During bipedal walking,it is critical to detect and adjust the robot postures by feedback control to maintain its normal state amidst multi-source random disturbances arising from some unavoidable uncertain factors.Th...During bipedal walking,it is critical to detect and adjust the robot postures by feedback control to maintain its normal state amidst multi-source random disturbances arising from some unavoidable uncertain factors.The radical basis function（RBF）neural network model of a five-link biped robot is established,and two certain disturbances and a randomly uncertain disturbance are then mixed with the optimal torques in the network model to study the performance of the biped robot by several evaluation indices and a specific Poincar′e map.In contrast with the simulations,the response varies as desired under optimal inputting while the output is fluctuating in the situation of disturbance driving.Simulation results from noise inputting also show that the dynamics of the robot is less sensitive to the disturbance of knee joint input of the swing leg than those of the other three joints,the response errors of the biped will be increasing with higher disturbance levels,and especially there are larger output fluctuations in the knee and hip joints of the swing leg.展开更多
In order to solve the problem of limited computational resources of multi-unmanned systems airborne navigation platform,a distributed cooperative positioning method based on confidence evaluation is proposed.Firstly,t...In order to solve the problem of limited computational resources of multi-unmanned systems airborne navigation platform,a distributed cooperative positioning method based on confidence evaluation is proposed.Firstly,the impact of ranging error,priori information,spatial geometric configuration and adjacent nodes count on cooperative positioning performance are analyzed individually.Secondly,a confidence evaluation method for measurement information of adjacent nodes is designed according to the cooperative positioning principle,which comprehensively considers the coupling relationship between influencing factors.Finally,a distributed cooperative navigation filter based on inter-vehicle ranging is designed.Simulation studies show that confidence evaluation method proposed in this paper can effectively characterize the contribution of measurement information to positioning results,and positioning accuracy under the proposed method is improved by more than 15%compared with the traditional screening methods based on optimal geometric configuration and closest distance.展开更多
In this paper, we conduct research on the teaching evaluation system based on the feedback tracking technology under the cloud computing environment. Teaching evaluation is an important work of teaching management dep...In this paper, we conduct research on the teaching evaluation system based on the feedback tracking technology under the cloud computing environment. Teaching evaluation is an important work of teaching management department. According to the traditional mode of work, must first into classroom, through questionnaire survey, fill out a form scale, obtain first-hand teaching evaluation data, and then input the data into the computer, after check again the data statistics and analysis which is not efficient. To optimize the procedure, we use the cloud computing to assist finalizing the general evaluation steps which is innovative and meaningful.展开更多
Classroom evaluation plays a critical role in shaping students’learning experiences,influencing not only their academic performance but also their motivation and engagement.In the context of primary Chinese language ...Classroom evaluation plays a critical role in shaping students’learning experiences,influencing not only their academic performance but also their motivation and engagement.In the context of primary Chinese language learning,oral feedback and written comments are two prevalent evaluation methods.This paper explores how these different types of feedback impact students’motivation,learning outcomes,and participation.By comparing the immediacy of oral feedback with the systematic nature of written comments,this study seeks to provide insights into how educators can utilize classroom evaluations more effectively to foster motivation in Chinese language learners.The findings indicate that both feedback methods have unique strengths,and a balanced approach may optimize learning outcomes.展开更多
