Decision-making of connected and automated vehicles(CAV)includes a sequence of driving maneuvers that improve safety and efficiency,characterized by complex scenarios,strong uncertainty,and high real-time requirements...Decision-making of connected and automated vehicles(CAV)includes a sequence of driving maneuvers that improve safety and efficiency,characterized by complex scenarios,strong uncertainty,and high real-time requirements.Deep reinforcement learning(DRL)exhibits excellent capability of real-time decision-making and adaptability to complex scenarios,and generalization abilities.However,it is arduous to guarantee complete driving safety and efficiency under the constraints of training samples and costs.This paper proposes a Mixture of Expert method(MoE)based on Soft Actor-Critic(SAC),where the upper-level discriminator dynamically decides whether to activate the lower-level DRL expert or the heuristic expert based on the features of the input state.To further enhance the performance of the DRL expert,a buffer zone is introduced in the reward function,preemptively applying penalties before insecure situations occur.In order to minimize collision and off-road rates,the Intelligent Driver Model(IDM)and Minimizing Overall Braking Induced by Lane changes(MOBIL)strategy are designed by heuristic experts.Finally,tested in typical simulation scenarios,MOE shows a 13.75%improvement in driving efficiency compared with the traditional DRL method with continuous action space.It ensures high safety with zero collision and zero off-road rates while maintaining high adaptability.展开更多
Peer-to-peer(P2P)energy trading in active distribution networks(ADNs)plays a pivotal role in promoting the efficient consumption of renewable energy sources.However,it is challenging to effectively coordinate the powe...Peer-to-peer(P2P)energy trading in active distribution networks(ADNs)plays a pivotal role in promoting the efficient consumption of renewable energy sources.However,it is challenging to effectively coordinate the power dispatch of ADNs and P2P energy trading while preserving the privacy of different physical interests.Hence,this paper proposes a soft actor-critic algorithm incorporating distributed trading control(SAC-DTC)to tackle the optimal power dispatch of ADNs and the P2P energy trading considering privacy preservation among prosumers.First,the soft actor-critic(SAC)algorithm is used to optimize the control strategy of device in ADNs to minimize the operation cost,and the primary environmental information of the ADN at this point is published to prosumers.Then,a distributed generalized fast dual ascent method is used to iterate the trading process of prosumers and maximize their revenues.Subsequently,the results of trading are encrypted based on the differential privacy technique and returned to the ADN.Finally,the social welfare value consisting of ADN operation cost and P2P market revenue is utilized as a reward value to update network parameters and control strategies of the deep reinforcement learning.Simulation results show that the proposed SAC-DTC algorithm reduces the ADN operation cost,boosts the P2P market revenue,maximizes the social welfare,and exhibits high computational accuracy,demonstrating its practical application to the operation of power systems and power markets.展开更多
Building integrated energy systems(BIESs)are pivotal for enhancing energy efficiency by accounting for a significant proportion of global energy consumption.Two key barriers that reduce the BIES operational efficiency...Building integrated energy systems(BIESs)are pivotal for enhancing energy efficiency by accounting for a significant proportion of global energy consumption.Two key barriers that reduce the BIES operational efficiency mainly lie in the renewable generation uncertainty and operational non-convexity of combined heat and power(CHP)units.To this end,this paper proposes a soft actor-critic(SAC)algorithm to solve the scheduling problem of BIES,which overcomes the model non-convexity and shows advantages in robustness and generalization.This paper also adopts a temporal fusion transformer(TFT)to enhance the optimal solution for the SAC algorithm by forecasting the renewable generation and energy demand.The TFT can effectively capture the complex temporal patterns and dependencies that span multiple steps.Furthermore,its forecasting results are interpretable due to the employment of a self-attention layer so as to assist in more trustworthy decision-making in the SAC algorithm.The proposed hybrid data-driven approach integrating TFT and SAC algorithm,i.e.,TFT-SAC approach,is trained and tested on a real-world dataset to validate its superior performance in reducing the energy cost and computational time compared with the benchmark approaches.The generalization performance for the scheduling policy,as well as the sensitivity analysis,are examined in the case studies.展开更多
Parking in a small parking lot within limited space poses a difficult task. It often leads to deviations between the final parking posture and the target posture. These deviations can lead to partial occupancy of adja...Parking in a small parking lot within limited space poses a difficult task. It often leads to deviations between the final parking posture and the target posture. These deviations can lead to partial occupancy of adjacent parking lots, which poses a safety threat to vehicles parked in these parking lots. However, previous studies have not addressed this issue. In this paper, we aim to evaluate the impact of parking deviation of existing vehicles next to the target parking lot(PDEVNTPL) on the automatic ego vehicle(AEV) parking, in terms of safety, comfort, accuracy, and efficiency of parking. A segmented parking training framework(SPTF) based on soft actor-critic(SAC) is proposed to improve parking performance. In the proposed method, the SAC algorithm incorporates strategy entropy into the objective function, to enable the AEV to learn parking strategies based on a more comprehensive understanding of the environment. Additionally, the SPTF simplifies complex parking tasks to maintain the high performance of deep reinforcement learning(DRL). The experimental results reveal that the PDEVNTPL has a detrimental influence on the AEV parking in terms of safety, accuracy, and comfort, leading to reductions of more than 27%, 54%, and 26%respectively. However, the SAC-based SPTF effectively mitigates this impact, resulting in a considerable increase in the parking success rate from 71% to 93%. Furthermore, the heading angle deviation is significantly reduced from 2.25 degrees to 0.43degrees.展开更多
