Energy consumption has become a key metric for evaluating how good an embedded system is,alongside more performance metrics like respecting operation deadlines and speed of execution.Schedulability improvement is no l...Energy consumption has become a key metric for evaluating how good an embedded system is,alongside more performance metrics like respecting operation deadlines and speed of execution.Schedulability improvement is no longer the only metric by which optimality is judged.In fact,energy efficiency is becoming a preferred choice with a fundamental objective to optimize the system's lifetime.In this work,we propose an optimal energy efficient scheduling algorithm for aperiodic real-time jobs to reduce CPU energy consumption.Specifically,we apply the concept of real-time process scheduling to a dynamic voltage and frequency scaling(DVFS)technique.We address a variant of earliest deadline first(EDF)scheduling algorithm called energy saving-dynamic voltage and frequency scaling(ES-DVFS)algorithm that is suited to unpredictable future energy production and irregular job arrivals.We prove that ES-DVFS cannot attain a total value greater than C/ˆSα,whereˆS is the minimum speed of any job and C is the available energy capacity.We also investigate the implications of having in advance,information about the largest job size and the minimum speed used for the competitive factor of ES-DVFS.We show that such advance knowledge makes possible the design of semi-on-line algorithm,ES-DVFS∗∗,that achieved a constant competitive factor of 0.5 which is proved as an optimal competitive factor.The experimental study demonstrates that substantial energy savings and highest percentage of feasible job sets can be obtained through our solution that combines EDF and DVFS optimally under the given aperiodic jobs and energy models.展开更多
Operation in multiple frequency bands simultaneously is an important enabler for future wireless communication systems. This article presents a new concept for scheduling transmissions in a wireless radio system opera...Operation in multiple frequency bands simultaneously is an important enabler for future wireless communication systems. This article presents a new concept for scheduling transmissions in a wireless radio system operating in multiple frequency bands: the Multiband Scheduler (MBS). The MBS ensures that the operation in multiple bands is transparent to higher network layers. Special attention is paid to achieving low delay and latency when operating the system in the multiband mode. In particular, we propose additions to the ARQ procedures in order to achieve this. Deployment details and assessment results are presented for two multiband deployment scenarios. The first scenario is operation in a spectrum sharing context where multiple bands are used: one dedicated band for basic service and one shared extension band for extended services. In the second scenario we consider multiband operation in a relay environment, where the two bands have different propagation properties and relays provide extra coverage and capacity in the whole cell.展开更多
The Controller Area Network (CAN) is a well established control network for automotive and automation control applications. Time-Triggered Controller Area Network (TTCAN) is a recent development which introduces a ses...The Controller Area Network (CAN) is a well established control network for automotive and automation control applications. Time-Triggered Controller Area Network (TTCAN) is a recent development which introduces a session layer,for message scheduling,to the existing CAN standard,which is a two layer standard comprising of a physical layer and a data link layer. TTCAN facilitates network communication in a time-triggered fashion,by introducing a Time Division Multiple Access style communication scheme. This allows deterministic network behavior,where maximum message latency times can be quantified and guaranteed. In order to solve the problem of determinate time latency and synchronization among several districted units in one auto panel CAN systems,this paper proposed a prototype design implementation for a shared-clock scheduler based on PIC18F458 MCU. This leads to improved CAN system performance and avoid the latency jitters and guarantee a deterministic communication pattern on the bus. The real runtime performance is satisfied.展开更多
There has been a number of algorithms designed to handle intra-query or inter-query scheduling in multiprocessor-based parallel database system. They all have the assumption that the processorsare identical. But in so...There has been a number of algorithms designed to handle intra-query or inter-query scheduling in multiprocessor-based parallel database system. They all have the assumption that the processorsare identical. But in some cases, such as shared nothing environment, this assumption would not be fullyvalid. In this paper we devise and evaluate a scheduling algorithm ELLIST to handle nonprecedence-basedheterogeneous malleable scheduling problem. It uses LLIST-NM as subroutine that handles nonmalleablescheduling without precedence and assuming the processors are identical. Even though the problem we considered is NP-hard in the strong sense, the schedule generated by our algorithm is seen experimentally toachieve results that are close to optimum when there are enough tasks to be scheduled.展开更多
Transmission line manipulations in a power system are necessary for the execution of preventative or corrective main- tenance in a network, thus ensuring the stability of the system. In this study, primal-dual interio...Transmission line manipulations in a power system are necessary for the execution of preventative or corrective main- tenance in a network, thus ensuring the stability of the system. In this study, primal-dual interior-point methods are used to minimize costs and losses in the generation and transmission of the predispatch active power flow in a hydroelectric system with previously scheduled line manipulations for preventative maintenance, over a period of twenty-four hours. The matrix structure of this problem and the modification that it imposes on the system is also broached in this study. From the computational standpoint, the effort required to solve a problem with or without line manipulations is similar, and the reasons for this are also discussed in this study. Computational results sustain our findings.展开更多
By thorough research on the prominent periodic and aperiodic scheduling algorithms,anon-line hard real-time scheduler is presented,which is applicable to the scheduling of packets over a link.This scheduler,based on b...By thorough research on the prominent periodic and aperiodic scheduling algorithms,anon-line hard real-time scheduler is presented,which is applicable to the scheduling of packets over a link.This scheduler,based on both Rate Monotonic,pinwheel scheduling algorithm Sr and Polling Serverscheduling algorithms,can rapidly judge the schedulability and then automatically generate a bus tablefor the scheduling algorithm to schedule the packets as the periodic packets.The implementation of thescheduler is simple and easy to use,and it is effective for the utilization of bus link.The orderly executionof the bus table can not only guarantee the performance of the hard real time but also avoid the blockageand interruption of the message transmission.So the scheduler perfectly meets the demand of hard real-time communication system on the field bus domain.展开更多
