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Remote Interference Source Localization: A Multi-UAV-Based Cooperative Framework

Chinese Journal of Electronics 2022年第3期31卷 442-455页

作者： WU Guangyu GU Jiangchun Department of Computer Science and Technology University of Science and Technology of China School of Communication Engineering Army Engineering University of PLA

Interference source localization with high accuracy and time efficiency is of crucial importance for protecting spectrum resources. Due to the flexibility of unmanned aerial vehicles(UAVs), exploiting UAVs to locate the interference source has attracted intensive research interests. The off-the-shelf UAV-based interference source localization schemes locate the interference sources by employing the UAV to keep searching until it arrives at the target. This obviously degrades time efficiency of localization. To balance the accuracy and the efficiency of searching and localization, this paper proposes a multi-UAV-based cooperative framework alone with its detailed scheme, where search and remote localization are iteratively performed with a swarm of UAVs. For searching, a low-complexity Q-learning algorithm is proposed to decide the direction of flight in every time interval for each UAV. In the following remote localization phase, a fast Fourier transformation based location prediction algorithm is proposed to estimate the location of the interference source by fusing the searching result of different UAVs in different time intervals. Numerical results reveal that in the proposed scheme outperforms the stateof-the-art schemes, in terms of the accuracy, the robustness and time efficiency of localization.

关键词： unmanned aerial vehicles low-complexity Q-learning algorithm path planning fast Fourier transforms remote interference source localization multi-robot systems UAV swarm mobile robots remote localization location prediction algorithm aerospace robotics autonomous aerial vehicles learning (artificial intelligence) fast Fourier transformation cooperative communication spectrum resources radiofrequency interference mobile radio flight direction multiUAV-based cooperative framework

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LightNestle: Quick and Accurate Neural Sequential Tensor Completion via Meta Learning 42

LightNestle: Quick and Accurate Neural Sequential Tensor Com...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Li, Yuhui Liang, Wei Xie, Kun Zhang, Dafang Xie, Songyou Li, KuanChing Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan411201 China

ISBN: (纸本)9798350334142

Network operation and maintenance rely heavily on network traffic monitoring. Due to the measurement overhead reduction, lack of measurement infrastructure, and unexpected transmission error, network traffic monitoring systems suffer from incomplete observed data and high data sparsity problems. Recent studies model missing data recovery as a tensor completion task and show good performance. Although promising, the current tensor completion models adopted in network traffic data recovery lack an effective and efficient retraining scheme to adapt to newly arrived data while retaining historical information. To solve the problem, we propose LightNestle, a novel sequential tensor completion scheme based on meta-learning, which designs (1) an expressive neural network to transfer spatial knowledge from previous embeddings to current embeddings;(2) an attention-based module to transfer temporal patterns into current embeddings in linear complexity;and (3) meta-learning-based algorithms to iteratively recover missing data and update transfer modules to catch up with learned knowledge. We conduct extensive experiments on two real-world network traffic datasets to assess our performance. Results show that our proposed methods achieve both fast retraining and high recovery accuracy. © 2023 IEEE.

关键词： Embeddings

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A Software Defect Prediction Method Using a Multivariate Heterogeneous Hybrid Deep Learning Algorithm

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computers, Materials & Continua 2025年第2期82卷 3251-3279页

作者： Qi Fei Haojun Hu Guisheng Yin Zhian Sun College of Computer Science and Technology Harbin Engineering UniversityHarbin150001China Information Technology Research Department Jiangsu Automation Research InstituteLianyungang222062China School of Computer Science and Technology Harbin Institute of TechnologyShenzhen518055China

Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.

关键词： Software defect prediction multiple heterogeneous data graph convolutional network models based on adjacency and spatial topologies CNN-BiLSTM TabNet

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Enhanced rate capability and cycle stability of Ti2C MXene for sodium storage through an aniline molecules welding strategy

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Tungsten 2025年第1期7卷 161-171页

作者： Hao Chen Ya-Qin Wang Dong-Ting Zhang Bei Zhao Min-Peng Li Chen-Yang Li Tian-Peng Xu Mao-Cheng Liu State Key Laboratory of Advanced Processing and Recycling of Non-Ferrous Metals Lanzhou University of Technology School of Materials Science and Engineering Lanzhou University of Technology School of Computer and Communication Technology Lanzhou University of Technology

MXenes obtained significant attention in the field of energy storage devices due to their characteristic layered structure,modifiable surface functional groups,large electrochemically active surface,and regulable interlayer ***,the self-restacking and sluggish ions diffusion kinetics performance of MXenes during the alkali metal ions insertion/extraction process severely impedes their cycle stability and rate *** paper proposes an aniline molecule welding strategy for welding p-phenylenediamine（PPDA） into the interlayers of Ti2C through a dehydration condensation *** welded PPDA molecules can contribute pillar effect to the layered structure of *** pillar effect effectively maintains the structural stability during the sodium ions insertion/extraction process and effectively expands the interlayer spacing of Ti2C from 1.16 to 1.38 nm,thereby enhancing ions diffusion kinetics performance and improving the long-term cycle *** Ti2C-PPDA demonstrates outstanding Na+storage capability,exhibiting a specific capacity of 100.2 mAh·g-1at a current density of 0.1 A·g-1over 960 cycles and delivering a remarkable rate capability 81.2 mAh·g-1at a current density of 5 A·*** study demonstrates that expanding interlayer spacing is a promising strategy to enhance the Na+storage capacity and improve long-term cycling stability,which provides significant guidance for the design of two-dimensional Na+storage materials with high-rate capability and cycle stability.