In this study,a novel residential virtual power plant(RVPP)scheduling method that leverages a gate recurrent unit(GRU)-integrated deep reinforcement learning(DRL)algorithm is proposed.In the proposed scheme,the GRU-in...In this study,a novel residential virtual power plant(RVPP)scheduling method that leverages a gate recurrent unit(GRU)-integrated deep reinforcement learning(DRL)algorithm is proposed.In the proposed scheme,the GRU-integrated DRL algorithm guides the RVPP to participate effectively in both the day-ahead and real-time markets,lowering the electricity purchase costs and consumption risks for end-users.The Lagrangian relaxation technique is introduced to transform the constrained Markov decision process(CMDP)into an unconstrained optimization problem,which guarantees that the constraints are strictly satisfied without determining the penalty coefficients.Furthermore,to enhance the scalability of the constrained soft actor-critic(CSAC)-based RVPP scheduling approach,a fully distributed scheduling architecture was designed to enable plug-and-play in the residential distributed energy resources(RDER).Case studies performed on the constructed RVPP scenario validated the performance of the proposed methodology in enhancing the responsiveness of the RDER to power tariffs,balancing the supply and demand of the power grid,and ensuring customer comfort.展开更多
To decrease the complexity of MAP algorithm, reduced state or reduced search techniques can be applied. In this paper we propose a reduced search soft output detection algorithm fully based on the principle of M a...To decrease the complexity of MAP algorithm, reduced state or reduced search techniques can be applied. In this paper we propose a reduced search soft output detection algorithm fully based on the principle of M algorithm for turbo equalization, which is a suboptimum version of the Lee algorithm. This algorithm is called soft output M algorithm (denoted as SO M algorithm), which applies the M strategy to both the forward recursion and the extended forward recursion of the Lee algorithm. Computer simulation results show that, by properly selecting and adjusting the breadth parameter and depth parameter during the iteration of turbo equalization, this algorithm can obtain good performance and complexity trade off.展开更多
In order to fully utilize the soft decision ability of the outer decoder in a concatenated system, reliability information (called soft output) from the inner decoder or equalizer is required. In this paper, based on...In order to fully utilize the soft decision ability of the outer decoder in a concatenated system, reliability information (called soft output) from the inner decoder or equalizer is required. In this paper, based on the analysis of typical implementations of soft output VA, a novel algorithm is proposed by utilizing the property of Viterbi algorithm. Compared with the typical implementations, less processing expense is required by the new algorithm for weighting the hard decisions of VA. Meanwhile, simulation results show that, deterioration in performance of this algorithm is usually small for decoding of convolutional code and negligible for equalization.展开更多
In order to solve the problem of high computing cost and low simulation accuracy caused by discontinuity of incision in traditional meshless model,this paper proposes a soft tissue deformation model based on the Marqu...In order to solve the problem of high computing cost and low simulation accuracy caused by discontinuity of incision in traditional meshless model,this paper proposes a soft tissue deformation model based on the Marquardt algorithm and enrichment function.The model is based on the element-free Galerkin method,in which Kelvin viscoelastic model and adjustment function are integrated.Marquardt algorithm is applied to fit the relation between force and displacement caused by surface deformation,and the enrichment function is applied to deal with the discontinuity in the meshless method.To verify the validity of the model,the Sensable Phantom Omni force tactile interactive device is used to simulate the deformations of stomach and heart.Experimental results show that the proposed model improves the real-time performance and accuracy of soft tissue deformation simulation,which provides a new perspective for the application of the meshless method in virtual surgery.展开更多
Vertical hot ring rolling(VHRR) process has the characteristics of nonlinearity,time-variation and being susceptible to disturbance.Furthermore,the ring's growth is quite fast within a short time,and the rolled ri...Vertical hot ring rolling(VHRR) process has the characteristics of nonlinearity,time-variation and being susceptible to disturbance.Furthermore,the ring's growth is quite fast within a short time,and the rolled ring's position is asymmetrical.All of these cause that the ring's dimensions cannot be measured directly.Through analyzing the relationships among the dimensions of ring blanks,the positions of rolls and the ring's inner and outer diameter,the soft measurement model of ring's dimensions is established based on the radial basis function neural network(RBFNN).A mass of data samples are obtained from VHRR finite element(FE) simulations to train and test the soft measurement NN model,and the model's structure parameters are deduced and optimized by genetic algorithm(GA).Finally,the soft measurement system of ring's dimensions is established and validated by the VHRR experiments.The ring's dimensions were measured artificially and calculated by the soft measurement NN model.The results show that the calculation values of GA-RBFNN model are close to the artificial measurement data.In addition,the calculation accuracy of GA-RBFNN model is higher than that of RBFNN model.The research results suggest that the soft measurement NN model has high precision and flexibility.The research can provide practical methods and theoretical guidance for the accurate measurement of VHRR process.展开更多
An improved immune algorithm is proposed in this paper. The problems, such as convergence speed and optimization precision, existing in the basic immune algorithm are well addressed. Besides, a fuzzy adaptive method i...An improved immune algorithm is proposed in this paper. The problems, such as convergence speed and optimization precision, existing in the basic immune algorithm are well addressed. Besides, a fuzzy adaptive method is presented by using the fuzzy system to realize the adaptive selection of two key parameters （possibility of crossover and mutation）. By comparing and analyzing the results of several benchmark functions, the performance of fuzzy immune algorithm （FIA） is approved. Not only the difficulty of parameters selection is relieved, but also the precision and stability are improved. At last, the FIA is ap- plied to optimization of the structure and parameters in radial basis function neural network （RBFNN） based on an orthogonal sequential method. And the availability of algorithm is proved by applying RBFNN in modeling in soft sensor of solvent tower.展开更多
