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
Good distribution of samples and weights can improve the computational accuracy and efficiency in the stochastic response analyses of aerospace problems with uncertain parameters.This work proposes a new Generalized L...Good distribution of samples and weights can improve the computational accuracy and efficiency in the stochastic response analyses of aerospace problems with uncertain parameters.This work proposes a new Generalized L2 Discrepancy based on a General Point(GL2D-GP)for generating samples and their corresponding weights.The proposed GL2D-GP is an extension of the existing discrepancy by introducing the non-same weights and a smaller box to measure probability errors.Minimizing the GL2D-GP can yield a weight optimization formula that generates a set of optimal non-identical weights for a given sample set.Through minimizing the GL2D-GP assigned to the set of optimal non-same weights,a new sample and weight generation method is developed.In the proposed method,the samples can be easily generated in terms of the generalized Halton formula with a series of optimal permutation vectors which are found by the intelligent evolutionary algorithm.Once the sample set is obtained,the optimal weights can be generated in terms of the weight optimization formula.Five numerical examples are presented to verify the high accuracy,efficiency,and strong robustness of the proposed sample generation method based on GL2D-GP.展开更多
Regular fastener detection is necessary to ensure the safety of railways.However,the number of abnormal fasteners is significantly lower than the number of normal fasteners in real railways.Existing supervised inspect...Regular fastener detection is necessary to ensure the safety of railways.However,the number of abnormal fasteners is significantly lower than the number of normal fasteners in real railways.Existing supervised inspectionmethods have insufficient detection ability in cases of imbalanced samples.To solve this problem,we propose an approach based on deep convolutional neural networks(DCNNs),which consists of three stages:fastener localization,abnormal fastener sample generation based on saliency detection,and fastener state inspection.First,a lightweight YOLOv5s is designed to achieve fast and precise localization of fastener regions.Then,the foreground clip region of a fastener image is extracted by the designed fastener saliency detection network(F-SDNet),combined with data augmentation to generate a large number of abnormal fastener samples and balance the number of abnormal and normal samples.Finally,a fastener inspection model called Fastener ResNet-8 is constructed by being trained with the augmented fastener dataset.Results show the effectiveness of our proposed method in solving the problem of sample imbalance in fastener detection.Qualitative and quantitative comparisons show that the proposed F-SDNet outperforms other state-of-the-art methods in clip region extraction,reaching MAE and max F-measure of 0.0215 and 0.9635,respectively.In addition,the FPS of the fastener state inspection model reached 86.2,and the average accuracy reached 98.7%on 614 augmented fastener test sets and 99.9%on 7505 real fastener datasets.展开更多
Deep learning technology is identified as a valid tool for transient stability assessment(TSA).Moreover,the superior performance of the TSA model depends on generously labeled samples.However,the power grid is dynamic...Deep learning technology is identified as a valid tool for transient stability assessment(TSA).Moreover,the superior performance of the TSA model depends on generously labeled samples.However,the power grid is dynamic,and some topologies or operation conditions change substantially.The traditional method generates a significant quantity of samples for each specific topology.Nonetheless,generating these labeled samples and establishing TSA models is very time-consuming.This paper proposes a high-quality sample generation framework based on data-driven methods to build a high-quality offline samples database for TSA model training and updating.Firstly,the representative topologies provided by the system operator are clustered into four different categories by density-based spatial clustering of applications with noise(DBSCAN).Thus the corresponding samples are collected.Then,when a new topology is encountered in the online application,scenario matching is used to match the most similar topology category.After that,instance-based transfer learning is implemented from a database of the best-matched topology category.Finally,a deep convolutional generative adversarial network(DCGAN)is constructed to mitigate the class imbalance problem.That is,unstable scenarios occur far more rarely than stable scenarios.Consequently,a high-quality and balanced TSA model training and updating database is constructed.The comprehensive test results on the Central China Power Grid illustrate that the proposed framework can generate high-quality and balanced TSA samples.Furthermore,the sample generation time is dramatically shortened.In addition,the metrics of accuracy,reliability and adaptability of the TSA model are significantly enhanced.展开更多
