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Joint Optimization of Resource Allocation and Trajectory Based on User Trajectory for UAV-Assisted Backscatter Communication System
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作者 Peizhong Xie Junjie Jiang +1 位作者 Ting Li Yin Lu 《China Communications》 SCIE CSCD 2024年第2期197-209,共13页
The Backscatter communication has gained widespread attention from academia and industry in recent years. In this paper, A method of resource allocation and trajectory optimization is proposed for UAV-assisted backsca... The Backscatter communication has gained widespread attention from academia and industry in recent years. In this paper, A method of resource allocation and trajectory optimization is proposed for UAV-assisted backscatter communication based on user trajectory. This paper will establish an optimization problem of jointly optimizing the UAV trajectories, UAV transmission power and BD scheduling based on the large-scale channel state signals estimated in advance of the known user trajectories, taking into account the constraints of BD data and working energy consumption, to maximize the energy efficiency of the system. The problem is a non-convex optimization problem in fractional form, and there is nonlinear coupling between optimization variables.An iterative algorithm is proposed based on Dinkelbach algorithm, block coordinate descent method and continuous convex optimization technology. First, the objective function is converted into a non-fractional programming problem based on Dinkelbach method,and then the block coordinate descent method is used to decompose the original complex problem into three independent sub-problems. Finally, the successive convex approximation method is used to solve the trajectory optimization sub-problem. The simulation results show that the proposed scheme and algorithm have obvious energy efficiency gains compared with the comparison scheme. 展开更多
关键词 energy efficiency joint optimization UAV-assisted backscatter communication user trajectory
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GCAGA: A Gini Coefficient-Based Optimization Strategy for Computation Offloading in Multi-User-Multi-Edge MEC System
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作者 Junqing Bai Qiuchao Dai Yingying Li 《Computers, Materials & Continua》 SCIE EI 2024年第6期5083-5103,共21页
To support the explosive growth of Information and Communications Technology(ICT),Mobile Edge Comput-ing(MEC)provides users with low latency and high bandwidth service by offloading computational tasks to the network... To support the explosive growth of Information and Communications Technology(ICT),Mobile Edge Comput-ing(MEC)provides users with low latency and high bandwidth service by offloading computational tasks to the network’s edge.However,resource-constrained mobile devices still suffer from a capacity mismatch when faced with latency-sensitive and compute-intensive emerging applications.To address the difficulty of running computationally intensive applications on resource-constrained clients,a model of the computation offloading problem in a network consisting of multiple mobile users and edge cloud servers is studied in this paper.Then a user benefit function EoU(Experience of Users)is proposed jointly considering energy consumption and time delay.The EoU maximization problem is decomposed into two steps,i.e.,resource allocation and offloading decision.The offloading decision is usually given by heuristic algorithms which are often faced with the challenge of slow convergence and poor stability.Thus,a combined offloading algorithm,i.e.,a Gini coefficient-based adaptive genetic algorithm(GCAGA),is proposed to alleviate the dilemma.The proposed algorithm optimizes the offloading decision by maximizing EoU and accelerates the convergence with the Gini coefficient.The simulation compares the proposed algorithm with the genetic algorithm(GA)and adaptive genetic algorithm(AGA).Experiment results show that the Gini coefficient and the adaptive heuristic operators can accelerate the convergence speed,and the proposed algorithm performs better in terms of convergence while obtaining higher EoU.The simulation code of the proposed algorithm is available:https://github.com/Grox888/Mobile_Edge_Computing/tree/GCAGA. 展开更多
关键词 Mobile edge computing multi-user-multi-edge joint optimization Gini coefficient adaptive genetic algorithm
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Optimal Design of the Modular Joint Drive Train for Enhancing Cobot Load Capacity and Dynamic Performance
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作者 Peng Li Zhenguo Nie +1 位作者 Zihao Li Xinjun Liu 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2024年第3期26-40,共15页
