Considering the special features of dynamic environment economic dispatch of power systems with high dimensionality,strong coupling,nonlinearity,and non-convexity,a GA-DE multi-objective optimization algorithm based o...Considering the special features of dynamic environment economic dispatch of power systems with high dimensionality,strong coupling,nonlinearity,and non-convexity,a GA-DE multi-objective optimization algorithm based on dual-population pseudo-parallel genetic algorithm-differential evolution is proposed in this paper.The algorithm is based on external elite archive and Pareto dominance,and it adopts the cooperative co-evolution mechanism of differential evolution and genetic algorithm.Average entropy and cubic chaoticmapping initialization strategies are proposed to increase population diversity.In the proposed method,we analyze the distribution of neighboring solutions and apply a new Pareto solution set pruning approach.Unlike traditional models,this work takes the transmission losses as an optimization target and overcomes complex model constraints through a dynamic relaxation constraint approach.To solve the uncertainty caused by integrating wind and photovoltaic energy in power system scheduling,a multi-objective dynamic environment economical dispatch model is set up that takes the system spinning reserve and network highest losses into account.In this paper,the DE algorithm is improved to form the DGAGE algorithm for the objective optimization of the overall power system,The DE algorithm part of DGAGE is combined with the JAYA algorithm to form the system scheduling HDJ algorithm for multiple energy sources connected to the grid.The effectiveness of the proposed method is demonstrated using CEC2022 and CEC2005 test functions,showing robust optimization performance.Validation on a classical 10-unit system confirms the feasibility of the proposed algorithm in addressing power system scheduling issues.This approach provides a novel solution for dynamic power dispatch systems.展开更多
The integration of large-scale-distributed new energy resources has led to heightened source‒load uncertainty.As energy prosumers,microgrids urgently require enhanced real-time regulation capabilities over controllabl...The integration of large-scale-distributed new energy resources has led to heightened source‒load uncertainty.As energy prosumers,microgrids urgently require enhanced real-time regulation capabilities over controllable resources amid uncertain environments,rendering real-time and rapid decision-making a critical issue.This paper proposes a tailored twin delayed deep deterministic policy gradient(TD3)reinforcement learning algorithm that explicitly accounts for source‒load uncertainty.First,following an expert experience-based methodology,Gaussian process regression was implemented using the radial basis function covariance with historical source and load data.The parameters were adaptively adjusted by maximum likelihood estimation to generate the expected curves of demand and wind‒solar power generation,along with their 95%confidence regions,which were treated as representative uncertainty scenarios.Second,the traditional scheduling model was transformed into a deep reinforcement learning(DRL)environment through a Markov process.To minimize the total operational cost of the microgrid,the tailored TD3 algorithm was applied to formulate rapid intraday scheduling decisions.Finally,simulations were conducted using real historical data from an actual region in Zhejiang province,China,to verify the efficacy of the proposed method.The results demonstrate the potential of the algorithm for achieving economic scheduling for microgrids.展开更多
Electroacoustic Tomography(EAT)is an imaging technique that detects ultrasound waves induced by electrical pulses,offering a solution for real-time electroporation monitoring.This study presents EAT system using a dua...Electroacoustic Tomography(EAT)is an imaging technique that detects ultrasound waves induced by electrical pulses,offering a solution for real-time electroporation monitoring.This study presents EAT system using a dual-frequency ultrasound array.The broadband nature of electroacoustic signals requires ultrasound detector to cover both the high-frequency range(around 6MHz)signals generated by small targets and the low-frequency range(around 1MHz)signals generated by large targets.In our EAT system,we use the 6 MHz array to detect high-frequency signals from the electrodes,and the 1 MHz array for the electrical field.To test this,we conducted simulations using COMSOL Multiphysics^(®) and MATLAB's k-Wave toolbox,followed by experiments using a custom-built setup with a dual-frequency transducer and real-time data acquisition.The results demonstrated that the dual-frequency EAT system could accurately and simultaneously monitor the electroporation process,effectively showing both the treatment area and electrode placement with the application of 1 kV electric pulses with 100 ns duration.The axial resolution of the 6MHz array for EAT was 0.45 mm,significantly better than the 2mm resolution achieved with the 1MHz array.These findings validate the potential of dual-frequency EAT as a superior method for real-time electroporation monitoring.展开更多
As a fundamental component in computer vision,edges can be categorized into four types based on discontinuities in reflectance,illumination,surface normal,or depth.While deep CNNs have significantly advanced generic e...As a fundamental component in computer vision,edges can be categorized into four types based on discontinuities in reflectance,illumination,surface normal,or depth.While deep CNNs have significantly advanced generic edge detection,real-time multi-class semantic edge detection under resource constraints remains challenging.To address this,we propose a lightweight framework based on PiDiNet that enables fine-grained semantic edge detection.Our model simultaneously predicts background and four edge categories from full-resolution inputs,balancing accuracy and efficiency.Key contributions include:a multi-channel output structure expanding binary edge prediction to five classes,supported by a deep supervision mechanism;a dynamic class-balancing strategy combining adaptive weighting with physical priors to handle extreme class imbalance;and maintained architectural efficiency enabling real-time inference.Extensive evaluations on BSDS-RIND show our approach achieves accuracy competitive with state-of-the-art methods while operating in real time.展开更多
In the era of the Internet of Things,distributed computing alleviates the problem of insufficient terminal computing power by integrating idle resources of heterogeneous devices.However,the imbalance between task exec...In the era of the Internet of Things,distributed computing alleviates the problem of insufficient terminal computing power by integrating idle resources of heterogeneous devices.However,the imbalance between task execution delay and node energy consumption,and the scheduling and adaptation challenges brought about by device heterogeneity,urgently need to be addressed.To tackle this problem,this paper constructs a multi-objective real-time task scheduling model that considers task real-time performance,execution delay,system energy consumption,and node interests.The model aims to minimize the delay upper bound and total energy consumption while maximizing system satisfaction.A real-time task scheduling algorithm based on bilateral matching game is proposed.By designing a bidirectional preference mechanism between tasks and computing nodes,combined with a multi-round stable matching strategy,accurate matching between tasks and nodes is achieved.Simulation results show that compared with the baseline scheme,the proposed algorithm significantly reduces the total execution cost,effectively balances the task execution delay and the energy consumption of compute nodes,and takes into account the interests of each network compute node.展开更多