In recent years,the soft subspace clustering algorithm has shown good results for high-dimensional data,which can assign different weights to each cluster class and use weights to measure the contribution of each dime...In recent years,the soft subspace clustering algorithm has shown good results for high-dimensional data,which can assign different weights to each cluster class and use weights to measure the contribution of each dimension in various features.The enhanced soft subspace clustering algorithm combines interclass separation and intraclass tightness information,which has strong results for image segmentation,but the clustering algorithm is vulnerable to noisy data and dependence on the initialized clustering center.However,the clustering algorithmis susceptible to the influence of noisydata and reliance on initializedclustering centers andfalls into a local optimum;the clustering effect is poor for brain MR images with unclear boundaries and noise effects.To address these problems,a soft subspace clustering algorithm for brain MR images based on genetic algorithm optimization is proposed,which combines the generalized noise technique,relaxes the equational weight constraint in the objective function as the boundary constraint,and uses a genetic algorithm as a method to optimize the initialized clustering center.The genetic algorithm finds the best clustering center and reduces the algorithm’s dependence on the initial clustering center.The experiment verifies the robustness of the algorithm,as well as the noise immunity in various ways and shows good results on the common dataset and the brain MR images provided by the Changshu First People’s Hospital with specific high accuracy for clinical medicine.展开更多
A computationally efficient soft-output detector with lattice-reduction （LR） for the multiple-input multiple-output （MIMO） systems is proposed. In the proposed scheme, the sorted QR de- composition is applied on t...A computationally efficient soft-output detector with lattice-reduction （LR） for the multiple-input multiple-output （MIMO） systems is proposed. In the proposed scheme, the sorted QR de- composition is applied on the lattice-reduced equivalent channel to obtain the tree structure. With the aid of the boundary control, the stack algorithm searches a small part of the whole search tree to generate a handful of candidate lists in the reduced lattice. The proposed soft-output algorithm achieves near-optimal perfor- mance in a coded MIMO system and the associated computational complexity is substantially lower than that of previously proposed methods.展开更多
It is well known that the kit completeness of parts processed in the previous stage is crucial for the subsequent manufacturing stage.This paper studies the flexible job shop scheduling problem(FJSP)with the objective...It is well known that the kit completeness of parts processed in the previous stage is crucial for the subsequent manufacturing stage.This paper studies the flexible job shop scheduling problem(FJSP)with the objective of material kitting,where a material kit is a collection of components that ensures that a batch of components can be ready at the same time during the product assembly process.In this study,we consider completion time variance and maximumcompletion time as scheduling objectives,continue the weighted summation process formultiple objectives,and design adaptive weighted summation parameters to optimize productivity and reduce the difference in completion time between components in the same kit.The Soft Actor Critic(SAC)algorithm is designed to be combined with the Adaptive Multi-Buffer Experience Replay(AMBER)mechanism to propose the SAC-AMBER algorithm.The AMBER mechanism optimizes the experience sampling and policy updating process and enhances learning efficiency by categorically storing the experience into the standard buffer,the high equipment utilization buffer,and the high productivity buffer.Experimental results show that the SAC-AMBER algorithm can effectively reduce the maximum completion time on multiple datasets,reduce the difference in component completion time in the same kit,and thus optimize the readiness of the part kits,demonstrating relatively good stability and convergence.Compared with traditional heuristics,meta-heuristics,and other deep reinforcement learning methods,the SAC-AMBER algorithm performs better in terms of solution quality and computational efficiency,and through extensive testing on multiple datasets,the algorithm has been confirmed to have good generalization ability,providing an effective solution to the FJSP problem.展开更多
This paper introduces fuzzy N-bipolar soft(FN-BS)sets,a novel mathematical framework designed to enhance multi-criteria decision-making(MCDM)processes under uncertainty.The study addresses a significant limitation in ...This paper introduces fuzzy N-bipolar soft(FN-BS)sets,a novel mathematical framework designed to enhance multi-criteria decision-making(MCDM)processes under uncertainty.The study addresses a significant limitation in existing models by unifying fuzzy logic,the consideration of bipolarity,and the ability to evaluate attributes on a multinary scale.The specific contributions of the FN-BS framework include:(1)a formal definition and settheoretic foundation,(2)the development of two innovative algorithms for solving decision-making(DM)problems,and(3)a comparative analysis demonstrating its superiority over established models.The proposed framework is applied to a real-world case study on selecting vaccination programs across multiple countries,showcasing consistent DM outcomes and exceptional adaptability to complex and uncertain scenarios.These results position FN-BS sets as a versatile and powerful tool for addressing dynamic DM challenges.展开更多
