Complex road conditions without signalized intersections when the traffic flow is nearly saturated result in high traffic congestion and accidents,reducing the traffic efficiency of intelligent vehicles.The complex ro...Complex road conditions without signalized intersections when the traffic flow is nearly saturated result in high traffic congestion and accidents,reducing the traffic efficiency of intelligent vehicles.The complex road traffic environment of smart vehicles and other vehicles frequently experiences conflicting start and stop motion.The fine-grained scheduling of autonomous vehicles(AVs)at non-signalized intersections,which is a promising technique for exploring optimal driving paths for both assisted driving nowadays and driverless cars in the near future,has attracted significant attention owing to its high potential for improving road safety and traffic efficiency.Fine-grained scheduling primarily focuses on signalized intersection scenarios,as applying it directly to non-signalized intersections is challenging because each AV can move freely without traffic signal control.This may cause frequent driving collisions and low road traffic efficiency.Therefore,this study proposes a novel algorithm to address this issue.Our work focuses on the fine-grained scheduling of automated vehicles at non-signal intersections via dual reinforced training(FS-DRL).For FS-DRL,we first use a grid to describe the non-signalized intersection and propose a convolutional neural network(CNN)-based fast decision model that can rapidly yield a coarse-grained scheduling decision for each AV in a distributed manner.We then load these coarse-grained scheduling decisions onto a deep Q-learning network(DQN)for further evaluation.We use an adaptive learning rate to maximize the reward function and employ parameterεto tradeoff the fast speed of coarse-grained scheduling in the CNN and optimal fine-grained scheduling in the DQN.In addition,we prove that using this adaptive learning rate leads to a converged loss rate with an extremely small number of training loops.The simulation results show that compared with Dijkstra,RNN,and ant colony-based scheduling,FS-DRL yields a high accuracy of 96.5%on the sample,with improved performance of approximately 61.54%-85.37%in terms of the average conflict and traffic efficiency.展开更多
This paper introduces autonomous driving image perception technology,including deep learning models(such as CNN and RNN)and their applications,analyzing the limitations of traditional algorithms.It elaborates on the s...This paper introduces autonomous driving image perception technology,including deep learning models(such as CNN and RNN)and their applications,analyzing the limitations of traditional algorithms.It elaborates on the shortcomings of Faster R-CNN and YOLO series models,proposes various improvement techniques such as data fusion,attention mechanisms,and model compression,and introduces relevant datasets,evaluation metrics,and testing frameworks to demonstrate the advantages of the improved models.展开更多
This study proposes an automatic control system for Autonomous Underwater Vehicle(AUV)docking,utilizing a digital twin(DT)environment based on the HoloOcean platform,which integrates six-degree-of-freedom(6-DOF)motion...This study proposes an automatic control system for Autonomous Underwater Vehicle(AUV)docking,utilizing a digital twin(DT)environment based on the HoloOcean platform,which integrates six-degree-of-freedom(6-DOF)motion equations and hydrodynamic coefficients to create a realistic simulation.Although conventional model-based and visual servoing approaches often struggle in dynamic underwater environments due to limited adaptability and extensive parameter tuning requirements,deep reinforcement learning(DRL)offers a promising alternative.In the positioning stage,the Twin Delayed Deep Deterministic Policy Gradient(TD3)algorithm is employed for synchronized depth and heading control,which offers stable training,reduced overestimation bias,and superior handling of continuous control compared to other DRL methods.During the searching stage,zig-zag heading motion combined with a state-of-the-art object detection algorithm facilitates docking station localization.For the docking stage,this study proposes an innovative Image-based DDPG(I-DDPG),enhanced and trained in a Unity-MATLAB simulation environment,to achieve visual target tracking.Furthermore,integrating a DT environment enables efficient and safe policy training,reduces dependence on costly real-world tests,and improves sim-to-real transfer performance.Both simulation and real-world experiments were conducted,demonstrating the effectiveness of the system in improving AUV control strategies and supporting the transition from simulation to real-world operations in underwater environments.The results highlight the scalability and robustness of the proposed system,as evidenced by the TD3 controller achieving 25%less oscillation than the adaptive fuzzy controller when reaching the target depth,thereby demonstrating superior stability,accuracy,and potential for broader and more complex autonomous underwater tasks.展开更多
While autonomous vehicles are vital components of intelligent transportation systems,ensuring the trustworthiness of decision-making remains a substantial challenge in realizing autonomous driving.Therefore,we present...While autonomous vehicles are vital components of intelligent transportation systems,ensuring the trustworthiness of decision-making remains a substantial challenge in realizing autonomous driving.Therefore,we present a novel robust reinforcement learning approach with safety guarantees to attain trustworthy decision-making for autonomous vehicles.The proposed technique ensures decision trustworthiness in terms of policy robustness and collision safety.Specifically,an adversary model is learned online to simulate the worst-case uncertainty by approximating the optimal adversarial perturbations on the observed states and environmental dynamics.In addition,an adversarial robust actor-critic algorithm is developed to enable the agent to learn robust policies against perturbations in observations and dynamics.Moreover,we devise a safety mask to guarantee the collision safety of the autonomous driving agent during both the training and testing processes using an interpretable knowledge model known as the Responsibility-Sensitive Safety Model.Finally,the proposed approach is evaluated through both simulations and experiments.These results indicate that the autonomous driving agent can make trustworthy decisions and drastically reduce the number of collisions through robust safety policies.展开更多
