In this paper, we consider the problem of automatic synthesis of decentralized supervisor for uncertain discrete event systems. In particular, we study the case when the uncontrolled plant is unknown a priori. To deal...In this paper, we consider the problem of automatic synthesis of decentralized supervisor for uncertain discrete event systems. In particular, we study the case when the uncontrolled plant is unknown a priori. To deal with the unknown plants, we first characterize the conormality of prefix-closed regular languages and propose formulas for computing the supremal conormal sublanguages; then sufficient conditions for the existence of decentralized supervisors are given in terms of language controllability and conormality and a learning-based algorithm to synthesize the supervisor automatically is proposed. Moreover, the paper also studies the on-line decentralized supervisory control of concurrent discrete event systems that are composed of multiple interacting unknown modules. We use the concept of modular controllability to characterize the necessary and sufficient conditions for the existence of the local supervisors, which consist of a set of local supervisor modules, one for each plant module and which determines its control actions based on the locally observed behaviors, and an on-line learning-based local synthesis algorithm is also presented. The correctness and convergence of the proposed algorithms are proved, and their implementation are illustrated through examples.展开更多
Rare bird has long been considered an important in the field of airport security,biological conservation,environmental monitoring,and so on.With the development and popularization of IOT-based video surveillance,all d...Rare bird has long been considered an important in the field of airport security,biological conservation,environmental monitoring,and so on.With the development and popularization of IOT-based video surveillance,all day and weather unattended bird monitoring becomes possible.However,the current mainstream bird recognition methods are mostly based on deep learning.These will be appropriate for big data applications,but the training sample size for rare bird is usually very short.Therefore,this paper presents a new sparse recognition model via improved part detection and our previous dictionary learning.There are two achievements in our work:(1)after the part localization with selective search,the gist feature of all bird image parts will be fused as data description;(2)the fused gist feature needs to be learned through our proposed intraclass dictionary learning with regularized K-singular value decomposition.According to above two innovations,the rare bird sparse recognition will be implemented by solving one l1-norm optimization.In the experiment with Caltech-UCSD Birds-200-2011 dataset,results show the proposed method can have better recognition performance than other SR methods for rare bird task with small sample size.展开更多
This paper adopts the research methods such as literature material method, questionnaire investigation method, expert interview method and mathematical statistics to investigate and study the conditions of fancy rope ...This paper adopts the research methods such as literature material method, questionnaire investigation method, expert interview method and mathematical statistics to investigate and study the conditions of fancy rope skipping curriculum in 22 universities in Sichuan. The research shows that the university teachers highly recognize the fancy rope skipping curriculum; construction of teaching resources should be strengthened; cultivation of the professional teaching staffs should be strengthened; in terms of teaching materials, more premium teaching materials with high pertinence and professionalism are needed; the promotion and implementation of the curriculum system of fancy rope skipping should be boosted in different steps and stages; cultivation of the professional teachers should be enlarged; related scientific research of the curriculum system of fancy rope skipping should be strengthened.展开更多
This paper aims to verify the family support situation for primary school children with intellectual disabilities learning in regular class and to explore various educational strategies to promote their development.A ...This paper aims to verify the family support situation for primary school children with intellectual disabilities learning in regular class and to explore various educational strategies to promote their development.A self-made questionnaire was used in this survey,and the parents of 380 intellectual disabled students were the subjects of this survey.It turns out that the overall family support for intellectual disabled children learning in regular class in China is good,but it is affected by the degree of obstacles.Factors such as grade,gender,and parental education had no significant effect on family support.It is the shared responsibility of the government,schools,and parents to promote the level of family support.Governments at all levels must implement family support projects,schools must carry out family education guidance to impart scientific parenting knowledge,and parents must take note of their own responsibilities,so as to promote the physical and mental development of children with intellectual disabilities.展开更多