Automation advancements prompts the extensive integration of collaborative robot(cobot)across a range of industries.Compared to the commonly used design approach of increasing the payload-to-weight ratio of cobot to e... Automation advancements prompts the extensive integration of collaborative robot(cobot)across a range of industries.Compared to the commonly used design approach of increasing the payload-to-weight ratio of cobot to enhance load capacity,equal attention should be paid to the dynamic response characteristics of cobot during the design process to make the cobot more flexible.In this paper,a new method for designing the drive train parameters of cobot is proposed.Firstly,based on the analysis of factors influencing the load capacity and dynamic response characteristics,design criteria for both aspects are established for cobot with all optimization design criteria normalized within the design domain.Secondly,with the cobot in the horizontal pose,the motor design scheme is discretized and it takes the joint motor diameter and gearbox speed ratio as optimization design variables.Finally,all the discrete values of the optimization objectives are obtained through the enumeration method and the Pareto front is used to select the optimal solution through multi-objective optimization.Base on the cobot design method proposed in this paper,a six-axis cobot is designed and compared with the commercial cobot.The result shows that the load capacity of the designed cobot in this paper reaches 8.4 kg,surpassing the 5 kg load capacity commercial cobot which is used as a benchmark.The minimum resonance frequency of the joints is 42.70 Hz. 展开更多
关键词 Multi-objective optimization Modular joint drive train design Load capacity Dynamic response performance
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Intelligent Reflecting Surface Assisted Transmission Optimization Strategies in Wireless Networks
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作者 He Xinxin Qi Xuan +2 位作者 Meng Wei Liu Wei Yin Changchuan 《China Communications》 SCIE CSCD 2024年第4期120-135,共16页
Wireless Power Transfer(WPT)technology can provide real-time power for many terminal devices in Internet of Things(IoT)through millimeterWave(mmWave)to support applications with large capacity and low latency.Although... Wireless Power Transfer(WPT)technology can provide real-time power for many terminal devices in Internet of Things(IoT)through millimeterWave(mmWave)to support applications with large capacity and low latency.Although the intelligent reflecting surface(IRS)can be adopted to create effective virtual links to address the mmWave blockage problem,the conventional solutions only adopt IRS in the downlink from the Base Station(BS)to the users to enhance the received signal strength.In practice,the reflection of IRS is also applicable to the uplink to improve the spectral efficiency.It is a challenging to jointly optimize IRS beamforming and system resource allocation for wireless energy acquisition and information transmission.In this paper,we first design a Low-Energy Adaptive Clustering Hierarchy(LEACH)clustering protocol for clustering and data collection.Then,the problem of maximizing the minimum system spectral efficiency is constructed by jointly optimizing the transmit power of sensor devices,the uplink and downlink transmission times,the active beamforming at the BS,and the IRS dynamic beamforming.To solve this non-convex optimization problem,we propose an alternating optimization(AO)-based joint solution algorithm.Simulation results show that the use of IRS dynamic beamforming can significantly improve the spectral efficiency of the system,and ensure the reliability of equipment communication and the sustainability of energy supply under NLOS link. 展开更多
关键词 intelligent reflecting surface(IRS) joint optimization millimeter wave wireless information transmission(WIT) wireless power transfer(WPT)
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Specific Order and Bisection Search Aided Joint 3D Beamforming and RIS Reflecting Optimization 被引量:1
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作者 Hongxing He Li Li +1 位作者 Peichang Zhang Xiaohu Tang 《China Communications》 SCIE CSCD 2023年第5期330-339,共10页
In this paper,a three-node transmission model is conceived,where the base station(BS)node leverages 3D beamforming,the reconfigurable intelligent surface(RIS)node can constructively reconfigure the wireless channel,th... In this paper,a three-node transmission model is conceived,where the base station(BS)node leverages 3D beamforming,the reconfigurable intelligent surface(RIS)node can constructively reconfigure the wireless channel,the user node only has a single antenna due to a limited price.Maximization of its downlink spectral efficiency is a joint optimization problem of three variables,namely phase-shift matrixΦof RIS,tilt angleθand beamforming vector w used in BS 3D beamforming.We solve this problem by employing the alternating optimization(AO)algorithm.But,in each iteration,a specific optimization order of firstlyΦ,secondlyθand finally w is proposed,which facilitates the search of optimalθin the way of narrowing its trust region and enabling unimodal property over the narrowed trust region.It finally results in a better combination of{Φ,θ,w}. 展开更多
