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Analysis model for damage of reinforced bars in RC beams under contact explosion
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作者 Chaozhi Yang Zhengxiang Huang +2 位作者 Xin Jia Wei Shang Jian Zhang 《Defence Technology(防务技术)》 SCIE EI CAS CSCD 2024年第11期104-118,共15页
The load-bearing capacity of reinforced concrete(RC) beams primarily relies on internal reinforced bars.However, limited research has been conducted on the dynamic response of these bars. To address this gap, this stu... The load-bearing capacity of reinforced concrete(RC) beams primarily relies on internal reinforced bars.However, limited research has been conducted on the dynamic response of these bars. To address this gap, this study has established an analytical model using dimensional analysis for calculating the deformation of reinforced bars within RC beams subjected to contact explosion. Comparison with experimental data reveals that the model has a relative error of 5.22%, effectively reflecting the deformation of reinforced bars. Additionally, based on this model, the study found that while concrete does influence the deformation of reinforced bars, this influence can be disregarded in comparison to the material properties of the bars themselves. The findings of this study have implications for calculating the residual load-bearing capacity of damaged RC beams, evaluating the extent of damage to RC beams after blast loading, and providing guidance for the blast-resistant design of RC structures. 展开更多
关键词 reinforced concrete beam Contact explosion reinforced bar Damage analysis Residual load-bearing capacity
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Reinforced tissue matrix to strengthen the abdominal wall following reversal of temporary ostomies or to treat incisional hernias
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作者 Spencer P Lake Corey R Deeken Amit K Agarwal 《World Journal of Gastrointestinal Surgery》 SCIE 2024年第3期823-832,共10页
BACKGROUND Abdominal wall deficiencies or weakness are a common complication of tem-porary ostomies,and incisional hernias frequently develop after colostomy or ileostomy takedown.The use of synthetic meshes to reinfo... BACKGROUND Abdominal wall deficiencies or weakness are a common complication of tem-porary ostomies,and incisional hernias frequently develop after colostomy or ileostomy takedown.The use of synthetic meshes to reinforce the abdominal wall has reduced hernia occurrence.Biologic meshes have also been used to enhance healing,particularly in contaminated conditions.Reinforced tissue matrices(R-TMs),which include a biologic scaffold of native extracellular matrix and a syn-thetic component for added strength/durability,are designed to take advantage of aspects of both synthetic and biologic materials.To date,RTMs have not been reported to reinforce the abdominal wall following stoma reversal.METHODS Twenty-eight patients were selected with a parastomal and/or incisional hernia who had received a temporary ileostomy or colostomy for fecal diversion after rectal cancer treatment or trauma.Following hernia repair and proximal stoma closure,RTM(OviTex®1S permanent or OviTex®LPR)was placed to reinforce the abdominal wall using a laparoscopic,robotic,or open surgical approach.Post-operative follow-up was performed at 1 month and 1 year.Hernia recurrence was determined by physical examination and,when necessary,via computed tomo-graphy scan.Secondary endpoints included length of hospital stay,time to return to work,and hospital readmissions.Evaluated complications of the wound/repair site included presence of surgical site infection,seroma,hematoma,wound dehiscence,or fistula formation.RESULTS The observational study cohort included 16 male and 12 female patients with average age of 58.5 years±16.3 years and average body mass index of 26.2 kg/m^(2)±4.1 kg/m^(2).Patients presented with a parastomal hernia(75.0%),in-cisional hernia(14.3%),or combined parastomal/incisional hernia(10.7%).Using a laparoscopic(53.6%),robotic(35.7%),or open(10.7%)technique,RTMs(OviTex®LPR:82.1%,OviTex®1S:17.9%)were placed using sublay(82.1%)or intraperitoneal onlay(IPOM;17.9%)mesh positioning.At 1-month and 1-year follow-ups,there were no hernia recurrences(0%).Average hospital stays were 2.1 d±1.2 d and return to work occurred at 8.3 post-operative days±3.0 post-operative days.Three patients(10.7%)were readmitted before the 1-month follow up due to mesh infection and/or gastrointestinal issues.Fistula and mesh infection were observed in two patients each(7.1%),leading to partial mesh removal in one patient(3.6%).There were no complications between 1 month and 1 year(0%).CONCLUSION RTMs were used successfully to treat parastomal and incisional hernias at ileostomy reversal,with no hernia recurrences and favorable outcomes after 1-month and 1-year. 展开更多
关键词 reinforced tissue matrix reinforced forestomach matrix ILEOSTOMY COLOSTOMY Ostomy takedown Incisional hernia Abdominal wall
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Trading in Fast-ChangingMarkets withMeta-Reinforcement Learning
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作者 Yutong Tian Minghan Gao +1 位作者 Qiang Gao Xiao-Hong Peng 《Intelligent Automation & Soft Computing》 2024年第2期175-188,共14页
