Federated learning has been used extensively in business inno-vation scenarios in various industries.This research adopts the federated learning approach for the first time to address the issue of bank-enterprise info...Federated learning has been used extensively in business inno-vation scenarios in various industries.This research adopts the federated learning approach for the first time to address the issue of bank-enterprise information asymmetry in the credit assessment scenario.First,this research designs a credit risk assessment model based on federated learning and feature selection for micro and small enterprises(MSEs)using multi-dimensional enterprise data and multi-perspective enterprise information.The proposed model includes four main processes:namely encrypted entity alignment,hybrid feature selection,secure multi-party computation,and global model updating.Secondly,a two-step feature selection algorithm based on wrapper and filter is designed to construct the optimal feature set in multi-source heterogeneous data,which can provide excellent accuracy and interpretability.In addition,a local update screening strategy is proposed to select trustworthy model parameters for aggregation each time to ensure the quality of the global model.The results of the study show that the model error rate is reduced by 6.22%and the recall rate is improved by 11.03%compared to the algorithms commonly used in credit risk research,significantly improving the ability to identify defaulters.Finally,the business operations of commercial banks are used to confirm the potential of the proposed model for real-world implementation.展开更多
Due to the rapid development of logistic industry, transportation cost is also increasing, and finding trends in transportation activities will impact positively in investment in transportation infrastructure. There i...Due to the rapid development of logistic industry, transportation cost is also increasing, and finding trends in transportation activities will impact positively in investment in transportation infrastructure. There is limited literature and data-driven analysis about trends in transportation mode. This thesis delves into the operational challenges of vehicle performance management within logistics clusters, a critical aspect of efficient supply chain operations. It aims to address the issues faced by logistics organizations in optimizing their vehicle fleets’ performance, essential for seamless logistics operations. The study’s core design involves the development of a predictive logistics model based on regression, focused on forecasting, and evaluating vehicle performance in logistics clusters. It encompasses a comprehensive literature review, research methodology, data sources, variables, feature engineering, and model training and evaluation and F-test analysis was done to identify and verify the relationships between attributes and the target variable. The findings highlight the model’s efficacy, with a low mean squared error (MSE) value of 3.42, indicating its accuracy in predicting performance metrics. The high R-squared (R2) score of 0.921 emphasizes its ability to capture relationships between input characteristics and performance metrics. The model’s training and testing accuracy further attest to its reliability and generalization capabilities. In interpretation, this research underscores the practical significance of the findings. The regression-based model provides a practical solution for the logistics industry, enabling informed decisions regarding resource allocation, maintenance planning, and delivery route optimization. This contributes to enhanced overall logistics performance and customer service. By addressing performance gaps and embracing modern logistics technologies, the study supports the ongoing evolution of vehicle performance management in logistics clusters, fostering increased competitiveness and sustainability in the logistics sector.展开更多
It is quite often that the theoretic model used in the Kalman filtering may not be sufficiently accurate for practical applications,due to the fact that the covariances of noises are not exactly known.Our previous wor...It is quite often that the theoretic model used in the Kalman filtering may not be sufficiently accurate for practical applications,due to the fact that the covariances of noises are not exactly known.Our previous work reveals that in such scenario the filter calculated mean square errors(FMSE)and the true mean square errors(TMSE)become inconsistent,while FMSE and TMSE are consistent in the Kalman filter with accurate models.This can lead to low credibility of state estimation regardless of using Kalman filters or adaptive Kalman filters.Obviously,it is important to study the inconsistency issue since it is vital to understand the quantitative influence induced by the inaccurate models.Aiming at this,the concept of credibility is adopted to discuss the inconsistency problem in this paper.In order to formulate the degree of the credibility,a trust factor is constructed based on the FMSE and the TMSE.However,the trust factor can not be directly computed since the TMSE cannot be found for practical applications.Based on the definition of trust factor,the estimation of the trust factor is