In the era of big data,data-driven technologies are increasingly leveraged by industry to facilitate autonomous learning and intelligent decision-making.However,the challenge of“small samples in big data”emerges whe...In the era of big data,data-driven technologies are increasingly leveraged by industry to facilitate autonomous learning and intelligent decision-making.However,the challenge of“small samples in big data”emerges when datasets lack the comprehensive information necessary for addressing complex scenarios,which hampers adaptability.Thus,enhancing data completeness is essential.Knowledge-guided virtual sample generation transforms domain knowledge into extensive virtual datasets,thereby reducing dependence on limited real samples and enabling zero-sample fault diagnosis.This study used building air conditioning systems as a case study.We innovatively used the large language model(LLM)to acquire domain knowledge for sample generation,significantly lowering knowledge acquisition costs and establishing a generalized framework for knowledge acquisition in engineering applications.This acquired knowledge guided the design of diffusion boundaries in mega-trend diffusion(MTD),while the Monte Carlo method was used to sample within the diffusion function to create information-rich virtual samples.Additionally,a noise-adding technique was introduced to enhance the information entropy of these samples,thereby improving the robustness of neural networks trained with them.Experimental results showed that training the diagnostic model exclusively with virtual samples achieved an accuracy of 72.80%,significantly surpassing traditional small-sample supervised learning in terms of generalization.This underscores the quality and completeness of the generated virtual samples.展开更多
The corrosion rate is a crucial factor that impacts the longevity of materials in different applications.After undergoing friction stir processing(FSP),the refined grain structure leads to a notable decrease in corros...The corrosion rate is a crucial factor that impacts the longevity of materials in different applications.After undergoing friction stir processing(FSP),the refined grain structure leads to a notable decrease in corrosion rate.However,a better understanding of the correlation between the FSP process parameters and the corrosion rate is still lacking.The current study used machine learning to establish the relationship between the corrosion rate and FSP process parameters(rotational speed,traverse speed,and shoulder diameter)for WE43 alloy.The Taguchi L27 design of experiments was used for the experimental analysis.In addition,synthetic data was generated using particle swarm optimization for virtual sample generation(VSG).The application of VSG has led to an increase in the prediction accuracy of machine learning models.A sensitivity analysis was performed using Shapley Additive Explanations to determine the key factors affecting the corrosion rate.The shoulder diameter had a significant impact in comparison to the traverse speed.A graphical user interface(GUI)has been created to predict the corrosion rate using the identified factors.This study focuses on the WE43 alloy,but its findings can also be used to predict the corrosion rate of other magnesium alloys.展开更多
Seismic reservoir prediction plays an important role in oil exploration and development.With the progress of artificial intelligence,many achievements have been made in machine learning seismic reservoir prediction.Ho...Seismic reservoir prediction plays an important role in oil exploration and development.With the progress of artificial intelligence,many achievements have been made in machine learning seismic reservoir prediction.However,due to the factors such as economic cost,exploration maturity,and technical limitations,it is often difficult to obtain a large number of training samples for machine learning.In this case,the prediction accuracy cannot meet the requirements.To overcome this shortcoming,we develop a new machine learning reservoir prediction method based on virtual sample generation.In this method,the virtual samples,which are generated in a high-dimensional hypersphere space,are more consistent with the original data characteristics.Furthermore,at the stage of model building after virtual sample generation,virtual samples screening and model iterative optimization are used to eliminate noise samples and ensure the rationality of virtual samples.The proposed method has been applied to standard function data and real seismic data.The results show that this method can improve the prediction accuracy of machine learning significantly.展开更多
Grid-connected current control is one.of the important control schemes in distributed generation systems.A lot of control methods have been developed,such as hysteresis control,dead-beat control,one-cycle control,etc....Grid-connected current control is one.of the important control schemes in distributed generation systems.A lot of control methods have been developed,such as hysteresis control,dead-beat control,one-cycle control,etc.Hysteresis current control has the advantages of simplicity,robustness and good large-signal response.Unfortunately,the switching frequency of the converter using hysteresis current control varies according to the parameters of the bus voltage,the filter inductor and the bandwidth.Increasing the hysteresis bandwidth and the filter inductance can reduce the switching frequency.展开更多
