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Approximate error correction scheme for three-dimensional surface codes based reinforcement learning
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作者 曲英杰 陈钊 +1 位作者 王伟杰 马鸿洋 《Chinese Physics B》 SCIE EI CAS CSCD 2023年第10期229-240,共12页
Quantum error correction technology is an important method to eliminate errors during the operation of quantum computers.In order to solve the problem of influence of errors on physical qubits,we propose an approximat... Quantum error correction technology is an important method to eliminate errors during the operation of quantum computers.In order to solve the problem of influence of errors on physical qubits,we propose an approximate error correction scheme that performs dimension mapping operations on surface codes.This error correction scheme utilizes the topological properties of error correction codes to map the surface code dimension to three dimensions.Compared to previous error correction schemes,the present three-dimensional surface code exhibits good scalability due to its higher redundancy and more efficient error correction capabilities.By reducing the number of ancilla qubits required for error correction,this approach achieves savings in measurement space and reduces resource consumption costs.In order to improve the decoding efficiency and solve the problem of the correlation between the surface code stabilizer and the 3D space after dimension mapping,we employ a reinforcement learning(RL)decoder based on deep Q-learning,which enables faster identification of the optimal syndrome and achieves better thresholds through conditional optimization.Compared to the minimum weight perfect matching decoding,the threshold of the RL trained model reaches 0.78%,which is 56%higher and enables large-scale fault-tolerant quantum computation. 展开更多
关键词 fault-tolerant quantum computing surface code approximate error correction reinforcement learning
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A theory for three-dimensional response of micropolar plates 被引量:1
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作者 Dianwu HUANG Linghui HE 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI CSCD 2024年第8期1403-1414,共12页
Through combined applications of the transfer-matrix method and asymptotic expansion technique,we formulate a theory to predict the three-dimensional response of micropolar plates.No ad hoc assumptions regarding throu... Through combined applications of the transfer-matrix method and asymptotic expansion technique,we formulate a theory to predict the three-dimensional response of micropolar plates.No ad hoc assumptions regarding through-thickness assumptions of the field variables are made,and the governing equations are two-dimensional,with the displacements and microrotations of the mid-plane as the unknowns.Once the deformation of the mid-plane is solved,a three-dimensional micropolar elastic field within the plate is generated,which is exact up to the second order except in the boundary region close to the plate edge.As an illustrative example,the bending of a clamped infinitely long plate caused by a uniformly distributed transverse force is analyzed and discussed in detail. 展开更多
关键词 micropolar plate TRANSFER-MATRIX asymptotic expansion three-dimensional response
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Oxygen tension modulates cell function in an in vitro three-dimensional glioblastoma tumor model 被引量:1
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作者 Sen Wang Siqi Yao +8 位作者 Na Pei Luge Bai Zhiyan Hao Dichen Li Jiankang He J.Miguel Oliveira Xiaoyan Xue Ling Wang Xinggang Mao 《Bio-Design and Manufacturing》 SCIE EI CAS CSCD 2024年第3期307-319,共13页
Hypoxia is a typical feature of the tumor microenvironment,one of the most critical factors affecting cell behavior and tumor progression.However,the lack of tumor models able to precisely emulate natural brain tumor ... Hypoxia is a typical feature of the tumor microenvironment,one of the most critical factors affecting cell behavior and tumor progression.However,the lack of tumor models able to precisely emulate natural brain tumor tissue has impeded the study of the effects of hypoxia on the progression and growth of tumor cells.This study reports a three-dimensional(3D)brain tumor model obtained by encapsulating U87MG(U87)cells in a hydrogel containing type I collagen.It also documents the effect of various oxygen concentrations(1%,7%,and 21%)in the culture environment on U87 cell morphology,proliferation,viability,cell cycle,apoptosis rate,and migration.Finally,it compares two-dimensional(2D)and 