As the complexity of flight missions continues to increase,sending a timely warning or providing assistance to pilots helps to reduce the probability of operational errors and flight accidents.Monitoring pilots’physi...As the complexity of flight missions continues to increase,sending a timely warning or providing assistance to pilots helps to reduce the probability of operational errors and flight accidents.Monitoring pilots’physiological data,real-time evaluation of mission load is a feasible technical way to achieve this.In this paper,a set of flight tasks including aircraft control,humancomputer interaction and mental arithmetic tests are designed to simulate five mission loads at different flight difficulty levels.A sensitivity analysis method based on a comprehensive test is proposed to select a set of sensitive physiological factors.Then,based on the SVM hierarchical combination classification method,the pilot mission load real-time evaluation model is established.The test results show significant differences in EMG,respiration rate(abdomen),heart rate,blood oxygen saturation,pupil area,fixation duration,number of fixations,and saccades.The high accuracy obtained from experiments proved that the proposed real-time evaluation model is applicable to meet the requirements of real working environments.The findings can provide methodological references for mission load evaluation research in other fields.展开更多
Background:Vision and vision-language foundation models,a subset of advanced artificial intelligence(AI)frameworks,have shown transformative potential in various medical fields.In ophthalmology,these models,particular...Background:Vision and vision-language foundation models,a subset of advanced artificial intelligence(AI)frameworks,have shown transformative potential in various medical fields.In ophthalmology,these models,particularly large language models and vision-based models,have demonstrated great potential to improve diagnostic accuracy,enhance treatment planning,and streamline clinical workflows.However,their deployment in ophthalmology has faced several challenges,particularly regarding generalizability and integration into clinical practice.This systematic review aims to summarize the current evidence on the use of vision and visionlanguage foundation models in ophthalmology,identifying key applications,outcomes,and challenges.Main text:A comprehensive search on PubMed,Web of Science,Scopus,and Google Scholar was conducted to identify studies published between January 2020 and July 2025.Studies were included if they developed or applied foundation models,such as vision-based models and large language models,to clinically relevant ophthalmic applications.A total of 10 studies met the inclusion criteria,covering areas such as retinal diseases,glaucoma,and ocular surface tumor.The primary outcome measures are model performance metrics,integration into clinical workflows,and the clinical utility of the models.Additionally,the review explored the limitations of foundation models,such as the reliance on large datasets,computational resources,and interpretability challenges.The majority of studies demonstrated that foundation models could achieve high diagnostic accuracy,with several reports indicating excellent performance comparable to or exceeding those of experienced clinicians.Foundation models achieved high accuracy rates up to 95%for diagnosing retinal diseases,and similar performances for detecting glaucoma progression.Despite promising results,concerns about algorithmic bias,overfitting,and the need for diverse training data were common.High computational demands,EHR compatibility,and the need for clinician validation also posed challenges.Additionally,model interpretability issues hindered clinician trust and adoption.Conclusions:Vision and vision-language foundation models in ophthalmology show significant potential for advancing diagnostic accuracy and treatment strategies,particularly in retinal diseases,glaucoma,and ocular oncology.However,challenges such as data quality,transparency,and ethical considerations must be addressed.Future research should focus on refining model performance,improving interpretability and generalizability,and exploring strategies for integrating these models into routine clinical practice to maximize their impact in clinical ophthalmology.展开更多
The status of an operator’s situation awareness is one of the critical factors that influence the quality of the missions.Thus the measurement method of the situation awareness status is an important topic to researc...The status of an operator’s situation awareness is one of the critical factors that influence the quality of the missions.Thus the measurement method of the situation awareness status is an important topic to research.So far,there are lots of methods designed for the measurement of situation awareness status,but there is no model that can measure it accurately in real-time,so this work is conducted to deal with such a gap.Firstly,collect the relevant physiological data of operators while they are performing a specific mission,simultaneously,measure their status of situation awareness by using the situation awareness global assessment technique(SAGAT),which is known for accuracy but cannot be used in real-time.And then,after the preprocessing of the raw data,use the physiological data as features,the SAGAT’s results as a label to train a fuzzy cognitive map(FCM),which is an explainable and powerful intelligent model.Also,a hybrid learning algorithm of particle swarm optimization(PSO)and gradient descent is proposed for the FCM training.The final results show that the learned FCM can assess the status of situation awareness accurately in real-time,and the proposed hybrid learning algorithm has better efficiency and accuracy.展开更多
This paper proposes a novel reinforcement learning(RL)architecture for the efficient scheduling and control of the heating,ventilation and air conditioning(HVAC)system in a commercial building while harnessing its de-...This paper proposes a novel reinforcement learning(RL)architecture for the efficient scheduling and control of the heating,ventilation and air conditioning(HVAC)system in a commercial building while harnessing its de-mand response(DR)potentials.With advances in automated building management systems,this can be achieved seamlessly by a smart autonomous RL agent which takes the best action,for example,a change in HVAC temper-ature set point,necessary to change the electricity usage pattern of a building in response to demand response signals,and with minimal thermal comfort impact to customers.Previous research in this area has tackled only individual aspects of the problem using RL.Specifically,due to the challenges in implementing demand response with whole-building models,simpler analytical models which poorly capture reality have been used instead.And where whole-building models are applied,RL is used for HVAC control mainly to achieve energy efficiency goals while demand response is neglected.Thus,in this research,we implement a holistic framework by designing an efficient RL controller for a whole-building model which learns to optimise and control the HVAC system for improved energy efficiency and thermal comfort levels in addition to achieving demand response goals.Our simulation results show that by applying reinforcement learning for normal HVAC operation,a maximum weekly energy reduction of up to 22%can be achieved compared to a handcrafted baseline controller.Furthermore,by employing a DR-aware RL controller during demand response periods,average power reductions or increases of up to 50%can be achieved on a weekly basis compared to the default RL controller,while keeping occupant thermal comfort levels within acceptable bounds.展开更多