Considering that the vehicle routing problem （VRP） with many extended features is widely used in actual life, such as multi-depot, heterogeneous types of vehicles, customer service priority and time windows etc., a ...Considering that the vehicle routing problem （VRP） with many extended features is widely used in actual life, such as multi-depot, heterogeneous types of vehicles, customer service priority and time windows etc., a mathematical model for multi-depot heterogeneous vehicle routing problem with soft time windows （MDHVRPSTW） is established. An improved ant colony optimization （IACO） is proposed for solving this model. First, MDHVRPSTW is transferred into different groups according to the nearest principle, and then the initial route is constructed by the scanning algorithm （SA）. Secondly, genetic operators are introduced, and crossover probability and mutation probability are adaptively adjusted in order to improve the global search ability of the algorithm. Moreover, the smooth mechanism is used to improve the performance of the ant colony optimization （ACO）. Finally, the 3-opt strategy is used to improve the local search ability. The proposed IACO was tested on three new instances that were generated randomly. The experimental results show that IACO is superior to the other three existing algorithms in terms of convergence speed and solution quality. Thus, the proposed method is effective and feasible, and the proposed model is meaningful.展开更多
Handling ralely happened safety-critical events remains a central challenge in developing reliable,learning-driven automated driving technologies.Existing reinforcement learning(RL)models,especially those trained on n...Handling ralely happened safety-critical events remains a central challenge in developing reliable,learning-driven automated driving technologies.Existing reinforcement learning(RL)models,especially those trained on normal driving data,often lack robustness when encountering uncovered scenarios,such as sudden full braking by the lead vehicle,due to the underrepresentation of these events in training data.This study addresses this gap by introducing a Double Soft Actor-Critic(Double SAC)framework tailored for car-following control in both routine and emergency situations,with distinct reward functions designed for each scenario.A unique stochastic training environment is proposed to incorporate simulated emergency braking scenarios,enhancing model resilience in diverse and challenging conditions.Simulation results demonstrate that the Double SAC algorithm outperforms traditional methods in key metrics such as safety,efficiency,and passenger comfort,especially in emergency braking situations.Additionally,with an increase in CAV penetration rates,the stability of platoon control is greatly improved,traffic oscillations are reduced,and the system exhibits enhanced cooperative control capabilities.展开更多
This paper presents an experimental study to compare the performance of model-free control strategies for pneumatic soft robots.Fabricated using soft materials,soft robots have gained much attention in academia and in...This paper presents an experimental study to compare the performance of model-free control strategies for pneumatic soft robots.Fabricated using soft materials,soft robots have gained much attention in academia and industry during recent years because of their inherent safety in human interaction.However,due to structural flexibility and compliance,mathematical models for these soft robots are nonlinear with an infinite degree of freedom(DOF).Therefore,accurate position(or orientation)control and optimization of their dynamic response remains a challenging task.Most existing soft robots currently employed in industrial and rehabilitation applications use model-free control algorithms such as PID.However,to the best of our knowledge,there has been no systematic study on the comparative performance of model-free control algorithms and their ability to optimize dynamic response,i.e.,reduce overshoot and settling time.In this paper,we present comparative performance of several variants of model-free PID-controllers based on extensive experimental results.Additionally,most of the existing work on modelfree control in pneumatic soft-robotic literature use manually tuned parameters,which is a time-consuming,labor-intensive task.We present a heuristic-based coordinate descent algorithm to tune the controller parameter automatically.We presented results for both manual tuning and automatic tuning using the Ziegler-Nichols method and proposed algorithm,respectively.We then used experimental results to statistically demonstrate that the presented automatic tuning algorithm results in high accuracy.The experiment results show that for soft robots,the PID-controller essentially reduces to the PI controller.This behavior was observed in both manual and automatic tuning experiments;we also discussed a rationale for removing the derivative term.展开更多
Due to the development of the novel materials,the past two decades have witnessed the rapid advances of soft electronics.The soft electronics have huge potential in the physical sign monitoring and health care.One of ...Due to the development of the novel materials,the past two decades have witnessed the rapid advances of soft electronics.The soft electronics have huge potential in the physical sign monitoring and health care.One of the important advantages of soft electronics is forming good interface with skin,which can increase the user scale and improve the signal quality.Therefore,it is easy to build the specific dataset,which is important to improve the performance of machine learning algorithm.At the same time,with the assistance of machine learning algorithm,the soft electronics have become more and more intelligent to realize real-time analysis and diagnosis.The soft electronics and machining learning algorithms complement each other very well.It is indubitable that the soft electronics will bring us to a healthier and more intelligent world in the near future.Therefore,in this review,we will give a careful introduction about the new soft material,physiological signal detected by soft devices,and the soft devices assisted by machine learning algorithm.Some soft materials will be discussed such as two-dimensional material,carbon nanotube,nanowire,nanomesh,and hydrogel.Then,soft sensors will be discussed according to the physiological signal types(pulse,respiration,human motion,intraocular pressure,phonation,etc.).After that,the soft electronics assisted by various algorithms will be reviewed,including some classical algorithms and powerful neural network algorithms.Especially,the soft device assisted by neural network will be introduced carefully.Finally,the outlook,challenge,and conclusion of soft system powered by machine learning algorithm will be discussed.展开更多
基金Supported by National Key R&D Program of China(Grant No.2022YFB2503203)National Natural Science Foundation of China(Grant No.U1964206).