Thedeployment of the Internet of Things(IoT)with smart sensors has facilitated the emergence of fog computing as an important technology for delivering services to smart environments such as campuses,smart cities,and ...Thedeployment of the Internet of Things(IoT)with smart sensors has facilitated the emergence of fog computing as an important technology for delivering services to smart environments such as campuses,smart cities,and smart transportation systems.Fog computing tackles a range of challenges,including processing,storage,bandwidth,latency,and reliability,by locally distributing secure information through end nodes.Consisting of endpoints,fog nodes,and back-end cloud infrastructure,it provides advanced capabilities beyond traditional cloud computing.In smart environments,particularly within smart city transportation systems,the abundance of devices and nodes poses significant challenges related to power consumption and system reliability.To address the challenges of latency,energy consumption,and fault tolerance in these environments,this paper proposes a latency-aware,faulttolerant framework for resource scheduling and data management,referred to as the FORD framework,for smart cities in fog environments.This framework is designed to meet the demands of time-sensitive applications,such as those in smart transportation systems.The FORD framework incorporates latency-aware resource scheduling to optimize task execution in smart city environments,leveraging resources from both fog and cloud environments.Through simulation-based executions,tasks are allocated to the nearest available nodes with minimum latency.In the event of execution failure,a fault-tolerantmechanism is employed to ensure the successful completion of tasks.Upon successful execution,data is efficiently stored in the cloud data center,ensuring data integrity and reliability within the smart city ecosystem.展开更多
In this paper,a bilevel optimization model of an integrated energy operator(IEO)–load aggregator(LA)is constructed to address the coordinate optimization challenge of multiple stakeholder island integrated energy sys...In this paper,a bilevel optimization model of an integrated energy operator(IEO)–load aggregator(LA)is constructed to address the coordinate optimization challenge of multiple stakeholder island integrated energy system(IIES).The upper level represents the integrated energy operator,and the lower level is the electricity-heatgas load aggregator.Owing to the benefit conflict between the upper and lower levels of the IIES,a dynamic pricing mechanism for coordinating the interests of the upper and lower levels is proposed,combined with factors such as the carbon emissions of the IIES,as well as the lower load interruption power.The price of selling energy can be dynamically adjusted to the lower LA in the mechanism,according to the information on carbon emissions and load interruption power.Mutual benefits and win-win situations are achieved between the upper and lower multistakeholders.Finally,CPLEX is used to iteratively solve the bilevel optimization model.The optimal solution is selected according to the joint optimal discrimination mechanism.Thesimulation results indicate that the sourceload coordinate operation can reduce the upper and lower operation costs.Using the proposed pricingmechanism,the carbon emissions and load interruption power of IEO-LA are reduced by 9.78%and 70.19%,respectively,and the capture power of the carbon capture equipment is improved by 36.24%.The validity of the proposed model and method is verified.展开更多
Recently,unmanned aerial vehicle(UAV)-aided free-space optical(FSO)communication has attracted widespread attentions.However,most of the existing research focuses on communication performance only.The authors investig...Recently,unmanned aerial vehicle(UAV)-aided free-space optical(FSO)communication has attracted widespread attentions.However,most of the existing research focuses on communication performance only.The authors investigate the integrated scheduling of communication,sensing,and control for UAV-aided FSO communication systems.Initially,a sensing-control model is established via the control theory.Moreover,an FSO communication channel model is established by considering the effects of atmospheric loss,atmospheric turbulence,geometrical loss,and angle-of-arrival fluctuation.Then,the relationship between the motion control of the UAV and radial displacement is obtained to link the control aspect and communication aspect.Assuming that the base station has instantaneous channel state information(CSI)or statistical CSI,the thresholds of the sensing-control pattern activation are designed,respectively.Finally,an integrated scheduling scheme for performing communication,sensing,and control is proposed.Numerical results indicate that,compared with conventional time-triggered scheme,the proposed integrated scheduling scheme obtains comparable communication and control performance,but reduces the sensing consumed power by 52.46%.展开更多
Background:Shift-based occupations have been consistently linked to adverse psychological outcomes;however,limited research has examined how work schedule type and physical activity are jointly associated with mental ...Background:Shift-based occupations have been consistently linked to adverse psychological outcomes;however,limited research has examined how work schedule type and physical activity are jointly associated with mental health and job stress in public transportation employees,a population frequently exposed to irregular hours and safety-critical responsibilities.This study investigated the associations between work schedule type and physical activity with mental health indicators and job stress among Seoul Metro employees.Methods:A cross-sectional survey was administered to 298 full-time male employees of Seoul Metro.Participants were categorized by work schedule(shift vs.regular)and physical activity level(regular,irregular,none)following American College of Sports Medicine(ACSM)guidelines.Mental health(sleep disturbance,depression,anxiety,loneliness)was assessed using validated binary indicators,and job stress was measured with the Korean Occupational Stress Scale–Short Form(KOSS-SF).Group differences were analyzed using chi-square tests,t-tests,and one-way ANOVA with effect sizes,and binary logistic and multiple regression analyses were conducted to identify predictors.Results:Shift workers reported significantly higher sleep disturbance and anxiety compared to regular daytime workers(p<0.05).Employees who participated in regular physical activity had lower odds of sleep disturbance and depression(p<0.05)and showed lower job stress scores compared with inactive workers.Work schedule type and physical activity were independently associated with mental health and job stress among transit employees.Conclusion:These findings underscore the dual influence of work schedule and physical activity on the psychological and occupational well-being of public transit employees.Promoting regular physical activity may buffer occupational stress among employees engaged in shift-based work.Workplace interventions that support physical activity participation and improve shift planning may enhance employee well-being.展开更多