3D cultures.For comparison purposes,cells cultured in flat culture dishes were used as the control(2D model).Cells cultured in the 3D model proliferated more slowly but had a higher apoptosis rate and proportion of cells in the resting phase(G0 phase)/gap I phase(G1 phase)than those cultured in the 2D model.Besides,the two models yielded significantly different cell morphologies.Finally,hypoxia(e.g.,1%O2)affected cell morphology,slowed cell growth,reduced cell viability,and increased the apoptosis rate in the 3D model.These results indicate that the constructed 3D model is effective for investigating the effects of biological and chemical factors on cell morphology and function,and can be more representative of the tumor microenvironment than 2D culture systems.The developed 3D glioblastoma tumor model is equally applicable to other studies in pharmacology and pathology. 展开更多
关键词 HYPOXIA GLIOMA three-dimensional glioma model In vitro
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UAV-Assisted Dynamic Avatar Task Migration for Vehicular Metaverse Services: A Multi-Agent Deep Reinforcement Learning Approach 被引量:1
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作者 Jiawen Kang Junlong Chen +6 位作者 Minrui Xu Zehui Xiong Yutao Jiao Luchao Han Dusit Niyato Yongju Tong Shengli Xie 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2024年第2期430-445,共16页
Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metavers... Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metaverses. However, avatar tasks include a multitude of human-to-avatar and avatar-to-avatar interactive applications, e.g., augmented reality navigation,which consumes intensive computing resources. It is inefficient and impractical for vehicles to process avatar tasks locally. Fortunately, migrating avatar tasks to the nearest roadside units(RSU)or unmanned aerial vehicles(UAV) for execution is a promising solution to decrease computation overhead and reduce task processing latency, while the high mobility of vehicles brings challenges for vehicles to independently perform avatar migration decisions depending on current and future vehicle status. To address these challenges, in this paper, we propose a novel avatar task migration system based on multi-agent deep reinforcement learning(MADRL) to execute immersive vehicular avatar tasks dynamically. Specifically, we first formulate the problem of avatar task migration from vehicles to RSUs/UAVs as a partially observable Markov decision process that can be solved by MADRL algorithms. We then design the multi-agent proximal policy optimization(MAPPO) approach as the MADRL algorithm for the avatar task migration problem. To overcome slow convergence resulting from the curse of dimensionality and non-stationary issues caused by shared parameters in MAPPO, we further propose a transformer-based MAPPO approach via sequential decision-making models for the efficient representation of relationships among agents. Finally, to motivate terrestrial or non-terrestrial edge servers(e.g., RSUs or UAVs) to share computation resources and ensure traceability of the sharing records, we apply smart contracts and blockchain technologies to achieve secure sharing management. Numerical results demonstrate that the proposed approach outperforms the MAPPO approach by around 2% and effectively reduces approximately 20% of the latency of avatar task execution in UAV-assisted vehicular Metaverses. 展开更多
关键词 AVATAR blockchain metaverses multi-agent deep reinforcement learning transformer UAVS
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Three-dimensional cell-based strategies for liver regeneration 被引量:1
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作者 DAN GUO XI XIA JIAN YANG 《BIOCELL》 SCIE 2024年第7期1023-1036,共14页
Liver regeneration and the development of effective therapies for liver failure remain formidable challenges in modern medicine.In recent years,the utilization of 3D cell-based strategies has emerged as a promising ap... Liver regeneration and the development of effective therapies for liver failure remain formidable challenges in modern medicine.In recent years,the utilization of 3D cell-based strategies has emerged as a promising approach for addressing these urgent clinical requirements.This review provides a thorough analysis of the application of 3D cell-based approaches to liver regeneration and their potential impact on patients with end-stage liver failure.Here,we discuss various 3D culture models that incorporate hepatocytes and stem cells to restore liver function and ameliorate the consequences of liver failure.Furthermore,we explored the challenges in transitioning these innovative strategies from preclinical studies to clinical applications.The collective insights presented herein highlight the significance of 3D cell-based strategies as a transformative paradigm for liver regeneration and improved patient care. 展开更多