Virtual testability demonstration test has many advantages,such as low cost,high efficiency,low risk and few restrictions.It brings new requirements to the fault sample generation.A fault sample simulation approach fo...Virtual testability demonstration test has many advantages,such as low cost,high efficiency,low risk and few restrictions.It brings new requirements to the fault sample generation.A fault sample simulation approach for virtual testability demonstration test based on stochastic process theory is proposed.First,the similarities and differences of fault sample generation between physical testability demonstration test and virtual testability demonstration test are discussed.Second,it is pointed out that the fault occurrence process subject to perfect repair is renewal process.Third,the interarrival time distribution function of the next fault event is given.Steps and flowcharts of fault sample generation are introduced.The number of faults and their occurrence time are obtained by statistical simulation.Finally,experiments are carried out on a stable tracking platform.Because a variety of types of life distributions and maintenance modes are considered and some assumptions are removed,the sample size and structure of fault sample simulation results are more similar to the actual results and more reasonable.The proposed method can effectively guide the fault injection in virtual testability demonstration test.展开更多
In view of class imbalance in data-driven modeling for Prognostics and Health Management(PHM),existing classification methods may fail in generating effective fault prediction models for the on-board high-speed train ...In view of class imbalance in data-driven modeling for Prognostics and Health Management(PHM),existing classification methods may fail in generating effective fault prediction models for the on-board high-speed train control equipment.A virtual sample generation solution based on Generative Adversarial Network(GAN)is proposed to overcome this shortcoming.Aiming at augmenting the sample classes with the imbalanced data problem,the GAN-based virtual sample generation strategy is embedded into the establishment of fault prediction models.Under the PHM framework of the on-board train control system,the virtual sample generation principle and the detailed procedures are presented.With the enhanced class-balancing mechanism and the designed sample augmentation logic,the PHM scheme of the on-board train control equipment has powerful data condition adaptability and can effectively predict the fault probability and life cycle status.Practical data from a specific type of on-board train control system is employed for the validation of the presented solution.The comparative results indicate that GAN-based sample augmentation is capable of achieving a desirable sample balancing level and enhancing the performance of correspondingly derived fault prediction models for the Condition-based Maintenance(CBM)operations.展开更多
In this paper, finite sample properties of virtual reference feedback tuning control are considered, by using the theory of finite sample properties from system identification. To design a controller in closed loop sy...In this paper, finite sample properties of virtual reference feedback tuning control are considered, by using the theory of finite sample properties from system identification. To design a controller in closed loop system structure, the idea of virtual reference feedback tuning is proposed to avoid the identification process corresponding to the plant model. After constructing one identification cost without any knowledge of plant model, the author derives one bound on the difference between the expected identification cost and its sample identification cost under the condition that the number of data points is finite. Also the correlation between the plant input and external noise is considered in the derivation of this bound. Furthermore, the author continues to derive one probability bound to quantify this difference by using some probability inequalities and control theory.展开更多
Solution styrene-butadiene rubber(SSBR)finds wide applications in high performance tire design and various other fields.This study aims to create a quantitative structure–property relationship(QSPR)model linking SSBR...Solution styrene-butadiene rubber(SSBR)finds wide applications in high performance tire design and various other fields.This study aims to create a quantitative structure–property relationship(QSPR)model linking SSBR's glass transition temperature(Tg)to its structural properties.A dataset of 68 sets of data from published literature was compiled to develop a predictive machine learning model for SSBR's structural design and synthesis using small sample sizes.To tackle small sample sizes,a framework combining generative adversarial networks(GAN)and the Tree-based Pipeline Optimization Tool(TPOT)is proposed.GAN is first used to generate additional samples that mirror the original dataset's distribution,expanding the dataset.The TPOT is then applied to automatically find the best model and parameter combinations,creating an optimal predictive model for the mixed dataset.Experimental results show that using GAN to enlarge the dataset and TPOT regression models significantly enhances model performance,increasing the R2 value from 0.745 to 0.985 and decreasing the RMSE from 7.676 to 1.569.The proposed GAN–TPOT framework demonstrates the potential of combining generative models with automated machine learning to improve materials science research.This combination accelerates research and development processes,enhances prediction and design accuracy,and introduces new perspectives and possibilities for the field.展开更多
基金supported by the National Natural Science Foundation of China(No.62306281)the Natural Science Foundation of Zhejiang Province(Nos.LQ23E060006 and LTGG24E050005)the Key Research Plan of Jiaxing City(No.2024BZ20016).