Variable Cycle Engine(VCE)serves as the core system in achieving future advanced fighters with cross-generational performance and mission versatility.However,the resultant complex configuration and strong coupling of ...Variable Cycle Engine(VCE)serves as the core system in achieving future advanced fighters with cross-generational performance and mission versatility.However,the resultant complex configuration and strong coupling of control parameters present significant challenges in designing acceleration and deceleration control schedules.To thoroughly explore the performance potential of engine,a global integration design method for acceleration and deceleration control schedule based on inner and outer loop optimization is proposed.The outer loop optimization module employs Integrated Surrogate-Assisted Co-Differential Evolutionary(ISACDE)algorithm to optimize the variable geometry adjustment laws based on B-spline curve,and the inner loop optimization module adopts the fixed-state method to design the open-loop fuel–air ratio control schedules,which are aimed at minimizing the acceleration and deceleration time under multiple constraints.Simulation results demonstrate that the proposed global integration design method not only furthest shortens the acceleration and deceleration time,but also effectively safeguards the engine from overlimit.展开更多
A collaborative optimization method for the sintering schedule of ternary cathode materials was proposed under microscopic coupling constraints.An oxygen vacancy concentration prediction model based on microscopic the...A collaborative optimization method for the sintering schedule of ternary cathode materials was proposed under microscopic coupling constraints.An oxygen vacancy concentration prediction model based on microscopic thermodynamics and a growth kinetics model based on neural networks were established.Then,optimization formulations were constructed in three stages to obtain an optimal sintering schedule that minimized energy consumption for different requirements.Simulations demonstrate that the models accurately predict the oxygen vacancy concentrations and grain size,with root mean square errors of approximately 5%and 3%,respectively.Furthermore,the optimized sintering schedule not only meets the required quality standards but also reduces sintering time by 12.31%and keeping temperature by 11.96%.This research provides new insights and methods for the preparation of ternary cathode materials.展开更多
The shop floor dynamic scheduling system based on human-computer interaction is the use of computer-aided decision-making and human-computer interaction to solve the dynamic scheduling problem.A human-computer interac...The shop floor dynamic scheduling system based on human-computer interaction is the use of computer-aided decision-making and human-computer interaction to solve the dynamic scheduling problem.A human-computer interaction interface based on Gantt chart is designed,which can not only comprehensively and quantitatively represent the scheduling process and scheduling scheme,but also have friendly human-computer interaction performance.The data transmission and interaction architecture is constructed to realize the rapid response to shop floor disturbance events.A priority calculation algorithm integrating priority rules and dispatcher preference is proposed,which realizes the automatic calculation of priority for the dispatcher's reference and reduces theirburden.A man-machine interactive shop floor dynamic scheduling strategy is proposed.When solving the dynamic flexible job shop scheduling problem caused by machine tool breakdown and urgent order,the origin moments obtained by using this strategy are 0.4190 and 0.3703 respectively.As can be seen from the origin moment indicator,the dynamic shop floor scheduling system based on the human-computer interaction is efficient and reliable in solving dynamic scheduling problems,and related strategies of this system are also feasible and stable.展开更多
This paper explains the goal conflict between schedule and quality in construction projects,including how schedule compression can lead to quality risks,and how quality control may cause delays.It analyzes the interna...This paper explains the goal conflict between schedule and quality in construction projects,including how schedule compression can lead to quality risks,and how quality control may cause delays.It analyzes the internal logic of collaborative management and influencing factors such as construction plans.The paper also introduces collaborative management methods,such as establishing a responsibility traceability system based on Work Breakdown Structure(WBS),and emphasizes the role of intelligent construction technologies and their future development directions.展开更多
The dwell scheduling problem for a multifunctional radar system is led to the formation of corresponding optimiza-tion problem.In order to solve the resulting optimization prob-lem,the dwell scheduling process in a sc...The dwell scheduling problem for a multifunctional radar system is led to the formation of corresponding optimiza-tion problem.In order to solve the resulting optimization prob-lem,the dwell scheduling process in a scheduling interval(SI)is formulated as a Markov decision process(MDP),where the state,action,and reward are specified for this dwell scheduling problem.Specially,the action is defined as scheduling the task on the left side,right side or in the middle of the radar idle time-line,which reduces the action space effectively and accelerates the convergence of the training.Through the above process,a model-free reinforcement learning framework is established.Then,an adaptive dwell scheduling method based on Q-learn-ing is proposed,where the converged Q value table after train-ing is utilized to instruct the scheduling process.Simulation results demonstrate that compared with existing dwell schedul-ing algorithms,the proposed one can achieve better scheduling performance considering the urgency criterion,the importance criterion and the desired execution time criterion comprehen-sively.The average running time shows the proposed algorithm has real-time performance.展开更多