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
Training samples for deep learning networks are typically obtained through various field experiments,which require significant manpower,resource and time consumption.However,it is possible to utilize simulated data to...Training samples for deep learning networks are typically obtained through various field experiments,which require significant manpower,resource and time consumption.However,it is possible to utilize simulated data to augment the training samples.In this paper,by comparing the actual experimental model with the simulated model generated by the gprMax[1]forward simulation method,the feasibility of obtaining simulated samples through gprMax simulation is validated.Subsequently,the samples generated by gprMax forward simulation are used for training the network to detect objects in existing real samples.At the same time,aiming at the detection and intelligent recognition of road sub-surface defects,the Swin-YOLOX algorithm is introduced,and the excellence of the detection network,which is improved by augmenting the simulated samples with real samples,is further verified.By comparing the prediction performance of the object detection models,it is observed that the model trained with mixed samples achieved a recall of 94.74%and a mean average precision(maP)of 97.71%,surpassing the model trained only on real samples by 12.95%and 15.64%,respectively.The feasibility and excellence of training the model with mixed samples are confirmed.The potential of using a fusion of simulated and existing real samples instead of repeatedly acquiring new real samples by field experiment is demonstrated by this study,thereby improving detection efficiency,saving resources,and providing a new approach to the problem of multiple interpretations in ground penetrating radar(GPR)data.展开更多
Since the scale and uncertainty of the power system have been rapidly increasing,the computation efficiency of constructing the security region boundary(SRB)has become a prominent problem.Based on the topological feat...Since the scale and uncertainty of the power system have been rapidly increasing,the computation efficiency of constructing the security region boundary(SRB)has become a prominent problem.Based on the topological features of historical operation data,a sample generation method for SRB identification is proposed to generate evenly distributed samples,which cover dominant security modes.The boundary sample pair(BSP)composed of a secure sample and an unsecure sample is defined to describe the feature of SRB.The resolution,sampling,and span indices are designed to evaluate the coverage degree of existing BSPs on the SRB and generate samples closer to the SRB.Based on the feature of flat distribution of BSPs over the SRB,the principal component analysis(PCA)is adopted to calculate the tangent vectors and normal vectors of SRB.Then,the sample distribution can be expanded along the tangent vector and corrected along the normal vector to cover different security modes.Finally,a sample set is randomly gen-erated based on the IEEE standard example and another new sample set is generated by the proposed method.The results indicate that the new sample set is closer to the SRB and covers different security modes with a small calculation time cost.展开更多
With the increasing complexity of power system structures and the increasing penetration of renewable energy,the number of possible power system operation modes increases dramatically.It is difficult to make manual po...With the increasing complexity of power system structures and the increasing penetration of renewable energy,the number of possible power system operation modes increases dramatically.It is difficult to make manual power flow adjustments to establish an initial convergent power flow that is suitable for operation mode analysis.At present,problems of low efficiency and long time consumption are encountered in the formulation of operation modes,resulting in a very limited number of generated operation modes.In this paper,we propose an intelligent power flow adjustment and generation model based on a deep network and reinforcement learning.First,a discriminator is trained to judge the power flow convergence,and the output of this discriminator is used to construct a value function.Then,the reinforcement learning method is adopted to learn a strategy for power flow convergence adjustment.Finally,a large number of convergent power flow samples are generated using the learned adjustment strategy.Compared with the traditional flow adjustment method,the proposed method has significant advantages that the learning of the power flow adjustment strategy does not depend on the parameters of the power system model.Therefore,this strategy can be automatically learned without manual intervention,which allows a large number of different operation modes to be efficiently formulated.The verification results of a case study show that the proposed method can independently learn a power flow adjustment strategy and generate various convergent power flows.展开更多
