This paper is devoted to development and study of models for operator training systems of heating power station processes management. It proposed a mathematical model describing the management processes of heating pow...This paper is devoted to development and study of models for operator training systems of heating power station processes management. It proposed a mathematical model describing the management processes of heating power units of the technological complex considering the relationship of technological variables in deviations effective in real time. A software complex is developed for the system of training of operators controlling processes in heating station units. Obtained results may be used in the course of development of computer training systems for operators of heating power stations with cross-linkage.展开更多
Intelligent fault diagnosis technology plays an indispensable role in ensuring the safety,stability,and efficiency of railway operations.However,existing studies have the following limitations.1)They are typical black-...Intelligent fault diagnosis technology plays an indispensable role in ensuring the safety,stability,and efficiency of railway operations.However,existing studies have the following limitations.1)They are typical black-box models that lacks interpretability as well as they fuse features by simply stacking them,overlooking the discrepancies in the importance of different features,which reduces the credibility and diagnosis accuracy of the models.2)They ignore the effects of potentially mistaken labels in the training datasets disrupting the ability of the models to learn the true data distribution,which degrades the generalization performance of intelligent diagnosis models,especially when the training samples are limited.To address the above items,an interpretable few-shot framework for fault diagnosis with noisy labels is proposed for train transmission systems.In the proposed framework,a feature extractor is constructed by stacked frequency band focus modules,which can capture signal features in different frequency bands and further adaptively concentrate on the features corresponding to the potential fault characteristic frequency.Then,according to prototypical network,a novel metric-based classifier is developed that is tolerant to mislabeled support samples in the case of limited samples.Besides,a new loss function is designed to decrease the impact of label mistakes in query datasets.Finally,fault simulation experiments of subway train transmission systems are designed and conducted,and the effectiveness as well as superiority of the proposed method are proved by ablation experiments and comparison with the existing methods.展开更多
The neuro-fuzzy network (NFN) is used to model the rules and experience of the process planner. NFN is to select the manufacturing operations sequences for the part features. A detailed description of the NFN system d...The neuro-fuzzy network (NFN) is used to model the rules and experience of the process planner. NFN is to select the manufacturing operations sequences for the part features. A detailed description of the NFN system development is given. The rule structure utilizes sigmoid functions to fuzzify the inputs, multiplication to combine the if Part of the rules and summation to integrate the fired rules. Expert knowledge from previous process Plans is used in determinning the initial network structure and parameters of the membership functions. A back-propagation (BP) training algorithm was developed to fine tune the knowledge to company standards using the input-output data from executions of previous plans. The method is illustrated by an industrial example.展开更多
Communication based train control systems （CBTC） must work even in the worst situation-- train crossing. This paper models the propagation characteristics in one of the most common and piv- otal scenarios--train cro...Communication based train control systems （CBTC） must work even in the worst situation-- train crossing. This paper models the propagation characteristics in one of the most common and piv- otal scenarios--train crossing in subway tunnels which is rarely mentioned in previous publications. Firstly, measurements for train crossing scenario at 2.4 GHz in a real subway line in Madrid have been made. The field measurement is the most reliable way to reveal the propagation characteristics involving shadowing effect and fast fading. Moreover, to precisely describe the fast fading distribu- tion and eliminate the inevitable weak points of traditional fitting way, a best numerical approxima- tion method using Legendre orthogonal polynomials has been proposed. Comparisons show that this method works better and is of greater physical significance. Finally, a complete statistical model is given and all the coefficients can be applied by system designers for the link and system level simu- lations.展开更多
Tree fruit architecture results from combination of the training system and pruning and thinning processes across multiple growth and development years.Further,the tree fruit architecture contributes to the light inte...Tree fruit architecture results from combination of the training system and pruning and thinning processes across multiple growth and development years.Further,the tree fruit architecture contributes to the light interception and improves tree growth,fruit quality,and fruit yield,in addition to easing the process of orchard management and harvest.Currently tree architectural traits are measured manually by researchers or growers,which is labor-intensive and time-consuming.In this study,the remote sensing techniques were evaluated to phenotype critical architectural traits with the final goal to assist tree fruit breeders,physiologists and growers in collecting architectural traits efficiently and in a standardized manner.For this,a consumer-grade red–green–blue(RGB)camera was used to collect apple tree side-images,while an unmanned aerial vehicle(UAV)integrated RGB camera was programmed to image tree canopy at 15 m above ground level to evaluate multiple tree fruit architectures.The sensing data were compared to ground reference data associated with tree orchard blocks within three training systems(Spindle,V-trellis,Biaxis),two rootstocks(‘WA 38 trees grafted on G41 and M9-Nic29)and two pruning methods(referred as bending and click pruning).The data were processed to extract architectural features from ground-based 2D images and UAV-based 3D digital surface model.The traits extracted from sensing data included box-counting fractal dimension(DBs),middle branch angle,number of branches,trunk basal diameter,and tree row volume(TRV).The results from ground-based sensing data indicated that there was a significant(P<0.0001)difference in DBs between Spindle and V-trellis training systems,and correlations between DBs with tree height(r=0.79)and total fruit yield per unit area(r=0.74)were significant(P<0.05).Moreover,correlations between average or total TRV and ground reference data,such as tree height and total fruit yield per unit area,were significant(P<0.05).With the reported findings,this study demonstrated the potential of sensing techniques for phenotyping tree fruit architectural traits.展开更多
文摘This paper is devoted to development and study of models for operator training systems of heating power station processes management. It proposed a mathematical model describing the management processes of heating power units of the technological complex considering the relationship of technological variables in deviations effective in real time. A software complex is developed for the system of training of operators controlling processes in heating station units. Obtained results may be used in the course of development of computer training systems for operators of heating power stations with cross-linkage.
