringHenan UniversityZhengzhou450046China Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions Ministry of EducationHenan UniversityKaifengHenan 475004China Henan Industrial Technology Academy of Spatio-Temporal Big Data Henan UniversityKaifengHenan 475004China Henan Technology Innovation Center of Spatial-Temporal Big Data Henan UniversityZhengzhou 450046China.

Urbanization has resulted in growing ecological pressures on cities,necessitating assessments of urban ecological ***-term characterization of regional dynamics and drivers is critical for environmental *** study proposes an enhanced ecological quality model(MRSEI)incorporating vegetation cover and EVI rather than just *** MRSEI model was applied to analyse ecological quality in Yulin City during 2000-2018 using Landsat TM/OLI data on Google Earth *** detectors also quantified anthropo-genic and environmental influences on the study *** results are summarized as follows:(1)MRSEI showed an average correlation coefficient of 0.840 with other indices,demonstrating higher representativeness than indi-vidual *** principal component analysis indicated a 12.88%increase in explained *** also exhibited significantly improved identification of roads,villages,and unused lands over RSEI,better matching ground conditions,and suitability for regional ecological assessment.(2)During 2000-2020,the average MRSEI in Yulin City was 0.481,peaking at 0.518 in 2018,indicating general ecological improvement over ***,conditions were better in the southeast than *** 38.81%of the area showed significant improvement,10.15%exhibited significant deterioration,concentrated in western Dingbian and Jingbian counties,highlighting areas requiring enhanced protection.(3)Ecological conditions in Yulin City remained stable over ***-high clusters were concentrated in eastern counties(Qingjian,Wubao,Jia,Fugu)and central lower-altitude areas near Yokoyama and ***-low clusters predominated in the northern Yuyang desert and high-altitude western Dingbian regions.(4)Enhanced vegetation cover had the greatest influence in improving Yulin’s ecological *** was the most impactful environmental driver,while precipitation and land use change interactions showed the strongest combined *** cont

关键词： Google Earth Engine improved remote sensing-based ecological indicator temporal and spatial variation geographical detector

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EPSCL: zero-shot segmentation with embedded pre-trained model and supervised contrastive learning on NIR-II vessel images

EPSCL: zero-shot segmentation with embedded pre-trained mode...

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Medical Imaging 2025: Image Processing

作者： Han, keyi Xiao, Anqi Li, Chunzhao Fu, Lidan Tian, Jie Hu, Zhenhua CAS Key Laboratory of Molecular Imaging Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Department of Neurosurgery Beijing Tiantan Hospital Capital Medical University Beijing100070 China China National Clinical Research Center for Neurological Diseases Beijing Tiantan Hospital Beijing100070 China Beijing Advanced Innovation Center for Big Data-based Precision Medicine School of Engineering Medicine Beihang University Beijing100191 China Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and Technology Xidian University Xi'an710071 China National Key Laboratory of Kidney Diseases Beijing100853 China

ISBN: (纸本)9781510685901

Blood vessel analysis is significant in many clinical domains, such as laryngology, ophthalmology, and neurosurgery. The second near-infrared window (NIR-II) fluorescence imaging is a promising technique in providing high-resolution and high-contrast blood vessel images, with improved depth penetration for better visualization of deeper blood vessels and structures. However, NIR-II vessel segmentation task is a zero-shot problem, since the lack of NIR-II blood vessel datasets and lots of labor to build the datasets. Moreover, existing methods face challenges in segmenting NIR-II fluorescence images due to the complexity introduced by the imaging modality's capacity to reveal intricate details, such as overlapping deeper vessels and mixed signals from deeper tissue organs and vessels. Therefore, we propose a zero-shot segmentation method for NIR-II fluorescence vessel images, which is an encoder-decoder framework with an embedded pre-trained model and a supervised contrastive learning strategy (EPSCL). Experiments on complex NIR-II fluorescence images show that EPSCL gets better performance and segments more vascular details compared with other popular methods. The proposed EPSCL has a great potential to serve as an effective segmentation method for NIR-II fluorescent images and assist blood vessel analysis. © 2025 SPIE.