How to find an effective trading policy is still an open question mainly due to the nonlinear and non-stationary dynamics in a financial market.Deep reinforcement learning,which has recently been used to develop tradi... How to find an effective trading policy is still an open question mainly due to the nonlinear and non-stationary dynamics in a financial market.Deep reinforcement learning,which has recently been used to develop trading strategies by automatically extracting complex features from a large amount of data,is struggling to deal with fastchanging markets due to sample inefficiency.This paper applies the meta-reinforcement learning method to tackle the trading challenges faced by conventional reinforcement learning(RL)approaches in non-stationary markets for the first time.In our work,the history trading data is divided into multiple task data and for each of these data themarket condition is relatively stationary.Then amodel agnosticmeta-learning(MAML)-based tradingmethod involving a meta-learner and a normal learner is proposed.A trading policy is learned by the meta-learner across multiple task data,which is then fine-tuned by the normal learner through a small amount of data from a new market task before trading in it.To improve the adaptability of the MAML-based method,an ordered multiplestep updating mechanism is also proposed to explore the changing dynamic within a task market.The simulation results demonstrate that the proposed MAML-based trading methods can increase the annualized return rate by approximately 180%,200%,and 160%,increase the Sharpe ratio by 180%,90%,and 170%,and decrease the maximum drawdown by 30%,20%,and 40%,compared to the traditional RL approach in three stock index future markets,respectively. 展开更多
关键词 Algorithmic trading reinforcement learning fast-changing market meta-reinforcement learning
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Evaluation of the Inhibitory Gel Aloe vera against Corrosion of Reinforcement Concrete in NaCl Medium
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作者 Malang Bodian Dame Keinde +3 位作者 Kinda Hannawi Modou Fall Aveline Darquennes Prince William Agbodjan 《Materials Sciences and Applications》 2024年第5期101-112,共12页
Most reinforced concrete structures in seaside locations suffer from corrosion damage to the reinforcement, limiting their durability and necessitating costly repairs. To improve their performance and durability, we h... Most reinforced concrete structures in seaside locations suffer from corrosion damage to the reinforcement, limiting their durability and necessitating costly repairs. To improve their performance and durability, we have investigated in this paper Aloe vera extracts as a green corrosion inhibitor for reinforcing steel in NaCl environments. Using electrochemical methods (zero-intensity chronopotentiometry, Tafel lines and electrochemical impedance spectroscopy), this experimental work investigated the effect of these Aloe vera (AV) extracts on corrosion inhibition of concrete reinforcing bar (HA, diameter 12mm) immersed in a 0.5M NaCl solution. The results show that Aloe vera extracts have an average corrosion-inhibiting efficacy of around 86% at an optimum concentration of 20%. 展开更多
关键词 reinforced Concrete Green Inhibitor Corrosion reinforceMENT Electrochemical Methods
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Characterization and Modeling of Reinforced Earth Structures
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作者 Tchamiè David Midikizi Oustasse Abdoulaye Sall +3 位作者 Déthié Sarr Cheikh Ibrahima Tine Ndeye Seynabou Ndiaye Makhaly Ba 《Open Journal of Applied Sciences》 2024年第10期2943-2954,共12页
The aim of this study is to characterize soil/reinforcement interaction in reinforced earth structures. The study showed that the internal behavior of this type of structure depends on a number of factors, including t... The aim of this study is to characterize soil/reinforcement interaction in reinforced earth structures. The study showed that the internal behavior of this type of structure depends on a number of factors, including the engineering backfill, the reinforcement and the soil/reinforcement interaction. The study also showed that the soil-reinforcement interaction phenomenon is a fairly complex mechanism that depends on the applied load, the geometry of the structure, the characteristics of the soil and a set of parameters characterizing the nailing: density, number and length of reinforcements, inclination of the reinforcements in relation to the sliding surface, mechanical characteristics of the reinforcements and, in particular, the relative stiffness of the reinforcements and the soil. The results showed that the tensile forces developed in the reinforcement are not entirely reversible, and that the soil at the interface undergoes permanent deformation, leading to the appearance of irreversible tensile forces in the reinforcement. 展开更多
关键词 reinforced Earth Structures MODELING Earth/reinforcement Interaction
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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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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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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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A Comparative Study on the Post-Buckling Behavior of Reinforced Thermoplastic Pipes(RTPs)Under External Pressure Considering Progressive Failure 被引量:1