关键词 three-dimensional Liver regeneration ORGANOIDS Stem cells Cell therapy
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Quafu-RL:The cloud quantum computers based quantum reinforcement learning 被引量:1
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作者 靳羽欣 许宏泽 +29 位作者 王正安 庄伟峰 黄凯旋 时运豪 马卫国 李天铭 陈驰通 许凯 冯玉龙 刘培 陈墨 李尚书 杨智鹏 钱辰 马运恒 肖骁 钱鹏 顾炎武 柴绪丹 普亚南 张翼鹏 魏世杰 曾进峰 李行 龙桂鲁 金贻荣 于海峰 范桁 刘东 胡孟军 《Chinese Physics B》 SCIE EI CAS CSCD 2024年第5期29-34,共6页
With the rapid advancement of quantum computing,hybrid quantum–classical machine learning has shown numerous potential applications at the current stage,with expectations of being achievable in the noisy intermediate... With the rapid advancement of quantum computing,hybrid quantum–classical machine learning has shown numerous potential applications at the current stage,with expectations of being achievable in the noisy intermediate-scale quantum(NISQ)era.Quantum reinforcement learning,as an indispensable study,has recently demonstrated its ability to solve standard benchmark environments with formally provable theoretical advantages over classical counterparts.However,despite the progress of quantum processors and the emergence of quantum computing clouds,implementing quantum reinforcement learning algorithms utilizing parameterized quantum circuits(PQCs)on NISQ devices remains infrequent.In this work,we take the first step towards executing benchmark quantum reinforcement problems on real devices equipped with at most 136 qubits on the BAQIS Quafu quantum computing cloud.The experimental results demonstrate that the policy agents can successfully accomplish objectives under modified conditions in both the training and inference phases.Moreover,we design hardware-efficient PQC architectures in the quantum model using a multi-objective evolutionary algorithm and develop a learning algorithm that is adaptable to quantum devices.We hope that the Quafu-RL can be a guiding example to show how to realize machine learning tasks by taking advantage of quantum computers on the quantum cloud platform. 展开更多
关键词 quantum cloud platform quantum reinforcement learning evolutionary quantum architecture search
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Cognitive interference decision method for air defense missile fuze based on reinforcement learning 被引量:1
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作者 Dingkun Huang Xiaopeng Yan +2 位作者 Jian Dai Xinwei Wang Yangtian Liu 《Defence Technology(防务技术)》 SCIE EI CAS CSCD 2024年第2期393-404,共12页
To solve the problem of the low interference success rate of air defense missile radio fuzes due to the unified interference form of the traditional fuze interference system,an interference decision method based Q-lea... To solve the problem of the low interference success rate of air defense missile radio fuzes due to the unified interference form of the traditional fuze interference system,an interference decision method based Q-learning algorithm is proposed.First,dividing the distance between the missile and the target into multiple states to increase the quantity of state spaces.Second,a multidimensional motion space is utilized,and the search range of which changes with the distance of the projectile,to select parameters and minimize the amount of ineffective interference parameters.The interference effect is determined by detecting whether the fuze signal disappears.Finally,a weighted reward function is used to determine the reward value based on the range state,output power,and parameter quantity information of the interference form.The effectiveness of the proposed method in selecting the range of motion space parameters and designing the discrimination degree of the reward function has been verified through offline experiments involving full-range missile rendezvous.The optimal interference form for each distance state has been obtained.Compared with the single-interference decision method,the proposed decision method can effectively improve the success rate of interference. 展开更多
关键词 Cognitive radio Interference decision Radio fuze reinforcement learning Interference strategy optimization
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Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications 被引量:4
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作者 Ding Wang Ning Gao +2 位作者 Derong Liu Jinna Li Frank L.Lewis 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2024年第1期18-36,共19页
Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and ... Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, respectively.Then, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation significantly.Finally, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence. 展开更多
关键词 Adaptive dynamic programming(ADP) advanced control complex environment data-driven control event-triggered design intelligent control neural networks nonlinear systems optimal control reinforcement learning(RL)
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The combined application of stem cells and three-dimensional bioprinting scaffolds for the repair of spinal cord injury 被引量:3
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作者 Dingyue Ju Chuanming Dong 《Neural Regeneration Research》 SCIE CAS CSCD 2024年第8期1751-1758,共8页
Spinal cord injury is considered one of the most difficult injuries to repair and has one of the worst prognoses for injuries to the nervous system.Following surgery,the poor regenerative capacity of nerve cells and t... Spinal cord injury is considered one of the most difficult injuries to repair and has one of the worst prognoses for injuries to the nervous system.Following surgery,the poor regenerative capacity of nerve cells and the generation of new scars can make it very difficult for the impaired nervous system to restore its neural functionality.Traditional treatments can only alleviate secondary injuries but cannot fundamentally repair the spinal cord.Consequently,there is a critical need to develop new treatments to promote functional repair after spinal cord injury.Over recent years,there have been seve ral developments in the use of stem cell therapy for the treatment of spinal cord injury.Alongside significant developments in the field of tissue engineering,three-dimensional bioprinting technology has become a hot research topic due to its ability to accurately print complex structures.This led to the loading of three-dimensional bioprinting scaffolds which provided precise cell localization.These three-dimensional bioprinting scaffolds co uld repair damaged neural circuits and had the potential to repair the damaged spinal cord.In this review,we discuss the mechanisms underlying simple stem cell therapy,the application of different types of stem cells for the treatment of spinal cord injury,and the different manufa cturing methods for three-dimensional bioprinting scaffolds.In particular,we focus on the development of three-dimensional bioprinting scaffolds for the treatment of spinal cord injury. 展开更多
关键词 BIOMATERIALS embryonic stem cells induced pluripotent stem cells mesenchymal stem cells nerve regeneration spinal cord injury stem cell therapy stem cells three-dimensional bioprinting
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Fabrication and Properties of Three-dimensional Braided Carbon Fiber Reinforced SiO2-rich Mullite Composites 被引量:2
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作者 ZHANG Wei MA Qingsong MAO Weiguo 《Journal of Wuhan University of Technology(Materials Science)》 SCIE EI CAS 2019年第4期798-803,共6页
In order to enhance the fracture toughness of mullite, three-dimensional braided carbon fiber reinforced mullite(C/mullite) composites were prepared using the Al2O3-SiO2 sol with a high solid content as raw material. ... In order to enhance the fracture toughness of mullite, three-dimensional braided carbon fiber reinforced mullite(C/mullite) composites were prepared using the Al2O3-SiO2 sol with a high solid content as raw material. Mullitization behavior of the sol was characterized. Then, the microstructure, mechanical properties and oxidation resistance of C/mullite composites were investigated. It is found that the SiO2-rich mullite with desirable sintering shrinkage can be synthesized at 1 300 ℃ from the sol with an Al2O3/SiO2 mass ratio of 1:1. The C/mullite composites with a total porosity of 21.5% were fabricated by repeating 18 cycles of vacuum impregnation-drying-heat treatment, showing a flexural strength of 234.5 MPa and a fracture toughness of 13.1 MPa·m1/2. Since carbon fibers were protected by compact matrix, the C/mullite composites show favorable oxidation resistance during 1 200 ℃-1 600 ℃ even if an open porosity of 10.3% was detected. 展开更多
关键词 MULLITE carbon fiber reinforcement SOL mechanical properties OXIDATION resistance
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Three-dimensional analysis of slopes reinforced with piles 被引量:8
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作者 高玉峰 叶茂 张飞 《Journal of Central South University》 SCIE EI CAS CSCD 2015年第6期2322-2327,共6页