关键词： Ti_2C Expanding interlayer spacing Sodium ions storage Diffusion kinetics Rate capability

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Designing new low alloyed Mg-RE alloys with high strength and ductility via high-speed extrusion

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International Journal of Minerals,Metallurgy and Materials 2023年第1期30卷 82-91页

作者： Jinshu Xie Zhi Zhang Shujuan Liu Jinghuai Zhang Jun Wang Yuying He Liwei Lu Yunlei Jiao Ruizhi Wu Key Laboratory of Superlight Material and Surface Technology Ministry of EducationCollege of Materials Science and Chemical EngineeringHarbin Engineering UniversityHarbin 150001China School of Materials Science and Engineering Harbin Institute of TechnologyHarbin 150001China Ningbo Branch Ordnance Science Institute of ChinaNingbo 315103China School of Materials Science and Engineering Hunan University of Science and TechnologyXiangtan 411201China

Two new low-alloyed Mg-2RE-0.8Mn-0.6Ca-0.5Zn(wt%,RE=Sm or Y)alloys are developed,which can be produced on an in-dustrial scale via relatively high-speed *** two alloys are not only comparable to commercial AZ31 alloy in extrudability,but also have superior mechanical properties,especially in terms of yield strength(YS).The excellent extrudability is related to less coarse second-phase particles and high initial melting point of the two as-cast *** high strength-ductility mainly comes from the formation of fine grains,nano-spaced submicron/nano precipitates,and weak ***,it is worth noting that the YS of the two alloys can maintain above 160 MPa at elevated temperature of 250°C,significantly higher than that of AZ31 alloy(YS:45 MPa).The Zn/Ca solute segregation at grain boundaries,the improved heat resistance of matrix due to addition of RE,and the high melting points of strengthening particles(Mn,MgZn_(2),and Mg-Zn-RE/Mg-Zn-RE-Ca)are mainly responsible for the excellent high-temperature strength.

关键词： magnesium alloys high-speed extrusion high strength high ductility solute segregation

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ANALYSIS OF TWO ANY ORDER SPECTRAL VOLUME METHODS FOR 1-D LINEAR HYPERBOLIC EQUATIONS WITH DEGENERATE VARIABLE COEFFICIENTS

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Journal of Computational Mathematics 2024年第6期42卷 1627-1655页

作者： Minqiang Xu Yanting Yuan Waixiang Cao Qingsong Zou College of Science Zhejiang University of TechnologyHangzhou 310023China School of Computer Science and Engineering Sun Yat-sen UniversityGuangzhou 510275China School of Mathematical Sciences Beijing Normal UniversityBeijing 100875China School of Computer Science and Engineering and Guangdong Province Key Laboratory of Computational ScienceSun Yat-sen UniversityGuangzhou 510275China

In this paper,we analyze two classes of spectral volume(SV)methods for one-dimensional hyperbolic equations with degenerate variable *** classes of SV methods are constructed by letting a piecewise k-th order(k≥1 is an integer)polynomial to satisfy the conservation law in each control volume,which is obtained by refining spectral volumes(SV)of the underlying mesh with k Gauss-Legendre points(LSV)or Radaus points(RSV)in each *** L^(2)-norm stability and optimal order convergence properties for both methods are rigorously proved for general non-uniform ***,we discover some very interesting superconvergence phenomena:At some special points,the SV flux function approximates the exact flux with(k+2)-th order and the SV solution itself approximates the exact solution with(k+3/2)-th order,some superconvergence behaviors for element averages errors have been also ***,these superconvergence phenomena are rigorously proved by using the so-called correction function *** theoretical findings are verified by several numerical experiments.

关键词： Spectral Volume Methods L^(2)stability Error estimates Superconvergence

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Renaissance of Fuzzy and Fast Matching Entity with DSHS Algorithm

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SN computer science 2024年第6期5卷 734页

作者： Kari, Venkatram Amalanathan, Geetha Mary School of Computer Science and Engineering Vellore Institute of Technology University

Entity matching is a crucial aspect of data management systems, requiring the identification of real-world entities from diverse expressions. Despite the human ability to recognize equivalences among entities, machines struggle due to variations in expression. This challenge underscores the necessity for a robust framework capable of matching entities based on their attributes. While previous studies have explored transformation techniques, NLP, and deep learning, they often fall short in accuracy and scalability or overlook internal constraints. Such shortcomings result in costly rework and compromised business decisions. To address these issues, proposed “Deep Semantic Homophonic Synonymy” (DSHS) algorithm, a hybrid approach integrating rule-based, NLP, and deep learning techniques. By employing semantic matching, rule-based preprocessing, NLP enrichment, phonetic analysis, abbreviation variations, and spelling corrections, the DSHS algorithm effectively overcomes entity matching challenges. This solution, comprising six modules including Preprocessing, Transformation, Translation, Word Embedding, Attribute Similarity, and Classification, outperforms existing EM models in terms of accuracy and scalability. This solution is a comprehensive entity matching, offering significant advancements in the field and providing a reliable framework for resolving complex matching requirements. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Data pipeline Data sync up MDDR Message queue Schema registry