During geothermal resource exploitation,the potential deterioration of mechanical properties in high-temperature granite subjected to cooling poses a significant safety concern.To address this,the present study invest...During geothermal resource exploitation,the potential deterioration of mechanical properties in high-temperature granite subjected to cooling poses a significant safety concern.To address this,the present study investigates the coupled thermo-mechanical behavior of granite during heating and cooling through a combination of laboratory tests and finite difference method analysis.Initial investigations involve X-ray diffraction,thermal expansion test,thermogravimetric analysis,and uniaxial compression test.Results show the significant variations of granite properties under different thermal conditions,attributed to temperature gradients,water evaporation,and mineral phase transitions.Subsequently,a model considering temperature-dependent parameters and real-time cooling rates was employed to simulate linear heating and nonlinear cooling processes.Simulation results indicate that the thermal cracking predominantly occurs during the heating stage,with tensile failure as the primary mode.Additionally,a faster real-time cooling rate at higher temperatures intensifies the thermal cracking behavior in granite.This study effectively elucidates the thermomechanical coupling behavior of granite during heating and cooling processes,providing insights into the mechanisms of mechanical property changes with rising or decreasing temperatures.展开更多
The intelligent environmental sensing systems are quickly transforming the sparse and retrospective monitoring to dense and decision-oriented environmental intelligence.This review brings together the manner in which ...The intelligent environmental sensing systems are quickly transforming the sparse and retrospective monitoring to dense and decision-oriented environmental intelligence.This review brings together the manner in which integration of Internet of Things(IoT)sensing,edge computing,and real-time analytics facilitates timely detection,interpretation,and prediction of the environmental conditions across the applications,such as urban air quality,watershed and coastal surveillance,industrial safety,agriculture,and disaster response.We define end-to-end architectural patterns to organize devices,edge nodes,and cloud services to satisfy latency,reliability,bandwidth,and governance constraints with emphasis on event-time processing,adaptive offloading,and hierarchical aggregation.Then we look at sensing and infrastructure foundations,emphasizing the effects of sensor modality and power autonomy,connectivity,and the practices of calibration on the practicable analytics and eventual plausibility.It is on this basis that we examine real-time analytics pipelines and Artificial Intelligence(AI)techniques to preprocess,sensor combine,anomaly detect,and short-horizon forecast,with a focus on edge-deployable models,quantification of uncertainties,and query resistance to drift and domain shift.Lastly,we address the realities of deployment that condition operational success,such as lifecycle engineering,provenance-aware data management,security and privacy risks,ethical governance,and evaluation methodologies,which place end-to-end latency and field generalization as a priority.This review offers cohesion to algorithmic capabilities and systems engineering and governance to define an overall framework,show open areas of research directions,and provide practical recommendations on how to design trustworthy,scalable,and sustainable environmental monitoring systems.展开更多
To investigate the energy relief effect of real-time drilling in preventing rockburst in high-stress rock,a series of high-stress real-time drilling uniaxial compression tests were conducted on red sandstone specimens...To investigate the energy relief effect of real-time drilling in preventing rockburst in high-stress rock,a series of high-stress real-time drilling uniaxial compression tests were conducted on red sandstone specimens using the SG4500 drilling rig.Results showed that the mechanical behavior(i.e.peak strength and rockburst intensity)of the rock was weakened under high-stress real-time drilling and exhibited a downward trend as the drilling diameter increased.The real-time drilling energy dissipation index(ERD)was proposed to characterize the energy relief during high-stress real-time drilling.The ERD exhibited a linear increase with the real-time drilling diameter.Furthermore,the elastic strain energy of post-drilling rock showed a linear relationship with the square of stress across different stress levels,which also applied to the peak elastic strain energy and the square of peak stress.This findingreveals the intrinsic link between the weakening effect of peak elastic strain energy and peak strength due to high-stress real-time drilling,confirmingthe consistency between energy relief and pressure relief effects.By establishing relationships among rockburst proneness,peak elastic strain energy,and peak strength,it was demonstrated that high-stress real-time drilling reduces rockburst proneness through energy dissipation.Specifically,both peak elastic strain energy and rockburst proneness decreased with larger drill bit diameters,consistent with reductions in peak strength,rockburst intensity,and fractal dimensions of high-stress real-time drilled rock.These results validate the energy relief mechanism of real-time drilling in mitigating rockburst risks.展开更多
An innovative real-time monitoring method for surrounding rock damage based on microseismic time-lapse double-difference tomography is proposed for delayed dynamic damage identification and insufficient detection of a...An innovative real-time monitoring method for surrounding rock damage based on microseismic time-lapse double-difference tomography is proposed for delayed dynamic damage identification and insufficient detection of adverse geological conditions in deep-buried tunnel construction.The installation techniques for microseismic sensors were optimized by mounting sensors at bolt ends which significantly improves signal-to-noise ratio(SNR)and anti-interference capability compared to conventional borehole placement.Subsequently,a 3D wave velocity evolution model that incorporates construction-induced disturbances was established,enabling the first visualization of spatiotemporal variations in surrounding rock wave velocity.It finds significant wave velocity reduction near the tunnel face,with roof and floor damage zones extending 40–50 m;wave velocities approaching undisturbed levels at 15 m ahead of the working face and on the laterally undisturbed side;pronounced spatial asymmetry in wave velocity distribution—values on the left side exceed those on the right,with a clear stress concentration or transition zone located 10–15 m;and systematically lower velocities behind the face than in front,indicating asymmetric rock damage development.These results provide essential theoretical support and practical guidance for optimizing dynamic construction strategies,enabling real-time adjustment of support parameters,and establishing safety early warning systems in deep-buried tunnel engineering.展开更多