关键词 reconfigurable intelligent surface vertical beamforming joint optimization
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Joint optimization for secure ambient backscatter communication in NOMA-enabled IoT networks
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作者 Wali Ullah Khan Furqan Jameel +2 位作者 Asim Ihsan Omer Waqar Manzoor Ahmed 《Digital Communications and Networks》 SCIE CSCD 2023年第1期264-269,共6页
Non-Orthogonal Multiple Access(NOMA)has emerged as a novel air interface technology for massive connectivity in Sixth-Generation(6G)era.The recent integration of NOMA in Backscatter Communication(BC)has triggered sign... Non-Orthogonal Multiple Access(NOMA)has emerged as a novel air interface technology for massive connectivity in Sixth-Generation(6G)era.The recent integration of NOMA in Backscatter Communication(BC)has triggered significant research interest due to its applications in low-powered Internet of Things(IoT)networks.However,the link security aspect of these networks has not been well investigated.This article provides a new optimization framework for improving the physical layer security of the NOMA ambient BC system.Our system model takes into account the simultaneous operation of NOMA IoT users and the Backscatter Node(BN)in the presence of multiple EavesDroppers(EDs).The EDs in the surrounding area can overhear the communication of Base Station(BS)and BN due to the wireless broadcast transmission.Thus,the chief aim is to enhance link security by optimizing the BN reflection coefficient and BS transmit power.To gauge the performance of the proposed scheme,we also present the suboptimal NOMA and conventional orthogonal multiple access as benchmark schemes.Monte Carlo simulation results demonstrate the superiority of the NOMA BC scheme over the pure NOMA scheme without the BC and conventional orthogonal multiple access schemes in terms of system secrecy rate. 展开更多
关键词 6G Non-orthogonal multiple access Ambient backscatter communication Internet-of-things joint optimization Physical layer security
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Joint On-Demand Pruning and Online Distillation in Automatic Speech Recognition Language Model Optimization
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作者 Soonshin Seo Ji-Hwan Kim 《Computers, Materials & Continua》 SCIE EI 2023年第12期2833-2856,共24页
Automatic speech recognition(ASR)systems have emerged as indispensable tools across a wide spectrum of applications,ranging from transcription services to voice-activated assistants.To enhance the performance of these... Automatic speech recognition(ASR)systems have emerged as indispensable tools across a wide spectrum of applications,ranging from transcription services to voice-activated assistants.To enhance the performance of these systems,it is important to deploy efficient models capable of adapting to diverse deployment conditions.In recent years,on-demand pruning methods have obtained significant attention within the ASR domain due to their adaptability in various deployment scenarios.However,these methods often confront substantial trade-offs,particularly in terms of unstable accuracy when reducing the model size.To address challenges,this study introduces two crucial empirical findings.Firstly,it proposes the incorporation of an online distillation mechanism during on-demand pruning training,which holds the promise of maintaining more consistent accuracy levels.Secondly,it proposes the utilization of the Mogrifier long short-term memory(LSTM)language model(LM),an advanced iteration of the conventional LSTM LM,as an effective alternative for pruning targets within the ASR framework.Through rigorous experimentation on the ASR system,employing the Mogrifier LSTM LM and training it using the suggested joint on-demand pruning and online distillation method,this study provides compelling evidence.The results exhibit that the proposed methods significantly outperform a benchmark model trained solely with on-demand pruning methods.Impressively,the proposed strategic configuration successfully reduces the parameter count by approximately 39%,all the while minimizing trade-offs. 展开更多
关键词 Automatic speech recognition neural language model Mogrifier long short-term memory PRUNING DISTILLATION efficient deployment optimization joint training
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Research on Coordinated Development and Optimization of Distribution Networks at All Levels in Distributed Power Energy Engineering 被引量:1
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作者 Zhuohan Jiang Jingyi Tu +2 位作者 Shuncheng Liu Jian Peng Guang Ouyang 《Energy Engineering》 EI 2023年第7期1655-1666,共12页