The transportation of oil and gas through pipelines is crucial for sustaining energy supply in industrial and civil sectors.However,the issue of pitting corrosion during pipeline operation poses an important threat to...The transportation of oil and gas through pipelines is crucial for sustaining energy supply in industrial and civil sectors.However,the issue of pitting corrosion during pipeline operation poses an important threat to the structural integrity and safety of pipelines.This problem not only affects the longevity of pipelines but also has the potential to cause secondary disasters,such as oil and gas leaks,leading to environmental pollution and endangering public safety.Therefore,the development of a highly stable,accurate,and reliable model for predicting pipeline pitting corrosion is of paramount importance.In this study,a novel prediction model for pipeline pitting corrosion depth that integrates the sparrow search algorithm(SSA),regularized extreme learning machine(RELM),principal component analysis(PCA),and residual correction is proposed.Initially,RELM is utilized to forecast pipeline pitting corrosion depth,and SSA is employed for optimizing RELM’s hyperparameters to enhance the model’s predictive capabilities.Subsequently,the residuals of the SSA-RELM model are obtained by subtracting the prediction results of the model from actual measurements.Moreover,PCA is applied to reduce the dimensionality of the original 10 features,yielding 7 new features with enhanced information content.Finally,residuals are predicted by using the seven features obtained by PCA,and the prediction result is combined with the output of the SSA-RELM model to derive the predicted pipeline pitting corrosion depth by incorporating multiple feature selection and residual correction.Case study demonstrates that the proposed model reduces mean squared error,mean absolute percentage error,and mean absolute error by 66.80%,42.71%,and 42.64%,respectively,compared with the SSA-RELM model.Research findings underscore the exceptional performance of the proposed integrated approach in predicting the depth of pipeline pitting corrosion.展开更多
The increasingly complex and interconnected train control information network is vulnerable to a variety of malicious traffic attacks,and the existing malicious traffic detection methods mainly rely on machine learnin...The increasingly complex and interconnected train control information network is vulnerable to a variety of malicious traffic attacks,and the existing malicious traffic detection methods mainly rely on machine learning,such as poor robustness,weak generalization,and a lack of ability to learn common features.Therefore,this paper proposes a malicious traffic identification method based on stacked sparse denoising autoencoders combined with a regularized extreme learning machine through particle swarm optimization.Firstly,the simulation environment of the Chinese train control system-3,was constructed for data acquisition.Then Pearson coefficient and other methods are used for pre-processing,then a stacked sparse denoising autoencoder is used to achieve nonlinear dimensionality reduction of features,and finally regularization extreme learning machine optimized by particle swarm optimization is used to achieve classification.Experimental data show that the proposed method has good training performance,with an average accuracy of 97.57%and a false negative rate of 2.43%,which is better than other alternative methods.In addition,ablation experiments were performed to evaluate the contribution of each component,and the results showed that the combination of methods was superior to individual methods.To further evaluate the generalization ability of the model in different scenarios,publicly available data sets of industrial control system networks were used.The results show that the model has robust detection capability in various types of network attacks.展开更多
This paper considers the problem of distributed online regularized optimization over a network that consists of multiple interacting nodes.Each node is endowed with a sequence of loss functions that are time-varying a...This paper considers the problem of distributed online regularized optimization over a network that consists of multiple interacting nodes.Each node is endowed with a sequence of loss functions that are time-varying and a regularization function that is fixed over time.A distributed forward-backward splitting algorithm is proposed for solving this problem and both fixed and adaptive learning rates are adopted.For both cases,we show that the regret upper bounds scale as O(VT),where T is the time horizon.In particular,those rates match the centralized counterpart.Finally,we show the effectiveness of the proposed algorithms over an online distributed regularized linear regression problem.展开更多
Exercise-induced tibia periostitis is one of the most common sports injuries in the sports training of athletics in regular institutions of higher learning. It often occurs. When it is serious, periosteal proliferatio...Exercise-induced tibia periostitis is one of the most common sports injuries in the sports training of athletics in regular institutions of higher learning. It often occurs. When it is serious, periosteal proliferation would occur, It will result in fatigue fi^cture sometimes. This is relevant to tibialis anterior muscle restrained contraction in the events in the sports process. If measures are not adopted, early prevention should be strengthened. This would not only affect seriously the sports achievement of long-distance runners, but also affects their future ordinary lives. Through analysis and summaries, the main reasons for the occurrence of tibia periostitis for long-distance runners are shown. From the perspectives of reasonably arranging the exercise load, strengthening the field management and strengthening the self-protection and so on, the prevention of tibia periostitis for long-distance runners has been explored. It also helps the long-distance runners to train scientifically, which can effectively avoid and lessen the occurrence of tibia periostitis and the adverse effect it brings.展开更多