Based on the upper bound of limit analysis, the plane-strain analysis of the slopes reinforced with a row of piles to the 3D case was extended. A 3D rotational failure mechanism was adopted to yield the upper bound of... Based on the upper bound of limit analysis, the plane-strain analysis of the slopes reinforced with a row of piles to the 3D case was extended. A 3D rotational failure mechanism was adopted to yield the upper bound of the factor of safety. Parametric studies were carried out to explore the end effects of the slope failures and the effects of the pile location and diameter on the safety of the reinforced slopes. The results demonstrate that the end effects nearly have no effects on the most suitable location of the installed piles but have significant influence on the safety of the slopes. For a slope constrained to a narrow width, the slope becomes more stable owing to the contribution of the end effects. When the slope is reinforced with a row of piles in small space between piles, the effects of group piles are significant for evaluating the safety of slopes. The presented method is more appropriate for assessing the stability of slopes reinforced with piles and can be also utilized in the design of plies stabilizing the unstable slopes. 展开更多
关键词 three-dimensional rotational failure mechanism stability of slopes limit analysis LANDSLIDES PILES
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Bridge Bidding via Deep Reinforcement Learning and Belief Monte Carlo Search
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作者 Zizhang Qiu Shouguang Wang +1 位作者 Dan You MengChu Zhou 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2024年第10期2111-2122,共12页
Contract Bridge,a four-player imperfect information game,comprises two phases:bidding and playing.While computer programs excel at playing,bidding presents a challenging aspect due to the need for information exchange... Contract Bridge,a four-player imperfect information game,comprises two phases:bidding and playing.While computer programs excel at playing,bidding presents a challenging aspect due to the need for information exchange with partners and interference with communication of opponents.In this work,we introduce a Bridge bidding agent that combines supervised learning,deep reinforcement learning via self-play,and a test-time search approach.Our experiments demonstrate that our agent outperforms WBridge5,a highly regarded computer Bridge software that has won multiple world championships,by a performance of 0.98 IMPs(international match points)per deal over 10000 deals,with a much cost-effective approach.The performance significantly surpasses previous state-of-the-art(0.85 IMPs per deal).Note 0.1 IMPs per deal is a significant improvement in Bridge bidding. 展开更多
关键词 Contract Bridge reinforcement learning SEARCH
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Distributed Graph Database Load Balancing Method Based on Deep Reinforcement Learning
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作者 Shuming Sha Naiwang Guo +1 位作者 Wang Luo Yong Zhang 《Computers, Materials & Continua》 SCIE EI 2024年第6期5105-5124,共20页
This paper focuses on the scheduling problem of workflow tasks that exhibit interdependencies.Unlike indepen-dent batch tasks,workflows typically consist of multiple subtasks with intrinsic correlations and dependenci... This paper focuses on the scheduling problem of workflow tasks that exhibit interdependencies.Unlike indepen-dent batch tasks,workflows typically consist of multiple subtasks with intrinsic correlations and dependencies.It necessitates the distribution of various computational tasks to appropriate computing node resources in accor-dance with task dependencies to ensure the smooth completion of the entire workflow.Workflow scheduling must consider an array of factors,including task dependencies,availability of computational resources,and the schedulability of tasks.Therefore,this paper delves into the distributed graph database workflow task scheduling problem and proposes a workflow scheduling methodology based on deep reinforcement learning(DRL).The method optimizes the maximum completion time(makespan)and response time of workflow tasks,aiming to enhance the responsiveness of workflow tasks while ensuring the minimization of the makespan.The experimental results indicate that the Q-learning Deep Reinforcement Learning(Q-DRL)algorithm markedly diminishes the makespan and refines the average response time within distributed graph database environments.In quantifying makespan,Q-DRL achieves mean reductions of 12.4%and 