基金supported in part by the National Key R&D Program of China under Grant 2022YFB4300601in part by the State Key Laboratory of Advanced Rail Autonomous Operation under Grant RAO2023ZZ003.
文摘Intelligent fault diagnosis technology plays an indispensable role in ensuring the safety,stability,and efficiency of railway operations.However,existing studies have the following limitations.1)They are typical black-box models that lacks interpretability as well as they fuse features by simply stacking them,overlooking the discrepancies in the importance of different features,which reduces the credibility and diagnosis accuracy of the models.2)They ignore the effects of potentially mistaken labels in the training datasets disrupting the ability of the models to learn the true data distribution,which degrades the generalization performance of intelligent diagnosis models,especially when the training samples are limited.To address the above items,an interpretable few-shot framework for fault diagnosis with noisy labels is proposed for train transmission systems.In the proposed framework,a feature extractor is constructed by stacked frequency band focus modules,which can capture signal features in different frequency bands and further adaptively concentrate on the features corresponding to the potential fault characteristic frequency.Then,according to prototypical network,a novel metric-based classifier is developed that is tolerant to mislabeled support samples in the case of limited samples.Besides,a new loss function is designed to decrease the impact of label mistakes in query datasets.Finally,fault simulation experiments of subway train transmission systems are designed and conducted,and the effectiveness as well as superiority of the proposed method are proved by ablation experiments and comparison with the existing methods.
文摘The neuro-fuzzy network (NFN) is used to model the rules and experience of the process planner. NFN is to select the manufacturing operations sequences for the part features. A detailed description of the NFN system development is given. The rule structure utilizes sigmoid functions to fuzzify the inputs, multiplication to combine the if Part of the rules and summation to integrate the fired rules. Expert knowledge from previous process Plans is used in determinning the initial network structure and parameters of the membership functions. A back-propagation (BP) training algorithm was developed to fine tune the knowledge to company standards using the input-output data from executions of previous plans. The method is illustrated by an industrial example.
基金Supported by the National Natural Science Foundation of China(No.60830001)Program for New Century Excellent Talents in University(No.NCET-09-0206)+2 种基金the Key Project of State Key Lab.of Rail Traffic Control and Safety(No.RCS2008ZZ006)Program for Changjiang Scholars and Innovative Research Team in University(No.IRT0949)the Project of State Key Lab.of Rail Traffic Control and Safety(No.RCS2008ZT005)
文摘Communication based train control systems （CBTC） must work even in the worst situation-- train crossing. This paper models the propagation characteristics in one of the most common and piv- otal scenarios--train crossing in subway tunnels which is rarely mentioned in previous publications. Firstly, measurements for train crossing scenario at 2.4 GHz in a real subway line in Madrid have been made. The field measurement is the most reliable way to reveal the propagation characteristics involving shadowing effect and fast fading. Moreover, to precisely describe the fast fading distribu- tion and eliminate the inevitable weak points of traditional fitting way, a best numerical approxima- tion method using Legendre orthogonal polynomials has been proposed. Comparisons show that this method works better and is of greater physical significance. Finally, a complete statistical model is given and all the coefficients can be applied by system designers for the link and system level simu- lations.
基金US Department of Agriculture(USDA)-National Institute for Food and Agriculture(NIFA)Agriculture and Food Research Initiative Competitive Project WNP08532(accession number 1008828)USDA-NIFA Hatch project WNP00011“Crop Improvement and Sustainable Production Systems”(accession number 1014919)Washington Tree Fruit Research Commission(grant number AP14-103A).
文摘Tree fruit architecture results from combination of the training system and pruning and thinning processes across multiple growth and development years.Further,the tree fruit architecture contributes to the light interception and improves tree growth,fruit quality,and fruit yield,in addition to easing the process of orchard management and harvest.Currently tree architectural traits are measured manually by researchers or growers,which is labor-intensive and time-consuming.In this study,the remote sensing techniques were evaluated to phenotype critical architectural traits with the final goal to assist tree fruit breeders,physiologists and growers in collecting architectural traits efficiently and in a standardized manner.For this,a consumer-grade red–green–blue(RGB)camera was used to collect apple tree side-images,while an unmanned aerial vehicle(UAV)integrated RGB camera was programmed to image tree canopy at 15 m above ground level to evaluate multiple tree fruit architectures.The sensing data were compared to ground reference data associated with tree orchard blocks within three training systems(Spindle,V-trellis,Biaxis),two rootstocks(‘WA 38 trees grafted on G41 and M9-Nic29)and two pruning methods(referred as bending and click pruning).The data were processed to extract architectural features from ground-based 2D images and UAV-based 3D digital surface model.The traits extracted from sensing data included box-counting fractal dimension(DBs),middle branch angle,number of branches,trunk basal diameter,and tree row volume(TRV).The results from ground-based sensing data indicated that there was a significant(P<0.0001)difference in DBs between Spindle and V-trellis training systems,and correlations between DBs with tree height(r=0.79)and total fruit yield per unit area(r=0.74)were significant(P<0.05).Moreover,correlations between average or total TRV and ground reference data,such as tree height and total fruit yield per unit area,were significant(P<0.05).With the reported findings,this study demonstrated the potential of sensing techniques for phenotyping tree fruit architectural traits.