Reinforcement Learning(RL)algorithms enhance intelligence of air combat AutonomousManeuver Decision(AMD)policy,but they may underperform in target combat environmentswith disturbances.To enhance the robustness of the ...Reinforcement Learning(RL)algorithms enhance intelligence of air combat AutonomousManeuver Decision(AMD)policy,but they may underperform in target combat environmentswith disturbances.To enhance the robustness of the AMD strategy learned by RL,thisstudy proposes a Tube-based Robust RL(TRRL)method.First,this study introduces a tube todescribe reachable trajectories under disturbances,formulates a method for calculating tubes basedon sum-of-squares programming,and proposes the TRRL algorithm that enhances robustness byutilizing tube size as a quantitative indicator.Second,this study introduces offline techniques forregressing the tube size function and establishing a tube library before policy learning,aiming toeliminate complex online tube solving and reduce the computational burden during training.Furthermore,an analysis of the tube library demonstrates that the mitigated AMD strategy achievesgreater robustness,as smaller tube sizes correspond to more cautious actions.This finding highlightsthat TRRL enhances robustness by promoting a conservative policy.To effectively balanceaggressiveness and robustness,the proposed TRRL algorithm introduces a“laziness factor”as aweight of robustness.Finally,combat simulations in an environment with disturbances confirm thatthe AMD policy learned by the TRRL algorithm exhibits superior air combat performance comparedto selected robust RL baselines.展开更多
Background:With the continuous development of information technology,most universities use mobile teaching platforms for classroom teaching.With the help of the national virtual simulation experimental teaching projec...Background:With the continuous development of information technology,most universities use mobile teaching platforms for classroom teaching.With the help of the national virtual simulation experimental teaching project sharing platform,students can enhance self-directed learning through the virtual simulation operations of the project.Purpose:To explore the application of virtual simulation experiment in teaching the fundamentals of nursing practice based on the Platform of the National Virtual Simulation Experiment Teaching Project during the COVID-19 pandemic analyze the impact of this teaching method on the autonomous learning ability of undergraduate nursing students.Methods:Convenience sampling was used to select 121 nursing undergraduates from Y University’s School of Nursing;the online teaching of fundamentals of nursing practice was conducted to the students.After taking the course,questionnaires were distributed to the undergraduate nursing students to collect their perceptions regarding the use of the virtual simulation experiment platform and autonomous learning competencies.Results:Most students expressed their preference for the virtual simulation teaching platform,and their satisfaction with the project evaluation was high 83.05%.They hoped to promote the application in future experimental teaching.Undergraduate nursing students believed that the virtual simulation teaching platform was conducive to cultivating clinical thinking ability,could stimulate learning interest,enhanced autonomous learning competencies.Conclusion:During the pandemic,the virtual simulation teaching platform for a lecture on in nursing education has achieved good results in both the aspects of teaching and student learning.Teachers efficiently used their training time and reduced their teaching burden.Moreover,the laboratory cost was also reduced.For undergraduate nursing students,the system was conducive to cultivating clinical thinking ability,stimulating their interest in learning,enhancing their learning and comprehension abilities and learning initiative.展开更多
This paper intends to promote a college English autonomous teaching and learning approach by introducing the whole process of its implementation and feedback from the learners. The theoretical and practical framework ...This paper intends to promote a college English autonomous teaching and learning approach by introducing the whole process of its implementation and feedback from the learners. The theoretical and practical framework of this approach is： with multiple autonomous learning research and practice models as its core, with process syllabus as its guidance, with task-based teaching as its essential principle, with group cooperation and reciprocal learning as its means, with extracurricular activities, online learning and self-access center as its learning environment, with formative assessment system as its guarantee and with cultivation of learners＇ comprehensive English practical skills and autonomy as its goal. Through this approach, we provide the learners with a favorable learning environment where they can learn by themselves and learn by reflection and practice so that they can learn how to learn and how to behave and how to survive.展开更多
Based on the literature review about autonomous learning,the study put forward four steps for using TED to enhance student autonomous learning,which are preparation,activity design,presentation and evaluation. By doin...Based on the literature review about autonomous learning,the study put forward four steps for using TED to enhance student autonomous learning,which are preparation,activity design,presentation and evaluation. By doing so,both teachers and students can achieve their teaching and learning objectives.展开更多
To solve problems of poor security guarantee and insufficient training efficiency in the conventional reinforcement learning methods for decision-making,this study proposes a hybrid framework to combine deep reinforce...To solve problems of poor security guarantee and insufficient training efficiency in the conventional reinforcement learning methods for decision-making,this study proposes a hybrid framework to combine deep reinforcement learning with rule-based decision-making methods.A risk assessment model for lane-change maneuvers considering uncertain predictions of surrounding vehicles is established as a safety filter to improve learning efficiency while correcting dangerous actions for safety enhancement.On this basis,a Risk-fused DDQN is constructed utilizing the model-based risk assessment and supervision mechanism.The proposed reinforcement learning algorithm sets up a separate experience buffer for dangerous trials and punishes such actions,which is shown to improve the sampling efficiency and training outcomes.Compared with conventional DDQN methods,the proposed algorithm improves the convergence value of cumulated reward by 7.6%and 2.2%in the two constructed scenarios in the simulation study and reduces the number of training episodes by 52.2%and 66.8%respectively.The success rate of lane change is improved by 57.3%while the time headway is increased at least by 16.5%in real vehicle tests,which confirms the higher training efficiency,scenario adaptability,and security of the proposed Risk-fused DDQN.展开更多