文摘Decision-making of connected and automated vehicles(CAV)includes a sequence of driving maneuvers that improve safety and efficiency,characterized by complex scenarios,strong uncertainty,and high real-time requirements.Deep reinforcement learning(DRL)exhibits excellent capability of real-time decision-making and adaptability to complex scenarios,and generalization abilities.However,it is arduous to guarantee complete driving safety and efficiency under the constraints of training samples and costs.This paper proposes a Mixture of Expert method(MoE)based on Soft Actor-Critic(SAC),where the upper-level discriminator dynamically decides whether to activate the lower-level DRL expert or the heuristic expert based on the features of the input state.To further enhance the performance of the DRL expert,a buffer zone is introduced in the reward function,preemptively applying penalties before insecure situations occur.In order to minimize collision and off-road rates,the Intelligent Driver Model(IDM)and Minimizing Overall Braking Induced by Lane changes(MOBIL)strategy are designed by heuristic experts.Finally,tested in typical simulation scenarios,MOE shows a 13.75%improvement in driving efficiency compared with the traditional DRL method with continuous action space.It ensures high safety with zero collision and zero off-road rates while maintaining high adaptability.
基金supported by the National Natural Science Foundation of China(No.52177085).
文摘Peer-to-peer(P2P)energy trading in active distribution networks(ADNs)plays a pivotal role in promoting the efficient consumption of renewable energy sources.However,it is challenging to effectively coordinate the power dispatch of ADNs and P2P energy trading while preserving the privacy of different physical interests.Hence,this paper proposes a soft actor-critic algorithm incorporating distributed trading control(SAC-DTC)to tackle the optimal power dispatch of ADNs and the P2P energy trading considering privacy preservation among prosumers.First,the soft actor-critic(SAC)algorithm is used to optimize the control strategy of device in ADNs to minimize the operation cost,and the primary environmental information of the ADN at this point is published to prosumers.Then,a distributed generalized fast dual ascent method is used to iterate the trading process of prosumers and maximize their revenues.Subsequently,the results of trading are encrypted based on the differential privacy technique and returned to the ADN.Finally,the social welfare value consisting of ADN operation cost and P2P market revenue is utilized as a reward value to update network parameters and control strategies of the deep reinforcement learning.Simulation results show that the proposed SAC-DTC algorithm reduces the ADN operation cost,boosts the P2P market revenue,maximizes the social welfare,and exhibits high computational accuracy,demonstrating its practical application to the operation of power systems and power markets.
文摘Building integrated energy systems(BIESs)are pivotal for enhancing energy efficiency by accounting for a significant proportion of global energy consumption.Two key barriers that reduce the BIES operational efficiency mainly lie in the renewable generation uncertainty and operational non-convexity of combined heat and power(CHP)units.To this end,this paper proposes a soft actor-critic(SAC)algorithm to solve the scheduling problem of BIES,which overcomes the model non-convexity and shows advantages in robustness and generalization.This paper also adopts a temporal fusion transformer(TFT)to enhance the optimal solution for the SAC algorithm by forecasting the renewable generation and energy demand.The TFT can effectively capture the complex temporal patterns and dependencies that span multiple steps.Furthermore,its forecasting results are interpretable due to the employment of a self-attention layer so as to assist in more trustworthy decision-making in the SAC algorithm.The proposed hybrid data-driven approach integrating TFT and SAC algorithm,i.e.,TFT-SAC approach,is trained and tested on a real-world dataset to validate its superior performance in reducing the energy cost and computational time compared with the benchmark approaches.The generalization performance for the scheduling policy,as well as the sensitivity analysis,are examined in the case studies.
基金supported by National Natural Science Foundation of China(52222215, 52272420, 52072051)。
文摘Parking in a small parking lot within limited space poses a difficult task. It often leads to deviations between the final parking posture and the target posture. These deviations can lead to partial occupancy of adjacent parking lots, which poses a safety threat to vehicles parked in these parking lots. However, previous studies have not addressed this issue. In this paper, we aim to evaluate the impact of parking deviation of existing vehicles next to the target parking lot(PDEVNTPL) on the automatic ego vehicle(AEV) parking, in terms of safety, comfort, accuracy, and efficiency of parking. A segmented parking training framework(SPTF) based on soft actor-critic(SAC) is proposed to improve parking performance. In the proposed method, the SAC algorithm incorporates strategy entropy into the objective function, to enable the AEV to learn parking strategies based on a more comprehensive understanding of the environment. Additionally, the SPTF simplifies complex parking tasks to maintain the high performance of deep reinforcement learning(DRL). The experimental results reveal that the PDEVNTPL has a detrimental influence on the AEV parking in terms of safety, accuracy, and comfort, leading to reductions of more than 27%, 54%, and 26%respectively. However, the SAC-based SPTF effectively mitigates this impact, resulting in a considerable increase in the parking success rate from 71% to 93%. Furthermore, the heading angle deviation is significantly reduced from 2.25 degrees to 0.43degrees.