Wind energy has been increasingly adopted to mitigate climate change.However,the variability of wind energy causes wind curtailment,resulting in considerable economic losses for wind farm owners.Wind curtailment can b...Wind energy has been increasingly adopted to mitigate climate change.However,the variability of wind energy causes wind curtailment,resulting in considerable economic losses for wind farm owners.Wind curtailment can be reduced using battery energy storage systems(BESS)as onsite backup sources.Yet,this auxiliary role may significantly weaken the economic potential of BESS in energy trading.Ideal BESS scheduling should balance onsite wind curtailment reduction and market bidding,but practical implementation is challenging due to coordination complexity and the stochastic nature of energy prices and wind generation.We investigate the joint-market bidding strategy of a co-located wind-battery system in the spot and Regulation Frequency Control Ancillary Service markets.We propose a novel deep reinforcement learning-based approach that decouples the system’s market participation into two related Markov decision processes for each facility,enabling the BESS to absorb onsite wind curtailment while performing joint-market bidding to maximize overall operational revenues.Using realistic wind farm data,we validated the coordinated bidding strategy,with outcomes surpassing the optimization-based benchmark in terms of higher revenue by approximately 25%and more wind curtailment reduction by 2.3 times.Our results show that joint-market bidding can significantly improve the financial performance of wind-battery systems compared to participating in each market separately.Simulations also show that using curtailed wind generation as a power source for charging the BESS can lead to additional financial gains.The successful implementation of our algorithm would encourage co-location of generation and storage assets to unlock wider system benefits.展开更多
基金co-supported by the Aeronautical Science Foundation of China(No.2020Z023053002)the National Natural Science Foundation of China(No.61305133。
文摘As the complexity of flight missions continues to increase,sending a timely warning or providing assistance to pilots helps to reduce the probability of operational errors and flight accidents.Monitoring pilots’physiological data,real-time evaluation of mission load is a feasible technical way to achieve this.In this paper,a set of flight tasks including aircraft control,humancomputer interaction and mental arithmetic tests are designed to simulate five mission loads at different flight difficulty levels.A sensitivity analysis method based on a comprehensive test is proposed to select a set of sensitive physiological factors.Then,based on the SVM hierarchical combination classification method,the pilot mission load real-time evaluation model is established.The test results show significant differences in EMG,respiration rate(abdomen),heart rate,blood oxygen saturation,pupil area,fixation duration,number of fixations,and saccades.The high accuracy obtained from experiments proved that the proposed real-time evaluation model is applicable to meet the requirements of real working environments.The findings can provide methodological references for mission load evaluation research in other fields.
基金supported by Natural Science Foundation of China(grant number 82201195).
文摘Background:Vision and vision-language foundation models,a subset of advanced artificial intelligence(AI)frameworks,have shown transformative potential in various medical fields.In ophthalmology,these models,particularly large language models and vision-based models,have demonstrated great potential to improve diagnostic accuracy,enhance treatment planning,and streamline clinical workflows.However,their deployment in ophthalmology has faced several challenges,particularly regarding generalizability and integration into clinical practice.This systematic review aims to summarize the current evidence on the use of vision and visionlanguage foundation models in ophthalmology,identifying key applications,outcomes,and challenges.Main text:A comprehensive search on PubMed,Web of Science,Scopus,and Google Scholar was conducted to identify studies published between January 2020 and July 2025.Studies were included if they developed or applied foundation models,such as vision-based models and large language models,to clinically relevant ophthalmic applications.A total of 10 studies met the inclusion criteria,covering areas such as retinal diseases,glaucoma,and ocular surface tumor.The primary outcome measures are model performance metrics,integration into clinical workflows,and the clinical utility of the models.Additionally,the review explored the limitations of foundation models,such as the reliance on large datasets,computational resources,and interpretability challenges.The majority of studies demonstrated that foundation models could achieve high diagnostic accuracy,with several reports indicating excellent performance comparable to or exceeding those of experienced clinicians.Foundation models achieved high accuracy rates up to 95%for diagnosing retinal diseases,and similar performances for detecting glaucoma progression.Despite promising results,concerns about algorithmic bias,overfitting,and the need for diverse training data were common.High computational demands,EHR compatibility,and the need for clinician validation also posed challenges.Additionally,model interpretability issues hindered clinician trust and adoption.Conclusions:Vision and vision-language foundation models in ophthalmology show significant potential for advancing diagnostic accuracy and treatment strategies,particularly in retinal diseases,glaucoma,and ocular oncology.However,challenges such as data quality,transparency,and ethical considerations must be addressed.Future research should focus on refining model performance,improving interpretability and generalizability,and exploring strategies for integrating these models into routine clinical practice to maximize their impact in clinical ophthalmology.