successfully modified to online estimation of the TMSE.More importantly,a necessary and sufficient condition is found,which turns out to be the basis for better design of Kalman filters with high performance.Accordingly,beyond trust factor estimation with Sage-Husa technique(TFE-SHT),three novel trust factor estimation methods,which are directly numerical solving method(TFE-DNS),the particle swarm optimization method(PSO)and expectation maximization-particle swarm optimization method(EM-PSO)are proposed.The analysis and simulation results both show that the proposed TFE-DNS is better than the TFE-SHT for the case of single unknown noise covariance.Meanwhile,the proposed EMPSO performs completely better than the EM and PSO on the estimation of the credibility degree and state when both noise covariances should be estimated online.展开更多
Despite some efforts and attempts have been made to improve the direction-of-arrival(DOA)estimation performance of the standard Capon beamformer(SCB)in array processing,rigorous statistical performance analyses of the...Despite some efforts and attempts have been made to improve the direction-of-arrival(DOA)estimation performance of the standard Capon beamformer(SCB)in array processing,rigorous statistical performance analyses of these modified Capon estimators are still lacking.This paper studies an improved Capon estimator(ICE)for estimating the DOAs of multiple uncorrelated narrowband signals,where the higherorder inverse(sample)array covariance matrix is used in the Capon-like cost function.By establishing the relationship between this nonparametric estimator and the parametric and classic subspace-based MUSIC(multiple signal classification),it is clarified that as long as the power order of the inverse covariance matrix is increased to reduce the influence of signal subspace components in the ICE,the estimation performance of the ICE becomes equivalent to that of the MUSIC regardless of the signal-to-noise ratio(SNR).Furthermore the statistical performance of the ICE is analyzed,and the large-sample mean-squared-error(MSE)expression of the estimated DOA is derived.Finally the effectiveness and the theoretical analysis of the ICE are substantiated through numerical examples,where the Cramer-Rao lower bound(CRB)is used to evaluate the validity of the derived asymptotic MSE expression.展开更多
Objective:To systematically study the key active ingredients of Jiuwei Huaban pill in the treatment of psoriasis and explore its mechanism of action.Methods:The chemical components of Jiuwei Huaban pill were systemati...Objective:To systematically study the key active ingredients of Jiuwei Huaban pill in the treatment of psoriasis and explore its mechanism of action.Methods:The chemical components of Jiuwei Huaban pill were systematically identified by UPLC-QTOF/MSE,and network pharmacology method was used to study the main pharmacodynamic substances of Jiuwei Huaban pill and discuss the mechanism of action.Molecular docking technology was used to verify the binding activity of key components and target proteins.Results:A total of 75 components of Jiuwei Huaban pill were identified,35 of which were key components for psoriasis treatment,including luteolin,baicalin,baicalin,paeoniflorin and geniposide.Enrichment analysis of 30 core target pathways showed that Jiuwei Huaban pill played its role in the treatment of psoriasis from the IL-17 signaling pathway,TNF signaling pathway,PI3K-Akt signaling pathway,Th17 signaling pathway,and MAPK signaling pathway.The key active components in Jiuwei Huaban pill and targets with the highest DGREE value in the"component target disease"network have strong binding activity.Conclusion:The active ingredients and mechanism of action of Jiuwei Huaban pill in the treatment of psoriasis were preliminarily clarified,which provids reference for quality control.展开更多
In this paper, the estimators of the scale parameter of the exponential distribution obtained by applying four methods, using complete data, are critically examined and compared. These methods are the Maximum Likeliho...In this paper, the estimators of the scale parameter of the exponential distribution obtained by applying four methods, using complete data, are critically examined and compared. These methods are the Maximum Likelihood Estimator (MLE), the Square-Error Loss Function (BSE), the Entropy Loss Function (BEN) and the Composite LINEX Loss Function (BCL). The performance of these four methods was compared based on three criteria: the Mean Square Error (MSE), the Akaike Information Criterion (AIC), and the Bayesian Information Criterion (BIC). Using Monte Carlo simulation based on relevant samples, the comparisons in this study suggest that the Bayesian method is better than the maximum likelihood estimator with respect to the estimation of the parameter that offers the smallest values of MSE, AIC, and BIC. Confidence intervals were then assessed to test the performance of the methods by comparing the 95% CI and average lengths (AL) for all estimation methods, showing that the Bayesian methods still offer the best performance in terms of generating the smallest ALs.展开更多