Inclusive education is an inevitable trend in today's international educational development. It is also one of the platforms for civilized society to fully embody the concepts of freedom, equality and mutual suppo...Inclusive education is an inevitable trend in today's international educational development. It is also one of the platforms for civilized society to fully embody the concepts of freedom, equality and mutual support. Our country attaches great importance to the development of inclusive education in order to promote it in all cities across the country in terms of policies and regulations, university studies, etc. The development of inclusive education in China still faces the problem of low educational quality and serious shortage of educational background. Such as the appointment of teachers, lack of standards and norms of behavior.展开更多
IntroductionChina started pilot inclusive education projects in 1980s to enroll more children with disabilities into schools,in response to the national goal of universalizing compulsory education to all and the inter...IntroductionChina started pilot inclusive education projects in 1980s to enroll more children with disabilities into schools,in response to the national goal of universalizing compulsory education to all and the international trend toward inclusion(Xiao,2007).National Education Statistics showed that about 50%of students with disabilities receiving education have been enrolled in regular schools in the last few years(Yan&Deng,2019).展开更多
Purpose Learning in a regular classroom(LRC)has become the main educational placement for students with disabilities in China.This study investigates the outcomes of LRC and their influential factors from the teachers...Purpose Learning in a regular classroom(LRC)has become the main educational placement for students with disabilities in China.This study investigates the outcomes of LRC and their influential factors from the teachers’experiences and perspectives.Design/Approach/Methods Seven inclusive education teachers participated in semi-structured interviews.Thematic analysis was conducted to analyze the qualitative data.Findings The results show that social participation and contribution,physical presence,academic performance,self-concept,and independence are the key outcomes of LRC from inclusive education teachers’experiences and perspectives.Moreover,the factors influencing the key desired outcomes include the severity of disabilities and problem behaviors,professional support,teaching practice,inclusive climate,and collaboration between schools and families.Originality/Value This study provides valuable insights into the outcomes and influential factors of inclusive education in the context of LRC in China.These findings inform efforts to improve inclusive education practices and enhance the educational experiences of students with special needs.展开更多
基金supported by the National Science Foundation(Nos.NSF-CNS-1239222,NSF-EECS-1253488)
文摘In this paper, we consider the problem of automatic synthesis of decentralized supervisor for uncertain discrete event systems. In particular, we study the case when the uncontrolled plant is unknown a priori. To deal with the unknown plants, we first characterize the conormality of prefix-closed regular languages and propose formulas for computing the supremal conormal sublanguages; then sufficient conditions for the existence of decentralized supervisors are given in terms of language controllability and conormality and a learning-based algorithm to synthesize the supervisor automatically is proposed. Moreover, the paper also studies the on-line decentralized supervisory control of concurrent discrete event systems that are composed of multiple interacting unknown modules. We use the concept of modular controllability to characterize the necessary and sufficient conditions for the existence of the local supervisors, which consist of a set of local supervisor modules, one for each plant module and which determines its control actions based on the locally observed behaviors, and an on-line learning-based local synthesis algorithm is also presented. The correctness and convergence of the proposed algorithms are proved, and their implementation are illustrated through examples.
文摘Rare bird has long been considered an important in the field of airport security,biological conservation,environmental monitoring,and so on.With the development and popularization of IOT-based video surveillance,all day and weather unattended bird monitoring becomes possible.However,the current mainstream bird recognition methods are mostly based on deep learning.These will be appropriate for big data applications,but the training sample size for rare bird is usually very short.Therefore,this paper presents a new sparse recognition model via improved part detection and our previous dictionary learning.There are two achievements in our work:(1)after the part localization with selective search,the gist feature of all bird image parts will be fused as data description;(2)the fused gist feature needs to be learned through our proposed intraclass dictionary learning with regularized K-singular value decomposition.According to above two innovations,the rare bird sparse recognition will be implemented by solving one l1-norm optimization.In the experiment with Caltech-UCSD Birds-200-2011 dataset,results show the proposed method can have better recognition performance than other SR methods for rare bird task with small sample size.