The uncertainty of distributed generation energy has dramatically challenged the coordinated development of distribution networks at all levels.This paper focuses on the multi-time-scale regulation model of distribute... The uncertainty of distributed generation energy has dramatically challenged the coordinated development of distribution networks at all levels.This paper focuses on the multi-time-scale regulation model of distributed generation energy under normal conditions.The simulation results of the example verify the self-optimization characteristics and the effectiveness of real-time dispatching of the distribution network control technology at all levels under multiple time scales. 展开更多
关键词 Distributed power generation energy engineering multiple time scales joint development of distribution network global optimization regional autonomy
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Identification and preoperative optimization of risk factors to prevent periprosthetic joint infection 被引量:5
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作者 Seung-Hoon Baek 《World Journal of Orthopedics》 2014年第3期362-367,共6页
Despite significant improvements over the past several decades in diagnosis,treatment and prevention of periprosthetic joint infection(PJI),it still remains a major challenge following total joint arthroplasty.Given t... Despite significant improvements over the past several decades in diagnosis,treatment and prevention of periprosthetic joint infection(PJI),it still remains a major challenge following total joint arthroplasty.Given the devastating nature and accelerated incidence of PJI,prevention is the most important strategy to deal with this challenging problem and should start from identifying risk factors.Understanding and well-organized optimization of these risk factors in individuals before elective arthroplasty are essential to the ultimate success in reducing the incidence of PJI.Even though some risk factors such as demographic characteristics are seldom changeable,they allow more accurate expectation regarding individual risks of PJI and thus,make proper counseling for shared preoperative decision-making possible.Others that increase the risk of PJI,but are potentially modifiable should be optimized prior to elective arthroplasty.Although remarkable advances have been achieved in past decades,many questions regarding standardized practice to prevent this catastrophic complication remain unanswered.The current study provide a comprehensive knowledge regarding risk factors based on general principles to control surgical siteinfection by the review of current literature and also share own practice at our institution to provide practical and better understandings. 展开更多
关键词 Total joint ARTHROPLASTY PERIPROSTHETIC joint infection Prevention Risk factors PREOPERATIVE optimization
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Joint optimization of maintenance inspection and spare provisioning for aircraft deteriorating parts 被引量:4
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作者 Jing Cai Xin Li Xi Chen 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2017年第6期1133-1140,共8页
With the wide application of condition based maintenance(CBM) in aircraft maintenance practice, the joint optimization of maintenance and inventory management, which can take full advantage of CBM and reduce the aircr... With the wide application of condition based maintenance(CBM) in aircraft maintenance practice, the joint optimization of maintenance and inventory management, which can take full advantage of CBM and reduce the aircraft operational cost, is receiving increasing attention. In order to optimize the inspection interval, maintenance decision and spare provisioning together for aircraft deteriorating parts, firstly, a joint inventory management strategy is presented, then, a joint optimization of maintenance inspection and spare provisioning for aircraft parts subject to the Wiener degradation process is proposed based on the strategy.Secondly, a combination of the genetic algorithm(GA) and the Monte Carol method is developed to minimize the total cost rate.Finally, a case study is conducted and the proposed joint optimization model is compared with the existing optimization model and the airline real case. The results demonstrate that the proposed model is more beneficial and effective. In addition, the sensitivity analysis of the proposed model shows that the lead time has higher influence on the optimal results than the urgent order cost and the corrective maintenance cost, which is consistent with the actual situation of aircraft maintenance practices and inventory management. 展开更多
关键词 joint optimization maintenance spare provision aircraft part Wiener process
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Joint Beamforming Optimization for Reconfigurable Intelligent Surface-Enabled MISO-OFDM Systems 被引量:9