The rapid advancement of Industry 4.0 has revolutionized manufacturing,shifting production from centralized control to decentralized,intelligent systems.Smart factories are now expected to achieve high adaptability an...The rapid advancement of Industry 4.0 has revolutionized manufacturing,shifting production from centralized control to decentralized,intelligent systems.Smart factories are now expected to achieve high adaptability and resource efficiency,particularly in mass customization scenarios where production schedules must accommodate dynamic and personalized demands.To address the challenges of dynamic task allocation,uncertainty,and realtime decision-making,this paper proposes Pathfinder,a deep reinforcement learning-based scheduling framework.Pathfinder models scheduling data through three key matrices:execution time(the time required for a job to complete),completion time(the actual time at which a job is finished),and efficiency(the performance of executing a single job).By leveraging neural networks,Pathfinder extracts essential features from these matrices,enabling intelligent decision-making in dynamic production environments.Unlike traditional approaches with fixed scheduling rules,Pathfinder dynamically selects from ten diverse scheduling rules,optimizing decisions based on real-time environmental conditions.To further enhance scheduling efficiency,a specialized reward function is designed to support dynamic task allocation and real-time adjustments.This function helps Pathfinder continuously refine its scheduling strategy,improving machine utilization and minimizing job completion times.Through reinforcement learning,Pathfinder adapts to evolving production demands,ensuring robust performance in real-world applications.Experimental results demonstrate that Pathfinder outperforms traditional scheduling approaches,offering improved coordination and efficiency in smart factories.By integrating deep reinforcement learning,adaptable scheduling strategies,and an innovative reward function,Pathfinder provides an effective solution to the growing challenges of multi-robot job scheduling in mass customization environments.展开更多
In the field of low-carbon building systems,the combination of renewable energy and hydrogen energy systems is gradually gaining prominence.However,the uncertainty of supply and demand and the multi-energy flow coupli...In the field of low-carbon building systems,the combination of renewable energy and hydrogen energy systems is gradually gaining prominence.However,the uncertainty of supply and demand and the multi-energy flow coupling characteristics of this system pose challenges for its optimized scheduling.In light of this,this study focuses on electro-thermal-hydrogen trigeneration systems,first modelling the system's scheduling optimization problem as a Markov decision process,thereby transforming it into a sequential decision problem.Based on this,this paper proposes a reinforcement learning algorithm based on deep deterministic policy gradient improvement,aiming to minimize system operating costs and enhance the system's sustainable operation capability.Experimental results show that compared to traditional reinforcement learning algorithms,the reinforcement learning algorithm based on deep deterministic policy gradient improvement achieves improvements of 12.5%and 22.8%in convergence speed and convergence value,respectively.Additionally,under uncertainty scenarios ranging from 10%to 30%,cost reductions of 2.82%,3.08%,and 2.52%were achieved,respectively,with an average cost reduction of 2.80%across 30 simulated scenarios.Compared to the original algorithm and rule-based algorithms in multi-uncertainty environments,the reinforcement learning algorithm based on improved deep deterministic policy gradients demonstrated superiority in terms of system operating costs and continuous operational capability,effectively enhancing the system's economic and sustainable performance.展开更多
The production mode of manufacturing industry presents characteristics of multiple varieties,small-batch and personalization,leading to frequent disturbances in workshop.Traditional centralized scheduling methods are ...The production mode of manufacturing industry presents characteristics of multiple varieties,small-batch and personalization,leading to frequent disturbances in workshop.Traditional centralized scheduling methods are difficult to achieve efficient and real-time production management under dynamic disturbance.In order to improve the intelligence and adaptability of production scheduler,a novel distributed scheduling architecture is proposed,which has the ability to autonomously allocate tasks and handle disturbances.All production tasks are scheduled through autonomous collaboration and decision-making between intelligent machines.Firstly,the multi-agent technology is applied to build a self-organizing manufacturing system,enabling each machine to be equipped with the ability of active information interaction and joint-action execution.Secondly,various self-organizing collaboration strategies are designed to effectively facilitate cooperation and competition among multiple agents,thereby flexibly achieving global perception of environmental state.To ensure the adaptability and superiority of production decisions in dynamic environment,deep reinforcement learning is applied to build a smart production scheduler:Based on the perceived environment state,the scheduler intelligently generates the optimal production strategy to guide the task allocation and resource configuration.The feasibility and effectiveness of the proposed method are verified through three experimental scenarios using a discrete manufacturing workshop as the test bed.Compared to heuristic dispatching rules,the proposed method achieves an average performance improvement of 34.0%in three scenarios in terms of order tardiness.The proposed system can provide a new reference for the design of smart manufacturing systems.展开更多
Frequent extreme disasters have led to frequent large-scale power outages in recent years.To quickly restore power,it is necessary to understand the damage information of the distribution network accurately.However,th...Frequent extreme disasters have led to frequent large-scale power outages in recent years.To quickly restore power,it is necessary to understand the damage information of the distribution network accurately.However,the public network communication system is easily damaged after disasters,causing the operation center to lose control of the distribution network.In this paper,we considered using satellites to transmit the distribution network data and focus on the resource scheduling problem of the satellite emergency communication system for the distribution network.Specifically,this paper first formulates the satellite beam-pointing problem and the accesschannel joint resource allocation problem.Then,this paper proposes the Priority-based Beam-pointing and Access-Channel joint optimization algorithm(PBAC),which uses convex optimization theory to solve the satellite beam pointing problem,and adopts the block coordinate descent method,Lagrangian dual method,and a greedy algorithm to solve the access-channel joint resource allocation problem,thereby obtaining the optimal resource scheduling scheme for the satellite network.Finally,this paper conducts comparative experiments with existing methods to verify the effec-tiveness of the proposed methods.The results show that the total weighted transmitted data of the proposed algorithm is increased by about 19.29∼26.29%compared with other algorithms.展开更多
