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RecStitchNet:Learning to stitch images with rectangular boundaries

Computational Visual Media 2024年第4期10卷 687-703页

作者： Yun Zhang Yu-Kun Lai Lang Nie Fang-Lue Zhang Lin Xu Key Lab of Film and TV Media Technology of Zhejiang Province College of Media EngineeringCommunication University of ZhejiangHangzhou 310018China School of Computer Science and Informatics Cardiff UniversityCardiff CF244AGUK Institute of Information Science Beijing Jiaotong UniversityBeijing 100091China School of Engineering and Computer Science Victoria University of WellingtonWellington 6012New Zealand STEM University of South AustraliaAdelaide 5095Australia

Irregular boundaries in image stitching naturally occur due to freely moving *** deal with this problem,existing methods focus on optimizing mesh warping to make boundaries regular using the traditional explicit ***,previous methods always depend on hand-crafted features(e.g.,keypoints and line segments).Thus,failures often happen in overlapping regions without distinctive *** this paper,we address this problem by proposing RecStitchNet,a reasonable and effective network for image stitching with rectangular *** that both stitching and imposing rectangularity are non-trivial tasks in the learning-based framework,we propose a three-step progressive learning based strategy,which not only simplifies this task,but gradually achieves a good balance between stitching and imposing *** the first step,we perform initial stitching by a pre-trained state-of-the-art image stitching model,to produce initially warped stitching results without considering the boundary ***,we use a regression network with a comprehensive objective regarding mesh,perception,and shape to further encourage the stitched meshes to have rectangular boundaries with high content ***,we propose an unsupervised instance-wise optimization strategy to refine the stitched meshes iteratively,which can effectively improve the stitching results in terms of feature alignment,as well as boundary and structure *** to the lack of stitching datasets and the difficulty of label generation,we propose to generate a stitching dataset with rectangular stitched images as pseudo-ground-truth labels,and the performance upper bound induced from the it can be broken by our unsupervised *** and quantitative results and evaluations demonstrate the advantages of our method over the state-of-the-art.

关键词： image stitching boundaries convolutional neural network

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Automating Maritime Risk Data Collection and Identification Leveraging Large Language Models 24

Automating Maritime Risk Data Collection and Identification ...

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24th IEEE International Conference on Data Mining Workshops, ICDMW 2024

作者： Huang, Donghao Fu, Xiuju Yin, Xiaofeng Pen, Haibo Wang, Zhaoxia Singapore Management University School of Computing and Information Systems Singapore Mastercard Research and Development Singapore Agency for Science Technology and Research Institute of High Performance Computing Singapore Tianjin University School of Electrical and Information Engineering China

ISBN: (纸本)9798331530631

Maritime risk research is crucial yet challenging for improving safety, efficiency, and sustainability in maritime operations. This paper presents an innovative method for automating the collection and identification of risk data related to global maritime risks from news sources, addressing the limitations of traditional manual methods. To evaluate the proposed method, different learning-based models, including conventional machine learning approaches and advanced Large Language Models (LLMs) such as GPT-4 and LLaMA-3.1, are comprehensively studied for comparison. In addition, not only do we use popular evaluation metrics to assess the proposed method, but we also introduce a new evaluation metric, called the "Ratio of Valid Categories (RVC),"to evaluate model reliability. The merits of the proposed method are demonstrated across different evaluation metrics. The research results show that the proposed LLM-based methods, particularly the GPT-4-based method, consistently outperform traditional models, significantly improving both the efficiency and accuracy of maritime risk data collection and identification. Our findings contribute to the expanding literature on LLM applications in risk management, demonstrating their potential to transform data collection and identification practices. © 2024 IEEE.

关键词： Contrastive Learning

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Novel Optimized Feature Selection Using Metaheuristics Applied to Physical Benchmark Datasets

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Computers, Materials & Continua 2023年第2期74卷 4027-4041页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Fadwa Alrowais Sunil Kumar Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt School of Computer Science University of Petroleum and Energy StudiesDehradun248001India Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