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作者 Keming Feng Xiao Li +1 位作者 Yu Han Yijian Chen 《China Communications》 SCIE CSCD 2021年第3期63-79,共17页
In this paper,we investigate the reconfigurable intelligent surface(RIS)-enabled multiple-input-single-output orthogonal frequency division multiplexing(MISO-OFDM)system under frequency-selective channels,and propose ... In this paper,we investigate the reconfigurable intelligent surface(RIS)-enabled multiple-input-single-output orthogonal frequency division multiplexing(MISO-OFDM)system under frequency-selective channels,and propose a low-complexity alternating optimization(AO)based joint beamforming and RIS phase shifts optimization algorithm to maximize the achievable rate.First,with fixed RIS phase shifts,we devise the optimal closedform transmit beamforming vectors corresponding to different subcarriers.Then,with given active beamforming vectors,near-optimal RIS reflection coefficients can be determined efficiently leveraging fractional programming(FP)combined with manifold optimization(MO)or majorization-minimization(MM)framework.Additionally,we also propose a heuristic RIS phase shifts design approach based on the sum of subcarrier gain maximization(SSGM)criterion requiring lower complexity.Numerical results indicate that the proposed MO/MM algorithm can achieve almost the same rate as the upper bound achieved by the semidefinite relaxation(SDR)algorithm,and the proposed SSGM based scheme is only slightly inferior to the upper bound while has much lower complexity.These results demonstrate the effectiveness of the proposed algorithms. 展开更多
关键词 reconfigurable intelligent surface(RIS) OFDM joint beamforming optimization fractional programming majorization-minimization manifold optimization
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An adaptive waveform-detection threshold joint optimization method for target tracking 被引量:5
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作者 王宏强 夏洪恩 +1 位作者 程永强 王璐璐 《Journal of Central South University》 SCIE EI CAS 2013年第11期3057-3064,共8页
The joint optimization of detection threshold and waveform parameters for target tracking which comes from the idea of cognitive radar is investigated for the modified probabilistic data association(MPDA)filter.The tr... The joint optimization of detection threshold and waveform parameters for target tracking which comes from the idea of cognitive radar is investigated for the modified probabilistic data association(MPDA)filter.The transmitted waveforms and detection threshold are adaptively selected to enhance the tracking performance.The modified Riccati equation is adopted to predict the error covariance which is used as the criterion function,while the optimization problem is solved through the genetic algorithm(GA).The detection probability,false alarm probability and measurement noise covariance are all considered together,which significantly improves the tracking performance of the joint detection and tracking system.Simulation results show that the proposed adaptive waveform-detection threshold joint optimization method outperforms the adaptive threshold method and the fixed parameters method,which will reduce the tracking error.The average reduction of range error between the adaptive joint method and the fixed parameters method is about 0.6 m,while that between the adaptive joint method and the adaptive threshold only method is about 0.3 m.Similar error reduction occurs for the velocity error and acceleration error. 展开更多
关键词 cognitive radar adaptive waveform selection target tracking joint optimization detection-tracking system
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Joint Power-Trajectory-Scheduling Optimization in A Mobile UAV-Enabled Network via Alternating Iteration 被引量:3
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作者 Xiaohan Qi Minxin Yuan +1 位作者 Qinyu Zhang Zhihua Yang 《China Communications》 SCIE CSCD 2022年第1期136-152,共17页
This work focuses on an unmanned aerial vehicle(UAV)-enabled mobile edge computing(MEC) system based on device-to-device(D2D) communication. In this system, the UAV exhibits caching,computing and relaying capabilities... This work focuses on an unmanned aerial vehicle(UAV)-enabled mobile edge computing(MEC) system based on device-to-device(D2D) communication. In this system, the UAV exhibits caching,computing and relaying capabilities to periodically provide specific service to cellular users and D2D receiver nodes in the appointed time slot. Besides, the D2D transmitter can provide additional caching services to D2D receiver to reduce the pressure of the UAV. Note that communication between multi-type nodes is mutually restricted and different links share spectrum resources. To achieve an improved balance between different types of node, we aim to maximize the overall energy efficiency while satisfying the quality-of-service requirements of the cellular nodes.To address this problem, we propose an alternating iteration algorithm to jointly optimize the scheduling strategies of the user, transmitting power of the UAV and D2D-TX nodes, and UAV trajectory. The successive convex approximation, penalty function, and Dinkelbach method are employed to transform the original problem into a group of solvable subproblems and the convergence of the method is proved. Simulation results show that the proposed scheme performs better than other benchmark algorithms, particularly in terms of balancing the tradeoff between minimizing UAV energy consumption and maximizing throughput. 展开更多