基金supported by the Sichuan Science and Technology Program(grant number 2022YFG0123).
文摘In this study,a novel residential virtual power plant(RVPP)scheduling method that leverages a gate recurrent unit(GRU)-integrated deep reinforcement learning(DRL)algorithm is proposed.In the proposed scheme,the GRU-integrated DRL algorithm guides the RVPP to participate effectively in both the day-ahead and real-time markets,lowering the electricity purchase costs and consumption risks for end-users.The Lagrangian relaxation technique is introduced to transform the constrained Markov decision process(CMDP)into an unconstrained optimization problem,which guarantees that the constraints are strictly satisfied without determining the penalty coefficients.Furthermore,to enhance the scalability of the constrained soft actor-critic(CSAC)-based RVPP scheduling approach,a fully distributed scheduling architecture was designed to enable plug-and-play in the residential distributed energy resources(RDER).Case studies performed on the constructed RVPP scenario validated the performance of the proposed methodology in enhancing the responsiveness of the RDER to power tariffs,balancing the supply and demand of the power grid,and ensuring customer comfort.
文摘To decrease the complexity of MAP algorithm, reduced state or reduced search techniques can be applied. In this paper we propose a reduced search soft output detection algorithm fully based on the principle of M algorithm for turbo equalization, which is a suboptimum version of the Lee algorithm. This algorithm is called soft output M algorithm (denoted as SO M algorithm), which applies the M strategy to both the forward recursion and the extended forward recursion of the Lee algorithm. Computer simulation results show that, by properly selecting and adjusting the breadth parameter and depth parameter during the iteration of turbo equalization, this algorithm can obtain good performance and complexity trade off.
文摘In order to fully utilize the soft decision ability of the outer decoder in a concatenated system, reliability information (called soft output) from the inner decoder or equalizer is required. In this paper, based on the analysis of typical implementations of soft output VA, a novel algorithm is proposed by utilizing the property of Viterbi algorithm. Compared with the typical implementations, less processing expense is required by the new algorithm for weighting the hard decisions of VA. Meanwhile, simulation results show that, deterioration in performance of this algorithm is usually small for decoding of convolutional code and negligible for equalization.
基金This work was supported,in part,by the National Nature Science Foundation of China under grant numbers 61502240,61502096,61304205,61773219in part,by the Natural Science Foundation of Jiangsu Province under grant number BK20191401+1 种基金in part,by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)fundin part,by the Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET)fund.
文摘In order to solve the problem of high computing cost and low simulation accuracy caused by discontinuity of incision in traditional meshless model,this paper proposes a soft tissue deformation model based on the Marquardt algorithm and enrichment function.The model is based on the element-free Galerkin method,in which Kelvin viscoelastic model and adjustment function are integrated.Marquardt algorithm is applied to fit the relation between force and displacement caused by surface deformation,and the enrichment function is applied to deal with the discontinuity in the meshless method.To verify the validity of the model,the Sensable Phantom Omni force tactile interactive device is used to simulate the deformations of stomach and heart.Experimental results show that the proposed model improves the real-time performance and accuracy of soft tissue deformation simulation,which provides a new perspective for the application of the meshless method in virtual surgery.
基金Project(51205299)supported by the National Natural Science Foundation of ChinaProject(2015M582643)supported by the China Postdoctoral Science Foundation+2 种基金Project(2014BAA008)supported by the Science and Technology Support Program of Hubei Province,ChinaProject(2014-IV-144)supported by the Fundamental Research Funds for the Central Universities of ChinaProject(2012AAA07-01)supported by the Major Science and Technology Achievements Transformation&Industrialization Program of Hubei Province,China
文摘Vertical hot ring rolling(VHRR) process has the characteristics of nonlinearity,time-variation and being susceptible to disturbance.Furthermore,the ring's growth is quite fast within a short time,and the rolled ring's position is asymmetrical.All of these cause that the ring's dimensions cannot be measured directly.Through analyzing the relationships among the dimensions of ring blanks,the positions of rolls and the ring's inner and outer diameter,the soft measurement model of ring's dimensions is established based on the radial basis function neural network(RBFNN).A mass of data samples are obtained from VHRR finite element(FE) simulations to train and test the soft measurement NN model,and the model's structure parameters are deduced and optimized by genetic algorithm(GA).Finally,the soft measurement system of ring's dimensions is established and validated by the VHRR experiments.The ring's dimensions were measured artificially and calculated by the soft measurement NN model.The results show that the calculation values of GA-RBFNN model are close to the artificial measurement data.In addition,the calculation accuracy of GA-RBFNN model is higher than that of RBFNN model.The research results suggest that the soft measurement NN model has high precision and flexibility.The research can provide practical methods and theoretical guidance for the accurate measurement of VHRR process.