Online examinations have become a dominant assessment mode,increasing concerns over academic integrity.To address the critical challenge of detecting cheating behaviours,this study proposes a hybrid deep learning appr...Online examinations have become a dominant assessment mode,increasing concerns over academic integrity.To address the critical challenge of detecting cheating behaviours,this study proposes a hybrid deep learning approach that combines visual detection and temporal behaviour classification.The methodology utilises object detection models—You Only Look Once(YOLOv12),Faster Region-based Convolutional Neural Network(RCNN),and Single Shot Detector(SSD)MobileNet—integrated with classification models such as Convolutional Neural Networks(CNN),Bidirectional Gated Recurrent Unit(Bi-GRU),and CNN-LSTM(Long Short-Term Memory).Two distinct datasets were used:the Online Exam Proctoring(EOP)dataset from Michigan State University and the School of Computer Science,Duy Tan Unievrsity(SCS-DTU)dataset collected in a controlled classroom setting.A diverse set of cheating behaviours,including book usage,unauthorised interaction,internet access,and mobile phone use,was categorised.Comprehensive experiments evaluated the models based on accuracy,precision,recall,training time,inference speed,and memory usage.We evaluate nine detector-classifier pairings under a unified budget and score them via a calibrated harmonic mean of detection and classification accuracies,enabling deployment-oriented selection under latency and memory constraints.Macro-Precision/Recall/F1 and Receiver Operating Characteristic-Area Under the Curve(ROC-AUC)are reported for the top configurations,revealing consistent advantages of object-centric pipelines for fine-grained cheating cues.The highest overall score is achieved by YOLOv12+CNN(97.15%accuracy),while SSD-MobileNet+CNN provides the best speed-efficiency trade-off for edge devices.This research provides valuable insights into selecting and deploying appropriate deep learning models for maintaining exam integrity under varying resource constraints.展开更多
The highly dynamic nature,strong uncertainty,and coupled multiple safety constraints inherent in carrier aircraft recovery operations pose severe challenges for real-time decision-making.Addressing bolter scenarios,th...The highly dynamic nature,strong uncertainty,and coupled multiple safety constraints inherent in carrier aircraft recovery operations pose severe challenges for real-time decision-making.Addressing bolter scenarios,this study proposes an intelligent decision-making framework based on a deep long short-term memory Q-network.This framework transforms the real-time sequencing for bolter recovery problem into a partially observable Markov decision process.It employs a stacked long shortterm memory network to accurately capture the long-range temporal dependencies of bolter event chains and fuel consumption.Furthermore,it integrates a prioritized experience replay training mechanism to construct a safe and adaptive scheduling system capable of millisecond-level real-time decision-making.Experimental demonstrates that,within large-scale mass recovery scenarios,the framework achieves zero safety violations in static environments and maintains a fuel safety violation rate below 10%in dynamic scenarios,with single-step decision times at the millisecond level.The model exhibits strong generalization capability,effectively responding to unforeseen emergent situations—such as multiple bolters and fuel emergencies—without requiring retraining.This provides robust support for efficient carrier-based aircraft recovery operations.展开更多
Given the different energy rates of multiple types of power generation units,different operation plans affect the economy of microgrids.Limited by load and power generation forecasting technologies,the economic superi...Given the different energy rates of multiple types of power generation units,different operation plans affect the economy of microgrids.Limited by load and power generation forecasting technologies,the economic superiority of day-ahead plans is unable to be fully utilized because of the fluctuation of loads and power sources.In this regard,a two-stage correction strategy-based real-time dispatch method for the economic operation of microgrids is proposed.Based on the optimal day-ahead economic operation plan,unbalanced power is validly allocated in two stages in terms of power increment and current power,which maintains the economy of the day-ahead plan.Further,for operating point offset during real-time correction,a rolling dispatch method is introduced to dynamically update the system operation plan.Finally,the results verify the effectiveness of the proposed method.展开更多
This paper proposes a dynamic-decision-based realtime dispatch method to coordinate the economic objective with multiple types of security dispatch objectives while reducing constraint violations in the process of adj...This paper proposes a dynamic-decision-based realtime dispatch method to coordinate the economic objective with multiple types of security dispatch objectives while reducing constraint violations in the process of adjusting the system operation point to the optimum.In each decision moment,the following tasks are executed in turn:①locally linearizing the system model at the current operation point with the online model identification by using measurements;②narrowing down the gaps between unsatisfied security requirements and their security thresholds in order of priority;③minimizing the generation cost;④minimizing the security indicators within their security thresholds.Compared with the existing real-time dispatch strategies,the proposed method can adjust the deviations caused by unpredictable power flow fluctuations,avoid dispatch bias caused by model parameter errors,and reduce the constraint violations in the dispatch decision process.The effectiveness of the proposed method is verified with the IEEE 39-bus system.展开更多
This paper develops a segmented real-time dispatch model for power-gas integrated systems(PGISs), where power-to-gas(P2G) devices and traditional automatic generation control units are cooperated to manage wind power ...This paper develops a segmented real-time dispatch model for power-gas integrated systems(PGISs), where power-to-gas(P2G) devices and traditional automatic generation control units are cooperated to manage wind power uncertainty. To improve the economics of the real-time dispatch in regard to the current high operation cost of P2Gs, the wind power uncertainty set is divided into several segments, and a segmented linear decision rule is developed, which assigns adjustment tasks differently when wind power uncertainty falls into different segments. Thus, the P2G operation with high costs can be reduced in real-time adjustment. Besides, a novel segmented stochastic robust optimization is proposed to improve the efficiency and robustness of PGIS dispatch under wind power uncertainty, which minimizes the expected cost under the empirical wind power distribution and builds up the security constraints based on the robust optimization. The expected cost is formulated using a Nataf conversion-based multi-point estimate method, and the optimal number of estimate points is determined through sensitivity analysis. Furthermore, a difference-ofconvex optimization with a partial relaxation rule is developed to solve the non-convex dispatch problem in a sequential optimization framework. Numerical simulations in two testing cases validate the effectiveness of the proposed model and solving method.展开更多