关键词 UAV MEC network D2D joint optimization energy efficiency
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Topology Optimization and Fatigue Analysis of Temporomandibular Joint Prosthesis 被引量:3
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作者 Musaddiq A. Al-Ali Muazez A. Al-Ali +1 位作者 Akihiro Takezawa Mitsuru Kitamura 《World Journal of Mechanics》 2017年第12期323-339,共17页
Treatment of bone tumors in the mandible often involves extensive excavation of affected bone, followed by mandibular reconstruction. Prosthetic implants may be needed to restore jaw functionality. The challenges of m... Treatment of bone tumors in the mandible often involves extensive excavation of affected bone, followed by mandibular reconstruction. Prosthetic implants may be needed to restore jaw functionality. The challenges of making prosthetic bone implants include stress shielding and extending the mechanical life of the implant. We have developed a design algorithm to improve the efficiency of prosthesis design. A finite element model of the patient case is constructed from a computer tomography scan, and the computer implements topology optimization techniques to design the prosthesis with limited stress shielding affected by highly biomechanical compatibility. Topology optimization facilitates the design of low weight structures by automatically introducing holes into the structure. This is governed by engineering predetermined constraints to meet certain job specifications. Such a design will be tested for fatigue life before it is ready to be manufactured and used. Topology optimization can be performed as a design process to achieve a final design that takes stress shielding into consideration. The problem of stress shielding is solved by matching the stiffness of the orthopedic implant to the original bone that is being replaced. The material we used was titanium alloy (Ti-6Al-7Nb). Volume fraction of the orthodox implant was used (0.2872 for the studied case) as volume constraints. Compliance of the bulk bone was set as a further constraint to match the stiffness of the bone with the designed structure. Our results show a good life expectancy for the designed parts, with 12% higher life expectancy for stress-based topology optimization than for compliance-based topology optimization. 展开更多
关键词 TOPOLOGY optimization TEMPOROMANDIBULAR joint FATIGUE STRESS SHIELDING
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Performance Analysis of Sparse Array based Massive MIMO via Joint Convex Optimization 被引量:2
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作者 Mengting Lou Jing Jin +5 位作者 Hanning Wang Dan Wu Liang Xia Qixing Wang Yifei Yuan Jiangzhou Wang 《China Communications》 SCIE CSCD 2022年第3期88-100,共13页
Massive multiple-input multiple-output(MIMO)technology enables higher data rate transmission in the future mobile communications.However,exploiting a large number of antenna elements at base station(BS)makes effective... Massive multiple-input multiple-output(MIMO)technology enables higher data rate transmission in the future mobile communications.However,exploiting a large number of antenna elements at base station(BS)makes effective implementation of massive MIMO challenging,due to the size and weight limits of the masssive MIMO that are located on each BS.Therefore,in order to miniaturize the massive MIMO,it is crucial to reduce the number of antenna elements via effective methods such as sparse array synthesis.In this paper,a multiple-pattern synthesis is considered towards convex optimization(CO).The joint convex optimization(JCO)based synthesis is proposed to construct a codebook for beamforming.Then,a criterion containing multiple constraints is developed,in which the sparse array is required to fullfill all constraints.Finally,extensive evaluations are performed under realistic simulation settings.The results show that with the same number of antenna elements,sparse array using the proposed JCO-based synthesis outperforms not only the uniform array,but also the sparse array with the existing CO-based synthesis method.Furthermore,with a half of the number of antenna elements that on the uniform array,the performance of the JCO-based sparse array approaches to that of the uniform array. 展开更多
关键词 B5G 6G sparse array joint convex optimization massive MIMO system-level simulation