Multi-source errors,as critical obstacles limiting the accuracy retention and machining performance of machine tools,hold fundamental and strategic significance for achieving high-precision,high-efficiency,and high-re...Multi-source errors,as critical obstacles limiting the accuracy retention and machining performance of machine tools,hold fundamental and strategic significance for achieving high-precision,high-efficiency,and high-reliability machining in modern manufacturing systems.However,these errors typically exhibit complex characteristics such as strong coupling,time-variance,and nonlinearity,which challenge traditional methods of error identification,modeling,and compensation in terms of adaptability,real-time capability,and integration.Therefore,it is imperative to establish a systematic and intelligent multi-source error control framework.Firstly,this work systematically reviews typical error sources and their evolution mechanisms,evaluates multi-scale detection technologies including laser interferometry,double ball-bar systems,multi-sensor fusion,and vision-based systems,and constructs an intelligent error identification and evaluation framework.Next,it reviews classical modeling methods such as homogeneous transformation matrices,screw theory,thermal equilibrium models,finite element analysis,and modal analysis,compares physical modeling,data-driven,and hybrid modeling strategies,and develops an integrated multi-source error modeling architecture centered on digital twin technology and artificial intelligence.Furthermore,key technologies,including geometric error mapping and real-time compensation,online thermal error prediction and active temperature control,dynamic error suppression,and adaptive control,are summarized.A multi-level integrated error compensation architecture is proposed by combining physical models,data models,and cyber-physical synchronization.This architecture encompasses core processes such as error traceability and decoupling,dynamic prediction,real-time compensation,and closed-loop optimization,emphasizing engineering implementation mechanisms based on cyber-physical collaboration,multi-physics coupling,and multi-scale fusion,thereby effectively enhancing accuracy stability and control robustness under complex operating conditions.Finally,frontier challenges such as constructing high-fidelity coupled models from heterogeneous multi-source data,edge-cloud collaborative control,and cross-platform interoperability are discussed.The application prospects of multi-source error evaluation are also envisioned,providing theoretical foundations and technical support for the precise management and optimization of the entire lifecycle accuracy of machine tools.展开更多
Teaching involves finding out about students'misunderstandings,intervening to change them and creating a context of learning that encourages students to engage with the subject matter.This theory of making student...Teaching involves finding out about students'misunderstandings,intervening to change them and creating a context of learning that encourages students to engage with the subject matter.This theory of making student learning possible is very much concerned with the content of what students have to learn in relation to how it should be taught.Evaluation implies collecting information about our work,interpreting the information and making judgments about which actions we should take to improve practice.Evaluation is an analytical process that is intrinsic to good teaching.展开更多
Accurate multi-source fusion is based on the reliability, quantity, and fusion mode of the sources. The problem of selecting the optimal set for participating in the fusion process is nondeterministic-polynomial-time-...Accurate multi-source fusion is based on the reliability, quantity, and fusion mode of the sources. The problem of selecting the optimal set for participating in the fusion process is nondeterministic-polynomial-time-hard and is neither sub-modular nor super-modular. Furthermore, in the case of the Kalman filter(KF) fusion algorithm, accurate statistical characteristics of noise are difficult to obtain, and this leads to an unsatisfactory fusion result. To settle the referred cases, a distributed and adaptive weighted fusion algorithm based on KF has been proposed in this paper. In this method, on the basis of the pseudo prior probability of the estimated state of each source, the reliability of the sources is evaluated and the optimal set is selected on a certain threshold. Experiments were performed on multi-source pedestrian dead reckoning for verifying the proposed algorithm. The results obtained from these experiments indicate that the optimal set can be selected accurately with minimal computation, and the fusion error is reduced by 16.6% as compared to the corresponding value resulting from the algorithm without improvements.The proposed adaptive source reliability and fusion weight evaluation is effective against the varied-noise multi-source fusion system, and the fusion error caused by inaccurate statistical characteristics of the noise is reduced by the adaptive weight evaluation.The proposed algorithm exhibits good robustness, adaptability,and value on applications.展开更多