文摘An improved immune algorithm is proposed in this paper. The problems, such as convergence speed and optimization precision, existing in the basic immune algorithm are well addressed. Besides, a fuzzy adaptive method is presented by using the fuzzy system to realize the adaptive selection of two key parameters （possibility of crossover and mutation）. By comparing and analyzing the results of several benchmark functions, the performance of fuzzy immune algorithm （FIA） is approved. Not only the difficulty of parameters selection is relieved, but also the precision and stability are improved. At last, the FIA is ap- plied to optimization of the structure and parameters in radial basis function neural network （RBFNN） based on an orthogonal sequential method. And the availability of algorithm is proved by applying RBFNN in modeling in soft sensor of solvent tower.
基金This work was supported in part by the National Natural Science Foundation of China under Grant 62171203in part by the Suzhou Key Supporting Subjects[Health Informatics(No.SZFCXK202147)]+2 种基金in part by the Changshu Science and Technology Program[No.CS202015,CS202246]in part by the Changshu City Health and Health Committee Science and Technology Program[No.csws201913]in part by the“333 High Level Personnel Training Project of Jiangsu Province”.
文摘In recent years,the soft subspace clustering algorithm has shown good results for high-dimensional data,which can assign different weights to each cluster class and use weights to measure the contribution of each dimension in various features.The enhanced soft subspace clustering algorithm combines interclass separation and intraclass tightness information,which has strong results for image segmentation,but the clustering algorithm is vulnerable to noisy data and dependence on the initialized clustering center.However,the clustering algorithmis susceptible to the influence of noisydata and reliance on initializedclustering centers andfalls into a local optimum;the clustering effect is poor for brain MR images with unclear boundaries and noise effects.To address these problems,a soft subspace clustering algorithm for brain MR images based on genetic algorithm optimization is proposed,which combines the generalized noise technique,relaxes the equational weight constraint in the objective function as the boundary constraint,and uses a genetic algorithm as a method to optimize the initialized clustering center.The genetic algorithm finds the best clustering center and reduces the algorithm’s dependence on the initial clustering center.The experiment verifies the robustness of the algorithm,as well as the noise immunity in various ways and shows good results on the common dataset and the brain MR images provided by the Changshu First People’s Hospital with specific high accuracy for clinical medicine.
文摘A computationally efficient soft-output detector with lattice-reduction （LR） for the multiple-input multiple-output （MIMO） systems is proposed. In the proposed scheme, the sorted QR de- composition is applied on the lattice-reduced equivalent channel to obtain the tree structure. With the aid of the boundary control, the stack algorithm searches a small part of the whole search tree to generate a handful of candidate lists in the reduced lattice. The proposed soft-output algorithm achieves near-optimal perfor- mance in a coded MIMO system and the associated computational complexity is substantially lower than that of previously proposed methods.
文摘It is well known that the kit completeness of parts processed in the previous stage is crucial for the subsequent manufacturing stage.This paper studies the flexible job shop scheduling problem(FJSP)with the objective of material kitting,where a material kit is a collection of components that ensures that a batch of components can be ready at the same time during the product assembly process.In this study,we consider completion time variance and maximumcompletion time as scheduling objectives,continue the weighted summation process formultiple objectives,and design adaptive weighted summation parameters to optimize productivity and reduce the difference in completion time between components in the same kit.The Soft Actor Critic(SAC)algorithm is designed to be combined with the Adaptive Multi-Buffer Experience Replay(AMBER)mechanism to propose the SAC-AMBER algorithm.The AMBER mechanism optimizes the experience sampling and policy updating process and enhances learning efficiency by categorically storing the experience into the standard buffer,the high equipment utilization buffer,and the high productivity buffer.Experimental results show that the SAC-AMBER algorithm can effectively reduce the maximum completion time on multiple datasets,reduce the difference in component completion time in the same kit,and thus optimize the readiness of the part kits,demonstrating relatively good stability and convergence.Compared with traditional heuristics,meta-heuristics,and other deep reinforcement learning methods,the SAC-AMBER algorithm performs better in terms of solution quality and computational efficiency,and through extensive testing on multiple datasets,the algorithm has been confirmed to have good generalization ability,providing an effective solution to the FJSP problem.
文摘This paper introduces fuzzy N-bipolar soft(FN-BS)sets,a novel mathematical framework designed to enhance multi-criteria decision-making(MCDM)processes under uncertainty.The study addresses a significant limitation in existing models by unifying fuzzy logic,the consideration of bipolarity,and the ability to evaluate attributes on a multinary scale.The specific contributions of the FN-BS framework include:(1)a formal definition and settheoretic foundation,(2)the development of two innovative algorithms for solving decision-making(DM)problems,and(3)a comparative analysis demonstrating its superiority over established models.The proposed framework is applied to a real-world case study on selecting vaccination programs across multiple countries,showcasing consistent DM outcomes and exceptional adaptability to complex and uncertain scenarios.These results position FN-BS sets as a versatile and powerful tool for addressing dynamic DM challenges.