文摘In the era of big data,data-driven technologies are increasingly leveraged by industry to facilitate autonomous learning and intelligent decision-making.However,the challenge of“small samples in big data”emerges when datasets lack the comprehensive information necessary for addressing complex scenarios,which hampers adaptability.Thus,enhancing data completeness is essential.Knowledge-guided virtual sample generation transforms domain knowledge into extensive virtual datasets,thereby reducing dependence on limited real samples and enabling zero-sample fault diagnosis.This study used building air conditioning systems as a case study.We innovatively used the large language model(LLM)to acquire domain knowledge for sample generation,significantly lowering knowledge acquisition costs and establishing a generalized framework for knowledge acquisition in engineering applications.This acquired knowledge guided the design of diffusion boundaries in mega-trend diffusion(MTD),while the Monte Carlo method was used to sample within the diffusion function to create information-rich virtual samples.Additionally,a noise-adding technique was introduced to enhance the information entropy of these samples,thereby improving the robustness of neural networks trained with them.Experimental results showed that training the diagnostic model exclusively with virtual samples achieved an accuracy of 72.80%,significantly surpassing traditional small-sample supervised learning in terms of generalization.This underscores the quality and completeness of the generated virtual samples.
文摘The corrosion rate is a crucial factor that impacts the longevity of materials in different applications.After undergoing friction stir processing(FSP),the refined grain structure leads to a notable decrease in corrosion rate.However,a better understanding of the correlation between the FSP process parameters and the corrosion rate is still lacking.The current study used machine learning to establish the relationship between the corrosion rate and FSP process parameters(rotational speed,traverse speed,and shoulder diameter)for WE43 alloy.The Taguchi L27 design of experiments was used for the experimental analysis.In addition,synthetic data was generated using particle swarm optimization for virtual sample generation(VSG).The application of VSG has led to an increase in the prediction accuracy of machine learning models.A sensitivity analysis was performed using Shapley Additive Explanations to determine the key factors affecting the corrosion rate.The shoulder diameter had a significant impact in comparison to the traverse speed.A graphical user interface(GUI)has been created to predict the corrosion rate using the identified factors.This study focuses on the WE43 alloy,but its findings can also be used to predict the corrosion rate of other magnesium alloys.
基金supported by National Natural Science Foundation of China under Grants 41874146 and 42030103。
文摘Seismic reservoir prediction plays an important role in oil exploration and development.With the progress of artificial intelligence,many achievements have been made in machine learning seismic reservoir prediction.However,due to the factors such as economic cost,exploration maturity,and technical limitations,it is often difficult to obtain a large number of training samples for machine learning.In this case,the prediction accuracy cannot meet the requirements.To overcome this shortcoming,we develop a new machine learning reservoir prediction method based on virtual sample generation.In this method,the virtual samples,which are generated in a high-dimensional hypersphere space,are more consistent with the original data characteristics.Furthermore,at the stage of model building after virtual sample generation,virtual samples screening and model iterative optimization are used to eliminate noise samples and ensure the rationality of virtual samples.The proposed method has been applied to standard function data and real seismic data.The results show that this method can improve the prediction accuracy of machine learning significantly.
文摘Grid-connected current control is one.of the important control schemes in distributed generation systems.A lot of control methods have been developed,such as hysteresis control,dead-beat control,one-cycle control,etc.Hysteresis current control has the advantages of simplicity,robustness and good large-signal response.Unfortunately,the switching frequency of the converter using hysteresis current control varies according to the parameters of the bus voltage,the filter inductor and the bandwidth.Increasing the hysteresis bandwidth and the filter inductance can reduce the switching frequency.