Using the existing positioning technology can easily obtain high-precision positioning information,which can save resources and reduce complexity when used in the communication field.In this paper,we propose a locatio...Using the existing positioning technology can easily obtain high-precision positioning information,which can save resources and reduce complexity when used in the communication field.In this paper,we propose a location-based user scheduling and beamforming scheme for the downlink of a massive multi-user input-output system.Specifically,we combine an analog outer beamformer with a digital inner beamformer.An outer beamformer can be selected from a codebook formed by antenna steering vectors,and then a reduced-complexity inner beamformer based on iterative orthogonal matrices and right triangular matrices(QR)decomposition is applied to cancel interuser interference.Then,we propose a low-complexity user selection algorithm using location information in this paper.We first derive the geometric angle between channel matrices,which represent the correlation between users.Furthermore,we derive the asymptotic signal to interference-plus-noise ratio(SINR)of the system in the context of two-stage beamforming using random matrix theory(RMT),taking into account inter-channel correlations and energies.Simulation results show that the algorithm can achieve higher system and speed while reducing computational complexity.展开更多
文摘Energy consumption has become a key metric for evaluating how good an embedded system is,alongside more performance metrics like respecting operation deadlines and speed of execution.Schedulability improvement is no longer the only metric by which optimality is judged.In fact,energy efficiency is becoming a preferred choice with a fundamental objective to optimize the system's lifetime.In this work,we propose an optimal energy efficient scheduling algorithm for aperiodic real-time jobs to reduce CPU energy consumption.Specifically,we apply the concept of real-time process scheduling to a dynamic voltage and frequency scaling(DVFS)technique.We address a variant of earliest deadline first(EDF)scheduling algorithm called energy saving-dynamic voltage and frequency scaling(ES-DVFS)algorithm that is suited to unpredictable future energy production and irregular job arrivals.We prove that ES-DVFS cannot attain a total value greater than C/ˆSα,whereˆS is the minimum speed of any job and C is the available energy capacity.We also investigate the implications of having in advance,information about the largest job size and the minimum speed used for the competitive factor of ES-DVFS.We show that such advance knowledge makes possible the design of semi-on-line algorithm,ES-DVFS∗∗,that achieved a constant competitive factor of 0.5 which is proved as an optimal competitive factor.The experimental study demonstrates that substantial energy savings and highest percentage of feasible job sets can be obtained through our solution that combines EDF and DVFS optimally under the given aperiodic jobs and energy models.
文摘Operation in multiple frequency bands simultaneously is an important enabler for future wireless communication systems. This article presents a new concept for scheduling transmissions in a wireless radio system operating in multiple frequency bands: the Multiband Scheduler (MBS). The MBS ensures that the operation in multiple bands is transparent to higher network layers. Special attention is paid to achieving low delay and latency when operating the system in the multiband mode. In particular, we propose additions to the ARQ procedures in order to achieve this. Deployment details and assessment results are presented for two multiband deployment scenarios. The first scenario is operation in a spectrum sharing context where multiple bands are used: one dedicated band for basic service and one shared extension band for extended services. In the second scenario we consider multiband operation in a relay environment, where the two bands have different propagation properties and relays provide extra coverage and capacity in the whole cell.
文摘The Controller Area Network (CAN) is a well established control network for automotive and automation control applications. Time-Triggered Controller Area Network (TTCAN) is a recent development which introduces a session layer,for message scheduling,to the existing CAN standard,which is a two layer standard comprising of a physical layer and a data link layer. TTCAN facilitates network communication in a time-triggered fashion,by introducing a Time Division Multiple Access style communication scheme. This allows deterministic network behavior,where maximum message latency times can be quantified and guaranteed. In order to solve the problem of determinate time latency and synchronization among several districted units in one auto panel CAN systems,this paper proposed a prototype design implementation for a shared-clock scheduler based on PIC18F458 MCU. This leads to improved CAN system performance and avoid the latency jitters and guarantee a deterministic communication pattern on the bus. The real runtime performance is satisfied.
文摘There has been a number of algorithms designed to handle intra-query or inter-query scheduling in multiprocessor-based parallel database system. They all have the assumption that the processorsare identical. But in some cases, such as shared nothing environment, this assumption would not be fullyvalid. In this paper we devise and evaluate a scheduling algorithm ELLIST to handle nonprecedence-basedheterogeneous malleable scheduling problem. It uses LLIST-NM as subroutine that handles nonmalleablescheduling without precedence and assuming the processors are identical. Even though the problem we considered is NP-hard in the strong sense, the schedule generated by our algorithm is seen experimentally toachieve results that are close to optimum when there are enough tasks to be scheduled.
文摘Transmission line manipulations in a power system are necessary for the execution of preventative or corrective main- tenance in a network, thus ensuring the stability of the system. In this study, primal-dual interior-point methods are used to minimize costs and losses in the generation and transmission of the predispatch active power flow in a hydroelectric system with previously scheduled line manipulations for preventative maintenance, over a period of twenty-four hours. The matrix structure of this problem and the modification that it imposes on the system is also broached in this study. From the computational standpoint, the effort required to solve a problem with or without line manipulations is similar, and the reasons for this are also discussed in this study. Computational results sustain our findings.
基金Supported by the Emphases Science and Technology Project Foundation of Sichuan Province(NO.02GG006-037)
文摘By thorough research on the prominent periodic and aperiodic scheduling algorithms,anon-line hard real-time scheduler is presented,which is applicable to the scheduling of packets over a link.This scheduler,based on both Rate Monotonic,pinwheel scheduling algorithm Sr and Polling Serverscheduling algorithms,can rapidly judge the schedulability and then automatically generate a bus tablefor the scheduling algorithm to schedule the packets as the periodic packets.The implementation of thescheduler is simple and easy to use,and it is effective for the utilization of bus link.The orderly executionof the bus table can not only guarantee the performance of the hard real time but also avoid the blockageand interruption of the message transmission.So the scheduler perfectly meets the demand of hard real-time communication system on the field bus domain.
基金supported by the Deanship of Scientific Research and Graduate Studies at King Khalid University under research grant number(R.G.P.2/93/45).