In order to recover ore as much as possible, a computer-controlled truck real-time dispatching model is conducted under the conditions of Qidashan lron Mine. It can not only acquire the optimization of shovel and truc...In order to recover ore as much as possible, a computer-controlled truck real-time dispatching model is conducted under the conditions of Qidashan lron Mine. It can not only acquire the optimization of shovel and truck operation, but also satisfy requirements of blending ores.The simulation results indicate the effectiveness of the model developed.展开更多
A large amount of renewable energy generation(REG)has been integrated into power systems,challenging the operational security of power networks.In a real-time dispatch,system operators need to estimate the ability of ...A large amount of renewable energy generation(REG)has been integrated into power systems,challenging the operational security of power networks.In a real-time dispatch,system operators need to estimate the ability of the power network to accommodate REG with a limited reserve capacity.The real-time dispatchable region(RTDR)is defined as the largest range of a power injection that the power network can accommodate in a certain dispatch interval for a given dispatch base point.State-of-the-art research on the RTDR adopts a DC power flow model regardless of the voltage profiles and reactive power,which can overlook potentially insecure operational states of the system.To address this issue,this paper proposes an AC power flow based RTDR model simultaneously considering the reactive power and voltage profiles constraints.The nonlinear constraints in our model are approximated using a linear power flow model together with a polytope approximation technique for quadratic constraints.An adaptive constraint generation algorithm is used to calculate the RTDR.Simulation results using the IEEE 5-bus and 30-bus systems illustrate the advantages of the proposed model.展开更多
As a key to improve the performance of the interbay automated material handling system （AMHS） in 300 mm semiconductor wafer fabrication system, the real- time overhead hoist transport （OHT） dispatching problem has...As a key to improve the performance of the interbay automated material handling system （AMHS） in 300 mm semiconductor wafer fabrication system, the real- time overhead hoist transport （OHT） dispatching problem has received much attention. This problem is first formu- lated as a special form of assignment problem and it is proved that more than one solution will be obtained by Hungarian algorithm simultaneously. Through proposing and strictly proving two propositions related to the char- acteristics of these solutions, a modified Hungarian algo- rithm is designed to distinguish these solutions. Finally, a new real-time OHT dispatching method is carefully designed by implementing the solution obtained by the modified Hungarian algorithm. The experimental results of discrete event simulations show that, compared with con- ventional Hungarian algorithm dispatching method, the proposed dispatching method that chooses the solution with the maximum variance respectively reduces on average 4 s of the average waiting time and average lead time of wafer lots, and its performance is rather stable in multiple dif- ferent scenarios of the interbay AMHS with different quantities of shortcuts. This research provides an efficient real-time OHT dispatching mechanism for the interbay AMHS with shortcuts and bypasses.展开更多
In the foundry industries,process design has traditionally relied on manuals and complex theoretical calculations.With the advent of 3D design in casting,computer-aided design(CAD)has been applied to integrate the fea...In the foundry industries,process design has traditionally relied on manuals and complex theoretical calculations.With the advent of 3D design in casting,computer-aided design(CAD)has been applied to integrate the features of casting process,thereby expanding the scope of design options.These technologies use parametric model design techniques for rapid component creation and use databases to access standard process parameters and design specifications.However,3D models are currently still created through inputting or calling parameters,which requires numerous verifications through calculations to ensure the design rationality.This process may be significantly slowed down due to repetitive modifications and extended design time.As a result,there are increasingly urgent demands for a real-time verification mechanism to address this issue.Therefore,this study proposed a novel closed-loop model and software development method that integrated contextual design with real-time verification,dynamically verifying relevant rules for designing 3D casting components.Additionally,the study analyzed three typical closed-loop scenarios of agile design in an independent developed intelligent casting process system.It is believed that foundry industries can potentially benefit from favorably reduced design cycles to yield an enhanced competitive product market.展开更多
Deep learning-based intelligent recognition algorithms are increasingly recognized for their potential to address the labor-intensive challenge of manual pest detection.However,their deployment on mobile devices has b...Deep learning-based intelligent recognition algorithms are increasingly recognized for their potential to address the labor-intensive challenge of manual pest detection.However,their deployment on mobile devices has been constrained by high computational demands.Here,we developed GBiDC-PEST,a mobile application that incorporates an improved,lightweight detection algorithm based on the You Only Look Once(YOLO)series singlestage architecture,for real-time detection of four tiny pests(wheat mites,sugarcane aphids,wheat aphids,and rice planthoppers).GBiDC-PEST incorporates several innovative modules,including GhostNet for lightweight feature extraction and architecture optimization by reconstructing the backbone,the bi-directional feature pyramid network(BiFPN)for enhanced multiscale feature fusion,depthwise convolution(DWConv)layers to reduce computational load,and the convolutional block attention module(CBAM)to enable precise feature focus.The newly developed GBiDC-PEST was trained and validated using a multitarget agricultural tiny pest dataset(Tpest-3960)that covered various field environments.GBiDC-PEST(2.8 MB)significantly reduced the model size to only 20%of the original model size,offering a smaller size than the YOLO series(v5-v10),higher detection accuracy than YOLOv10n and v10s,and faster detection speed than v8s,v9c,v10m and v10b.In Android deployment experiments,GBiDCPEST demonstrated enhanced performance in detecting pests against complex backgrounds,and the accuracy for wheat mites and rice planthoppers was improved by 4.5-7.5%compared with the original model.The GBiDC-PEST optimization algorithm and its mobile deployment proposed in this study offer a robust technical framework for the rapid,onsite identification and localization of tiny pests.This advancement provides valuable insights for effective pest monitoring,counting,and control in various agricultural settings.展开更多
基金funded by the Major Humanities and Social Sciences Research Projects in Zhejiang Higher Education Institutions,grant number 2023QN131National Innovation Training Program Project in China,grant number 202410451009.