Nowadays, English as a world language becomes more and more important. Consequently, English learning becomes more and more popular. As we know, an important object for English learners is to improve their communicati...Nowadays, English as a world language becomes more and more important. Consequently, English learning becomes more and more popular. As we know, an important object for English learners is to improve their communicative competence. So autonomous learning is a good way to improve communicative competence. In this paper, two terms, autonomous learning and communicative competence, and their relationship will be introduced from the perspective of English learning. Autonomous learning is self-managed learning, which is contrary to passive learning and mechanical learning, according to intrinsic property of language learning. Communicative competence is a concept introduced by Dell Hymes and is discussed and refined by many other linguists. According to Hymes, communicative competence is the ability not only to apply the grammatical rules of language in order to form grammatically correct sentences but also to know when and where to use these sentences and to whom. Communicative competence includes 4 aspects: Possibility, feasibility, appropriateness and performance. Improving communicative competence is the result of autonomous learning, autonomous learning is the motivation of improving communicative competence. English, of course, is a bridge connecting China to the world, and fostering students'communicative competence through autonomous learning is the vital element of improving English learning in China.展开更多
The thesis introduces a comparative study of students'autonomous listening practice in a web-based autonomous learning center and the traditional teacher-dominated listening practice in a traditional language lab....The thesis introduces a comparative study of students'autonomous listening practice in a web-based autonomous learning center and the traditional teacher-dominated listening practice in a traditional language lab.The purpose of the study is to find how students'listening strategies differ in these two approaches and thereby to find which one better facilitates students'listening proficiency.展开更多
The paper, with the backdrop of web-based autonomous learning put forward by the recent college English teaching reform, aims to explore teachers＇ roles in this learning process in students＇ perception through the m...The paper, with the backdrop of web-based autonomous learning put forward by the recent college English teaching reform, aims to explore teachers＇ roles in this learning process in students＇ perception through the means of questionnaires and interviews. It further analyzes the possible reasons why students perceive their teachers＇ roles in such a way, in the hope of providing some implications for web-based college English autonomous learning.展开更多
Autonomous learning is one of the objectives of multi-media college English teaching. On basis of the test of students＇ autonomous learning ability and the analysis of the results, this paper attempts to explore the ...Autonomous learning is one of the objectives of multi-media college English teaching. On basis of the test of students＇ autonomous learning ability and the analysis of the results, this paper attempts to explore the feasibility of fostering the autonomous learning ability in college English teaching.展开更多
Autonomous study emphasizes the learner's initiative,enthusiasm and creativity.In all fields of education,there is growing emphasis on "learner-centered" teaching methods and the ability of learner auton...Autonomous study emphasizes the learner's initiative,enthusiasm and creativity.In all fields of education,there is growing emphasis on "learner-centered" teaching methods and the ability of learner autonomy.Many experts and scholars have found that learning strategies plays an important role in English language learning,but the importance of affective strategy use in English learning is often ignored by people.Therefore,this paper focuses on the frequencies of affective strategies use in English learning and their relationships so as to enable college students to use positive affective strategies effectively to improve their autonomous learning ability.展开更多
Self-Explaining Autonomous Systems(SEAS)have emerged as a strategic frontier within Artificial Intelligence(AI),responding to growing demands for transparency and interpretability in autonomous decisionmaking.This stu...Self-Explaining Autonomous Systems(SEAS)have emerged as a strategic frontier within Artificial Intelligence(AI),responding to growing demands for transparency and interpretability in autonomous decisionmaking.This study presents a comprehensive bibliometric analysis of SEAS research published between 2020 and February 2025,drawing upon 1380 documents indexed in Scopus.The analysis applies co-citation mapping,keyword co-occurrence,and author collaboration networks using VOSviewer,MASHA,and Python to examine scientific production,intellectual structure,and global collaboration patterns.The results indicate a sustained annual growth rate of 41.38%,with an h-index of 57 and an average of 21.97 citations per document.A normalized citation rate was computed to address temporal bias,enabling balanced evaluation across publication cohorts.Thematic analysis reveals four consolidated research fronts:interpretability in machine learning,explainability in deep neural networks,transparency in generative models,and optimization strategies in autonomous control.Author co-citation analysis identifies four distinct research communities,and keyword evolution shows growing interdisciplinary links with medicine,cybersecurity,and industrial automation.The United States leads in scientific output and citation impact at the geographical level,while countries like India and China show high productivity with varied influence.However,international collaboration remains limited at 7.39%,reflecting a fragmented research landscape.As discussed in this study,SEAS research is expanding rapidly yet remains epistemologically dispersed,with uneven integration of ethical and human-centered perspectives.This work offers a structured and data-driven perspective on SEAS development,highlights key contributors and thematic trends,and outlines critical directions for advancing responsible and transparent autonomous systems.展开更多