Currently,data security and privacy protection are becoming more and more important.Access control is a method of authorization for users through predefined policies.Token-based access control(TBAC)enhances the manage...Currently,data security and privacy protection are becoming more and more important.Access control is a method of authorization for users through predefined policies.Token-based access control(TBAC)enhances the manageability of authorization through the token.However,traditional access control policies lack the ability to dynamically adjust based on user access behavior.Incorporating user reputation evaluation into access control can provide valuable feedback to enhance system security and flexibility.As a result,this paper proposes a blockchain-empowered TBAC system and introduces a user reputation evaluation module to provide feedback on access control.The TBAC system divides the access control process into three stages:policy upload,token request,and resource request.The user reputation evaluation module evaluates the user’s token reputation and resource reputation for the token request and resource request stages of the TBAC system.The proposed system is implemented using the Hyperledger Fabric blockchain.The TBAC system is evaluated to prove that it has high processing performance.The user reputation evaluation model is proved to be more conservative and sensitive by comparative study with other methods.In addition,the security analysis shows that the TBAC system has a certain anti-attack ability and can maintain stable operation under the Distributed Denial of Service(DDoS)attack environment.展开更多
Sentiment analysis attracts the attention of Egyptian Decisionmakers in the education sector.It offers a viable method to assess education quality services based on the students’feedback as well as that provides an u...Sentiment analysis attracts the attention of Egyptian Decisionmakers in the education sector.It offers a viable method to assess education quality services based on the students’feedback as well as that provides an understanding of their needs.As machine learning techniques offer automated strategies to process big data derived from social media and other digital channels,this research uses a dataset for tweets’sentiments to assess a few machine learning techniques.After dataset preprocessing to remove symbols,necessary stemming and lemmatization is performed for features extraction.This is followed by several machine learning techniques and a proposed Long Short-Term Memory(LSTM)classifier optimized by the Salp Swarm Algorithm(SSA)and measured the corresponding performance.Then,the validity and accuracy of commonly used classifiers,such as Support Vector Machine,Logistic Regression Classifier,and Naive Bayes classifier,were reviewed.Moreover,LSTM based on the SSA classification model was compared with Support Vector Machine(SVM),Logistic Regression(LR),and Naive Bayes(NB).Finally,as LSTM based SSA achieved the highest accuracy,it was applied to predict the sentiments of students’feedback and evaluate their association with the course outcome evaluations for education quality purposes.展开更多
This paper deals with the effect of grade entitlement on English programs in Japanese universities. For years, teachers and administrators have noted that even though Japan is one of the highest spending countries on ...This paper deals with the effect of grade entitlement on English programs in Japanese universities. For years, teachers and administrators have noted that even though Japan is one of the highest spending countries on English education, there has not been a commensurate increase in English ability. Most research to explain this disparity has thus far focused on methodology, class size and teacher qualifications. Recent research dealing with academic entitlement at US universities may offer an alternative explanation for some of the lack of success that Japanese universities have experienced. Ellen Greenberger, one of the author＇s of Self-entitled college students： Contributions of personality, parenting and motivational factors, which appeared in 2008 in The Journal of Youth and Adolescence claims that in recent years, the number of students appealing their grades and expecting to be rewarded for effort rather than results has increased. Greenberger＇s paper motivated this study. 200 Japanese students completed a survey that presented a number of grade scenarios. The students were asked, based on test grades and completed assignments, what grade a student should receive in each hypothetical situation. Similarly, full-time and part-time teachers were asked what grade they would give in each situation. This paper explains the different situations, students＇ and teachers＇ responses to the scenarios, and then discusses the implications for English education in Japan.展开更多
当前,大语言模型(Large Language Model,LLM)凭借先进的文本处理能力为智能作文评价提供了优化契机,但其反馈内容质量和语义分析能力仍有不足,而其常出现的主题浅显、润色过度等问题恰暴露其反馈尚无法满足语文作文以“主题、表达”为...当前,大语言模型(Large Language Model,LLM)凭借先进的文本处理能力为智能作文评价提供了优化契机,但其反馈内容质量和语义分析能力仍有不足,而其常出现的主题浅显、润色过度等问题恰暴露其反馈尚无法满足语文作文以“主题、表达”为核心的评价要求,因此有必要评估其反馈质量。为此,文章首先通过文献阅读、专家咨询研发“大语言模型作文评价反馈质量评估标准”,利用10个LLM依据指定作文评分标准对50篇记叙文和50篇议论文分别进行评价。然后,文章基于该标准对LLM反馈进行打分,计算评分者间一致性并对研究结果进行描述性分析与质性总结。研究发现:LLM的反馈在文体稳定性、丰富性和深度理解上有提升空间;LLM的反馈缺乏引导思考、批判评价与判断语言文采的类师能力;LLM善于在主题、材料、结构、感情与思想层面给出反馈;LLM的反馈呈现一定程度的学科专业性。最后,文章根据研究结果提出建议,以期为推动LLM与语文作文评价的深度融合提供参考。展开更多