文摘Thedeployment of the Internet of Things(IoT)with smart sensors has facilitated the emergence of fog computing as an important technology for delivering services to smart environments such as campuses,smart cities,and smart transportation systems.Fog computing tackles a range of challenges,including processing,storage,bandwidth,latency,and reliability,by locally distributing secure information through end nodes.Consisting of endpoints,fog nodes,and back-end cloud infrastructure,it provides advanced capabilities beyond traditional cloud computing.In smart environments,particularly within smart city transportation systems,the abundance of devices and nodes poses significant challenges related to power consumption and system reliability.To address the challenges of latency,energy consumption,and fault tolerance in these environments,this paper proposes a latency-aware,faulttolerant framework for resource scheduling and data management,referred to as the FORD framework,for smart cities in fog environments.This framework is designed to meet the demands of time-sensitive applications,such as those in smart transportation systems.The FORD framework incorporates latency-aware resource scheduling to optimize task execution in smart city environments,leveraging resources from both fog and cloud environments.Through simulation-based executions,tasks are allocated to the nearest available nodes with minimum latency.In the event of execution failure,a fault-tolerantmechanism is employed to ensure the successful completion of tasks.Upon successful execution,data is efficiently stored in the cloud data center,ensuring data integrity and reliability within the smart city ecosystem.
基金supported by the Central Government Guides Local Science and Technology Development Fund Project(2023ZY0020)Key R&D and Achievement Transformation Project in InnerMongolia Autonomous Region(2022YFHH0019)+3 种基金the Fundamental Research Funds for Inner Mongolia University of Science&Technology(2022053)Natural Science Foundation of Inner Mongolia(2022LHQN05002)National Natural Science Foundation of China(52067018)Metallurgical Engineering First-Class Discipline Construction Project in Inner Mongolia University of Science and Technology,Control Science and Engineering Quality Improvement and Cultivation Discipline Project in Inner Mongolia University of Science and Technology。
文摘In this paper,a bilevel optimization model of an integrated energy operator(IEO)–load aggregator(LA)is constructed to address the coordinate optimization challenge of multiple stakeholder island integrated energy system(IIES).The upper level represents the integrated energy operator,and the lower level is the electricity-heatgas load aggregator.Owing to the benefit conflict between the upper and lower levels of the IIES,a dynamic pricing mechanism for coordinating the interests of the upper and lower levels is proposed,combined with factors such as the carbon emissions of the IIES,as well as the lower load interruption power.The price of selling energy can be dynamically adjusted to the lower LA in the mechanism,according to the information on carbon emissions and load interruption power.Mutual benefits and win-win situations are achieved between the upper and lower multistakeholders.Finally,CPLEX is used to iteratively solve the bilevel optimization model.The optimal solution is selected according to the joint optimal discrimination mechanism.Thesimulation results indicate that the sourceload coordinate operation can reduce the upper and lower operation costs.Using the proposed pricingmechanism,the carbon emissions and load interruption power of IEO-LA are reduced by 9.78%and 70.19%,respectively,and the capture power of the carbon capture equipment is improved by 36.24%.The validity of the proposed model and method is verified.
文摘Recently,unmanned aerial vehicle(UAV)-aided free-space optical(FSO)communication has attracted widespread attentions.However,most of the existing research focuses on communication performance only.The authors investigate the integrated scheduling of communication,sensing,and control for UAV-aided FSO communication systems.Initially,a sensing-control model is established via the control theory.Moreover,an FSO communication channel model is established by considering the effects of atmospheric loss,atmospheric turbulence,geometrical loss,and angle-of-arrival fluctuation.Then,the relationship between the motion control of the UAV and radial displacement is obtained to link the control aspect and communication aspect.Assuming that the base station has instantaneous channel state information(CSI)or statistical CSI,the thresholds of the sensing-control pattern activation are designed,respectively.Finally,an integrated scheduling scheme for performing communication,sensing,and control is proposed.Numerical results indicate that,compared with conventional time-triggered scheme,the proposed integrated scheduling scheme obtains comparable communication and control performance,but reduces the sensing consumed power by 52.46%.
文摘Background:Shift-based occupations have been consistently linked to adverse psychological outcomes;however,limited research has examined how work schedule type and physical activity are jointly associated with mental health and job stress in public transportation employees,a population frequently exposed to irregular hours and safety-critical responsibilities.This study investigated the associations between work schedule type and physical activity with mental health indicators and job stress among Seoul Metro employees.Methods:A cross-sectional survey was administered to 298 full-time male employees of Seoul Metro.Participants were categorized by work schedule(shift vs.regular)and physical activity level(regular,irregular,none)following American College of Sports Medicine(ACSM)guidelines.Mental health(sleep disturbance,depression,anxiety,loneliness)was assessed using validated binary indicators,and job stress was measured with the Korean Occupational Stress Scale–Short Form(KOSS-SF).Group differences were analyzed using chi-square tests,t-tests,and one-way ANOVA with effect sizes,and binary logistic and multiple regression analyses were conducted to identify predictors.Results:Shift workers reported significantly higher sleep disturbance and anxiety compared to regular daytime workers(p<0.05).Employees who participated in regular physical activity had lower odds of sleep disturbance and depression(p<0.05)and showed lower job stress scores compared with inactive workers.Work schedule type and physical activity were independently associated with mental health and job stress among transit employees.Conclusion:These findings underscore the dual influence of work schedule and physical activity on the psychological and occupational well-being of public transit employees.Promoting regular physical activity may buffer occupational stress among employees engaged in shift-based work.Workplace interventions that support physical activity participation and improve shift planning may enhance employee well-being.