关键词： Image segmentation

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UAV Swarm-enabled Collaborative Post-disaster Communications in Low Altitude Economy via a Two-stage Optimization Approach

arXiv

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arXiv 2025年

作者： Zheng, Xiaoya Sun, Geng Li, Jiahui Wang, Jiacheng Wu, Qingqing Niyato, Dusit Jamalipour, Abbas College of Computer Science d Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University 639798 Singapore College of Computing and ta Science Nanyang Technological University 639798 Singapore Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China School of Electrical and Computer Engiering The University of Sydney SydneyNSW2006 Australia

The low-altitude economy (LAE), as a new economic paradigm, plays an indispensable role in cargo transportation, healthcare, infrastructure inspection, and especially post-disaster communication. Specifically, unmanned aerial vehicles (UAVs), as one of the core technologies of the LAE, can be deployed to provide communication coverage, facilitate data collection, and relay data for trapped users, thereby significantly enhancing the efficiency of post-disaster response efforts. However, conventional UAV self-organizing networks exhibit low reliability in long-range cases due to their limited onboard energy and transmit ability. Therefore, in this paper, we design an efficient and robust UAV-swarm enabled collaborative self-organizing network to facilitate post-disaster communications. Specifically, a ground device transmits data to UAV swarms, which then use collaborative beamforming (CB) technique to form virtual antenna arrays and relay the data to a remote access point (AP) efficiently. Then, we formulate a rescue-oriented post-disaster transmission rate maximization optimization problem (RPTRMOP), aimed at maximizing the transmission rate of the whole network. Given the challenges of solving the formulated RPTRMOP by using traditional algorithms, we propose a two-stage optimization approach to address it. In the first stage, the optimal traffic routing and the theoretical upper bound on the transmission rate of the network are derived. In the second stage, we transform the formulated RPTRMOP into a variant named V-RPTRMOP based on the obtained optimal traffic routing, aimed at rendering the actual transmission rate closely approaches its theoretical upper bound by optimizing the excitation current weight and the placement of each participating UAV via a diffusion model-enabled particle swarm optimization (DM-PSO) algorithm. Simulation results show the effectiveness of the proposed two-stage optimization approach in improving the transmission rate of the construct

关键词： Unmanned aerial vehicles (UAV)

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SSAM: a span spatial attention model for recognizing named entities

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Scientific Reports 2025年第1期15卷 1-13页

作者： Wang, Kai Wen, Kunjian Chen, Yanping Qin, Yongbin Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and Technology Guizhou University Guiyang China State Key Laboratory of Public Big Data Guizhou University Guiyang China ByteDance Ltd. Beijing China

Mapping a sentence into a two-dimensional (2D) representation can flatten nested semantic structures and build multi-granular span dependencies in named entity recognition. Existing approaches to recognizing named entities often classify each entity span independently, which ignores the spatial structures between neighboring spans. To address this issue, we propose a Span Spatial Attention Model (SSAM) that consists of a token encoder, a span generation module, and a 2D spatial attention network. The SSAM employs a two-channel span generation strategy to capture multi-granular features. Unlike traditional attention implemented on a sequential sentence representation, spatial attention is applied to a 2D sentence representation, enabling the model to learn the spatial structures of the sentence. This allows the SSAM to adaptively encode important features and suppress non-essential information in the 2D sentence representation. Experimental results on the GENIA, ACE2005, and ACE2004 datasets demonstrate that our proposed model achieves state-of-the-art performance, with F1-scores of 81.82%, 89.04%, and 89.24%, respectively. The code is available at https://***/Gzuwkj/SpatialAttentionForNer. © The Author(s) 2025.

关键词： Named entity recognition Natural language processing Spatial attention

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GeoMinLM: A Large Language Model in Geology and Mineral Survey in Yunnan Province

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Ore Geology Reviews 2025年 182卷

作者： Fu, Yu Wang, Mingguo Wang, Chengbin Dong, Shuaixian Chen, Jianguo Wang, Jiyuan Yu, Hongping Huang, Jing Chang, Liheng Wang, Bo School of Earth Resources & Ministry of Natural Resources Key Laboratory of Resource Quantitative Evaluation and Information Engineering China University of Geosciences Wuhan430074 China Key Laboratory of Geological Survey and Evaluation of Ministry of Education China University of Geosciences Wuhan430074 China Yunnan Geological Big Data Center Geological Survey and Mapping Institute of Yunnan Province Kunming650218 China Yunnan Gold & Mining Group Co. Ltd. Kunming650299 China International Mining Research Center China Geological Survey Beijing100037 China