Enhancing Autonomous Decision-Making (ADM) for unmanned combat aerial vehicle formations in beyond-visual-range air combat is pivotal for future battlefields, whereas the predominant reinforcement learning technique f...Enhancing Autonomous Decision-Making (ADM) for unmanned combat aerial vehicle formations in beyond-visual-range air combat is pivotal for future battlefields, whereas the predominant reinforcement learning technique for ADM has been proven to be inadequately fitting complex tactical Unit Coordination (UC), limiting the integrity of decision-making for formations. This study proposes a knowledge-enhanced ADM method, with a focus on UC, to elevate formation combat effectiveness. The main innovation is integrating data mining technique with tactical knowledge mining and integration. Foremost, based on Frequent Event Arrangement Mining (FEAM) theory, a cross-channel UC knowledge mining method is designed by introducing data flow, which is capable of capturing dynamic coordinative action sequences. Then, a dual-mode knowledge integration method is proposed by employing the Graph Attention Network (GAT) and attenuated structural similarity, bolstering the interplay between autonomous UC tactics fitting and knowledge injection. The experimental results demonstrate that the algorithm surpasses the existing methods, providing more strategic maneuver trajectories and a win rate of more than 90% in different scenarios. The method is promising to augment the autonomous operational capabilities of unmanned formations and drive the evolution of combat effectiveness.展开更多
The research topic of the author’s PhD dissertation is“The Impact of Motivation Cultivation on English Autonomous Learning among University Students in Hunan,China—A Mediating Role of Learning Strategy.”Within thi...The research topic of the author’s PhD dissertation is“The Impact of Motivation Cultivation on English Autonomous Learning among University Students in Hunan,China—A Mediating Role of Learning Strategy.”Within this topic,three key variables are identified:the dependent variable(DV),the independent variable(IV),and the mediating variable(MV).Specifically,the DV refers to English autonomous learning,the IV refers to motivation,and the MV refers to learning strategy.The research establishes that the MV(learning strategy)is an integral component of information processing theory(IPT).Consequently,the dissertation incorporates integrative and instrumental motivation theories alongside IPT as its foundational theoretical framework.This paper aims to explore the theoretical framework of the PhD dissertation in detail,focusing on the interplay of these three theories.展开更多
Through the research into college students＇ English autonomous learning ability of the non-English major students. That the cause why university students＇ English autonomous learning ability is weak is proved to be ...Through the research into college students＇ English autonomous learning ability of the non-English major students. That the cause why university students＇ English autonomous learning ability is weak is proved to be that they do not value the use of learning strategies. The use of learning strategies can promote the formation and enhancement of autonomous learning ability of the learners. Metacognitive strategy is a high-level management skill which can enable the learners to plan, regulate, monitor and evaluate actively their own learning process. Massive researches have proved whether metacognitive strategy is used successfully or not can directly affect the student learning result. So, it is necessary for teachers to cultivate and train the students to use metacogitive strategy in the university English teaching.展开更多
Ensuring that autonomous vehicles maintain high precision and rapid response capabilities in complex and dynamic driving environments is a critical challenge in the field of autonomous driving.This study aims to enhan...Ensuring that autonomous vehicles maintain high precision and rapid response capabilities in complex and dynamic driving environments is a critical challenge in the field of autonomous driving.This study aims to enhance the learning efficiency ofmulti-sensor feature fusion in autonomous driving tasks,thereby improving the safety and responsiveness of the system.To achieve this goal,we propose an innovative multi-sensor feature fusion model that integrates three distinct modalities:visual,radar,and lidar data.The model optimizes the feature fusion process through the introduction of two novel mechanisms:Sparse Channel Pooling(SCP)and Residual Triplet-Attention(RTA).Firstly,the SCP mechanism enables the model to adaptively filter out salient feature channels while eliminating the interference of redundant features.This enhances the model’s emphasis on critical features essential for decisionmaking and strengthens its robustness to environmental variability.Secondly,the RTA mechanism addresses the issue of feature misalignment across different modalities by effectively aligning key cross-modal features.This alignment reduces the computational overhead associated with redundant features and enhances the overall efficiency of the system.Furthermore,this study incorporates a reinforcement learning module designed to optimize strategies within a continuous action space.By integrating thismodulewith the feature fusion learning process,the entire system is capable of learning efficient driving strategies in an end-to-end manner within the CARLA autonomous driving simulator.Experimental results demonstrate that the proposedmodel significantly enhances the perception and decision-making accuracy of the autonomous driving system in complex traffic scenarios while maintaining real-time responsiveness.This work provides a novel perspective and technical pathway for the application of multi-sensor data fusion in autonomous driving.展开更多
Multiple UAVs cooperative target search has been widely used in various environments,such as emergency rescue and traffic monitoring.However,uncertain communication network among UAVs exhibits unstable links and rapid...Multiple UAVs cooperative target search has been widely used in various environments,such as emergency rescue and traffic monitoring.However,uncertain communication network among UAVs exhibits unstable links and rapid topological fluctuations due to mission complexity and unpredictable environmental states.This limitation hinders timely information sharing and insightful path decisions for UAVs,resulting in inefficient or even failed collaborative search.Aiming at this issue,this paper proposes a multi-UAV cooperative search strategy by developing a real-time trajectory decision that incorporates autonomous connectivity to reinforce multi-UAV collaboration and achieve search acceleration in uncertain search environments.Specifically,an autonomous connectivity strategy based on node cognitive information and network states is introduced to enable effective message transmission and adapt to the dynamic network environment.Based on the fused information,we formalize the trajectory planning as a multiobjective optimization problem by jointly considering search performance and UAV energy harnessing.A multi-agent deep reinforcement learning based algorithm is proposed to solve it,where the reward-guided real-time path is determined to achieve an energyefficient search.Finally,extensive experimental results show that the proposed algorithm outperforms existing works in terms of average search rate and coverage rate with reduced energy consumption under uncertain search environments.展开更多
基金Supported by National Natural Science Foundation of China(Grant No.61803206)Jiangsu Provincial Natural Science Foundation(Grant No.222300420468)Jiangsu Provincial key R&D Program(Grant No.BE2017008-2).
