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Enhancing Relational Triple Extraction in Specific Domains:Semantic Enhancement and Synergy of Large Language Models and Small Pre-Trained Language Models

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Computers, Materials & Continua 2024年第5期79卷 2481-2503页

作者： Jiakai Li Jianpeng Hu Geng Zhang School of electronic and Electrical engineering Shanghai University of Engineering ScienceShanghai201620China

In the process of constructing domain-specific knowledge graphs,the task of relational triple extraction plays a critical role in transforming unstructured text into structured *** relational triple extraction models facemultiple challenges when processing domain-specific data,including insufficient utilization of semantic interaction information between entities and relations,difficulties in handling challenging samples,and the scarcity of domain-specific *** address these issues,our study introduces three innovative components:Relation semantic enhancement,data augmentation,and a voting strategy,all designed to significantly improve the model’s performance in tackling domain-specific relational triple extraction *** first propose an innovative attention interaction *** method significantly enhances the semantic interaction capabilities between entities and relations by integrating semantic information fromrelation ***,we propose a voting strategy that effectively combines the strengths of large languagemodels(LLMs)and fine-tuned small pre-trained language models(SLMs)to reevaluate challenging samples,thereby improving the model’s adaptability in specific ***,we explore the use of LLMs for data augmentation,aiming to generate domain-specific datasets to alleviate the scarcity of domain *** conducted on three domain-specific datasets demonstrate that our model outperforms existing comparative models in several aspects,with F1 scores exceeding the State of the Art models by 2%,1.6%,and 0.6%,respectively,validating the effectiveness and generalizability of our approach.

关键词： Relational triple extraction semantic interaction large language models data augmentation specific domains

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A deep Q-learning model for sequential task offloading in edge AI systems

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Intelligent and Converged Networks 2024年第3期5卷 207-221页

作者： Dong Liu Shiheng Gu Xinyu Fan Xu Zheng Department of Computer science and engineering University of Electronic Science and Technology of ChinaChengdu 610000China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of ChinaShenzhen 515100China Department of information and Software engineering University of Electronic Science and Technology of ChinaChengdu 610000China

Currently,edge Artificial Intelligence(AI)systems have significantly facilitated the functionalities of intelligent devices such as smartphones and smart cars,and supported diverse applications and *** fundamental supports come from continuous data analysis and computation over these *** the resource constraints of terminal devices,multi-layer edge artificial intelligence systems improve the overall computing power of the system by scheduling computing tasks to edge and cloud servers for *** efforts tend to ignore the nature of strong pipelined characteristics of processing tasks in edge AI systems,such as the encryption,decryption and consensus algorithm supporting the implementation of Blockchain ***,this paper proposes a new pipelined task scheduling algorithm(referred to as PTS-RDQN),which utilizes the system representation ability of deep reinforcement learning and integrates multiple dimensional information to achieve global task ***,a co-optimization strategy based on Rainbow Deep Q-Learning(RainbowDQN)is proposed to allocate computation tasks for mobile devices,edge and cloud servers,which is able to comprehensively consider the balance of task turnaround time,link quality,and other factors,thus effectively improving system performance and user *** addition,a task scheduling strategy based on PTS-RDQN is proposed,which is capable of realizing dynamic task allocation according to device *** results based on many simulation experiments show that the proposed method can effectively improve the resource utilization,and provide an effective task scheduling strategy for the edge computing system with cloud-edge-end architecture.

关键词： edge computing task scheduling reinforcement learning Rainbow Deep Q-Learning(RainbowDQN)

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Heat management technology for solid-state high voltage and high repetitive pulse generators:Towards better effects and reliability

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High Voltage 2024年第1期9卷 2-21页

作者： Yanan Wang Linyuan Ren Zihao Yang Saikang Shen Zichen Deng Qi Yuan Weidong Ding Zhenjie Ding School of Electrical engineering Xi'an Jiaotong UniversityXi'anChina School of electronic and information engineering Xi'an Jiaotong UniversityXi'anChina

Solid-state high voltage high repetitive pulse generators have a broad prospect in various *** high power and high-frequency operation of the pulse generator suffer from the massive heat dissipation problem,which limits the improvement of the output parameters and even affects the *** article focuses on heat management technology for high voltage high repetitive pulse ***,the typical circuit topology of the high repetitive pulse generators was *** the perspective of different application requirements,the demands of the heat management design were ***,the heat generation characteristics and difficulties of solid-state high voltage high repetitive pulse generators were ***,the different stateof-art cooling techniques were reviewed,and their applicability and limitations for the high voltage high repetitive generators were ***,a flow chart for heat management design was given.