In recent years, the development of artificial intelligence and big data technologies has led to the advancement of tools and solutions for transforming the geological and mineral survey paradigm, which requires a large amount of geological knowledge in a complex and arduous working environment. The large language model (LLM) has a significant advantage in answering generative intelligent questions. However, LLMs for general fields have limitations in answering professional questions in a vertical domain like geology. To overcome this challenge, we proposed and developed GeoMinLM, an LLM for geological and mineral exploration scenarios in Yunnan Province, and explored its applications in intelligent Q&A. Leveraging a proprietary dataset of 5.16 million words in geology and mineral exploration, we trained GeoMinLM based on Baichuan-2, achieving superior performance through fine-tuning and hyperparameter optimization. By integrating expert knowledge via a knowledge graph, we significantly reduced hallucinations and enhanced professionalism. This study proves that GeoMinLM is helpful for accurate information retrieval and knowledge dissemination, thereby supporting the intelligent advancement of geological and mineral fields. © 2025 The Author(s)

关键词： knowledge graph

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Frequency-Assisted Temporal Upsampling Artifacts Representation Learning for Face Forgery Detection

IEEE Transactions on Biometrics, Behavior, and Identity Scie...

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IEEE Transactions on Biometrics, Behavior, and Identity Science 2025年

作者： Sun, Rui Yu, Xiaolu Wang, Fei Da, Zikai Zhang, Yifan Gao, Jun Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) School of Computer and Information Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei 230009 China

Recently, the highly realistic deepfake images have aroused the potential for deepfake abuse, and both the detection and its generalization remain challenging. Existing deepfake video detection methods strive to capture distinguishing features between fake and real faces through temporal modeling. However, these works provide local supervision between sampled video frames, but overlook intra-frame generative artifacts, which can function as an useful indicators for detection. To mitigate the issue, we propose a temporal-frequential convolutional network based on the representation of upsampling artifacts, which can automatically capture incoherent artifacts representation in different ends. Specifically, we design the Upsampling Artifacts Representation Module (UARM), which captures upsampling artifacts introduced in generation end based on the relationship between pixels, and then we probe the deep coupling of intra-frame global frequency information with inter-frame incoherence, and design the Frequency-Assisted Temporal Incoherence Module (FATIM), which can facilitate the detection of temporal information in RGB domain by frequency domain information, and the Self-tuned Convolutions Module (SCM) is designed to supplement information and adjust it automatically in the learning process. In addition, the Information Fusion Module (IFM) is designed to couple multiple information and establish a more comprehensive representation. With the above modules, our proposed method can learn robust features in both generation end and detection end. Extensive experiments demonstrate that our method outperforms other state-of-the-art video detection methods by a large margin on the challenging DFDC and FF++ in low-quality. © 2019 IEEE.

关键词： DeepFake detection Multiple end Temporal-frequential convolution Upsampling artifacts

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Study on the Characteristics of Cross-Domain knowledge Diffusion from Science to Policy: Evidence from Overton data

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Proceedings of the Association for Information Science and Technology 2023年第1期60卷 368-378页

作者： Ren, Chao Yang, Menghui School of Information Resource Management Renmin University of China China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education China

The cross-domain knowledge diffusion from science to policy is a prevalent phenomenon that demands academic attention. To investigate the characteristics of cross-domain knowledge diffusion from science to policy, this study suggests using the citation of policies to scientific articles as a basis for quantifying the diffusion strength, breadth, and speed. The study reveals that the strength and breadth of cross-domain knowledge diffusion from scientific papers to policies conform to a power-law distribution, while the speed follows a logarithmic normal distribution. Moreover, the papers with the highest diffusion strength, breadth, and fastest diffusion speed are predominantly from world-renowned universities, scholars, and top journals. The papers with the highest diffusion strength and breadth are mostly from social sciences, especially economics, those with the fastest diffusion speed are mainly from medical and life sciences, followed by social sciences. The findings indicate that cross-domain knowledge diffusion from science to policy follows the Matthew effect, whereby individuals or institutions with high academic achievements are more likely to achieve successful cross-domain knowledge diffusion. Furthermore, papers in the field of economics tend to have the higher cross-domain knowledge diffusion strength and breadth, while those in medical and life sciences have the faster cross-domain knowledge diffusion speed. 86 Annual Meeting of the Association for Information Science & Technology | Oct. 27 – 31, 2023 | London, United Kingdom. Author(s) retain copyright, but ASIS&T receives an exclusive publication license.