文摘Complex road conditions without signalized intersections when the traffic flow is nearly saturated result in high traffic congestion and accidents,reducing the traffic efficiency of intelligent vehicles.The complex road traffic environment of smart vehicles and other vehicles frequently experiences conflicting start and stop motion.The fine-grained scheduling of autonomous vehicles(AVs)at non-signalized intersections,which is a promising technique for exploring optimal driving paths for both assisted driving nowadays and driverless cars in the near future,has attracted significant attention owing to its high potential for improving road safety and traffic efficiency.Fine-grained scheduling primarily focuses on signalized intersection scenarios,as applying it directly to non-signalized intersections is challenging because each AV can move freely without traffic signal control.This may cause frequent driving collisions and low road traffic efficiency.Therefore,this study proposes a novel algorithm to address this issue.Our work focuses on the fine-grained scheduling of automated vehicles at non-signal intersections via dual reinforced training(FS-DRL).For FS-DRL,we first use a grid to describe the non-signalized intersection and propose a convolutional neural network(CNN)-based fast decision model that can rapidly yield a coarse-grained scheduling decision for each AV in a distributed manner.We then load these coarse-grained scheduling decisions onto a deep Q-learning network(DQN)for further evaluation.We use an adaptive learning rate to maximize the reward function and employ parameterεto tradeoff the fast speed of coarse-grained scheduling in the CNN and optimal fine-grained scheduling in the DQN.In addition,we prove that using this adaptive learning rate leads to a converged loss rate with an extremely small number of training loops.The simulation results show that compared with Dijkstra,RNN,and ant colony-based scheduling,FS-DRL yields a high accuracy of 96.5%on the sample,with improved performance of approximately 61.54%-85.37%in terms of the average conflict and traffic efficiency.
文摘This paper introduces autonomous driving image perception technology,including deep learning models(such as CNN and RNN)and their applications,analyzing the limitations of traditional algorithms.It elaborates on the shortcomings of Faster R-CNN and YOLO series models,proposes various improvement techniques such as data fusion,attention mechanisms,and model compression,and introduces relevant datasets,evaluation metrics,and testing frameworks to demonstrate the advantages of the improved models.
基金supported by the National Science and Technology Council,Taiwan[Grant NSTC 111-2628-E-006-005-MY3]supported by the Ocean Affairs Council,Taiwansponsored in part by Higher Education Sprout Project,Ministry of Education to the Headquarters of University Advancement at National Cheng Kung University(NCKU).
文摘This study proposes an automatic control system for Autonomous Underwater Vehicle(AUV)docking,utilizing a digital twin(DT)environment based on the HoloOcean platform,which integrates six-degree-of-freedom(6-DOF)motion equations and hydrodynamic coefficients to create a realistic simulation.Although conventional model-based and visual servoing approaches often struggle in dynamic underwater environments due to limited adaptability and extensive parameter tuning requirements,deep reinforcement learning(DRL)offers a promising alternative.In the positioning stage,the Twin Delayed Deep Deterministic Policy Gradient(TD3)algorithm is employed for synchronized depth and heading control,which offers stable training,reduced overestimation bias,and superior handling of continuous control compared to other DRL methods.During the searching stage,zig-zag heading motion combined with a state-of-the-art object detection algorithm facilitates docking station localization.For the docking stage,this study proposes an innovative Image-based DDPG(I-DDPG),enhanced and trained in a Unity-MATLAB simulation environment,to achieve visual target tracking.Furthermore,integrating a DT environment enables efficient and safe policy training,reduces dependence on costly real-world tests,and improves sim-to-real transfer performance.Both simulation and real-world experiments were conducted,demonstrating the effectiveness of the system in improving AUV control strategies and supporting the transition from simulation to real-world operations in underwater environments.The results highlight the scalability and robustness of the proposed system,as evidenced by the TD3 controller achieving 25%less oscillation than the adaptive fuzzy controller when reaching the target depth,thereby demonstrating superior stability,accuracy,and potential for broader and more complex autonomous underwater tasks.