文摘Considering the special features of dynamic environment economic dispatch of power systems with high dimensionality,strong coupling,nonlinearity,and non-convexity,a GA-DE multi-objective optimization algorithm based on dual-population pseudo-parallel genetic algorithm-differential evolution is proposed in this paper.The algorithm is based on external elite archive and Pareto dominance,and it adopts the cooperative co-evolution mechanism of differential evolution and genetic algorithm.Average entropy and cubic chaoticmapping initialization strategies are proposed to increase population diversity.In the proposed method,we analyze the distribution of neighboring solutions and apply a new Pareto solution set pruning approach.Unlike traditional models,this work takes the transmission losses as an optimization target and overcomes complex model constraints through a dynamic relaxation constraint approach.To solve the uncertainty caused by integrating wind and photovoltaic energy in power system scheduling,a multi-objective dynamic environment economical dispatch model is set up that takes the system spinning reserve and network highest losses into account.In this paper,the DE algorithm is improved to form the DGAGE algorithm for the objective optimization of the overall power system,The DE algorithm part of DGAGE is combined with the JAYA algorithm to form the system scheduling HDJ algorithm for multiple energy sources connected to the grid.The effectiveness of the proposed method is demonstrated using CEC2022 and CEC2005 test functions,showing robust optimization performance.Validation on a classical 10-unit system confirms the feasibility of the proposed algorithm in addressing power system scheduling issues.This approach provides a novel solution for dynamic power dispatch systems.
基金supported in part by Science and Technology Project of State Grid Corporation of China(No.5400-202319829A-4-1-KJ).
文摘The integration of large-scale-distributed new energy resources has led to heightened source‒load uncertainty.As energy prosumers,microgrids urgently require enhanced real-time regulation capabilities over controllable resources amid uncertain environments,rendering real-time and rapid decision-making a critical issue.This paper proposes a tailored twin delayed deep deterministic policy gradient(TD3)reinforcement learning algorithm that explicitly accounts for source‒load uncertainty.First,following an expert experience-based methodology,Gaussian process regression was implemented using the radial basis function covariance with historical source and load data.The parameters were adaptively adjusted by maximum likelihood estimation to generate the expected curves of demand and wind‒solar power generation,along with their 95%confidence regions,which were treated as representative uncertainty scenarios.Second,the traditional scheduling model was transformed into a deep reinforcement learning(DRL)environment through a Markov process.To minimize the total operational cost of the microgrid,the tailored TD3 algorithm was applied to formulate rapid intraday scheduling decisions.Finally,simulations were conducted using real historical data from an actual region in Zhejiang province,China,to verify the efficacy of the proposed method.The results demonstrate the potential of the algorithm for achieving economic scheduling for microgrids.
基金supported by the National Institute of Health(R37CA240806,U01CA288351,and R50CA283816)support from UCI Chao Family Comprehensive Cancer Center(P30CA062203).
文摘Electroacoustic Tomography(EAT)is an imaging technique that detects ultrasound waves induced by electrical pulses,offering a solution for real-time electroporation monitoring.This study presents EAT system using a dual-frequency ultrasound array.The broadband nature of electroacoustic signals requires ultrasound detector to cover both the high-frequency range(around 6MHz)signals generated by small targets and the low-frequency range(around 1MHz)signals generated by large targets.In our EAT system,we use the 6 MHz array to detect high-frequency signals from the electrodes,and the 1 MHz array for the electrical field.To test this,we conducted simulations using COMSOL Multiphysics^(®) and MATLAB's k-Wave toolbox,followed by experiments using a custom-built setup with a dual-frequency transducer and real-time data acquisition.The results demonstrated that the dual-frequency EAT system could accurately and simultaneously monitor the electroporation process,effectively showing both the treatment area and electrode placement with the application of 1 kV electric pulses with 100 ns duration.The axial resolution of the 6MHz array for EAT was 0.45 mm,significantly better than the 2mm resolution achieved with the 1MHz array.These findings validate the potential of dual-frequency EAT as a superior method for real-time electroporation monitoring.
基金supported by the National Natural Science Foundation of China 62402171.
文摘As a fundamental component in computer vision,edges can be categorized into four types based on discontinuities in reflectance,illumination,surface normal,or depth.While deep CNNs have significantly advanced generic edge detection,real-time multi-class semantic edge detection under resource constraints remains challenging.To address this,we propose a lightweight framework based on PiDiNet that enables fine-grained semantic edge detection.Our model simultaneously predicts background and four edge categories from full-resolution inputs,balancing accuracy and efficiency.Key contributions include:a multi-channel output structure expanding binary edge prediction to five classes,supported by a deep supervision mechanism;a dynamic class-balancing strategy combining adaptive weighting with physical priors to handle extreme class imbalance;and maintained architectural efficiency enabling real-time inference.Extensive evaluations on BSDS-RIND show our approach achieves accuracy competitive with state-of-the-art methods while operating in real time.
基金Supported by the National Program on Key Basic Research Project(2020YFA0713600)the National Natural Science Foundation of China(62272214)。
文摘In the era of the Internet of Things,distributed computing alleviates the problem of insufficient terminal computing power by integrating idle resources of heterogeneous devices.However,the imbalance between task execution delay and node energy consumption,and the scheduling and adaptation challenges brought about by device heterogeneity,urgently need to be addressed.To tackle this problem,this paper constructs a multi-objective real-time task scheduling model that considers task real-time performance,execution delay,system energy consumption,and node interests.The model aims to minimize the delay upper bound and total energy consumption while maximizing system satisfaction.A real-time task scheduling algorithm based on bilateral matching game is proposed.By designing a bidirectional preference mechanism between tasks and computing nodes,combined with a multi-round stable matching strategy,accurate matching between tasks and nodes is achieved.Simulation results show that compared with the baseline scheme,the proposed algorithm significantly reduces the total execution cost,effectively balances the task execution delay and the energy consumption of compute nodes,and takes into account the interests of each network compute node.