基金National Natural Science Foundation of China(51105369)
文摘Virtual testability demonstration test has many advantages,such as low cost,high efficiency,low risk and few restrictions.It brings new requirements to the fault sample generation.A fault sample simulation approach for virtual testability demonstration test based on stochastic process theory is proposed.First,the similarities and differences of fault sample generation between physical testability demonstration test and virtual testability demonstration test are discussed.Second,it is pointed out that the fault occurrence process subject to perfect repair is renewal process.Third,the interarrival time distribution function of the next fault event is given.Steps and flowcharts of fault sample generation are introduced.The number of faults and their occurrence time are obtained by statistical simulation.Finally,experiments are carried out on a stable tracking platform.Because a variety of types of life distributions and maintenance modes are considered and some assumptions are removed,the sample size and structure of fault sample simulation results are more similar to the actual results and more reasonable.The proposed method can effectively guide the fault injection in virtual testability demonstration test.
基金supported by National Natural Science Foundation of China(U2268206,T2222015)Beijing Natural Science Foundation(4232031)+1 种基金Key Fields Project of DEGP(2021ZDZX1110)Shenzhen Science and Technology Program(CJGJZD20220517141801004).
文摘In view of class imbalance in data-driven modeling for Prognostics and Health Management(PHM),existing classification methods may fail in generating effective fault prediction models for the on-board high-speed train control equipment.A virtual sample generation solution based on Generative Adversarial Network(GAN)is proposed to overcome this shortcoming.Aiming at augmenting the sample classes with the imbalanced data problem,the GAN-based virtual sample generation strategy is embedded into the establishment of fault prediction models.Under the PHM framework of the on-board train control system,the virtual sample generation principle and the detailed procedures are presented.With the enhanced class-balancing mechanism and the designed sample augmentation logic,the PHM scheme of the on-board train control equipment has powerful data condition adaptability and can effectively predict the fault probability and life cycle status.Practical data from a specific type of on-board train control system is employed for the validation of the presented solution.The comparative results indicate that GAN-based sample augmentation is capable of achieving a desirable sample balancing level and enhancing the performance of correspondingly derived fault prediction models for the Condition-based Maintenance(CBM)operations.
基金supported by Jiangxi Provincial National Science Foundation under Grant No.20142BAB206020
文摘In this paper, finite sample properties of virtual reference feedback tuning control are considered, by using the theory of finite sample properties from system identification. To design a controller in closed loop system structure, the idea of virtual reference feedback tuning is proposed to avoid the identification process corresponding to the plant model. After constructing one identification cost without any knowledge of plant model, the author derives one bound on the difference between the expected identification cost and its sample identification cost under the condition that the number of data points is finite. Also the correlation between the plant input and external noise is considered in the derivation of this bound. Furthermore, the author continues to derive one probability bound to quantify this difference by using some probability inequalities and control theory.
基金supported by the National Science Fund for Excellent Young Scholars(52122311)the National Natural Science Foundation of China(52373222,92367111).
文摘Solution styrene-butadiene rubber(SSBR)finds wide applications in high performance tire design and various other fields.This study aims to create a quantitative structure–property relationship(QSPR)model linking SSBR's glass transition temperature(Tg)to its structural properties.A dataset of 68 sets of data from published literature was compiled to develop a predictive machine learning model for SSBR's structural design and synthesis using small sample sizes.To tackle small sample sizes,a framework combining generative adversarial networks(GAN)and the Tree-based Pipeline Optimization Tool(TPOT)is proposed.GAN is first used to generate additional samples that mirror the original dataset's distribution,expanding the dataset.The TPOT is then applied to automatically find the best model and parameter combinations,creating an optimal predictive model for the mixed dataset.Experimental results show that using GAN to enlarge the dataset and TPOT regression models significantly enhances model performance,increasing the R2 value from 0.745 to 0.985 and decreasing the RMSE from 7.676 to 1.569.The proposed GAN–TPOT framework demonstrates the potential of combining generative models with automated machine learning to improve materials science research.This combination accelerates research and development processes,enhances prediction and design accuracy,and introduces new perspectives and possibilities for the field.