基金supported in part by the Start-Up Grant-Nanyang Assistant Professorship Grant of Nanyang Technological Universitythe Agency for Science,Technology and Research(A*STAR)under Advanced Manufacturing and Engineering(AME)Young Individual Research under Grant(A2084c0156)+2 种基金the MTC Individual Research Grant(M22K2c0079)the ANR-NRF Joint Grant(NRF2021-NRF-ANR003 HM Science)the Ministry of Education(MOE)under the Tier 2 Grant(MOE-T2EP50222-0002)。
文摘While autonomous vehicles are vital components of intelligent transportation systems,ensuring the trustworthiness of decision-making remains a substantial challenge in realizing autonomous driving.Therefore,we present a novel robust reinforcement learning approach with safety guarantees to attain trustworthy decision-making for autonomous vehicles.The proposed technique ensures decision trustworthiness in terms of policy robustness and collision safety.Specifically,an adversary model is learned online to simulate the worst-case uncertainty by approximating the optimal adversarial perturbations on the observed states and environmental dynamics.In addition,an adversarial robust actor-critic algorithm is developed to enable the agent to learn robust policies against perturbations in observations and dynamics.Moreover,we devise a safety mask to guarantee the collision safety of the autonomous driving agent during both the training and testing processes using an interpretable knowledge model known as the Responsibility-Sensitive Safety Model.Finally,the proposed approach is evaluated through both simulations and experiments.These results indicate that the autonomous driving agent can make trustworthy decisions and drastically reduce the number of collisions through robust safety policies.
文摘Reinforcement Learning(RL)algorithms enhance intelligence of air combat AutonomousManeuver Decision(AMD)policy,but they may underperform in target combat environmentswith disturbances.To enhance the robustness of the AMD strategy learned by RL,thisstudy proposes a Tube-based Robust RL(TRRL)method.First,this study introduces a tube todescribe reachable trajectories under disturbances,formulates a method for calculating tubes basedon sum-of-squares programming,and proposes the TRRL algorithm that enhances robustness byutilizing tube size as a quantitative indicator.Second,this study introduces offline techniques forregressing the tube size function and establishing a tube library before policy learning,aiming toeliminate complex online tube solving and reduce the computational burden during training.Furthermore,an analysis of the tube library demonstrates that the mitigated AMD strategy achievesgreater robustness,as smaller tube sizes correspond to more cautious actions.This finding highlightsthat TRRL enhances robustness by promoting a conservative policy.To effectively balanceaggressiveness and robustness,the proposed TRRL algorithm introduces a“laziness factor”as aweight of robustness.Finally,combat simulations in an environment with disturbances confirm thatthe AMD policy learned by the TRRL algorithm exhibits superior air combat performance comparedto selected robust RL baselines.
基金The research was carried out at the project of Jilin Province Higher Education Society(JGJX2022D61).
文摘Background:With the continuous development of information technology,most universities use mobile teaching platforms for classroom teaching.With the help of the national virtual simulation experimental teaching project sharing platform,students can enhance self-directed learning through the virtual simulation operations of the project.Purpose:To explore the application of virtual simulation experiment in teaching the fundamentals of nursing practice based on the Platform of the National Virtual Simulation Experiment Teaching Project during the COVID-19 pandemic analyze the impact of this teaching method on the autonomous learning ability of undergraduate nursing students.Methods:Convenience sampling was used to select 121 nursing undergraduates from Y University’s School of Nursing;the online teaching of fundamentals of nursing practice was conducted to the students.After taking the course,questionnaires were distributed to the undergraduate nursing students to collect their perceptions regarding the use of the virtual simulation experiment platform and autonomous learning competencies.Results:Most students expressed their preference for the virtual simulation teaching platform,and their satisfaction with the project evaluation was high 83.05%.They hoped to promote the application in future experimental teaching.Undergraduate nursing students believed that the virtual simulation teaching platform was conducive to cultivating clinical thinking ability,could stimulate learning interest,enhanced autonomous learning competencies.Conclusion:During the pandemic,the virtual simulation teaching platform for a lecture on in nursing education has achieved good results in both the aspects of teaching and student learning.Teachers efficiently used their training time and reduced their teaching burden.Moreover,the laboratory cost was also reduced.For undergraduate nursing students,the system was conducive to cultivating clinical thinking ability,stimulating their interest in learning,enhancing their learning and comprehension abilities and learning initiative.
文摘This paper intends to promote a college English autonomous teaching and learning approach by introducing the whole process of its implementation and feedback from the learners. The theoretical and practical framework of this approach is： with multiple autonomous learning research and practice models as its core, with process syllabus as its guidance, with task-based teaching as its essential principle, with group cooperation and reciprocal learning as its means, with extracurricular activities, online learning and self-access center as its learning environment, with formative assessment system as its guarantee and with cultivation of learners＇ comprehensive English practical skills and autonomy as its goal. Through this approach, we provide the learners with a favorable learning environment where they can learn by themselves and learn by reflection and practice so that they can learn how to learn and how to behave and how to survive.
文摘Based on the literature review about autonomous learning,the study put forward four steps for using TED to enhance student autonomous learning,which are preparation,activity design,presentation and evaluation. By doing so,both teachers and students can achieve their teaching and learning objectives.