基金supported by the Science Fund for Creative Research Groups of National Natural Science Foundation of China(51221004)the National Natural Science Foundation of China(11172260,11372270,and 51375434)+2 种基金the Higher School Specialized Research Fund for the Doctoral Program(20110101110016)the Science and technology project of Zhejiang Province(2013C31086)the Fundamental Research Funds forthe Central Universities of China(2013XZZX005)
文摘During bipedal walking,it is critical to detect and adjust the robot postures by feedback control to maintain its normal state amidst multi-source random disturbances arising from some unavoidable uncertain factors.The radical basis function（RBF）neural network model of a five-link biped robot is established,and two certain disturbances and a randomly uncertain disturbance are then mixed with the optimal torques in the network model to study the performance of the biped robot by several evaluation indices and a specific Poincar′e map.In contrast with the simulations,the response varies as desired under optimal inputting while the output is fluctuating in the situation of disturbance driving.Simulation results from noise inputting also show that the dynamics of the robot is less sensitive to the disturbance of knee joint input of the swing leg than those of the other three joints,the response errors of the biped will be increasing with higher disturbance levels,and especially there are larger output fluctuations in the knee and hip joints of the swing leg.
基金supported in part by National Natural Science Foundation of China(Nos.62073163,62103285,62203228)National Defense Basic Research Program(No.JCKY2020605C009)+1 种基金Aeronautic Science Foundation of China(Nos.ASFC-2020Z071052001,202055052003)Foundation Strengthening Program Technology 173 Field Fund(No.2021-JCJQ-JJ-0308)。
文摘In order to solve the problem of limited computational resources of multi-unmanned systems airborne navigation platform,a distributed cooperative positioning method based on confidence evaluation is proposed.Firstly,the impact of ranging error,priori information,spatial geometric configuration and adjacent nodes count on cooperative positioning performance are analyzed individually.Secondly,a confidence evaluation method for measurement information of adjacent nodes is designed according to the cooperative positioning principle,which comprehensively considers the coupling relationship between influencing factors.Finally,a distributed cooperative navigation filter based on inter-vehicle ranging is designed.Simulation studies show that confidence evaluation method proposed in this paper can effectively characterize the contribution of measurement information to positioning results,and positioning accuracy under the proposed method is improved by more than 15%compared with the traditional screening methods based on optimal geometric configuration and closest distance.
文摘In this paper, we conduct research on the teaching evaluation system based on the feedback tracking technology under the cloud computing environment. Teaching evaluation is an important work of teaching management department. According to the traditional mode of work, must first into classroom, through questionnaire survey, fill out a form scale, obtain first-hand teaching evaluation data, and then input the data into the computer, after check again the data statistics and analysis which is not efficient. To optimize the procedure, we use the cloud computing to assist finalizing the general evaluation steps which is innovative and meaningful.
文摘Classroom evaluation plays a critical role in shaping students’learning experiences,influencing not only their academic performance but also their motivation and engagement.In the context of primary Chinese language learning,oral feedback and written comments are two prevalent evaluation methods.This paper explores how these different types of feedback impact students’motivation,learning outcomes,and participation.By comparing the immediacy of oral feedback with the systematic nature of written comments,this study seeks to provide insights into how educators can utilize classroom evaluations more effectively to foster motivation in Chinese language learners.The findings indicate that both feedback methods have unique strengths,and a balanced approach may optimize learning outcomes.
基金financially supported by National Natural Science Foundation of China(Grant Nos.52375447,52305477 and 52105457)the Shandong Provincial Natural Science Foundation of China(Grant Nos.ZR2023QE057,ZR2024QE100 and ZR2024ME255)+2 种基金the Shandong Provincial Science and Technology SMEs Innovation Capacity Improvement Project(Grant No.2024TSGC0239)the Special Fund of Taishan Scholars Project,the Shandong Province Youth Science and Technology Talent Support Project(Grant No.SDAST2024QTA043)the Open Funding of Key Lab of Industrial Fluid Energy Conservation and Pollution Control,Ministry of Education(Grant Nos.CK-2024-0031,CK-2024-0035 and CK-2024-0036).