On-line ion-exchange separation and preconcentration were combined with flow-injection hydride generation atomic absorption spectrometry (HGAAS) to determine ultra-trace amounts of antimony in water samples. Antimony(...On-line ion-exchange separation and preconcentration were combined with flow-injection hydride generation atomic absorption spectrometry (HGAAS) to determine ultra-trace amounts of antimony in water samples. Antimony(Ⅲ) was preconcentrated on a micro-column packed with CPG-8Q chelating ion-exchanger using time-based sample loading and eluted by 4 mol l^(-1) HCl directly into the hydride generation AAS system. A detection limit (3σ) of 0.0015μg l^(-1) Sb(Ⅲ) was obtained on the basis of a 20 fold enrichment and with a sampling frequency of 60h^(-1). The precision was 1.0% r.s.d.(n=11) at the 0.5μg l^(-1) Sb(Ⅲ) level. Recoveries for the analysis of antimony in tap water, snow water and sea water samples were in the range 97-102%.展开更多
Monodisperse aerosols are essential in many applications, such as filter testing, aerosol instrument calibration, and experiments for validating models. This paper describes the design principle, construction, and per...Monodisperse aerosols are essential in many applications, such as filter testing, aerosol instrument calibration, and experiments for validating models. This paper describes the design principle, construction, and performance of a monodisperse-aerosol generation system that comprises an atomizer, virtual impactor, microcontroller-based isokinetic probe, wind tunnel, and velocity measurement device. The size distribution of the produced monodisperse aerosols was determined by an optical particle counter. The effects of atomizer characteristics, the rates of minor and major flow, and solution criteria were investigated. It was found that all these parameters affect the generation of monodisperse aerosol. Finally, the expected geometric standard deviation （〈1.25） of monodisperse aerosol particles was obtained with the most suitable atomizer for 10% oleic acid in ethyl alcohol solution with 5%-15% minor flow, where the ratio between the nozzle-to-probe distance and acceleration-nozzle-exit diameter was 0.66. The con- structed monodisperse-aerosol-generation system can be used for instrumental calibration and aerosol research.展开更多
The steady-state security region(SSR)offers ro-bust support for the security assessment and control of new power systems with high uncertainty and fluctuation.However,accurately solving the steady-state security regio...The steady-state security region(SSR)offers ro-bust support for the security assessment and control of new power systems with high uncertainty and fluctuation.However,accurately solving the steady-state security region boundary(SS-RB),which is high-dimensional,non-convex,and non-linear,presents a significant challenge.To address this problem,this paper proposes a method for approximating the SSRB in power systems using the feature non-linear converter and improved oblique decision tree.First,to better characterize the SSRB,boundary samples are generated using the proposed sampling method.These samples are distributed within a limited distance near the SSRB.Then,to handle the high-dimensionality,non-convexity and non-linearity of the SSRB,boundary samples are converted from the original power injection space to a new fea-ture space using the designed feature non-linear converter.Con-sequently,in this feature space,boundary samples are linearly separated using the proposed information gain rate based weighted oblique decision tree.Finally,the effectiveness and generality of the proposed sampling method are verified on the WECC 3-machine 9-bus system and IEEE 118-bus system.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
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the support of the National Natural Science Foundation of China(Nos.12372190,12272077)the Fundamental Research Funds for the Central Universities,China(Nos.DUT20RC(5)009,DUT20GJ216).
文摘Good distribution of samples and weights can improve the computational accuracy and efficiency in the stochastic response analyses of aerospace problems with uncertain parameters.This work proposes a new Generalized L2 Discrepancy based on a General Point(GL2D-GP)for generating samples and their corresponding weights.The proposed GL2D-GP is an extension of the existing discrepancy by introducing the non-same weights and a smaller box to measure probability errors.Minimizing the GL2D-GP can yield a weight optimization formula that generates a set of optimal non-identical weights for a given sample set.Through minimizing the GL2D-GP assigned to the set of optimal non-same weights,a new sample and weight generation method is developed.In the proposed method,the samples can be easily generated in terms of the generalized Halton formula with a series of optimal permutation vectors which are found by the intelligent evolutionary algorithm.Once the sample set is obtained,the optimal weights can be generated in terms of the weight optimization formula.Five numerical examples are presented to verify the high accuracy,efficiency,and strong robustness of the proposed sample generation method based on GL2D-GP.
基金supported in part by the National Natural Science Foundation of China (Grant Nos.51975347 and 51907117)in part by the Shanghai Science and Technology Program (Grant No.22010501600).