基金Supported by National Key Research and Development Program of China(Grant No.2022YFE0117100)National Science Foundation of China(Grant No.52102468,52325212)Fundamental Research Funds for the Central Universities。
文摘To solve problems of poor security guarantee and insufficient training efficiency in the conventional reinforcement learning methods for decision-making,this study proposes a hybrid framework to combine deep reinforcement learning with rule-based decision-making methods.A risk assessment model for lane-change maneuvers considering uncertain predictions of surrounding vehicles is established as a safety filter to improve learning efficiency while correcting dangerous actions for safety enhancement.On this basis,a Risk-fused DDQN is constructed utilizing the model-based risk assessment and supervision mechanism.The proposed reinforcement learning algorithm sets up a separate experience buffer for dangerous trials and punishes such actions,which is shown to improve the sampling efficiency and training outcomes.Compared with conventional DDQN methods,the proposed algorithm improves the convergence value of cumulated reward by 7.6%and 2.2%in the two constructed scenarios in the simulation study and reduces the number of training episodes by 52.2%and 66.8%respectively.The success rate of lane change is improved by 57.3%while the time headway is increased at least by 16.5%in real vehicle tests,which confirms the higher training efficiency,scenario adaptability,and security of the proposed Risk-fused DDQN.
文摘Nowadays, English as a world language becomes more and more important. Consequently, English learning becomes more and more popular. As we know, an important object for English learners is to improve their communicative competence. So autonomous learning is a good way to improve communicative competence. In this paper, two terms, autonomous learning and communicative competence, and their relationship will be introduced from the perspective of English learning. Autonomous learning is self-managed learning, which is contrary to passive learning and mechanical learning, according to intrinsic property of language learning. Communicative competence is a concept introduced by Dell Hymes and is discussed and refined by many other linguists. According to Hymes, communicative competence is the ability not only to apply the grammatical rules of language in order to form grammatically correct sentences but also to know when and where to use these sentences and to whom. Communicative competence includes 4 aspects: Possibility, feasibility, appropriateness and performance. Improving communicative competence is the result of autonomous learning, autonomous learning is the motivation of improving communicative competence. English, of course, is a bridge connecting China to the world, and fostering students'communicative competence through autonomous learning is the vital element of improving English learning in China.
文摘The thesis introduces a comparative study of students'autonomous listening practice in a web-based autonomous learning center and the traditional teacher-dominated listening practice in a traditional language lab.The purpose of the study is to find how students'listening strategies differ in these two approaches and thereby to find which one better facilitates students'listening proficiency.
文摘The paper, with the backdrop of web-based autonomous learning put forward by the recent college English teaching reform, aims to explore teachers＇ roles in this learning process in students＇ perception through the means of questionnaires and interviews. It further analyzes the possible reasons why students perceive their teachers＇ roles in such a way, in the hope of providing some implications for web-based college English autonomous learning.
文摘Autonomous learning is one of the objectives of multi-media college English teaching. On basis of the test of students＇ autonomous learning ability and the analysis of the results, this paper attempts to explore the feasibility of fostering the autonomous learning ability in college English teaching.
文摘Autonomous study emphasizes the learner's initiative,enthusiasm and creativity.In all fields of education,there is growing emphasis on "learner-centered" teaching methods and the ability of learner autonomy.Many experts and scholars have found that learning strategies plays an important role in English language learning,but the importance of affective strategy use in English learning is often ignored by people.Therefore,this paper focuses on the frequencies of affective strategies use in English learning and their relationships so as to enable college students to use positive affective strategies effectively to improve their autonomous learning ability.
基金partially funded by the Programa Nacional de Becas y Crédito Educativo of Peru and the Universitat de València,Spain.
文摘Self-Explaining Autonomous Systems(SEAS)have emerged as a strategic frontier within Artificial Intelligence(AI),responding to growing demands for transparency and interpretability in autonomous decisionmaking.This study presents a comprehensive bibliometric analysis of SEAS research published between 2020 and February 2025,drawing upon 1380 documents indexed in Scopus.The analysis applies co-citation mapping,keyword co-occurrence,and author collaboration networks using VOSviewer,MASHA,and Python to examine scientific production,intellectual structure,and global collaboration patterns.The results indicate a sustained annual growth rate of 41.38%,with an h-index of 57 and an average of 21.97 citations per document.A normalized citation rate was computed to address temporal bias,enabling balanced evaluation across publication cohorts.Thematic analysis reveals four consolidated research fronts:interpretability in machine learning,explainability in deep neural networks,transparency in generative models,and optimization strategies in autonomous control.Author co-citation analysis identifies four distinct research communities,and keyword evolution shows growing interdisciplinary links with medicine,cybersecurity,and industrial automation.The United States leads in scientific output and citation impact at the geographical level,while countries like India and China show high productivity with varied influence.However,international collaboration remains limited at 7.39%,reflecting a fragmented research landscape.As discussed in this study,SEAS research is expanding rapidly yet remains epistemologically dispersed,with uneven integration of ethical and human-centered perspectives.This work offers a structured and data-driven perspective on SEAS development,highlights key contributors and thematic trends,and outlines critical directions for advancing responsible and transparent autonomous systems.