基金supported by the Basic Research on Dynamic Real-time Modeling and Onboard Adaptive Modeling of Aero Engine,China(No.QZPY202308)。
文摘Variable Cycle Engine(VCE)serves as the core system in achieving future advanced fighters with cross-generational performance and mission versatility.However,the resultant complex configuration and strong coupling of control parameters present significant challenges in designing acceleration and deceleration control schedules.To thoroughly explore the performance potential of engine,a global integration design method for acceleration and deceleration control schedule based on inner and outer loop optimization is proposed.The outer loop optimization module employs Integrated Surrogate-Assisted Co-Differential Evolutionary(ISACDE)algorithm to optimize the variable geometry adjustment laws based on B-spline curve,and the inner loop optimization module adopts the fixed-state method to design the open-loop fuel–air ratio control schedules,which are aimed at minimizing the acceleration and deceleration time under multiple constraints.Simulation results demonstrate that the proposed global integration design method not only furthest shortens the acceleration and deceleration time,but also effectively safeguards the engine from overlimit.
基金supported by the National Natural Science Foundation of China(No.62033014)the Application Projects of Integrated Standardization and New Paradigm for Intelligent Manufacturing from the Ministry of Industry and Information Technology of China in 2016,and the Fundamental Research Funds for the Central Universities of Central South University,China(No.2021zzts0700).
文摘A collaborative optimization method for the sintering schedule of ternary cathode materials was proposed under microscopic coupling constraints.An oxygen vacancy concentration prediction model based on microscopic thermodynamics and a growth kinetics model based on neural networks were established.Then,optimization formulations were constructed in three stages to obtain an optimal sintering schedule that minimized energy consumption for different requirements.Simulations demonstrate that the models accurately predict the oxygen vacancy concentrations and grain size,with root mean square errors of approximately 5%and 3%,respectively.Furthermore,the optimized sintering schedule not only meets the required quality standards but also reduces sintering time by 12.31%and keeping temperature by 11.96%.This research provides new insights and methods for the preparation of ternary cathode materials.
基金supported by the Tianjin Enterprise Science and Technology Commissioner Project(Grant No.23YDTPJC00740,Grant No.24YDTPJC00610)the Tianjin Tiankai Higher Education Science and Technology Innovation Park Enterprise R&D Special Project(Grant No.23YFZXYC00027).
文摘The shop floor dynamic scheduling system based on human-computer interaction is the use of computer-aided decision-making and human-computer interaction to solve the dynamic scheduling problem.A human-computer interaction interface based on Gantt chart is designed,which can not only comprehensively and quantitatively represent the scheduling process and scheduling scheme,but also have friendly human-computer interaction performance.The data transmission and interaction architecture is constructed to realize the rapid response to shop floor disturbance events.A priority calculation algorithm integrating priority rules and dispatcher preference is proposed,which realizes the automatic calculation of priority for the dispatcher's reference and reduces theirburden.A man-machine interactive shop floor dynamic scheduling strategy is proposed.When solving the dynamic flexible job shop scheduling problem caused by machine tool breakdown and urgent order,the origin moments obtained by using this strategy are 0.4190 and 0.3703 respectively.As can be seen from the origin moment indicator,the dynamic shop floor scheduling system based on the human-computer interaction is efficient and reliable in solving dynamic scheduling problems,and related strategies of this system are also feasible and stable.
文摘This paper explains the goal conflict between schedule and quality in construction projects,including how schedule compression can lead to quality risks,and how quality control may cause delays.It analyzes the internal logic of collaborative management and influencing factors such as construction plans.The paper also introduces collaborative management methods,such as establishing a responsibility traceability system based on Work Breakdown Structure(WBS),and emphasizes the role of intelligent construction technologies and their future development directions.
基金supported by the National Natural Science Foundation of China(6177109562031007).
文摘The dwell scheduling problem for a multifunctional radar system is led to the formation of corresponding optimiza-tion problem.In order to solve the resulting optimization prob-lem,the dwell scheduling process in a scheduling interval(SI)is formulated as a Markov decision process(MDP),where the state,action,and reward are specified for this dwell scheduling problem.Specially,the action is defined as scheduling the task on the left side,right side or in the middle of the radar idle time-line,which reduces the action space effectively and accelerates the convergence of the training.Through the above process,a model-free reinforcement learning framework is established.Then,an adaptive dwell scheduling method based on Q-learn-ing is proposed,where the converged Q value table after train-ing is utilized to instruct the scheduling process.Simulation results demonstrate that compared with existing dwell schedul-ing algorithms,the proposed one can achieve better scheduling performance considering the urgency criterion,the importance criterion and the desired execution time criterion comprehen-sively.The average running time shows the proposed algorithm has real-time performance.
基金supported by National Natural Science Foundation of China under Grant No.62372110Fujian Provincial Natural Science of Foundation under Grants 2023J02008,2024H0009.
文摘The rapid advancement of Industry 4.0 has revolutionized manufacturing,shifting production from centralized control to decentralized,intelligent systems.Smart factories are now expected to achieve high adaptability and resource efficiency,particularly in mass customization scenarios where production schedules must accommodate dynamic and personalized demands.To address the challenges of dynamic task allocation,uncertainty,and realtime decision-making,this paper proposes Pathfinder,a deep reinforcement learning-based scheduling framework.Pathfinder models scheduling data through three key matrices:execution time(the time required for a job to complete),completion time(the actual time at which a job is finished),and efficiency(the performance of executing a single job).By leveraging neural networks,Pathfinder extracts essential features from these matrices,enabling intelligent decision-making in dynamic production environments.Unlike traditional approaches with fixed scheduling rules,Pathfinder dynamically selects from ten diverse scheduling rules,optimizing decisions based on real-time environmental conditions.To further enhance scheduling efficiency,a specialized reward function is designed to support dynamic task allocation and real-time adjustments.This function helps Pathfinder continuously refine its scheduling strategy,improving machine utilization and minimizing job completion times.Through reinforcement learning,Pathfinder adapts to evolving production demands,ensuring robust performance in real-world applications.Experimental results demonstrate that Pathfinder outperforms traditional scheduling approaches,offering improved coordination and efficiency in smart factories.By integrating deep reinforcement learning,adaptable scheduling strategies,and an innovative reward function,Pathfinder provides an effective solution to the growing challenges of multi-robot job scheduling in mass customization environments.