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作者 DING Xin-dong WANG Shu-qing +1 位作者 LIU Wen-cheng YE Xiao-han 《China Ocean Engineering》 SCIE EI CSCD 2024年第2期233-246,共14页
The collapse pressure is a key parameter when RTPs are applied in harsh deep-water environments.To investigate the collapse of RTPs,numerical simulations and hydrostatic pressure tests are conducted.For the numerical ... The collapse pressure is a key parameter when RTPs are applied in harsh deep-water environments.To investigate the collapse of RTPs,numerical simulations and hydrostatic pressure tests are conducted.For the numerical simulations,the eigenvalue analysis and Riks analysis are combined,in which the Hashin failure criterion and fracture energy stiffness degradation model are used to simulate the progressive failure of composites,and the“infinite”boundary conditions are applied to eliminate the boundary effects.As for the hydrostatic pressure tests,RTP specimens were placed in a hydrostatic chamber after filled with water.It has been observed that the cross-section of the middle part collapses when it reaches the maximum pressure.The collapse pressure obtained from the numerical simulations agrees well with that in the experiment.Meanwhile,the applicability of NASA SP-8007 formula on the collapse pressure prediction was also discussed.It has a relatively greater difference because of the ignorance of the progressive failure of composites.For the parametric study,it is found that RTPs have much higher first-ply-failure pressure when the winding angles are between 50°and 70°.Besides,the effect of debonding and initial ovality,and the contribution of the liner and coating are also discussed. 展开更多
关键词 reinforced thermoplastic pipes post-buckling behavior progressive failure of composites DEBONDING initial ovality
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Toward Trustworthy Decision-Making for Autonomous Vehicles:A Robust Reinforcement Learning Approach with Safety Guarantees
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作者 Xiangkun He Wenhui Huang Chen Lv 《Engineering》 SCIE EI CAS CSCD 2024年第2期77-89,共13页
While autonomous vehicles are vital components of intelligent transportation systems,ensuring the trustworthiness of decision-making remains a substantial challenge in realizing autonomous driving.Therefore,we present... While autonomous vehicles are vital components of intelligent transportation systems,ensuring the trustworthiness of decision-making remains a substantial challenge in realizing autonomous driving.Therefore,we present a novel robust reinforcement learning approach with safety guarantees to attain trustworthy decision-making for autonomous vehicles.The proposed technique ensures decision trustworthiness in terms of policy robustness and collision safety.Specifically,an adversary model is learned online to simulate the worst-case uncertainty by approximating the optimal adversarial perturbations on the observed states and environmental dynamics.In addition,an adversarial robust actor-critic algorithm is developed to enable the agent to learn robust policies against perturbations in observations and dynamics.Moreover,we devise a safety mask to guarantee the collision safety of the autonomous driving agent during both the training and testing processes using an interpretable knowledge model known as the Responsibility-Sensitive Safety Model.Finally,the proposed approach is evaluated through both simulations and experiments.These results indicate that the autonomous driving agent can make trustworthy decisions and drastically reduce the number of collisions through robust safety policies. 展开更多
关键词 Autonomous vehicle DECISION-MAKING reinforcement learning Adversarial attack Safety guarantee
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A deep reinforcement learning approach to gasoline blending real-time optimization under uncertainty
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作者 Zhiwei Zhu Minglei Yang +3 位作者 Wangli He Renchu He Yunmeng Zhao Feng Qian 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2024年第7期183-192,共10页
The gasoline inline blending process has widely used real-time optimization techniques to achieve optimization objectives,such as minimizing the cost of production.However,the effectiveness of real-time optimization i... The gasoline inline blending process has widely used real-time optimization techniques to achieve optimization objectives,such as minimizing the cost of production.However,the effectiveness of real-time optimization in gasoline blending relies on accurate blending models and is challenged by stochastic disturbances.Thus,we propose a real-time optimization algorithm based on the soft actor-critic(SAC)deep reinforcement learning strategy to optimize gasoline blending without relying on a single blending model and to be robust against disturbances.Our approach constructs the environment using nonlinear blending models and feedstocks with disturbances.The algorithm incorporates the Lagrange multiplier and path constraints in reward design to manage sparse product constraints.Carefully abstracted states facilitate algorithm convergence,and the normalized action vector in each optimization period allows the agent to generalize to some extent across different target production scenarios.Through these well-designed components,the algorithm based on the SAC outperforms real-time optimization methods based on either nonlinear or linear programming.It even demonstrates comparable performance with the time-horizon based real-time optimization method,which requires knowledge of uncertainty models,confirming its capability to handle uncertainty without accurate models.Our simulation illustrates a promising approach to free real-time optimization of the gasoline blending process from uncertainty models that are difficult to acquire in practice. 展开更多