In data mining and machine learning,feature selection is a critical part of the process of selecting the optimal subset of features based on the target *** are 2n potential feature subsets for every n features in a dataset,making it difficult to pick the best set of features using standard ***,in this research,a new metaheuristics-based feature selection technique based on an adaptive squirrel search optimization algorithm(ASSOA)has been *** using metaheuristics to pick features,it is common for the selection of features to vary across runs,which can lead to *** of this,we used the adaptive squirrel search to balance exploration and exploitation duties more evenly in the optimization *** the selection of the best subset of features,we recommend using the binary ASSOA search strategy we developed *** to the suggested approach,the number of features picked is reduced while maximizing classification accuracy.A ten-feature dataset from the University of California,Irvine(UCI)repository was used to test the proposed method’s performance *** other state-of-the-art approaches,including binary grey wolf optimization(bGWO),binary hybrid grey wolf and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hybrid GWO and genetic algorithm 4028 CMC,2023,vol.74,no.2(bGWO-GA),binary firefly algorithm(bFA),and *** results confirm the superiority and effectiveness of the proposed algorithm for solving the problem of feature selection.

关键词： Metaheuristics adaptive squirrel search algorithm optimization methods binary optimizer

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An automatic isotropic/anisotropic hybrid grid generation technique for viscous flow simulations based on an artificial neural network

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Chinese Journal of Aeronautics 2022年第4期35卷 102-117页

作者： Peng LU Nianhua WANG Xinghua CHANG Laiping ZHANG Yadong WU School of Information Engineering Southwest University of Science and TechnologyMianyang 621010China School of Intelligent Manufacturing Engineering Chongqing University of Arts and ScienceChongqing 402160China Stake Key Laboratory of Aerodynamics China Aerodynamics Research and Development CenterMianyang 621000China Unmanned Systems Research Center National Innovation Institute of Defense TechnologyBeijing 100071China School of Computer Science and Technology Sichuan University of Science&EngineeringYibin 644005China

Based on the author’s previous research, a novel hybrid grid generation technique is developed by introducing an Artificial Neural Network(ANN) approach for realistic viscous flow simulations. An initial hybrid grid over a typical geometry with anisotropic quadrilaterals in the boundary layer and isotropic triangles in the off-body region is generated by the classical mesh generation method to train two ANNs on how to predict the advancing direction of the new point and to control the grid size. After inputting the initial discretized fronts, the ANN-based Advancing Layer Method(ALM) is adopted to generate the anisotropic quadrilaterals in boundary layers. When the high aspect ratio of the anisotropic grid reaches a specified value, the ANN-based Advancing Front Method(AFM) is adopted to generate isotropic triangles in the off-body computational *** initial isotropic triangles are smoothed to further improve the grid quality. Three typical cases are tested and compared with experimental data to validate the effectiveness of grids generated by the ANN-based hybrid grid generation method. The experimental results show that the two ANNs can predict the advancing direction and the grid size very well, and improve the adaptability of the isotropic/anisotropic hybrid grid generation for viscous flow simulations.

关键词： Advancing front method Advancing layer method Anisotropic quadrilateral grid generation Artificial neural network Isotropic triangular grid generation Machine learning

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A New Tree Structure for Local Diagnosis

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Journal of information science and engineering 2024年第2期40卷 231-244页

作者： Chen, Meirun Li, Xiao-Yan Lin, Cheng-Kuan Pai, Kung-Jui School of Mathematics and Statistics Xiamen University of Technology Xiamen361024 China College of Computer and Data Science Fuzhou University Fuzhou350108 China Department of Computer Science National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Undergraduate Degree Program of Systems Engineering and Technology National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Computer Science and Information Engineering Chung Cheng Institute of Technology National Defense University Taoyuan30010 Taiwan Department of Industrial Engineering and Management Ming Chi University of Technology New Taipei City243303 Taiwan

Diagnosability is an important parameter to measure the fault tolerance of a multiprocessor system. If we only care about the state of a node, instead of doing the global diagnosis, Hsu and Tan proposed the idea of local diagnosis. Chiang and Tan provided an extended star structure to diagnose a node under comparison model. In this work, we evaluate the local diagnosability better by proposing a tree structure around this node. We provide the corresponding algorithm to diagnose the node. Simulation results are presented for different failure probability of a node in the tree and different percentage of faulty nodes in the tree, showing the performance of our algorithm. © 2024 Institute of information science. All rights reserved.