关键词： pulse state high

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Joint Transmit and Reflective Beamforming Design for Active IRS-Aided SWIPT Systems

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Chinese Journal of electronics 2024年第2期33卷 536-548页

作者： Weiping SHI Qingqing WU Di WU Feng SHU Jiangzhou WANG School of electronic and Optical engineering Nanjing University of Science and Technology Department of electronic engineering Shanghai Jiao Tong University School of information and Communication engineering Hainan University Department of Automation Shanghai Jiao Tong University School of engineering University of Kent

To further improve the performance of passive intelligent reflecting surface(IRS)-assisted communication systems and mitigate the serious path loss due to “double-fading ” of IRS-assisted links, an active IRS-aided simultaneous wireless information and power transfer(SWIPT) system is investigated. This paper jointly optimizes the transmit beamforming at the base station(BS) and the phase shifts at the active IRS in order to maximize the power collected by the energy harvesting receiver under both perfect and imperfect channel state information(CSI)states, subject to the signal-to-interference-noise ratio constraint of the information decoding receiver, and the power constraints of the BS/IRS. Under perfect CSI, the alternating optimization algorithm is utilized for obtaining the transmit beamforming at the BS and the phase shifts at the active IRS. For each subproblem, we first transform nonconvex objective function and constraints into convex ones by performing a first-order Taylor expansion. Then, each subproblem is solved by using the interior point method. Given that obtaining perfect CSI is impractical, two robust beamforming designs are proposed for imperfect CSI case. Under the bounded CSI error model, we first transform the non-convex optimization problem into two semidefinite programming subproblems, and then solve each subproblem based on S-procedure and sequential rank-one constraint relaxation(SROCR) techniques. Under the stochastic CSI error model, the alternating optimization method is applied in an iterative manner based on Bernstein-type inequality and SROCR technique. Simulation results show that both robust and non-robust schemes for active IRS-assisted SWIPT systems can achieve extremely superior performance over conventional passive IRS-assisted systems under the same overall power budget.

关键词： Wireless communication Semidefinite programming Array signal processing Simulation Stochastic processes Transforms Receivers

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MAUN:Memory-Augmented Deep Unfolding Network for Hyperspectral Image Reconstruction

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IEEE/CAA Journal of Automatica Sinica 2024年第5期11卷 1139-1150页

作者： Qian Hu Jiayi Ma Yuan Gao Junjun Jiang Yixuan Yuan electronic information School Wuhan UniversityWuhan 430072China School of Computer science and Technology Harbin Institute of TechnologyHarbin 150001China Department of electronic engineering Chinese University of Hong KongHong Kong 999077China

Spectral compressive imaging has emerged as a powerful technique to collect the 3D spectral information as 2D *** algorithm for restoring the original 3D hyperspectral images(HSIs)from compressive measurements is pivotal in the imaging *** approaches painstakingly designed networks to directly map compressive measurements to HSIs,resulting in the lack of interpretability without exploiting the imaging *** some recent works have introduced the deep unfolding framework for explainable reconstruction,the performance of these methods is still limited by the weak information transmission between iterative *** this paper,we propose a Memory-Augmented deep Unfolding Network,termed MAUN,for explainable and accurate HSI ***,MAUN implements a novel CNN scheme to facilitate a better extrapolation step of the fast iterative shrinkage-thresholding algorithm,introducing an extra momentum incorporation step for each iteration to alleviate the information ***,to exploit the high correlation of intermediate images from neighboring iterations,we customize a cross-stage transformer(CSFormer)as the deep denoiser to simultaneously capture self-similarity from both in-stage and cross-stage features,which is the first attempt to model the long-distance dependencies between iteration *** experiments demonstrate that the proposed MAUN is superior to other state-of-the-art methods both visually and *** code is publicly available at https://***/HuQ1an/MAUN.