文摘This paper adopts the research methods such as literature material method, questionnaire investigation method, expert interview method and mathematical statistics to investigate and study the conditions of fancy rope skipping curriculum in 22 universities in Sichuan. The research shows that the university teachers highly recognize the fancy rope skipping curriculum; construction of teaching resources should be strengthened; cultivation of the professional teaching staffs should be strengthened; in terms of teaching materials, more premium teaching materials with high pertinence and professionalism are needed; the promotion and implementation of the curriculum system of fancy rope skipping should be boosted in different steps and stages; cultivation of the professional teachers should be enlarged; related scientific research of the curriculum system of fancy rope skipping should be strengthened.
基金supported by The Final Achievement of the 13th Five-Year Plan of Philosophy and Social Sciences in Guangdong Province in 2020“Research on the Relationship Between Family Support,School Support and School Adaptation of Regular Primary School Students(No.:GD20XJY27).
文摘This paper aims to verify the family support situation for primary school children with intellectual disabilities learning in regular class and to explore various educational strategies to promote their development.A self-made questionnaire was used in this survey,and the parents of 380 intellectual disabled students were the subjects of this survey.It turns out that the overall family support for intellectual disabled children learning in regular class in China is good,but it is affected by the degree of obstacles.Factors such as grade,gender,and parental education had no significant effect on family support.It is the shared responsibility of the government,schools,and parents to promote the level of family support.Governments at all levels must implement family support projects,schools must carry out family education guidance to impart scientific parenting knowledge,and parents must take note of their own responsibilities,so as to promote the physical and mental development of children with intellectual disabilities.
基金Supported by the Natural Science Foundation of Shandong Province of China(ZR2022QE091)the Special fund for Taishan Industry Leading Talent Project(tsls20230605)Key R&D Program of Shandong Province,China(2023CXGC010407).
文摘The transportation of oil and gas through pipelines is crucial for sustaining energy supply in industrial and civil sectors.However,the issue of pitting corrosion during pipeline operation poses an important threat to the structural integrity and safety of pipelines.This problem not only affects the longevity of pipelines but also has the potential to cause secondary disasters,such as oil and gas leaks,leading to environmental pollution and endangering public safety.Therefore,the development of a highly stable,accurate,and reliable model for predicting pipeline pitting corrosion is of paramount importance.In this study,a novel prediction model for pipeline pitting corrosion depth that integrates the sparrow search algorithm(SSA),regularized extreme learning machine(RELM),principal component analysis(PCA),and residual correction is proposed.Initially,RELM is utilized to forecast pipeline pitting corrosion depth,and SSA is employed for optimizing RELM’s hyperparameters to enhance the model’s predictive capabilities.Subsequently,the residuals of the SSA-RELM model are obtained by subtracting the prediction results of the model from actual measurements.Moreover,PCA is applied to reduce the dimensionality of the original 10 features,yielding 7 new features with enhanced information content.Finally,residuals are predicted by using the seven features obtained by PCA,and the prediction result is combined with the output of the SSA-RELM model to derive the predicted pipeline pitting corrosion depth by incorporating multiple feature selection and residual correction.Case study demonstrates that the proposed model reduces mean squared error,mean absolute percentage error,and mean absolute error by 66.80%,42.71%,and 42.64%,respectively,compared with the SSA-RELM model.Research findings underscore the exceptional performance of the proposed integrated approach in predicting the depth of pipeline pitting corrosion.