基金supported by the National Natural Science Foundation of China(61305133)the Aeronautical Science Foundation of China grant number 2020Z023053002.
文摘The status of an operator’s situation awareness is one of the critical factors that influence the quality of the missions.Thus the measurement method of the situation awareness status is an important topic to research.So far,there are lots of methods designed for the measurement of situation awareness status,but there is no model that can measure it accurately in real-time,so this work is conducted to deal with such a gap.Firstly,collect the relevant physiological data of operators while they are performing a specific mission,simultaneously,measure their status of situation awareness by using the situation awareness global assessment technique(SAGAT),which is known for accuracy but cannot be used in real-time.And then,after the preprocessing of the raw data,use the physiological data as features,the SAGAT’s results as a label to train a fuzzy cognitive map(FCM),which is an explainable and powerful intelligent model.Also,a hybrid learning algorithm of particle swarm optimization(PSO)and gradient descent is proposed for the FCM training.The final results show that the learned FCM can assess the status of situation awareness accurately in real-time,and the proposed hybrid learning algorithm has better efficiency and accuracy.
基金This research was funded by Australian Renewable Energy Agency(ARENA)as part of ARENA’s Advancing Renewables Program under Grant 2018/ARP017.
文摘This paper proposes a novel reinforcement learning(RL)architecture for the efficient scheduling and control of the heating,ventilation and air conditioning(HVAC)system in a commercial building while harnessing its de-mand response(DR)potentials.With advances in automated building management systems,this can be achieved seamlessly by a smart autonomous RL agent which takes the best action,for example,a change in HVAC temper-ature set point,necessary to change the electricity usage pattern of a building in response to demand response signals,and with minimal thermal comfort impact to customers.Previous research in this area has tackled only individual aspects of the problem using RL.Specifically,due to the challenges in implementing demand response with whole-building models,simpler analytical models which poorly capture reality have been used instead.And where whole-building models are applied,RL is used for HVAC control mainly to achieve energy efficiency goals while demand response is neglected.Thus,in this research,we implement a holistic framework by designing an efficient RL controller for a whole-building model which learns to optimise and control the HVAC system for improved energy efficiency and thermal comfort levels in addition to achieving demand response goals.Our simulation results show that by applying reinforcement learning for normal HVAC operation,a maximum weekly energy reduction of up to 22%can be achieved compared to a handcrafted baseline controller.Furthermore,by employing a DR-aware RL controller during demand response periods,average power reductions or increases of up to 50%can be achieved on a weekly basis compared to the default RL controller,while keeping occupant thermal comfort levels within acceptable bounds.
基金This work has been supported in part by the FIT Academic Funding of Monash University,Australia and the Australian Research Council(ARC)Discovery Early Career Researcher Award(DECRA)under Grant DE230100046.
文摘Wind energy has been increasingly adopted to mitigate climate change.However,the variability of wind energy causes wind curtailment,resulting in considerable economic losses for wind farm owners.Wind curtailment can be reduced using battery energy storage systems(BESS)as onsite backup sources.Yet,this auxiliary role may significantly weaken the economic potential of BESS in energy trading.Ideal BESS scheduling should balance onsite wind curtailment reduction and market bidding,but practical implementation is challenging due to coordination complexity and the stochastic nature of energy prices and wind generation.We investigate the joint-market bidding strategy of a co-located wind-battery system in the spot and Regulation Frequency Control Ancillary Service markets.We propose a novel deep reinforcement learning-based approach that decouples the system’s market participation into two related Markov decision processes for each facility,enabling the BESS to absorb onsite wind curtailment while performing joint-market bidding to maximize overall operational revenues.Using realistic wind farm data,we validated the coordinated bidding strategy,with outcomes surpassing the optimization-based benchmark in terms of higher revenue by approximately 25%and more wind curtailment reduction by 2.3 times.Our results show that joint-market bidding can significantly improve the financial performance of wind-battery systems compared to participating in each market separately.Simulations also show that using curtailed wind generation as a power source for charging the BESS can lead to additional financial gains.The successful implementation of our algorithm would encourage co-location of generation and storage assets to unlock wider system benefits.