基金National Natural Science Foundation of China,Grant/Award Number:52104120Hunan Provincial Key Laboratory of Key Technology on Hydropower Development,Grant/Award Number:PKLHD202303。
文摘During geothermal resource exploitation,the potential deterioration of mechanical properties in high-temperature granite subjected to cooling poses a significant safety concern.To address this,the present study investigates the coupled thermo-mechanical behavior of granite during heating and cooling through a combination of laboratory tests and finite difference method analysis.Initial investigations involve X-ray diffraction,thermal expansion test,thermogravimetric analysis,and uniaxial compression test.Results show the significant variations of granite properties under different thermal conditions,attributed to temperature gradients,water evaporation,and mineral phase transitions.Subsequently,a model considering temperature-dependent parameters and real-time cooling rates was employed to simulate linear heating and nonlinear cooling processes.Simulation results indicate that the thermal cracking predominantly occurs during the heating stage,with tensile failure as the primary mode.Additionally,a faster real-time cooling rate at higher temperatures intensifies the thermal cracking behavior in granite.This study effectively elucidates the thermomechanical coupling behavior of granite during heating and cooling processes,providing insights into the mechanisms of mechanical property changes with rising or decreasing temperatures.
基金supported by Jiangxi Polytechnic Institute Key Research Topics in Educational Reform 2025-JGJG-07.
文摘The intelligent environmental sensing systems are quickly transforming the sparse and retrospective monitoring to dense and decision-oriented environmental intelligence.This review brings together the manner in which integration of Internet of Things(IoT)sensing,edge computing,and real-time analytics facilitates timely detection,interpretation,and prediction of the environmental conditions across the applications,such as urban air quality,watershed and coastal surveillance,industrial safety,agriculture,and disaster response.We define end-to-end architectural patterns to organize devices,edge nodes,and cloud services to satisfy latency,reliability,bandwidth,and governance constraints with emphasis on event-time processing,adaptive offloading,and hierarchical aggregation.Then we look at sensing and infrastructure foundations,emphasizing the effects of sensor modality and power autonomy,connectivity,and the practices of calibration on the practicable analytics and eventual plausibility.It is on this basis that we examine real-time analytics pipelines and Artificial Intelligence(AI)techniques to preprocess,sensor combine,anomaly detect,and short-horizon forecast,with a focus on edge-deployable models,quantification of uncertainties,and query resistance to drift and domain shift.Lastly,we address the realities of deployment that condition operational success,such as lifecycle engineering,provenance-aware data management,security and privacy risks,ethical governance,and evaluation methodologies,which place end-to-end latency and field generalization as a priority.This review offers cohesion to algorithmic capabilities and systems engineering and governance to define an overall framework,show open areas of research directions,and provide practical recommendations on how to design trustworthy,scalable,and sustainable environmental monitoring systems.
基金supported by the National Natural Science Foundation of China(Grant No.42077244)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(Grant No.KYCX24_0434).
文摘To investigate the energy relief effect of real-time drilling in preventing rockburst in high-stress rock,a series of high-stress real-time drilling uniaxial compression tests were conducted on red sandstone specimens using the SG4500 drilling rig.Results showed that the mechanical behavior(i.e.peak strength and rockburst intensity)of the rock was weakened under high-stress real-time drilling and exhibited a downward trend as the drilling diameter increased.The real-time drilling energy dissipation index(ERD)was proposed to characterize the energy relief during high-stress real-time drilling.The ERD exhibited a linear increase with the real-time drilling diameter.Furthermore,the elastic strain energy of post-drilling rock showed a linear relationship with the square of stress across different stress levels,which also applied to the peak elastic strain energy and the square of peak stress.This findingreveals the intrinsic link between the weakening effect of peak elastic strain energy and peak strength due to high-stress real-time drilling,confirmingthe consistency between energy relief and pressure relief effects.By establishing relationships among rockburst proneness,peak elastic strain energy,and peak strength,it was demonstrated that high-stress real-time drilling reduces rockburst proneness through energy dissipation.Specifically,both peak elastic strain energy and rockburst proneness decreased with larger drill bit diameters,consistent with reductions in peak strength,rockburst intensity,and fractal dimensions of high-stress real-time drilled rock.These results validate the energy relief mechanism of real-time drilling in mitigating rockburst risks.
基金support of the National Natural Science Foundation of China(No.52274176)the Guangdong Province Key Areas R&D Program(No.2022B0101070001)+5 种基金Chongqing Elite Innovation and Entrepreneurship Leading talent Project(No.CQYC20220302517)the Chongqing Natural Science Foundation Innovation and Development Joint Fund(No.CSTB2022NSCQ-LZX0079)the National Key Research and Development Program Young Scientists Project(No.2022YFC2905700)the Chongqing Municipal Education Commission“Shuangcheng Economic Circle Construction in Chengdu-Chongqing Area”Science and Technology Innovation Project(No.KJCX2020031)the Fundamental Research Funds for the Central Universities(No.2024CDJGF-009)the Key Project for Technological Innovation and Application Development in Chongqing(No.CSTB2025TIAD-KPX0029).