11.9%over established First-fit and Random scheduling strategies,respectively.Additionally,Q-DRL surpasses the performance of both DRL-Cloud and Improved Deep Q-learning Network(IDQN)algorithms,with improvements standing at 4.4%and 2.6%,respectively.With reference to average response time,the Q-DRL approach exhibits a significantly enhanced performance in the scheduling of workflow tasks,decreasing the average by 2.27%and 4.71%when compared to IDQN and DRL-Cloud,respectively.The Q-DRL algorithm also demonstrates a notable increase in the efficiency of system resource utilization,reducing the average idle rate by 5.02%and 9.30%in comparison to IDQN and DRL-Cloud,respectively.These findings support the assertion that Q-DRL not only upholds a lower average idle rate but also effectively curtails the average response time,thereby substantially improving processing efficiency and optimizing resource utilization within distributed graph database systems. 展开更多
关键词 reinforcement learning WORKFLOW task scheduling load balancing
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Development of a toroidal soft x-ray imaging system and application for investigating three-dimensional plasma on J-TEXT
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作者 赵传旭 李建超 +9 位作者 张晓卿 王能超 丁永华 杨州军 江中和 严伟 李杨波 毛飞越 任正康 the J-TEXT Team 《Plasma Science and Technology》 SCIE EI CAS CSCD 2024年第3期94-99,共6页
A toroidal soft x-ray imaging(T-SXRI)system has been developed to investigate threedimensional(3D)plasma physics on J-TEXT.This T-SXRI system consists of three sets of SXR arrays.Two sets are newly developed and locat... A toroidal soft x-ray imaging(T-SXRI)system has been developed to investigate threedimensional(3D)plasma physics on J-TEXT.This T-SXRI system consists of three sets of SXR arrays.Two sets are newly developed and located on the vacuum chamber wall at toroidal positionsφof 126.4°and 272.6°,respectively,while one set was established previously atφ=65.50.Each set of SXR arrays consists of three arrays viewing the plasma poloidally,and hence can be used separately to obtain SXR images via the tomographic method.The sawtooth precursor oscillations are measured by T-SXRI,and the corresponding images of perturbative SXR signals are successfully reconstructed at these three toroidal positions,hence providing measurement of the 3D structure of precursor oscillations.The observed 3D structure is consistent with the helical structure of the m/n=1/1 mode.The experimental observation confirms that the T-SXRI system is able to observe 3D structures in the J-TEXT plasma. 展开更多
关键词 SXR imaging J-TEXT tokamak three-dimensional measurement MHD
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Intraoperative application of three-dimensional printed guides in total hip arthroplasty: A systematic review
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作者 Tim P Crone Bart M W Cornelissen +2 位作者 Jakob Van Oldenrijk Pieter Koen Bos Ewout S Veltman 《World Journal of Orthopedics》 2024年第7期660-667,共8页
BACKGROUND Acetabular component positioning in total hip arthroplasty(THA)is of key importance to ensure satisfactory post-operative outcomes and to minimize the risk of complications.The majority of acetabular compon... BACKGROUND Acetabular component positioning in total hip arthroplasty(THA)is of key importance to ensure satisfactory post-operative outcomes and to minimize the risk of complications.The majority of acetabular components are aligned freehand,without the use of navigation methods.Patient specific instruments(PSI)and three-dimensional(3D)printing of THA placement guides are increasingly used in primary THA to ensure optimal positioning.AIM To summarize the literature on 3D printing in THA and how they improve acetabular component alignment.METHODS PubMed was used to identify and access scientific studies reporting on different 3D printing methods used in THA.Eight studies with 236 hips in 228 patients were included.The studies could be divided into two main categories;3D printed models and 3D printed guides.RESULTS 3D printing in THA helped improve preoperative cup size planning and post-operative Harris hip scores between intervention and control groups(P=0.019,P=0.009).Otherwise,outcome measures were heterogeneous and thus difficult to compare.The overarching consensus between the studies is that the use of 3D guidance tools can assist in improving THA cup positioning and reduce the need for revision THA and the associated costs.CONCLUSION The implementation of 3D printing and PSI for primary THA can significantly improve the positioning accuracy of the acetabular cup component and reduce the number of complications caused by malpositioning. 展开更多