文摘Regular fastener detection is necessary to ensure the safety of railways.However,the number of abnormal fasteners is significantly lower than the number of normal fasteners in real railways.Existing supervised inspectionmethods have insufficient detection ability in cases of imbalanced samples.To solve this problem,we propose an approach based on deep convolutional neural networks(DCNNs),which consists of three stages:fastener localization,abnormal fastener sample generation based on saliency detection,and fastener state inspection.First,a lightweight YOLOv5s is designed to achieve fast and precise localization of fastener regions.Then,the foreground clip region of a fastener image is extracted by the designed fastener saliency detection network(F-SDNet),combined with data augmentation to generate a large number of abnormal fastener samples and balance the number of abnormal and normal samples.Finally,a fastener inspection model called Fastener ResNet-8 is constructed by being trained with the augmented fastener dataset.Results show the effectiveness of our proposed method in solving the problem of sample imbalance in fastener detection.Qualitative and quantitative comparisons show that the proposed F-SDNet outperforms other state-of-the-art methods in clip region extraction,reaching MAE and max F-measure of 0.0215 and 0.9635,respectively.In addition,the FPS of the fastener state inspection model reached 86.2,and the average accuracy reached 98.7%on 614 augmented fastener test sets and 99.9%on 7505 real fastener datasets.
基金supported by the Technology Project from China Electric Power Planning&Engineering Institute(No.K202312)。
文摘Deep learning technology is identified as a valid tool for transient stability assessment(TSA).Moreover,the superior performance of the TSA model depends on generously labeled samples.However,the power grid is dynamic,and some topologies or operation conditions change substantially.The traditional method generates a significant quantity of samples for each specific topology.Nonetheless,generating these labeled samples and establishing TSA models is very time-consuming.This paper proposes a high-quality sample generation framework based on data-driven methods to build a high-quality offline samples database for TSA model training and updating.Firstly,the representative topologies provided by the system operator are clustered into four different categories by density-based spatial clustering of applications with noise(DBSCAN).Thus the corresponding samples are collected.Then,when a new topology is encountered in the online application,scenario matching is used to match the most similar topology category.After that,instance-based transfer learning is implemented from a database of the best-matched topology category.Finally,a deep convolutional generative adversarial network(DCGAN)is constructed to mitigate the class imbalance problem.That is,unstable scenarios occur far more rarely than stable scenarios.Consequently,a high-quality and balanced TSA model training and updating database is constructed.The comprehensive test results on the Central China Power Grid illustrate that the proposed framework can generate high-quality and balanced TSA samples.Furthermore,the sample generation time is dramatically shortened.In addition,the metrics of accuracy,reliability and adaptability of the TSA model are significantly enhanced.
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Training samples for deep learning networks are typically obtained through various field experiments,which require significant manpower,resource and time consumption.However,it is possible to utilize simulated data to augment the training samples.In this paper,by comparing the actual experimental model with the simulated model generated by the gprMax[1]forward simulation method,the feasibility of obtaining simulated samples through gprMax simulation is validated.Subsequently,the samples generated by gprMax forward simulation are used for training the network to detect objects in existing real samples.At the same time,aiming at the detection and intelligent recognition of road sub-surface defects,the Swin-YOLOX algorithm is introduced,and the excellence of the detection network,which is improved by augmenting the simulated samples with real samples,is further verified.By comparing the prediction performance of the object detection models,it is observed that the model trained with mixed samples achieved a recall of 94.74%and a mean average precision(maP)of 97.71%,surpassing the model trained only on real samples by 12.95%and 15.64%,respectively.The feasibility and excellence of training the model with mixed samples are confirmed.The potential of using a fusion of simulated and existing real samples instead of repeatedly acquiring new real samples by field experiment is demonstrated by this study,thereby improving detection efficiency,saving resources,and providing a new approach to the problem of multiple interpretations in ground penetrating radar(GPR)data.
文摘Since the scale and uncertainty of the power system have been rapidly increasing,the computation efficiency of constructing the security region boundary(SRB)has become a prominent problem.Based on the topological features of historical operation data,a sample generation method for SRB identification is proposed to generate evenly distributed samples,which cover dominant security modes.The boundary sample pair(BSP)composed of a secure sample and an unsecure sample is defined to describe the feature of SRB.The resolution,sampling,and span indices are designed to evaluate the coverage degree of existing BSPs on the SRB and generate samples closer to the SRB.Based on the feature of flat distribution of BSPs over the SRB,the principal component analysis(PCA)is adopted to calculate the tangent vectors and normal vectors of SRB.Then,the sample distribution can be expanded along the tangent vector and corrected along the normal vector to cover different security modes.Finally,a sample set is randomly gen-erated based on the IEEE standard example and another new sample set is generated by the proposed method.The results indicate that the new sample set is closer to the SRB and covers different security modes with a small calculation time cost.