文摘Enhancing Autonomous Decision-Making (ADM) for unmanned combat aerial vehicle formations in beyond-visual-range air combat is pivotal for future battlefields, whereas the predominant reinforcement learning technique for ADM has been proven to be inadequately fitting complex tactical Unit Coordination (UC), limiting the integrity of decision-making for formations. This study proposes a knowledge-enhanced ADM method, with a focus on UC, to elevate formation combat effectiveness. The main innovation is integrating data mining technique with tactical knowledge mining and integration. Foremost, based on Frequent Event Arrangement Mining (FEAM) theory, a cross-channel UC knowledge mining method is designed by introducing data flow, which is capable of capturing dynamic coordinative action sequences. Then, a dual-mode knowledge integration method is proposed by employing the Graph Attention Network (GAT) and attenuated structural similarity, bolstering the interplay between autonomous UC tactics fitting and knowledge injection. The experimental results demonstrate that the algorithm surpasses the existing methods, providing more strategic maneuver trajectories and a win rate of more than 90% in different scenarios. The method is promising to augment the autonomous operational capabilities of unmanned formations and drive the evolution of combat effectiveness.
基金Swan College of Central South University of Forestry and Technology Teaching Reform Research Project“The Impact of Teachers’Task-Based Teaching Method on English Interpreting Learning among University Students in Hunan,China”(SWXYJGPJ27)Swan College of Central South University of Forestry and Technology Scientific Research Project“The Impact of Integrative Motivation and Instrumental Motivation on English Autonomous Learning among University Students in Hunan,China--A Mediating Role of Learning Strategy”(SYXY202441)。
文摘The research topic of the author’s PhD dissertation is“The Impact of Motivation Cultivation on English Autonomous Learning among University Students in Hunan,China—A Mediating Role of Learning Strategy.”Within this topic,three key variables are identified:the dependent variable(DV),the independent variable(IV),and the mediating variable(MV).Specifically,the DV refers to English autonomous learning,the IV refers to motivation,and the MV refers to learning strategy.The research establishes that the MV(learning strategy)is an integral component of information processing theory(IPT).Consequently,the dissertation incorporates integrative and instrumental motivation theories alongside IPT as its foundational theoretical framework.This paper aims to explore the theoretical framework of the PhD dissertation in detail,focusing on the interplay of these three theories.
文摘Through the research into college students＇ English autonomous learning ability of the non-English major students. That the cause why university students＇ English autonomous learning ability is weak is proved to be that they do not value the use of learning strategies. The use of learning strategies can promote the formation and enhancement of autonomous learning ability of the learners. Metacognitive strategy is a high-level management skill which can enable the learners to plan, regulate, monitor and evaluate actively their own learning process. Massive researches have proved whether metacognitive strategy is used successfully or not can directly affect the student learning result. So, it is necessary for teachers to cultivate and train the students to use metacogitive strategy in the university English teaching.
文摘Ensuring that autonomous vehicles maintain high precision and rapid response capabilities in complex and dynamic driving environments is a critical challenge in the field of autonomous driving.This study aims to enhance the learning efficiency ofmulti-sensor feature fusion in autonomous driving tasks,thereby improving the safety and responsiveness of the system.To achieve this goal,we propose an innovative multi-sensor feature fusion model that integrates three distinct modalities:visual,radar,and lidar data.The model optimizes the feature fusion process through the introduction of two novel mechanisms:Sparse Channel Pooling(SCP)and Residual Triplet-Attention(RTA).Firstly,the SCP mechanism enables the model to adaptively filter out salient feature channels while eliminating the interference of redundant features.This enhances the model’s emphasis on critical features essential for decisionmaking and strengthens its robustness to environmental variability.Secondly,the RTA mechanism addresses the issue of feature misalignment across different modalities by effectively aligning key cross-modal features.This alignment reduces the computational overhead associated with redundant features and enhances the overall efficiency of the system.Furthermore,this study incorporates a reinforcement learning module designed to optimize strategies within a continuous action space.By integrating thismodulewith the feature fusion learning process,the entire system is capable of learning efficient driving strategies in an end-to-end manner within the CARLA autonomous driving simulator.Experimental results demonstrate that the proposedmodel significantly enhances the perception and decision-making accuracy of the autonomous driving system in complex traffic scenarios while maintaining real-time responsiveness.This work provides a novel perspective and technical pathway for the application of multi-sensor data fusion in autonomous driving.
基金supported by National Natural Science Foundation of China(No.62202449 and No.62472410)National Key Research and Development Program of China(2021YFB2900102)。
文摘Multiple UAVs cooperative target search has been widely used in various environments,such as emergency rescue and traffic monitoring.However,uncertain communication network among UAVs exhibits unstable links and rapid topological fluctuations due to mission complexity and unpredictable environmental states.This limitation hinders timely information sharing and insightful path decisions for UAVs,resulting in inefficient or even failed collaborative search.Aiming at this issue,this paper proposes a multi-UAV cooperative search strategy by developing a real-time trajectory decision that incorporates autonomous connectivity to reinforce multi-UAV collaboration and achieve search acceleration in uncertain search environments.Specifically,an autonomous connectivity strategy based on node cognitive information and network states is introduced to enable effective message transmission and adapt to the dynamic network environment.Based on the fused information,we formalize the trajectory planning as a multiobjective optimization problem by jointly considering search performance and UAV energy harnessing.A multi-agent deep reinforcement learning based algorithm is proposed to solve it,where the reward-guided real-time path is determined to achieve an energyefficient search.Finally,extensive experimental results show that the proposed algorithm outperforms existing works in terms of average search rate and coverage rate with reduced energy consumption under uncertain search environments.