关键词： Deep folding iteration

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Improved PSO-Extreme Learning Machine Algorithm for Indoor Localization

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China Communications 2024年第5期21卷 113-122页

作者： Qiu Wanqing Zhang Qingmiao Zhao Junhui Yang Lihua School of information engineering East China Jiaotong UniversityNanchang 330013China School of electronic and information engineering Beijing Jiaotong UniversityBeijing 100044China

Wi Fi and fingerprinting localization method have been a hot topic in indoor positioning because of their universality and location-related *** basic assumption of fingerprinting localization is that the received signal strength indication(RSSI)distance is accord with the location ***,how to efficiently match the current RSSI of the user with the RSSI in the fingerprint database is the key to achieve high-accuracy *** this paper,a particle swarm optimization-extreme learning machine(PSO-ELM)algorithm is proposed on the basis of the original fingerprinting ***,we collect the RSSI of the experimental area to construct the fingerprint database,and the ELM algorithm is applied to the online stages to determine the corresponding relation between the location of the terminal and the RSSI it ***,PSO algorithm is used to improve the bias and weight of ELM neural network,and the global optimal results are ***,extensive simulation results are *** is shown that the proposed algorithm can effectively reduce mean error of localization and improve positioning accuracy when compared with K-Nearest Neighbor(KNN),Kmeans and Back-propagation(BP)algorithms.

关键词： extreme learning machine fingerprinting localization indoor localization machine learning particle swarm optimization

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Deployment of Edge Computing Nodes in IoT:Effective Implementation of Simulated Annealing Method Based on User Location

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China Communications 2024年第1期21卷 279-296页

作者： Junhui Zhao Ziyang Zhang Zhenghao Yi Xiaoting Ma Qingmiao Zhang School of electronic and information engineering Beijing Jiaotong UniversityBeijing 100044China School of information engineering East China Jiaotong UniversityNanchang 330013China

Edge computing paradigm for 5G architecture has been considered as one of the most effective ways to realize low latency and highly reliable communication,which brings computing tasks and network resources to the edge of *** deployment of edge computing nodes is a key factor affecting the service performance of edge computing *** this paper,we propose a method for deploying edge computing nodes based on user *** the combination of Simulation of Urban Mobility(SUMO)and Network Simulator-3(NS-3),a simulation platform is built to generate data of hotspot areas in Io T *** effectively using the data generated by the communication between users in Io T scenario,the location area of the user terminal can be *** this basis,the deployment problem is expressed as a mixed integer linear problem,which can be solved by Simulated Annealing(SA)*** analysis of the results shows that,compared with the traditional method,the proposed method has faster convergence speed and better performance.

关键词： deployment problem edge computing internet of things machine learning

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CMMCAN:Lightweight Feature Extraction and Matching Network for Endoscopic Images Based on Adaptive Attention

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Computers, Materials & Continua 2024年第8期80卷 2761-2783页

作者： Nannan Chong Fan Yang School of electronic and information engineering Hebei University of TechnologyTianjin300401China School of information and Intelligence engineering Tianjin Renai CollegeTianjin301636China

In minimally invasive surgery,endoscopes or laparoscopes equipped with miniature cameras and tools are used to enter the human body for therapeutic purposes through small incisions or natural ***,in clinical operating environments,endoscopic images often suffer from challenges such as low texture,uneven illumination,and non-rigid structures,which affect feature observation and *** can severely impact surgical navigation or clinical diagnosis due to missing feature points in endoscopic images,leading to treatment and postoperative recovery issues for *** address these challenges,this paper introduces,for the first time,a Cross-Channel Multi-Modal Adaptive Spatial Feature Fusion(ASFF)module based on the lightweight architecture of ***,a novel lightweight feature extraction and matching network based on attention mechanism is *** network dynamically adjusts attention weights for cross-modal information from grayscale images and optical flow images through a dual-branch Siamese *** extracts static and dynamic information features ranging from low-level to high-level,and from local to global,ensuring robust feature extraction across different widths,noise levels,and blur *** and local matching are performed through a multi-level cascaded attention mechanism,with cross-channel attention introduced to simultaneously extract low-level and high-level *** ablation experiments and comparative studies are conducted on the HyperKvasir,EAD,M2caiSeg,CVC-ClinicDB,and UCL synthetic *** results demonstrate that the proposed network improves upon the baseline EfficientViT-B3 model by 75.4%in accuracy(Acc),while also enhancing runtime performance and storage *** compared with the complex DenseDescriptor feature extraction network,the difference in Acc is less than 7.22%,and IoU calculation results on specific datasets outperform complex dense ***