关键词 Deep reinforcement learning Gasoline blending Real-time optimization PETROLEUM Computer simulation Neural networks
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Stability behavior of the Lanxi ancient flood control levee after reinforcement with upside-down hanging wells and grouting curtain
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作者 QIN Zipeng TIAN Yan +4 位作者 GAO Siyuan ZHOU Jianfen HE Xiaohui HE Weizhong GAO Jingquan 《Journal of Mountain Science》 SCIE CSCD 2024年第1期84-99,共16页
The stability of the ancient flood control levees is mainly influenced by water level fluctuations, groundwater concentration and rainfalls. This paper takes the Lanxi ancient levee as a research object to study the e... The stability of the ancient flood control levees is mainly influenced by water level fluctuations, groundwater concentration and rainfalls. This paper takes the Lanxi ancient levee as a research object to study the evolution laws of its seepage, displacement and stability before and after reinforcement with the upside-down hanging wells and grouting curtain through numerical simulation methods combined with experiments and observations. The study results indicate that the filled soil is less affected by water level fluctuations and groundwater concentration after reinforcement. A high groundwater level is detrimental to the levee's long-term stability, and the drainage issues need to be fully considered. The deformation of the reinforced levee is effectively controlled since the fill deformation is mainly borne by the upside-down hanging wells. The safety factors of the levee before reinforcement vary significantly with the water level. The minimum value of the safety factors is 0.886 during the water level decreasing period, indicating a very high risk of the instability. While it reached 1.478 after reinforcement, the stability of the ancient levee is improved by a large margin. 展开更多
关键词 Stability analysis Multiple factors Antiseepage reinforcement Upside-down hanging well Grouting curtain Ancient levee
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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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Combining reinforcement learning with mathematical programming:An approach for optimal design of heat exchanger networks
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作者 Hui Tan Xiaodong Hong +4 位作者 Zuwei Liao Jingyuan Sun Yao Yang Jingdai Wang Yongrong Yang 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2024年第5期63-71,共9页
Heat integration is important for energy-saving in the process industry.It is linked to the persistently challenging task of optimal design of heat exchanger networks(HEN).Due to the inherent highly nonconvex nonlinea... Heat integration is important for energy-saving in the process industry.It is linked to the persistently challenging task of optimal design of heat exchanger networks(HEN).Due to the inherent highly nonconvex nonlinear and combinatorial nature of the HEN problem,it is not easy to find solutions of high quality for large-scale problems.The reinforcement learning(RL)method,which learns strategies through ongoing exploration and exploitation,reveals advantages in such area.However,due to the complexity of the HEN design problem,the RL method for HEN should be dedicated and designed.A hybrid strategy combining RL with mathematical programming is proposed to take better advantage of both methods.An insightful state representation of the HEN structure as well as a customized reward function is introduced.A Q-learning algorithm is applied to update the HEN structure using theε-greedy strategy.Better results are obtained from three literature cases of different scales. 展开更多
关键词 Heat exchanger network reinforcement learning Mathematical programming Process design
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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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Reinforcement Learning-Based Energy Management for Hybrid Power Systems:State-of-the-Art Survey,Review,and Perspectives
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作者 Xiaolin Tang Jiaxin Chen +4 位作者 Yechen Qin Teng Liu Kai Yang Amir Khajepour Shen Li 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2024年第3期1-25,共25页