关键词： Diffusion

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CroPrompt: Cross-task Interactive Prompting for Zero-shot Spoken Language Understanding

CroPrompt: Cross-task Interactive Prompting for Zero-shot Sp...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Libo Qin Fuxuan Wei Qiguang Chen Jingxuan Zhou Shijue Huang Jiasheng Si Wenpeng Lu Wanxiang Che School of Computer Science and Engineering Central South University China Key Laboratory of Data Intelligence and Advanced Computing in Provincial Universities Soochow University China Research Center for Social Computing and Information Retrieval Harbin Institute of Technology China Harbin Institute of Technology Shenzhen China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology (Shandong Academy of Sciences)

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Slot filling and intent detection are two highly correlated tasks in spoken language understanding (SLU). Recent SLU research attempts to explore zero-shot prompting techniques in large language models to alleviate the data scarcity problem. Nevertheless, the existing prompting work ignores the cross-task interaction information for SLU, which leads to sub-optimal performance. To solve this problem, we present the pioneering work of Cross-task Interactive Prompting (CroPrompt) for SLU, which enables the model to interactively leverage the information exchange across the correlated tasks in SLU. Additionally, we further introduce a multi-task self-consistency mechanism to mitigate the error propagation caused by the intent information injection. We conduct extensive experiments on the standard SLU benchmark and the results reveal that CroPrompt consistently outperforms the existing prompting approaches. In addition, the multi-task self-consistency mechanism can effectively ease the error propagation issue, thereby enhancing the performance. We hope this work can inspire more research on cross-task prompting for SLU.

关键词： Large language models Signal processing Benchmark testing Multitasking Filling Acoustics Speech processing Information exchange Standards

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SoAy: A Solution-based LLM API-using Methodology for Academic Information Seeking 25

SoAy: A Solution-based LLM API-using Methodology for Academi...

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Proceedings of the 31st ACM SIGKDD Conference on knowledge Discovery and data Mining V.1

作者： Yuanchun Wang Jifan Yu Zijun Yao Jing Zhang Yuyang Xie Shangqing Tu Yiyang Fu Youhe Feng Jinkai Zhang Jingyao Zhang Bowen Huang Yuanyao Li Huihui Yuan Lei Hou Juanzi Li Jie Tang School of Information Renmin University of China Beijing China & Key Laboratory of Data Engineering and Knowledge Engineering MOE Beijing China Institute of Education Tsinghua University Beijing China Department of Computer Science and Technology Tsinghua University Beijing China School of Information Renmin University of China Beijing China & Engineering Research Center of Database and Business Intelligence MOE Beijing China School of Information Renmin University of China Beijing China Zhipu AI Beijing China

ISBN: (纸本)9798400712456

Applying large language models (LLMs) to academic API usage shows promise in reducing researchers' efforts to seek academic information. However, current LLM methods for using APIs struggle with the complex API coupling commonly encountered in academic queries. To address this, we introduce SoAy, a solution-based LLM methodology for academic information seeking. SoAy enables LLMs to generate code for invoking APIs, guided by a pre-constructed API calling sequence referred to as a solution. This solution simplifies the model's understanding of complex API relationships, while the generated code enhances reasoning efficiency. LLMs are aligned with this solution-oriented, code-based reasoning method by automatically enumerating valid API coupling sequences and transforming them into queries and executable *** evaluate SoAy, we introduce SoAyBench, an evaluation benchmark accompanied by SoAyEval, built upon a cloned environment of APIs from AMiner. Experimental results demonstrate a 34.58-75.99% performance improvement compared to state-of-the-art LLM API-based baselines. All datasets, codes, tuned models, and deployed online services are publicly accessible at https://***/RUCKBReasoning/SoAy.

关键词： academic information seeking

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