关键词 Total hip arthroplasty three-dimensional printing Hip replacement surgery three-dimensional planning Surgical guides
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Nano-scale Reinforcements and Properties of Al-Si-Cu Alloy Processed by High-Pressure Torsion
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作者 DONG Ying WU Siyuan +4 位作者 HE Ziyang LIANG Chen CHENG Feng HE Zuwei QIAN Chenhao 《Journal of Wuhan University of Technology(Materials Science)》 SCIE EI CAS CSCD 2024年第5期1253-1259,共7页
To improve the comprehensive mechanical properties of Al-Si-Cu alloy,it was treated by a high-pressure torsion process,and the effect of the deformation degree on the microstructure and properties of the Al-Si-Cu allo... To improve the comprehensive mechanical properties of Al-Si-Cu alloy,it was treated by a high-pressure torsion process,and the effect of the deformation degree on the microstructure and properties of the Al-Si-Cu alloy was studied.The results show that the reinforcements(β-Si andθ-CuAl_(2)phases)of the Al-Si-Cu alloy are dispersed in theα-Al matrix phase with finer phase size after the treatment.The processed samples exhibit grain sizes in the submicron or even nanometer range,which effectively improves the mechanical properties of the material.The hardness and strength of the deformed alloy are both significantly raised to 268 HV and 390.04 MPa by 10 turns HPT process,and the fracture morphology shows that the material gradually transits from brittle to plastic before and after deformation.The elements interdiffusion at the interface between the phases has also been effectively enhanced.In addition,it is found that the severe plastic deformation at room temperature induces a ternary eutectic reaction,resulting in the formation of ternary Al+Si+CuAl_(2)eutectic. 展开更多
关键词 Al-Si-Cu alloy high-pressure torsion nano-scale reinforcements ternary eutectic
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QoS Routing Optimization Based on Deep Reinforcement Learning in SDN
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作者 Yu Song Xusheng Qian +2 位作者 Nan Zhang Wei Wang Ao Xiong 《Computers, Materials & Continua》 SCIE EI 2024年第5期3007-3021,共15页
To enhance the efficiency and expediency of issuing e-licenses within the power sector, we must confront thechallenge of managing the surging demand for data traffic. Within this realm, the network imposes stringentQu... To enhance the efficiency and expediency of issuing e-licenses within the power sector, we must confront thechallenge of managing the surging demand for data traffic. Within this realm, the network imposes stringentQuality of Service (QoS) requirements, revealing the inadequacies of traditional routing allocation mechanismsin accommodating such extensive data flows. In response to the imperative of handling a substantial influx of datarequests promptly and alleviating the constraints of existing technologies and network congestion, we present anarchitecture forQoS routing optimizationwith in SoftwareDefinedNetwork (SDN), leveraging deep reinforcementlearning. This innovative approach entails the separation of SDN control and transmission functionalities, centralizingcontrol over data forwardingwhile integrating deep reinforcement learning for informed routing decisions. Byfactoring in considerations such as delay, bandwidth, jitter rate, and packet loss rate, we design a reward function toguide theDeepDeterministic PolicyGradient (DDPG) algorithmin learning the optimal routing strategy to furnishsuperior QoS provision. In our empirical investigations, we juxtapose the performance of Deep ReinforcementLearning (DRL) against that of Shortest Path (SP) algorithms in terms of data packet transmission delay. Theexperimental simulation results show that our proposed algorithm has significant efficacy in reducing networkdelay and improving the overall transmission efficiency, which is superior to the traditional methods. 展开更多
关键词 Deep reinforcement learning SDN route optimization QOS
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Role Dynamic Allocation of Human-Robot Cooperation Based on Reinforcement Learning in an Installation of Curtain Wall
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作者 Zhiguang Liu Shilin Wang +2 位作者 Jian Zhao Jianhong Hao Fei Yu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第1期473-487,共15页