文摘The increasingly complex and interconnected train control information network is vulnerable to a variety of malicious traffic attacks,and the existing malicious traffic detection methods mainly rely on machine learning,such as poor robustness,weak generalization,and a lack of ability to learn common features.Therefore,this paper proposes a malicious traffic identification method based on stacked sparse denoising autoencoders combined with a regularized extreme learning machine through particle swarm optimization.Firstly,the simulation environment of the Chinese train control system-3,was constructed for data acquisition.Then Pearson coefficient and other methods are used for pre-processing,then a stacked sparse denoising autoencoder is used to achieve nonlinear dimensionality reduction of features,and finally regularization extreme learning machine optimized by particle swarm optimization is used to achieve classification.Experimental data show that the proposed method has good training performance,with an average accuracy of 97.57%and a false negative rate of 2.43%,which is better than other alternative methods.In addition,ablation experiments were performed to evaluate the contribution of each component,and the results showed that the combination of methods was superior to individual methods.To further evaluate the generalization ability of the model in different scenarios,publicly available data sets of industrial control system networks were used.The results show that the model has robust detection capability in various types of network attacks.
基金This work was supported in part by the National Natural Science Foundation of China(Nos.62022042,62273181 and 62073166)in part by the Fundamental Research Funds for the Central Universities(No.30919011105)in part by the Open Project of the Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment(No.GDSC202017).
文摘This paper considers the problem of distributed online regularized optimization over a network that consists of multiple interacting nodes.Each node is endowed with a sequence of loss functions that are time-varying and a regularization function that is fixed over time.A distributed forward-backward splitting algorithm is proposed for solving this problem and both fixed and adaptive learning rates are adopted.For both cases,we show that the regret upper bounds scale as O(VT),where T is the time horizon.In particular,those rates match the centralized counterpart.Finally,we show the effectiveness of the proposed algorithms over an online distributed regularized linear regression problem.
文摘Exercise-induced tibia periostitis is one of the most common sports injuries in the sports training of athletics in regular institutions of higher learning. It often occurs. When it is serious, periosteal proliferation would occur, It will result in fatigue fi^cture sometimes. This is relevant to tibialis anterior muscle restrained contraction in the events in the sports process. If measures are not adopted, early prevention should be strengthened. This would not only affect seriously the sports achievement of long-distance runners, but also affects their future ordinary lives. Through analysis and summaries, the main reasons for the occurrence of tibia periostitis for long-distance runners are shown. From the perspectives of reasonably arranging the exercise load, strengthening the field management and strengthening the self-protection and so on, the prevention of tibia periostitis for long-distance runners has been explored. It also helps the long-distance runners to train scientifically, which can effectively avoid and lessen the occurrence of tibia periostitis and the adverse effect it brings.
文摘Inclusive education is an inevitable trend in today's international educational development. It is also one of the platforms for civilized society to fully embody the concepts of freedom, equality and mutual support. Our country attaches great importance to the development of inclusive education in order to promote it in all cities across the country in terms of policies and regulations, university studies, etc. The development of inclusive education in China still faces the problem of low educational quality and serious shortage of educational background. Such as the appointment of teachers, lack of standards and norms of behavior.
文摘IntroductionChina started pilot inclusive education projects in 1980s to enroll more children with disabilities into schools,in response to the national goal of universalizing compulsory education to all and the international trend toward inclusion(Xiao,2007).National Education Statistics showed that about 50%of students with disabilities receiving education have been enrolled in regular schools in the last few years(Yan&Deng,2019).
文摘Purpose Learning in a regular classroom(LRC)has become the main educational placement for students with disabilities in China.This study investigates the outcomes of LRC and their influential factors from the teachers’experiences and perspectives.Design/Approach/Methods Seven inclusive education teachers participated in semi-structured interviews.Thematic analysis was conducted to analyze the qualitative data.Findings The results show that social participation and contribution,physical presence,academic performance,self-concept,and independence are the key outcomes of LRC from inclusive education teachers’experiences and perspectives.Moreover,the factors influencing the key desired outcomes include the severity of disabilities and problem behaviors,professional support,teaching practice,inclusive climate,and collaboration between schools and families.Originality/Value This study provides valuable insights into the outcomes and influential factors of inclusive education in the context of LRC in China.These findings inform efforts to improve inclusive education practices and enhance the educational experiences of students with special needs.