文摘Multi-source errors,as critical obstacles limiting the accuracy retention and machining performance of machine tools,hold fundamental and strategic significance for achieving high-precision,high-efficiency,and high-reliability machining in modern manufacturing systems.However,these errors typically exhibit complex characteristics such as strong coupling,time-variance,and nonlinearity,which challenge traditional methods of error identification,modeling,and compensation in terms of adaptability,real-time capability,and integration.Therefore,it is imperative to establish a systematic and intelligent multi-source error control framework.Firstly,this work systematically reviews typical error sources and their evolution mechanisms,evaluates multi-scale detection technologies including laser interferometry,double ball-bar systems,multi-sensor fusion,and vision-based systems,and constructs an intelligent error identification and evaluation framework.Next,it reviews classical modeling methods such as homogeneous transformation matrices,screw theory,thermal equilibrium models,finite element analysis,and modal analysis,compares physical modeling,data-driven,and hybrid modeling strategies,and develops an integrated multi-source error modeling architecture centered on digital twin technology and artificial intelligence.Furthermore,key technologies,including geometric error mapping and real-time compensation,online thermal error prediction and active temperature control,dynamic error suppression,and adaptive control,are summarized.A multi-level integrated error compensation architecture is proposed by combining physical models,data models,and cyber-physical synchronization.This architecture encompasses core processes such as error traceability and decoupling,dynamic prediction,real-time compensation,and closed-loop optimization,emphasizing engineering implementation mechanisms based on cyber-physical collaboration,multi-physics coupling,and multi-scale fusion,thereby effectively enhancing accuracy stability and control robustness under complex operating conditions.Finally,frontier challenges such as constructing high-fidelity coupled models from heterogeneous multi-source data,edge-cloud collaborative control,and cross-platform interoperability are discussed.The application prospects of multi-source error evaluation are also envisioned,providing theoretical foundations and technical support for the precise management and optimization of the entire lifecycle accuracy of machine tools.
文摘Teaching involves finding out about students'misunderstandings,intervening to change them and creating a context of learning that encourages students to engage with the subject matter.This theory of making student learning possible is very much concerned with the content of what students have to learn in relation to how it should be taught.Evaluation implies collecting information about our work,interpreting the information and making judgments about which actions we should take to improve practice.Evaluation is an analytical process that is intrinsic to good teaching.
文摘Accurate multi-source fusion is based on the reliability, quantity, and fusion mode of the sources. The problem of selecting the optimal set for participating in the fusion process is nondeterministic-polynomial-time-hard and is neither sub-modular nor super-modular. Furthermore, in the case of the Kalman filter(KF) fusion algorithm, accurate statistical characteristics of noise are difficult to obtain, and this leads to an unsatisfactory fusion result. To settle the referred cases, a distributed and adaptive weighted fusion algorithm based on KF has been proposed in this paper. In this method, on the basis of the pseudo prior probability of the estimated state of each source, the reliability of the sources is evaluated and the optimal set is selected on a certain threshold. Experiments were performed on multi-source pedestrian dead reckoning for verifying the proposed algorithm. The results obtained from these experiments indicate that the optimal set can be selected accurately with minimal computation, and the fusion error is reduced by 16.6% as compared to the corresponding value resulting from the algorithm without improvements.The proposed adaptive source reliability and fusion weight evaluation is effective against the varied-noise multi-source fusion system, and the fusion error caused by inaccurate statistical characteristics of the noise is reduced by the adaptive weight evaluation.The proposed algorithm exhibits good robustness, adaptability,and value on applications.
基金supported by NSFC under Grant No.62341102National Key R&D Program of China under Grant No.2018YFA0701604.