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Bearing Failure Optimization of Composite Double-Lap Bolted Joints Based on a Three-Step Strategy Marked By Feasible Region Reduction and Model Decoupling 被引量:1
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作者 Fengrui Liu Wanting Yao +2 位作者 Xinhong Shi Libin Zhao Jianyu Zhang 《Computers, Materials & Continua》 SCIE EI 2020年第2期977-999,共23页
To minimize the mass and increase the bearing failure load of composite double-lap bolted joints,a three-step optimization strategy including feasible region reduction,optimization model decoupling and optimization wa... To minimize the mass and increase the bearing failure load of composite double-lap bolted joints,a three-step optimization strategy including feasible region reduction,optimization model decoupling and optimization was presented.In feasible region reduction,the dimensions of the feasible design region were reduced by selecting dominant design variables from numerous multilevel parameters by sensitivity analyses,and the feasible regions of variables were reduced by influence mechanism analyses.In model decoupling,the optimization model with a large number of variables was divided into various sub-models with fewer variables by variance analysis.In the third step,the optimization sub-models were solved one by one using a genetic algorithm,and the modified characteristic curve method was adopted as the failure prediction method.Based on the proposed optimization method,optimization of a double-lap single-bolt joint was performed using the ANSYS®code.The results show that the bearing failure load increased by 13.5%and that the mass decreased by 8.7%compared with those of the initial design of the joint,which validated the effectiveness of the three-step optimization strategy. 展开更多
关键词 COMPOSITE bolted joints sensitivity analysis optimization
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Thermo-mechanical fatigue reliability optimization of PBGA solder joints based on ANN-PSO 被引量:2
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作者 周继承 肖小清 +2 位作者 恩云飞 陈妮 王湘中 《Journal of Central South University of Technology》 EI 2008年第5期689-693,共5页
Based on a method combined artificial neural network (ANN) with particle swarm optimization (PSO) algorithm, the thermo-mechanical fatigue reliability of plastic ball grid array (PBGA) solder joints was studied. The s... Based on a method combined artificial neural network (ANN) with particle swarm optimization (PSO) algorithm, the thermo-mechanical fatigue reliability of plastic ball grid array (PBGA) solder joints was studied. The simulation experiments of accelerated thermal cycling test were performed by ANSYS software. Based on orthogonal array experiments, a back-propagation artificial neural network (BPNN) was used to establish the nonlinear multivariate relationship between thermo-mechanical fatigue reliability and control factors. Then, PSO was applied to obtaining the optimal levels of control factors by using the output of BPNN as the affinity measure. The results show that the control factors, such as print circuit board (PCB) size, PCB thickness, substrate size, substrate thickness, PCB coefficient of thermal expansion (CTE), substrate CTE, silicon die CTE, and solder joint CTE, have a great influence on thermo-mechanical fatigue reliability of PBGA solder joints. The ratio of signal to noise of ANN-PSO method is 51.77 dB and its error is 33.3% less than that of Taguchi method. Moreover, the running time of ANN-PSO method is only 2% of that of the BPNN. These conclusions are verified by the confirmative experiments. 展开更多
关键词 thermo-meehanical fatigue reliability solder joints plastic ball grid array finite element analysis Taguehi method artificial neural network particle swarm optimization
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An Approach of Distributed Joint Optimization for Cluster-based Wireless Sensor Networks 被引量:11
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作者 Zhixin Liu Yazhou Yuan +1 位作者 Xinping Guan Xinbin Li 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI 2015年第3期267-273,共7页
Wireless sensor networks (WSNs) are energyconstrained, so energy saving is one of the most important issues in typical applications. The clustered WSN topology is considered in this paper. To achieve the balance of en... Wireless sensor networks (WSNs) are energyconstrained, so energy saving is one of the most important issues in typical applications. The clustered WSN topology is considered in this paper. To achieve the balance of energy consumption and utility of network resources, we explicitly model and factor the effect of power and rate. A novel joint optimization model is proposed with the protection for cluster head. By the mean of a choice of two appropriate sub-utility functions, the distributed iterative algorithm is obtained. The convergence of the proposed iterative algorithm is proved analytically. We consider general dual decomposition method to realize variable separation and distributed computation, which is practical in large-scale sensor networks. Numerical results show that the proposed joint optimal algorithm converges to the optimal power allocation and rate transmission, and validate the performance in terms of prolonging of network lifetime and improvement of throughput. © 2014 Chinese Association of Automation. 展开更多