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The microstructure evolution and deformation mechanism in a casting AM80 magnesium alloy under ultra-high strain rate loading

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Journal of Magnesium and Alloys 2022年第11期10卷 3205-3216页

作者： Pengcheng Guo Xiao Liu Biwu Zhu Wenhui Liu Liqiang Zhang School of Mechanical and Electrical Engineering Central South University of Forestry and TechnologyChangsha 410004China School of Material Science and Engineering Hunan University of Science and TechnologyXiangtanHunan 411201China

Ultra-high strain rate impact tests were conducted by Split-Hopkinson pressure bar to investigate the microstructure evolution and impact deformation mechanism of a solution treated casting AM80 Mg alloy at 25, 150 and 250 ℃ with a strain rate of 5000 s^(-1). The microcrack and dynamic recrystallization(DRX) preferentially nucleate at grain boundary(GB) and twin boundary(TB), especially at the intersections between GBs and TBs, and then propagate along twin direction. In contrast, the adiabatic shear bands preferentially occur at high-density twined regions. At 25 ℃, the dominated deformation mechanisms are basal slip and twinning. As deformation temperature increases to 150and 250℃, the deformation gradually shifts to be dominated by a coordinated mechanism among non-basal slip, twinning and DRX. The flow stress behavior and deformation mechanism indicate that the degree of decrease in flow stress with temperature is associated with the change of deformation mode.

关键词： AM80 mg alloy Deformation mechanism Ultra-high strain rate Twinning Dynamic recrystallization

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Cat Swarm Algorithm Generated Based on Genetic Programming Framework Applied in Digital Watermarking

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computers, Materials & Continua 2025年第5期83卷 3135-3163页

作者： Shu-Chuan Chu Libin Fu Jeng-Shyang Pan Xingsi Xue Min Liu School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210000China College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266000China Department of Information Management Chaoyang University of TechnologyTaichung40601TaiwanChina School of Information Science and Engineering Fujian University of TechnologyFuzhou350000China Key Laboratory of Hydrometeorological Disaster Mechanism and Warning of Ministry of Water Resource Nanjing University of Information Science and TechnologyNanjing210000China

Evolutionary algorithms have been extensively utilized in practical ***,manually designed population updating formulas are inherently prone to the subjective influence of the *** programming(GP),characterized by its tree-based solution structure,is a widely adopted technique for optimizing the structure of mathematical models tailored to real-world *** paper introduces a GP-based framework(GPEAs)for the autonomous generation of update formulas,aiming to reduce human *** modifications to tree-based GP have been instigated,encompassing adjustments to its initialization process and fundamental update operations such as crossover and mutation within the *** designing suitable function sets and terminal sets tailored to the selected evolutionary algorithm,and ultimately derive an improved update *** Cat Swarm Optimization Algorithm(CSO)is chosen as a case study,and the GP-EAs is employed to regenerate the speed update formulas of the *** validate the feasibility of the GP-EAs,the comprehensive performance of the enhanced algorithm(GP-CSO)was evaluated on the CEC2017 benchmark ***,GP-CSO is applied to deduce suitable embedding factors,thereby improving the robustness of the digital watermarking *** experimental results indicate that the update formulas generated through training with GP-EAs possess excellent performance scalability and practical application proficiency.

关键词： Cat swarm algorithm genetic programming digital watermarking update mode mode generation framework

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medIKAL: Integrating Knowledge Graphs as Assistants of LLMs for Enhanced Clinical Diagnosis on EMRs 31

medIKAL: Integrating Knowledge Graphs as Assistants of LLMs ...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Jia, Mingyi Duan, Junwen Song, Yan Wang, Jianxin Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University China University of Science and Technology of China China

ISBN: (纸本)9798891761964

Electronic Medical Records (EMRs), while integral to modern healthcare, present challenges for clinical reasoning and diagnosis due to their complexity and information redundancy. To address this, we proposed medIKAL (Integrating Knowledge Graphs as Assistants of LLMs), a framework that combines Large Language Models (LLMs) with knowledge graphs (KGs) to enhance diagnostic capabilities. medIKAL assigns weighted importance to entities in medical records based on their type, enabling precise localization of candidate diseases within KGs. It innovatively employs a residual network-like approach, allowing initial diagnosis by LLMs to be merged into KG search results. Through a path-based reranking algorithm and a fill-in-the-blank style prompt template, it further refined the diagnostic process. We validated medIKAL's effectiveness through extensive experiments on a newly introduced open-sourced Chinese EMR dataset, demonstrating its potential to improve clinical diagnosis in real-world settings. The code and dataset are publicly available at https://***/CSU-NLP-Group/mediKAL. © 2025 Association for Computational Linguistics.

关键词： Knowledge graph

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