基金supported by the Science and Technology Project of the State Grid Corporation of China(No.5400-201935258A-0-0-00)the National Natural Science Foundation of China(No.51777104)
文摘With the increasing complexity of power system structures and the increasing penetration of renewable energy,the number of possible power system operation modes increases dramatically.It is difficult to make manual power flow adjustments to establish an initial convergent power flow that is suitable for operation mode analysis.At present,problems of low efficiency and long time consumption are encountered in the formulation of operation modes,resulting in a very limited number of generated operation modes.In this paper,we propose an intelligent power flow adjustment and generation model based on a deep network and reinforcement learning.First,a discriminator is trained to judge the power flow convergence,and the output of this discriminator is used to construct a value function.Then,the reinforcement learning method is adopted to learn a strategy for power flow convergence adjustment.Finally,a large number of convergent power flow samples are generated using the learned adjustment strategy.Compared with the traditional flow adjustment method,the proposed method has significant advantages that the learning of the power flow adjustment strategy does not depend on the parameters of the power system model.Therefore,this strategy can be automatically learned without manual intervention,which allows a large number of different operation modes to be efficiently formulated.The verification results of a case study show that the proposed method can independently learn a power flow adjustment strategy and generate various convergent power flows.
文摘On-line ion-exchange separation and preconcentration were combined with flow-injection hydride generation atomic absorption spectrometry (HGAAS) to determine ultra-trace amounts of antimony in water samples. Antimony(Ⅲ) was preconcentrated on a micro-column packed with CPG-8Q chelating ion-exchanger using time-based sample loading and eluted by 4 mol l^(-1) HCl directly into the hydride generation AAS system. A detection limit (3σ) of 0.0015μg l^(-1) Sb(Ⅲ) was obtained on the basis of a 20 fold enrichment and with a sampling frequency of 60h^(-1). The precision was 1.0% r.s.d.(n=11) at the 0.5μg l^(-1) Sb(Ⅲ) level. Recoveries for the analysis of antimony in tap water, snow water and sea water samples were in the range 97-102%.
文摘Monodisperse aerosols are essential in many applications, such as filter testing, aerosol instrument calibration, and experiments for validating models. This paper describes the design principle, construction, and performance of a monodisperse-aerosol generation system that comprises an atomizer, virtual impactor, microcontroller-based isokinetic probe, wind tunnel, and velocity measurement device. The size distribution of the produced monodisperse aerosols was determined by an optical particle counter. The effects of atomizer characteristics, the rates of minor and major flow, and solution criteria were investigated. It was found that all these parameters affect the generation of monodisperse aerosol. Finally, the expected geometric standard deviation （〈1.25） of monodisperse aerosol particles was obtained with the most suitable atomizer for 10% oleic acid in ethyl alcohol solution with 5%-15% minor flow, where the ratio between the nozzle-to-probe distance and acceleration-nozzle-exit diameter was 0.66. The con- structed monodisperse-aerosol-generation system can be used for instrumental calibration and aerosol research.
基金This work was supported by the National Key Research and Development Program of China(No.2018AAA0101504)the Science and Technology Project of State Grid Corporation of China"fundamental theory of human inthe-loop hybrid-augmented intelligence for power grid dispatch and control".
文摘The steady-state security region(SSR)offers ro-bust support for the security assessment and control of new power systems with high uncertainty and fluctuation.However,accurately solving the steady-state security region boundary(SS-RB),which is high-dimensional,non-convex,and non-linear,presents a significant challenge.To address this problem,this paper proposes a method for approximating the SSRB in power systems using the feature non-linear converter and improved oblique decision tree.First,to better characterize the SSRB,boundary samples are generated using the proposed sampling method.These samples are distributed within a limited distance near the SSRB.Then,to handle the high-dimensionality,non-convexity and non-linearity of the SSRB,boundary samples are converted from the original power injection space to a new fea-ture space using the designed feature non-linear converter.Con-sequently,in this feature space,boundary samples are linearly separated using the proposed information gain rate based weighted oblique decision tree.Finally,the effectiveness and generality of the proposed sampling method are verified on the WECC 3-machine 9-bus system and IEEE 118-bus system.
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.