关键词 ALGORITHMS Distributed computer systems Energy conservation Energy utilization Iterative methods optimization Parallel algorithms Power control
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Prediction of high-embankment settlement combining joint denoising technique and enhanced GWO-v-SVR method
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作者 Qi Zhang Qian Su +2 位作者 Zongyu Zhang Zhixing Deng De Chen 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2024年第1期317-332,共16页
Reliable long-term settlement prediction of a high embankment relates to mountain infrastructure safety.This study developed a novel hybrid model(NHM)that combines a joint denoising technique with an enhanced gray wol... Reliable long-term settlement prediction of a high embankment relates to mountain infrastructure safety.This study developed a novel hybrid model(NHM)that combines a joint denoising technique with an enhanced gray wolf optimizer(EGWO)-n-support vector regression(n-SVR)method.High-embankment field measurements were preprocessed using the joint denoising technique,which in-cludes complete ensemble empirical mode decomposition,singular value decomposition,and wavelet packet transform.Furthermore,high-embankment settlements were predicted using the EGWO-n-SVR method.In this method,the standard gray wolf optimizer(GWO)was improved to obtain the EGWO to better tune the n-SVR model hyperparameters.The proposed NHM was then tested in two case studies.Finally,the influences of the data division ratio and kernel function on the EGWO-n-SVR forecasting performance and prediction efficiency were investigated.The results indicate that the NHM suppresses noise and restores details in high-embankment field measurements.Simultaneously,the NHM out-performs other alternative prediction methods in prediction accuracy and robustness.This demonstrates that the proposed NHM is effective in predicting high-embankment settlements with noisy field mea-surements.Moreover,the appropriate data division ratio and kernel function for EGWO-n-SVR are 7:3 and radial basis function,respectively. 展开更多
关键词 High embankment Settlement prediction joint denoising technique Enhanced gray wolf optimizer Support vector regression
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Joint Active and Passive Beamforming Design for Hybrid RIS-Aided Integrated Sensing and Communication
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作者 Chu Hongyun Yang Mengyao +1 位作者 Pan Xue Xiao Ge 《China Communications》 SCIE CSCD 2024年第10期101-112,共12页
Integrated sensing and communication(ISAC) is considered an effective technique to solve spectrum congestion in the future. In this paper, we consider a hybrid reconfigurable intelligent surface(RIS)-assisted downlink... Integrated sensing and communication(ISAC) is considered an effective technique to solve spectrum congestion in the future. In this paper, we consider a hybrid reconfigurable intelligent surface(RIS)-assisted downlink ISAC system that simultaneously serves multiple single-antenna communication users and senses multiple targets. Hybrid RIS differs from fully passive RIS in that it is composed of both active and passive elements, with the active elements having the effect of amplifying the signal in addition to phase-shifting. We maximize the achievable sum rate of communication users by collaboratively improving the beamforming matrix at the dual function base station(DFBS) and the phase-shifting matrix of the hybrid RIS, subject to the transmit power constraint at the DFBS, the signal-to-interference-plus-noise-ratio(SINR) constraint of the radar echo signal and the RIS constraint are satisfied at the same time. The builtin RIS-assisted ISAC design problem model is significantly non-convex due to the fractional objective function of this optimization problem and the coupling of the optimization variables in the objective function and constraints. As a result, we provide an effective alternating optimization approach based on fractional programming(FP) with block coordinate descent(BCD)to solve the optimization variables. Results from simulations show that the hybrid RIS-assisted ISAC system outperforms the other benchmark solutions. 展开更多
关键词 alternating optimization fractional programming hybrid reconfigurable intelligent surface integrated sensing and communication joint active and passive beamforming
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