文摘An innovative real-time monitoring method for surrounding rock damage based on microseismic time-lapse double-difference tomography is proposed for delayed dynamic damage identification and insufficient detection of adverse geological conditions in deep-buried tunnel construction.The installation techniques for microseismic sensors were optimized by mounting sensors at bolt ends which significantly improves signal-to-noise ratio(SNR)and anti-interference capability compared to conventional borehole placement.Subsequently,a 3D wave velocity evolution model that incorporates construction-induced disturbances was established,enabling the first visualization of spatiotemporal variations in surrounding rock wave velocity.It finds significant wave velocity reduction near the tunnel face,with roof and floor damage zones extending 40–50 m;wave velocities approaching undisturbed levels at 15 m ahead of the working face and on the laterally undisturbed side;pronounced spatial asymmetry in wave velocity distribution—values on the left side exceed those on the right,with a clear stress concentration or transition zone located 10–15 m;and systematically lower velocities behind the face than in front,indicating asymmetric rock damage development.These results provide essential theoretical support and practical guidance for optimizing dynamic construction strategies,enabling real-time adjustment of support parameters,and establishing safety early warning systems in deep-buried tunnel engineering.
文摘Online examinations have become a dominant assessment mode,increasing concerns over academic integrity.To address the critical challenge of detecting cheating behaviours,this study proposes a hybrid deep learning approach that combines visual detection and temporal behaviour classification.The methodology utilises object detection models—You Only Look Once(YOLOv12),Faster Region-based Convolutional Neural Network(RCNN),and Single Shot Detector(SSD)MobileNet—integrated with classification models such as Convolutional Neural Networks(CNN),Bidirectional Gated Recurrent Unit(Bi-GRU),and CNN-LSTM(Long Short-Term Memory).Two distinct datasets were used:the Online Exam Proctoring(EOP)dataset from Michigan State University and the School of Computer Science,Duy Tan Unievrsity(SCS-DTU)dataset collected in a controlled classroom setting.A diverse set of cheating behaviours,including book usage,unauthorised interaction,internet access,and mobile phone use,was categorised.Comprehensive experiments evaluated the models based on accuracy,precision,recall,training time,inference speed,and memory usage.We evaluate nine detector-classifier pairings under a unified budget and score them via a calibrated harmonic mean of detection and classification accuracies,enabling deployment-oriented selection under latency and memory constraints.Macro-Precision/Recall/F1 and Receiver Operating Characteristic-Area Under the Curve(ROC-AUC)are reported for the top configurations,revealing consistent advantages of object-centric pipelines for fine-grained cheating cues.The highest overall score is achieved by YOLOv12+CNN(97.15%accuracy),while SSD-MobileNet+CNN provides the best speed-efficiency trade-off for edge devices.This research provides valuable insights into selecting and deploying appropriate deep learning models for maintaining exam integrity under varying resource constraints.
基金supported by the National Natural Science Foundation of China(Grant No.62403486)。
文摘The highly dynamic nature,strong uncertainty,and coupled multiple safety constraints inherent in carrier aircraft recovery operations pose severe challenges for real-time decision-making.Addressing bolter scenarios,this study proposes an intelligent decision-making framework based on a deep long short-term memory Q-network.This framework transforms the real-time sequencing for bolter recovery problem into a partially observable Markov decision process.It employs a stacked long shortterm memory network to accurately capture the long-range temporal dependencies of bolter event chains and fuel consumption.Furthermore,it integrates a prioritized experience replay training mechanism to construct a safe and adaptive scheduling system capable of millisecond-level real-time decision-making.Experimental demonstrates that,within large-scale mass recovery scenarios,the framework achieves zero safety violations in static environments and maintains a fuel safety violation rate below 10%in dynamic scenarios,with single-step decision times at the millisecond level.The model exhibits strong generalization capability,effectively responding to unforeseen emergent situations—such as multiple bolters and fuel emergencies—without requiring retraining.This provides robust support for efficient carrier-based aircraft recovery operations.
文摘Given the different energy rates of multiple types of power generation units,different operation plans affect the economy of microgrids.Limited by load and power generation forecasting technologies,the economic superiority of day-ahead plans is unable to be fully utilized because of the fluctuation of loads and power sources.In this regard,a two-stage correction strategy-based real-time dispatch method for the economic operation of microgrids is proposed.Based on the optimal day-ahead economic operation plan,unbalanced power is validly allocated in two stages in terms of power increment and current power,which maintains the economy of the day-ahead plan.Further,for operating point offset during real-time correction,a rolling dispatch method is introduced to dynamically update the system operation plan.Finally,the results verify the effectiveness of the proposed method.
基金This work was supported by the National Natural Science Foundation of China(No.51761145106)the Guangdong Provincial Natural Science Foundation of China(No.2018B030306041)+1 种基金the Fundamental Research Funds for the Central Universities(No.2019SJ01)the China Scholarship Council(No.201806155019).
文摘This paper proposes a dynamic-decision-based realtime dispatch method to coordinate the economic objective with multiple types of security dispatch objectives while reducing constraint violations in the process of adjusting the system operation point to the optimum.In each decision moment,the following tasks are executed in turn:①locally linearizing the system model at the current operation point with the online model identification by using measurements;②narrowing down the gaps between unsatisfied security requirements and their security thresholds in order of priority;③minimizing the generation cost;④minimizing the security indicators within their security thresholds.Compared with the existing real-time dispatch strategies,the proposed method can adjust the deviations caused by unpredictable power flow fluctuations,avoid dispatch bias caused by model parameter errors,and reduce the constraint violations in the dispatch decision process.The effectiveness of the proposed method is verified with the IEEE 39-bus system.
基金supported by the National Natural Science Foundation of China (No. 51907025)Fundamental Research Funds for the Central Universities。
文摘This paper develops a segmented real-time dispatch model for power-gas integrated systems(PGISs), where power-to-gas(P2G) devices and traditional automatic generation control units are cooperated to manage wind power uncertainty. To improve the economics of the real-time dispatch in regard to the current high operation cost of P2Gs, the wind power uncertainty set is divided into several segments, and a segmented linear decision rule is developed, which assigns adjustment tasks differently when wind power uncertainty falls into different segments. Thus, the P2G operation with high costs can be reduced in real-time adjustment. Besides, a novel segmented stochastic robust optimization is proposed to improve the efficiency and robustness of PGIS dispatch under wind power uncertainty, which minimizes the expected cost under the empirical wind power distribution and builds up the security constraints based on the robust optimization. The expected cost is formulated using a Nataf conversion-based multi-point estimate method, and the optimal number of estimate points is determined through sensitivity analysis. Furthermore, a difference-ofconvex optimization with a partial relaxation rule is developed to solve the non-convex dispatch problem in a sequential optimization framework. Numerical simulations in two testing cases validate the effectiveness of the proposed model and solving method.