The new energy vehicle plays a crucial role in green transportation,and the energy management strategy of hybrid power systems is essential for ensuring energy-efficient driving.This paper presents a state-of-the-art ... The new energy vehicle plays a crucial role in green transportation,and the energy management strategy of hybrid power systems is essential for ensuring energy-efficient driving.This paper presents a state-of-the-art survey and review of reinforcement learning-based energy management strategies for hybrid power systems.Additionally,it envisions the outlook for autonomous intelligent hybrid electric vehicles,with reinforcement learning as the foundational technology.First of all,to provide a macro view of historical development,the brief history of deep learning,reinforcement learning,and deep reinforcement learning is presented in the form of a timeline.Then,the comprehensive survey and review are conducted by collecting papers from mainstream academic databases.Enumerating most of the contributions based on three main directions—algorithm innovation,powertrain innovation,and environment innovation—provides an objective review of the research status.Finally,to advance the application of reinforcement learning in autonomous intelligent hybrid electric vehicles,future research plans positioned as“Alpha HEV”are envisioned,integrating Autopilot and energy-saving control. 展开更多
关键词 New energy vehicle Hybrid power system reinforcement learning Energy management strategy
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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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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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Unleashing the Power of Multi-Agent Reinforcement Learning for Algorithmic Trading in the Digital Financial Frontier and Enterprise Information Systems
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作者 Saket Sarin Sunil K.Singh +4 位作者 Sudhakar Kumar Shivam Goyal Brij Bhooshan Gupta Wadee Alhalabi Varsha Arya 《Computers, Materials & Continua》 SCIE EI 2024年第8期3123-3138,共16页
In the rapidly evolving landscape of today’s digital economy,Financial Technology(Fintech)emerges as a trans-formative force,propelled by the dynamic synergy between Artificial Intelligence(AI)and Algorithmic Trading... In the rapidly evolving landscape of today’s digital economy,Financial Technology(Fintech)emerges as a trans-formative force,propelled by the dynamic synergy between Artificial Intelligence(AI)and Algorithmic Trading.Our in-depth investigation delves into the intricacies of merging Multi-Agent Reinforcement Learning(MARL)and Explainable AI(XAI)within Fintech,aiming to refine Algorithmic Trading strategies.Through meticulous examination,we uncover the nuanced interactions of AI-driven agents as they collaborate and compete within the financial realm,employing sophisticated deep learning techniques to enhance the clarity and adaptability of trading decisions.These AI-infused Fintech platforms harness collective intelligence to unearth trends,mitigate risks,and provide tailored financial guidance,fostering benefits for individuals and enterprises navigating the digital landscape.Our research holds the potential to revolutionize finance,opening doors to fresh avenues for investment and asset management in the digital age.Additionally,our statistical evaluation yields encouraging results,with metrics such as Accuracy=0.85,Precision=0.88,and F1 Score=0.86,reaffirming the efficacy of our approach within Fintech and emphasizing its reliability and innovative prowess. 展开更多
关键词 Neurodynamic Fintech multi-agent reinforcement learning algorithmic trading digital financial frontier
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Multi-circular formation control with reinforced transient profiles for nonholonomic vehicles:A path-following framework
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作者 Jintao Zhang Xingling Shao +1 位作者 Wendong Zhang Zongyu Zuo 《Defence Technology(防务技术)》 SCIE EI CAS CSCD 2024年第1期278-287,共10页
This article investigates a multi-circular path-following formation control with reinforced transient profiles for nonholonomic vehicles connected by a digraph.A multi-circular formation controller endowed with the fe... This article investigates a multi-circular path-following formation control with reinforced transient profiles for nonholonomic vehicles connected by a digraph.A multi-circular formation controller endowed with the feature of spatial-temporal decoupling is devised for a group of vehicles guided by a virtual leader evolving along an implicit path,which allows for a circumnavigation on multiple circles with an anticipant angular spacing.In addition,notice that it typically imposes a stringent time constraint on time-sensitive enclosing scenarios,hence an improved prescribed performance control(IPPC)using novel tighter behavior boundaries is presented to enhance transient capabilities with an ensured appointed-time convergence free from any overshoots.The significant merits are that coordinated circumnavigation along different circles can be realized via executing geometric and dynamic assignments independently with modified transient profiles.Furthermore,all variables existing in the entire system are analyzed to be convergent.Simulation and experimental results are provided to validate the utility of suggested solution. 展开更多
关键词 Multi-circular formation reinforced transient profiles Nonholonomic vehicles Path following
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