A real-time adaptive roles allocation method based on reinforcement learning is proposed to improve humanrobot cooperation performance for a curtain wall installation task.This method breaks the traditional idea that ... A real-time adaptive roles allocation method based on reinforcement learning is proposed to improve humanrobot cooperation performance for a curtain wall installation task.This method breaks the traditional idea that the robot is regarded as the follower or only adjusts the leader and the follower in cooperation.In this paper,a self-learning method is proposed which can dynamically adapt and continuously adjust the initiative weight of the robot according to the change of the task.Firstly,the physical human-robot cooperation model,including the role factor is built.Then,a reinforcement learningmodel that can adjust the role factor in real time is established,and a reward and actionmodel is designed.The role factor can be adjusted continuously according to the comprehensive performance of the human-robot interaction force and the robot’s Jerk during the repeated installation.Finally,the roles adjustment rule established above continuously improves the comprehensive performance.Experiments of the dynamic roles allocation and the effect of the performance weighting coefficient on the result have been verified.The results show that the proposed method can realize the role adaptation and achieve the dual optimization goal of reducing the sum of the cooperator force and the robot’s Jerk. 展开更多
关键词 Human-robot cooperation roles allocation reinforcement learning
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Reinforcement Learning in Process Industries:Review and Perspective
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作者 Oguzhan Dogru Junyao Xie +6 位作者 Om Prakash Ranjith Chiplunkar Jansen Soesanto Hongtian Chen Kirubakaran Velswamy Fadi Ibrahim Biao Huang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2024年第2期283-300,共18页
This survey paper provides a review and perspective on intermediate and advanced reinforcement learning(RL)techniques in process industries. It offers a holistic approach by covering all levels of the process control ... This survey paper provides a review and perspective on intermediate and advanced reinforcement learning(RL)techniques in process industries. It offers a holistic approach by covering all levels of the process control hierarchy. The survey paper presents a comprehensive overview of RL algorithms,including fundamental concepts like Markov decision processes and different approaches to RL, such as value-based, policy-based, and actor-critic methods, while also discussing the relationship between classical control and RL. It further reviews the wide-ranging applications of RL in process industries, such as soft sensors, low-level control, high-level control, distributed process control, fault detection and fault tolerant control, optimization,planning, scheduling, and supply chain. The survey paper discusses the limitations and advantages, trends and new applications, and opportunities and future prospects for RL in process industries. Moreover, it highlights the need for a holistic approach in complex systems due to the growing importance of digitalization in the process industries. 展开更多
关键词 Process control process systems engineering reinforcement learning
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Reinforcement learning based edge computing in B5G
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作者 Jiachen Yang Yiwen Sun +4 位作者 Yutian Lei Zhuo Zhang Yang Li Yongjun Bao Zhihan Lv 《Digital Communications and Networks》 SCIE CSCD 2024年第1期1-6,共6页
The development of communication technology will promote the application of Internet of Things,and Beyond 5G will become a new technology promoter.At the same time,Beyond 5G will become one of the important supports f... The development of communication technology will promote the application of Internet of Things,and Beyond 5G will become a new technology promoter.At the same time,Beyond 5G will become one of the important supports for the development of edge computing technology.This paper proposes a communication task allocation algorithm based on deep reinforcement learning for vehicle-to-pedestrian communication scenarios in edge computing.Through trial and error learning of agent,the optimal spectrum and power can be determined for transmission without global information,so as to balance the communication between vehicle-to-pedestrian and vehicle-to-infrastructure.The results show that the agent can effectively improve vehicle-to-infrastructure communication rate as well as meeting the delay constraints on the vehicle-to-pedestrian link. 展开更多
关键词 reinforcement learning Edge computing Beyond 5G Vehicle-to-pedestrian
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