关键词： comparison model diagnosis algorithm fault diagnosis local diagnosability tree

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Self-powered sensor based on compressible ionic gel electrolyte for simultaneous determination of temperature and pressure

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InfoMat 2024年第7期6卷 62-75页

作者： Junjie Zou Yanan Ma Chenxu Liu Yimei Xie Xingyao Dai Xinhui Li Shuxuan Li Shaohui Peng Yang Yue Shuo Wang Ce-Wen Nan Xin Zhang State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center of Smart Materials and Devices&International School of Materials Science and EngineeringWuhan University of TechnologyWuhanthe People's Republic of China Hubei Key Laboratory of Energy Storage and Power Battery School of MathematicsPhysics and Optoelectronic EngineeringHubei University of Automotive TechnologyShiyanthe People's Republic of China Information Materials and Intelligent Sensing Laboratory of Anhui Province Key Laboratory of Structure and Functional Regulation of Hybrid Materials of Ministry of EducationInstitutes of Physical Science and Information TechnologyAnhui UniversityHefeithe People's Republic of China State Key Lab of New Ceramics and Fine Processing School of Materials Science and EngineeringTsinghua UniversityBeijingthe People's Republic of China

The simultaneous detection of multiple stimuli,such as pressure and temperature,has long been a persistent challenge for developing electronic skin(eskin)to emulate the functionality of human ***,the demand for integrated power supply units is an additional pressing concern to achieve its lightweightness and ***,we propose a self-powered dual temperature–pressure(SPDM)sensor,which utilizes a compressible ionic gel electrolyte driven by the potential difference between MXene and Al *** SPDM sensor exhibits a rapid and timely response to changes in pressure-induced deformation,while exhibiting a slow and hysteretic response to temperature *** distinct response characteristics enable the differentiation of current signals generated by different stimuli through machine learning,resulting in an impressive accuracy rate of 99.1%.Furthermore,the developed SPDM sensor exhibits a wide pressure detection range of 0–800 kPa and a broad temperature detection range of 5–75C,encompassing the environmental conditions encountered in daily human *** dual-mode coupled strategy by machine learning provides an effective approach for temperature and pressure detection and discrimination,showcasing its potential applications in wearable electronics,intelligent robots,human–machine interactions,and so on.

关键词： e-skin gel electrolyte machine learning self-powered dual-mode sensor

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A platform for integrated spectrometers based on solution-processable semiconductors

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Light(science & Applications) 2023年第9期12卷 1739-1748页

作者： Yanhao Li Xiong Jiang Yimu Chen Yuhan Wang Yunkai Wu De Yu Kaiyang Wang Sai Bai Shumin Xiao Qinghai Song Ministry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information System Guangdong Provincial Key Laboratory of Semiconductor Optoelectronic Materials and Intelligent Photonic SystemsHarbin Institute of Technology(Shenzhen)ShenzhenGuangdong 518055China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengduSichuan 611731China Collaborative Innovation Center of Extreme Optics Shanxi UniversityTaiyuanShan`xi 030006China

Acquiring real-time spectral information in point-of-care diagnosis,internet-of-thing,and other lab-on-chip applications require spectrometers with hetero-integration capability and miniaturized *** to conventional semiconductors integrated by heteroepitaxy,solution-processable semiconductors provide a much-flexible integration platform due to their solution-processability,and,therefore,more suitable for the multi-material integrated ***,solution-processable semiconductors are usually incompatible with the micro-fabrication *** work proposes a facile and universal platform to fabricate integrated spectrometers with semiconductor substitutability by unprecedently involving the conjugated mode of the bound states in the continuum(conjugated-BIC)***,exploiting the conjugated-BIC photonics,which remains unexplored in conventional lasing studies,renders the broadband photodiodes with ultra-narrowband detection ability,detection wavelength tunability,and on-chip integration ability while ensuring the device *** based on these ultra-narrowband photodiode arrays exhibit high spectral resolution and wide/tunable spectral *** fabrication processes are compatible with solution-processable semiconductors photodiodes like perovskites and quantum dots,which can be potentially extended to conventional *** from the spectrometers directly constitute the incident spectra without being computation-intensive,latency-sensitive,and *** an example,the integrated spectrometers based on perovskite photodiodes are capable of realizing narrowband/broadband light reconstruction and in-situ hyperspectral imaging.