文摘In order to recover ore as much as possible, a computer-controlled truck real-time dispatching model is conducted under the conditions of Qidashan lron Mine. It can not only acquire the optimization of shovel and truck operation, but also satisfy requirements of blending ores.The simulation results indicate the effectiveness of the model developed.
基金This work was supported in part by the Science and Technology Program of Guangzhou under Grant 201904010215the State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources under Grant LAPS19011the Fundamental Research Funds for the Central Universities.
文摘A large amount of renewable energy generation(REG)has been integrated into power systems,challenging the operational security of power networks.In a real-time dispatch,system operators need to estimate the ability of the power network to accommodate REG with a limited reserve capacity.The real-time dispatchable region(RTDR)is defined as the largest range of a power injection that the power network can accommodate in a certain dispatch interval for a given dispatch base point.State-of-the-art research on the RTDR adopts a DC power flow model regardless of the voltage profiles and reactive power,which can overlook potentially insecure operational states of the system.To address this issue,this paper proposes an AC power flow based RTDR model simultaneously considering the reactive power and voltage profiles constraints.The nonlinear constraints in our model are approximated using a linear power flow model together with a polytope approximation technique for quadratic constraints.An adaptive constraint generation algorithm is used to calculate the RTDR.Simulation results using the IEEE 5-bus and 30-bus systems illustrate the advantages of the proposed model.
基金Supported by National Natural Science Foundation of China(Grant No.51275307)
文摘As a key to improve the performance of the interbay automated material handling system （AMHS） in 300 mm semiconductor wafer fabrication system, the real- time overhead hoist transport （OHT） dispatching problem has received much attention. This problem is first formu- lated as a special form of assignment problem and it is proved that more than one solution will be obtained by Hungarian algorithm simultaneously. Through proposing and strictly proving two propositions related to the char- acteristics of these solutions, a modified Hungarian algo- rithm is designed to distinguish these solutions. Finally, a new real-time OHT dispatching method is carefully designed by implementing the solution obtained by the modified Hungarian algorithm. The experimental results of discrete event simulations show that, compared with con- ventional Hungarian algorithm dispatching method, the proposed dispatching method that chooses the solution with the maximum variance respectively reduces on average 4 s of the average waiting time and average lead time of wafer lots, and its performance is rather stable in multiple dif- ferent scenarios of the interbay AMHS with different quantities of shortcuts. This research provides an efficient real-time OHT dispatching mechanism for the interbay AMHS with shortcuts and bypasses.
基金the financial support of the Natural Science Foundation of Hubei Province,China (Grant No.2022CFB770)。
文摘In the foundry industries,process design has traditionally relied on manuals and complex theoretical calculations.With the advent of 3D design in casting,computer-aided design(CAD)has been applied to integrate the features of casting process,thereby expanding the scope of design options.These technologies use parametric model design techniques for rapid component creation and use databases to access standard process parameters and design specifications.However,3D models are currently still created through inputting or calling parameters,which requires numerous verifications through calculations to ensure the design rationality.This process may be significantly slowed down due to repetitive modifications and extended design time.As a result,there are increasingly urgent demands for a real-time verification mechanism to address this issue.Therefore,this study proposed a novel closed-loop model and software development method that integrated contextual design with real-time verification,dynamically verifying relevant rules for designing 3D casting components.Additionally,the study analyzed three typical closed-loop scenarios of agile design in an independent developed intelligent casting process system.It is believed that foundry industries can potentially benefit from favorably reduced design cycles to yield an enhanced competitive product market.
基金support of the Natural Science Foundation of Jiangsu Province,China(BK20240977)the China Scholarship Council(201606850024)+1 种基金the National High Technology Research and Development Program of China(2016YFD0701003)the Postgraduate Research&Practice Innovation Program of Jiangsu Province,China(SJCX23_1488)。
文摘Deep learning-based intelligent recognition algorithms are increasingly recognized for their potential to address the labor-intensive challenge of manual pest detection.However,their deployment on mobile devices has been constrained by high computational demands.Here,we developed GBiDC-PEST,a mobile application that incorporates an improved,lightweight detection algorithm based on the You Only Look Once(YOLO)series singlestage architecture,for real-time detection of four tiny pests(wheat mites,sugarcane aphids,wheat aphids,and rice planthoppers).GBiDC-PEST incorporates several innovative modules,including GhostNet for lightweight feature extraction and architecture optimization by reconstructing the backbone,the bi-directional feature pyramid network(BiFPN)for enhanced multiscale feature fusion,depthwise convolution(DWConv)layers to reduce computational load,and the convolutional block attention module(CBAM)to enable precise feature focus.The newly developed GBiDC-PEST was trained and validated using a multitarget agricultural tiny pest dataset(Tpest-3960)that covered various field environments.GBiDC-PEST(2.8 MB)significantly reduced the model size to only 20%of the original model size,offering a smaller size than the YOLO series(v5-v10),higher detection accuracy than YOLOv10n and v10s,and faster detection speed than v8s,v9c,v10m and v10b.In Android deployment experiments,GBiDCPEST demonstrated enhanced performance in detecting pests against complex backgrounds,and the accuracy for wheat mites and rice planthoppers was improved by 4.5-7.5%compared with the original model.The GBiDC-PEST optimization algorithm and its mobile deployment proposed in this study offer a robust technical framework for the rapid,onsite identification and localization of tiny pests.This advancement provides valuable insights for effective pest monitoring,counting,and control in various agricultural settings.