基金The National Natural Science Foundation of China(No.61074147)the Natural Science Foundation of Guangdong Province(No.S2011010005059)+2 种基金the Foundation of Enterprise-University-Research Institute Cooperation from Guangdong Province and Ministry of Education of China(No.2012B091000171,2011B090400460)the Science and Technology Program of Guangdong Province(No.2012B050600028)the Science and Technology Program of Huadu District,Guangzhou(No.HD14ZD001)
文摘Considering that the vehicle routing problem （VRP） with many extended features is widely used in actual life, such as multi-depot, heterogeneous types of vehicles, customer service priority and time windows etc., a mathematical model for multi-depot heterogeneous vehicle routing problem with soft time windows （MDHVRPSTW） is established. An improved ant colony optimization （IACO） is proposed for solving this model. First, MDHVRPSTW is transferred into different groups according to the nearest principle, and then the initial route is constructed by the scanning algorithm （SA）. Secondly, genetic operators are introduced, and crossover probability and mutation probability are adaptively adjusted in order to improve the global search ability of the algorithm. Moreover, the smooth mechanism is used to improve the performance of the ant colony optimization （ACO）. Finally, the 3-opt strategy is used to improve the local search ability. The proposed IACO was tested on three new instances that were generated randomly. The experimental results show that IACO is superior to the other three existing algorithms in terms of convergence speed and solution quality. Thus, the proposed method is effective and feasible, and the proposed model is meaningful.
基金supported in part by the lconic Specialised Cultivation Project of Yanshan University(Grant No.2022BZZD005)the Hebei Natural Science Foundation Grant No.F2024203083.
文摘Handling ralely happened safety-critical events remains a central challenge in developing reliable,learning-driven automated driving technologies.Existing reinforcement learning(RL)models,especially those trained on normal driving data,often lack robustness when encountering uncovered scenarios,such as sudden full braking by the lead vehicle,due to the underrepresentation of these events in training data.This study addresses this gap by introducing a Double Soft Actor-Critic(Double SAC)framework tailored for car-following control in both routine and emergency situations,with distinct reward functions designed for each scenario.A unique stochastic training environment is proposed to incorporate simulated emergency braking scenarios,enhancing model resilience in diverse and challenging conditions.Simulation results demonstrate that the Double SAC algorithm outperforms traditional methods in key metrics such as safety,efficiency,and passenger comfort,especially in emergency braking situations.Additionally,with an increase in CAV penetration rates,the stability of platoon control is greatly improved,traffic oscillations are reduced,and the system exhibits enhanced cooperative control capabilities.
文摘This paper presents an experimental study to compare the performance of model-free control strategies for pneumatic soft robots.Fabricated using soft materials,soft robots have gained much attention in academia and industry during recent years because of their inherent safety in human interaction.However,due to structural flexibility and compliance,mathematical models for these soft robots are nonlinear with an infinite degree of freedom(DOF).Therefore,accurate position(or orientation)control and optimization of their dynamic response remains a challenging task.Most existing soft robots currently employed in industrial and rehabilitation applications use model-free control algorithms such as PID.However,to the best of our knowledge,there has been no systematic study on the comparative performance of model-free control algorithms and their ability to optimize dynamic response,i.e.,reduce overshoot and settling time.In this paper,we present comparative performance of several variants of model-free PID-controllers based on extensive experimental results.Additionally,most of the existing work on modelfree control in pneumatic soft-robotic literature use manually tuned parameters,which is a time-consuming,labor-intensive task.We present a heuristic-based coordinate descent algorithm to tune the controller parameter automatically.We presented results for both manual tuning and automatic tuning using the Ziegler-Nichols method and proposed algorithm,respectively.We then used experimental results to statistically demonstrate that the presented automatic tuning algorithm results in high accuracy.The experiment results show that for soft robots,the PID-controller essentially reduces to the PI controller.This behavior was observed in both manual and automatic tuning experiments;we also discussed a rationale for removing the derivative term.
基金supported by National Natural Science Foundation of China(No.62201624,32000939,21775168,22174167,51861145202,U20A20168)the Guangdong Basic and Applied Basic Research Foundation(2019A1515111183)+3 种基金Shenzhen Research Funding Program(JCYJ20190807160401657,JCYJ201908073000608,JCYJ20150831192224146)the National Key R&D Program(2018YFC2001202)the support of the Research Fund from Tsinghua University Initiative Scientific Research Programthe support from Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province(No.2020B1212060077)。
文摘Due to the development of the novel materials,the past two decades have witnessed the rapid advances of soft electronics.The soft electronics have huge potential in the physical sign monitoring and health care.One of the important advantages of soft electronics is forming good interface with skin,which can increase the user scale and improve the signal quality.Therefore,it is easy to build the specific dataset,which is important to improve the performance of machine learning algorithm.At the same time,with the assistance of machine learning algorithm,the soft electronics have become more and more intelligent to realize real-time analysis and diagnosis.The soft electronics and machining learning algorithms complement each other very well.It is indubitable that the soft electronics will bring us to a healthier and more intelligent world in the near future.Therefore,in this review,we will give a careful introduction about the new soft material,physiological signal detected by soft devices,and the soft devices assisted by machine learning algorithm.Some soft materials will be discussed such as two-dimensional material,carbon nanotube,nanowire,nanomesh,and hydrogel.Then,soft sensors will be discussed according to the physiological signal types(pulse,respiration,human motion,intraocular pressure,phonation,etc.).After that,the soft electronics assisted by various algorithms will be reviewed,including some classical algorithms and powerful neural network algorithms.Especially,the soft device assisted by neural network will be introduced carefully.Finally,the outlook,challenge,and conclusion of soft system powered by machine learning algorithm will be discussed.