文摘Currently,data security and privacy protection are becoming more and more important.Access control is a method of authorization for users through predefined policies.Token-based access control(TBAC)enhances the manageability of authorization through the token.However,traditional access control policies lack the ability to dynamically adjust based on user access behavior.Incorporating user reputation evaluation into access control can provide valuable feedback to enhance system security and flexibility.As a result,this paper proposes a blockchain-empowered TBAC system and introduces a user reputation evaluation module to provide feedback on access control.The TBAC system divides the access control process into three stages:policy upload,token request,and resource request.The user reputation evaluation module evaluates the user’s token reputation and resource reputation for the token request and resource request stages of the TBAC system.The proposed system is implemented using the Hyperledger Fabric blockchain.The TBAC system is evaluated to prove that it has high processing performance.The user reputation evaluation model is proved to be more conservative and sensitive by comparative study with other methods.In addition,the security analysis shows that the TBAC system has a certain anti-attack ability and can maintain stable operation under the Distributed Denial of Service(DDoS)attack environment.
文摘Sentiment analysis attracts the attention of Egyptian Decisionmakers in the education sector.It offers a viable method to assess education quality services based on the students’feedback as well as that provides an understanding of their needs.As machine learning techniques offer automated strategies to process big data derived from social media and other digital channels,this research uses a dataset for tweets’sentiments to assess a few machine learning techniques.After dataset preprocessing to remove symbols,necessary stemming and lemmatization is performed for features extraction.This is followed by several machine learning techniques and a proposed Long Short-Term Memory(LSTM)classifier optimized by the Salp Swarm Algorithm(SSA)and measured the corresponding performance.Then,the validity and accuracy of commonly used classifiers,such as Support Vector Machine,Logistic Regression Classifier,and Naive Bayes classifier,were reviewed.Moreover,LSTM based on the SSA classification model was compared with Support Vector Machine(SVM),Logistic Regression(LR),and Naive Bayes(NB).Finally,as LSTM based SSA achieved the highest accuracy,it was applied to predict the sentiments of students’feedback and evaluate their association with the course outcome evaluations for education quality purposes.
文摘This paper deals with the effect of grade entitlement on English programs in Japanese universities. For years, teachers and administrators have noted that even though Japan is one of the highest spending countries on English education, there has not been a commensurate increase in English ability. Most research to explain this disparity has thus far focused on methodology, class size and teacher qualifications. Recent research dealing with academic entitlement at US universities may offer an alternative explanation for some of the lack of success that Japanese universities have experienced. Ellen Greenberger, one of the author＇s of Self-entitled college students： Contributions of personality, parenting and motivational factors, which appeared in 2008 in The Journal of Youth and Adolescence claims that in recent years, the number of students appealing their grades and expecting to be rewarded for effort rather than results has increased. Greenberger＇s paper motivated this study. 200 Japanese students completed a survey that presented a number of grade scenarios. The students were asked, based on test grades and completed assignments, what grade a student should receive in each hypothetical situation. Similarly, full-time and part-time teachers were asked what grade they would give in each situation. This paper explains the different situations, students＇ and teachers＇ responses to the scenarios, and then discusses the implications for English education in Japan.
文摘当前,大语言模型(Large Language Model,LLM)凭借先进的文本处理能力为智能作文评价提供了优化契机,但其反馈内容质量和语义分析能力仍有不足,而其常出现的主题浅显、润色过度等问题恰暴露其反馈尚无法满足语文作文以“主题、表达”为核心的评价要求,因此有必要评估其反馈质量。为此,文章首先通过文献阅读、专家咨询研发“大语言模型作文评价反馈质量评估标准”,利用10个LLM依据指定作文评分标准对50篇记叙文和50篇议论文分别进行评价。然后,文章基于该标准对LLM反馈进行打分,计算评分者间一致性并对研究结果进行描述性分析与质性总结。研究发现:LLM的反馈在文体稳定性、丰富性和深度理解上有提升空间;LLM的反馈缺乏引导思考、批判评价与判断语言文采的类师能力;LLM善于在主题、材料、结构、感情与思想层面给出反馈;LLM的反馈呈现一定程度的学科专业性。最后,文章根据研究结果提出建议,以期为推动LLM与语文作文评价的深度融合提供参考。