关键词： Feature extraction and matching lightweighted network medical images endoscopic attention

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A Polarization Control Operator for Polarized Electromagnetic Wave Designing

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Chinese Journal of electronics 2024年第5期33卷 1253-1260页

作者： Shuo CUI Yaoyao LI Shijian ZHANG Ling CHEN Cheng CAO Donglin SU School of electronic and information engineering Beihang University Shenyuan Honors College Beihang University Research Institute for Frontier science Beihang University

To describe and control the polarization state of electromagnetic waves, a polarization control operator of the complex vector form is proposed. Distinct from traditional descriptors, the proposed operator employs an angle parameter to configure the polarization state of the polarized wave. By setting the parameter in the proposed operator,the amplitude of the field components can be modified, resulting in changes in the magnitude and direction of the field vector, and thus realizing control of the polarization state of the electromagnetic wave. The physical meaning,orthogonal decomposition, and discrete property of the proposed operator are demonstrated through mathematical derivation. In the simulation examples, the polarization control operator with fixed and time-varying parameters is applied to the circularly polarized wave. The propagation waveform, the trajectory projection and the waveform cross section in different reception directions of the new electromagnetic waves are observed. The simulation results indicate that complex electromagnetic waves with more flexible polarization states can be obtained with the aid of the polarization operator.

关键词： Polarization Simulation Modulation Electromagnetic compatibility Vectors Trajectory Electromagnetics Electromagnetic wave Polarization control operator Polarization descriptor Polarization state MIMO

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Proposal for valleytronic materials:Ferrovalley metal and valley gapless semiconductor

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Frontiers of physics 2024年第2期19卷 131-139页

作者： San-Dong Guo Yu-Ling Tao Guangzhao Wang Shaobo Chen Dong Huang Yee Sin Ang School of electronic engineering Xi’an University of Posts and TelecommunicationsXi’an 710121China Key Laboratory of Extraordinary Bond engineering and Advanced Materials Technology of Chongqing School of Electronic Information EngineeringYangtze Normal UniversityChongqing 408100China College of electronic and information engineering Anshun UniversityAnshun 561000China science Mathematics and Technology(SMT)Singapore University of Technology and Design(SUTD)8 Somapah RoadSingapore 487372Singapore

Valleytronic materials can provide new degrees of freedom to future electronic *** this work,the concepts of the ferrovalley metal(FVM)and valley gapless semiconductor(VGS)are proposed,which can be achieved in valleytronic bilayer systems by electric field *** valleytronic bilayer systems,the interaction between out-of-plane ferroelectricity and A-type antiferromagnetism can induce layer-polarized anomalous valley Hall(LP-AVH)*** K and−K valleys of FVM are both metallic,and electron and hole carriers simultaneously *** the extreme case,the FVM can become VGS by analogizing spin gapless semiconductor(SGS).Moreover,it is proposed that the valley splitting enhancement and valley polarization reversal can be achieved by electric field engineering in valleytronic bilayer *** the bilayer RuBr_(2)as an example,our proposal is confirmed by the first-principle *** FVM and VGS can be achieved in bilayer RuBr_(2)by applying electric *** appropriate electric field range,increasing electric field can enhance valley splitting,and the valley polarization can be reversed by flipping electric field *** effectively tune valley properties by electric field in bilayer systems,the parent monolayer should possess out-of-plane magnetization,and have large valley *** results shed light on the possible role of electric field in tuning valleytronic bilayer systems,and provide a way to design the ferrovalley-related material by electric field.

关键词： valleytronics electric field bilayer

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