关键词： semiconductors process integration

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Multifunctional Semiconducting Fibers for Visual Detection of Sarin Gas

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Advanced Fiber Materials 2023年第5期5卷 1632-1642页

作者： Maomao Yao Ruilong Zhou Mingshuai Yuan Haoyu Wang Le Wang Hengda Sun Yanyan Fu Ru Xiao Hongzhi Wang Gang Wang Meifang Zhu State Key Laboratory for Modification of Chemical Fibers and Polymer Materials College of Materials Science and EngineeringDonghua UniversityShanghai 201620China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijing 100049China State Key Lab of Transducer Technology Shanghai Institute of Microsystem and Information TechnologyChinese Academy of SciencesShanghai 200050China

There is a growing need for protective instruments that can be used in extreme environments,including those encountered during exoplanet exploration,anti-terrorism activities,and in chemical *** instruments should have the ability to detect external threats visually and monitor internal physiological signals in real time for maximum *** address this need,multifunctional semiconducting fibers with visual detection ranging from yellow to red and near-field communication(NFC)capabilities have been developed for use in personal protective clothing.A composite conductive yarn with semiconducting fluorescent probe molecules is embroidered on the clothing,forming an NFC coil that allows for the visual monitoring of atmospheric safety through color *** fluorescence detection system was able to selectively detect diethyl chlorophosphate(DCP),a substitute for the toxic gas sarin,with a detection limit of 6.08 ppb,which is lower than the life-threatening concentration of sarin ***,an intelligent protective suit with the abovementioned dual functions was fabricated with good mechanical cycle stability and ***-time physiological signals such as the temperature and humidity of the wearer could be read through the NFC *** intelligent protective suits can quickly provide an early warning to the identified low-dose DCP and evaluate the health of wearer according to the changes in physiological *** study offers a smart,low-cost strategy for designing intelligent protective devices for extreme environments.

关键词： Multifunctional semiconducting fibers Visual detection Near-field communication(NFC) Fluorescence detection Intelligent protective suits

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Refinery: Graph Solver as a Service: Refinement-based Generation and Analysis of Consistent Models 24

Refinery: Graph Solver as a Service: Refinement-based Genera...

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46th International Conference on Software engineering: Companion, ICSE-Companion 2024

作者： Marussy, Kristóf Ficsor, Attila Semeráth, Oszkár Varró, Dániel Budapest University of Technology and Economics Department of Measurement and Information Systems Budapest Hungary Linköping University Department of Computer and Information Science Linköping Sweden McGill University Department of Electrical and Computer Engineering Montreal Canada

ISBN: (纸本)9798400705021

Various software and systems engineering scenarios rely on the systematic construction of consistent graph models. However, automatically generating a diverse set of consistent graph models for complex domain specifications is challenging. First, the graph generation problem must be specified with mathematical precision. Moreover, graph generation is a computationally complex task, which necessitates specialized logic solvers. Refinery is a novel open-source software framework to automatically synthesize a diverse set of consistent domain-specific graph models. The framework offers an expressive high-level specification language using partial models to succinctly formulate a wide range of graph generation challenges. Moreover, it provides a modern cloud-based architecture for a scalable graph solver as a service, which uses logic reasoning rules to efficiently synthesize a diverse set of solutions to graph generation problems by partial model refinement. Applications include system-level architecture synthesis, test generation for modeling tools or traffic scenario synthesis for autonomous vehicles. Video demonstration: https://***/Qy-3udNsWsM. Continuously deployed at: https://***/. © 2024 IEEE Computer Society. All rights reserved.

关键词： Open source software

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Cooperative Caching for Scalable Video Coding Using Value-Decomposed Dimensional Networks

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China Communications 2022年第9期19卷 146-161页

作者： Youjia Chen Yuekai Cai Haifeng Zheng Jinsong Hu Jun Li Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information EngineeringFuzhou UniversityFuzhou 350000China School of Electronic and Optical Engineering Nanjing University of Science and TechnologyNanjing 210000China

Scalable video coding(SVC)has been widely used in video-on-demand(VOD)service,to efficiently satisfy users’different video quality requirements and dynamically adjust video stream to timevariant wireless *** the 5G network structure,we consider a cooperative caching scheme inside each cluster with SVC to economically utilize the limited caching storage.A novel multi-agent deep reinforcement learning(MADRL)framework is proposed to jointly optimize the video access delay and users’satisfaction,where an aggregation node is introduced helping individual agents to achieve global observations and overall system ***,to cope with the large action space caused by the large number of videos and users,a dimension decomposition method is embedded into the neural network in each agent,which greatly reduce the computational complexity and memory cost of the reinforcement *** results show that:1)the proposed value-decomposed dimensional network(VDDN)algorithm achieves an obvious performance gain versus the traditional MADRL;2)the proposed VDDN algorithm can handle an extremely large action space and quickly converge with a low computational complexity.

关键词： cooperative caching multi-agent deep reinforcement learning scalable video coding value-decomposition network

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