基金the Science and Technology Projects of State Grid Jiangsu Electric Power Company “Design, Regulation and Application of Electric-Hydrogen-Heat Integrated Energy Systems for Low Carbon Buildings, J2024184”.
文摘In the field of low-carbon building systems,the combination of renewable energy and hydrogen energy systems is gradually gaining prominence.However,the uncertainty of supply and demand and the multi-energy flow coupling characteristics of this system pose challenges for its optimized scheduling.In light of this,this study focuses on electro-thermal-hydrogen trigeneration systems,first modelling the system's scheduling optimization problem as a Markov decision process,thereby transforming it into a sequential decision problem.Based on this,this paper proposes a reinforcement learning algorithm based on deep deterministic policy gradient improvement,aiming to minimize system operating costs and enhance the system's sustainable operation capability.Experimental results show that compared to traditional reinforcement learning algorithms,the reinforcement learning algorithm based on deep deterministic policy gradient improvement achieves improvements of 12.5%and 22.8%in convergence speed and convergence value,respectively.Additionally,under uncertainty scenarios ranging from 10%to 30%,cost reductions of 2.82%,3.08%,and 2.52%were achieved,respectively,with an average cost reduction of 2.80%across 30 simulated scenarios.Compared to the original algorithm and rule-based algorithms in multi-uncertainty environments,the reinforcement learning algorithm based on improved deep deterministic policy gradients demonstrated superiority in terms of system operating costs and continuous operational capability,effectively enhancing the system's economic and sustainable performance.
基金supported by the Scientific Research Foundation of Nanjing Institute of Technology(No.YKJ202425)the National Natural Science Foundation of China(No.72301130).
文摘The production mode of manufacturing industry presents characteristics of multiple varieties,small-batch and personalization,leading to frequent disturbances in workshop.Traditional centralized scheduling methods are difficult to achieve efficient and real-time production management under dynamic disturbance.In order to improve the intelligence and adaptability of production scheduler,a novel distributed scheduling architecture is proposed,which has the ability to autonomously allocate tasks and handle disturbances.All production tasks are scheduled through autonomous collaboration and decision-making between intelligent machines.Firstly,the multi-agent technology is applied to build a self-organizing manufacturing system,enabling each machine to be equipped with the ability of active information interaction and joint-action execution.Secondly,various self-organizing collaboration strategies are designed to effectively facilitate cooperation and competition among multiple agents,thereby flexibly achieving global perception of environmental state.To ensure the adaptability and superiority of production decisions in dynamic environment,deep reinforcement learning is applied to build a smart production scheduler:Based on the perceived environment state,the scheduler intelligently generates the optimal production strategy to guide the task allocation and resource configuration.The feasibility and effectiveness of the proposed method are verified through three experimental scenarios using a discrete manufacturing workshop as the test bed.Compared to heuristic dispatching rules,the proposed method achieves an average performance improvement of 34.0%in three scenarios in terms of order tardiness.The proposed system can provide a new reference for the design of smart manufacturing systems.
基金supported by the Science and Technology Project of the State Grid Corporation of China(5400-202255158A-1-1-ZN).
文摘Frequent extreme disasters have led to frequent large-scale power outages in recent years.To quickly restore power,it is necessary to understand the damage information of the distribution network accurately.However,the public network communication system is easily damaged after disasters,causing the operation center to lose control of the distribution network.In this paper,we considered using satellites to transmit the distribution network data and focus on the resource scheduling problem of the satellite emergency communication system for the distribution network.Specifically,this paper first formulates the satellite beam-pointing problem and the accesschannel joint resource allocation problem.Then,this paper proposes the Priority-based Beam-pointing and Access-Channel joint optimization algorithm(PBAC),which uses convex optimization theory to solve the satellite beam pointing problem,and adopts the block coordinate descent method,Lagrangian dual method,and a greedy algorithm to solve the access-channel joint resource allocation problem,thereby obtaining the optimal resource scheduling scheme for the satellite network.Finally,this paper conducts comparative experiments with existing methods to verify the effec-tiveness of the proposed methods.The results show that the total weighted transmitted data of the proposed algorithm is increased by about 19.29∼26.29%compared with other algorithms.
基金supported by the National Natural Science Foundation of China(61901341).
文摘Using the existing positioning technology can easily obtain high-precision positioning information,which can save resources and reduce complexity when used in the communication field.In this paper,we propose a location-based user scheduling and beamforming scheme for the downlink of a massive multi-user input-output system.Specifically,we combine an analog outer beamformer with a digital inner beamformer.An outer beamformer can be selected from a codebook formed by antenna steering vectors,and then a reduced-complexity inner beamformer based on iterative orthogonal matrices and right triangular matrices(QR)decomposition is applied to cancel interuser interference.Then,we propose a low-complexity user selection algorithm using location information in this paper.We first derive the geometric angle between channel matrices,which represent the correlation between users.Furthermore,we derive the asymptotic signal to interference-plus-noise ratio(SINR)of the system in the context of two-stage beamforming using random matrix theory(RMT),taking into account inter-channel correlations and energies.Simulation results show that the algorithm can achieve higher system and speed while reducing computational complexity.