Local markets in East Africa have been destroyed by raging fires,leading to the loss of life and property in the nearby communities.Electrical circuits,arson,and neglected charcoal stoves are the major causes of these...Local markets in East Africa have been destroyed by raging fires,leading to the loss of life and property in the nearby communities.Electrical circuits,arson,and neglected charcoal stoves are the major causes of these fires.Previous methods,i.e.,satellites,are expensive to maintain and cause unnecessary delays.Also,unit-smoke detectors are highly prone to false alerts.In this paper,an Interval Type-2 TSK fuzzy model for an intelligent lightweight fire intensity detection algorithm with decision-making in low-power devices is proposed using a sparse inference rules approach.A free open–source MATLAB/Simulink fuzzy toolbox integrated into MATLAB 2018a is used to investigate the performance of the Interval Type-2 fuzzy model.Two crisp input parameters,namely:FIT and FIG��are used.Results show that the Interval Type-2 model achieved an accuracy value of FIO�=98.2%,MAE=1.3010,MSE=1.6938 and RMSE=1.3015 using regression analysis.The study shall assist the firefighting personnel in fully understanding and mitigating the current level of fire danger.As a result,the proposed solution can be fully implemented in low-cost,low-power fire detection systems to monitor the state of fire with improved accuracy and reduced false alerts.Through informed decision-making in low-cost fire detection devices,early warning notifications can be provided to aid in the rapid evacuation of people,thereby improving fire safety surveillance,management,and protection for the market community.展开更多
Cohesion is an important soil strength parameter for the overall structure and quality of building foundations as well as slope stability. For a civil engineering project, cohesion (c) can be determined directly from ...Cohesion is an important soil strength parameter for the overall structure and quality of building foundations as well as slope stability. For a civil engineering project, cohesion (c) can be determined directly from mainly unconfined compression tests, direct shear tests, and triaxial tests of soil. However, it’s quite challenging to collect soil samples as there are time and cost constraints, as well as a diversity of soil deposits. Hence, this research aims to demonstrate a simplified method in order to determine the strength parameter of cohesive soil. Here, we propose an alternative solution adopting statistical correlations and machine learning techniques to establish correlations between the liquid limit, plastic limit, moisture content and %fine of soil with the strength parameter. In laboratory testing, these parameters can be obtained easily and these tests are relatively simple, quick to perform and also comparatively inexpensive. Hence, several test results were used from 100 boreholes which were soft soil or silty clay-type soil. Using the collected in-situ and lab test results of soil samples, a Multiple Linear Regression (MLR), Random Forest Regression (RFR) and Machine Learning (ML) model will be developed to establish a relationship between cohesion and the available test results. In order to assess the performances of both models, several performance indicators like: correlation coefficient (R<sup>2</sup>), mean squared error (MSE), root mean square error (RMSE), and mean average error (MAE) will be used. These correlation coefficients will be used to demonstrate the prediction capacity and accuracy of both models. It should be noted that this approach will substitute the conventional testing required for strength parameters, which is both expensive and time-consuming.展开更多
基金funded by the State Grid Jiangsu Electric Power Company(Grant No.JS2020112)the National Natural Science Foundation of China(Grant No.62272236).
文摘Federated learning has been used extensively in business inno-vation scenarios in various industries.This research adopts the federated learning approach for the first time to address the issue of bank-enterprise information asymmetry in the credit assessment scenario.First,this research designs a credit risk assessment model based on federated learning and feature selection for micro and small enterprises(MSEs)using multi-dimensional enterprise data and multi-perspective enterprise information.The proposed model includes four main processes:namely encrypted entity alignment,hybrid feature selection,secure multi-party computation,and global model updating.Secondly,a two-step feature selection algorithm based on wrapper and filter is designed to construct the optimal feature set in multi-source heterogeneous data,which can provide excellent accuracy and interpretability.In addition,a local update screening strategy is proposed to select trustworthy model parameters for aggregation each time to ensure the quality of the global model.The results of the study show that the model error rate is reduced by 6.22%and the recall rate is improved by 11.03%compared to the algorithms commonly used in credit risk research,significantly improving the ability to identify defaulters.Finally,the business operations of commercial banks are used to confirm the potential of the proposed model for real-world implementation.
文摘Due to the rapid development of logistic industry, transportation cost is also increasing, and finding trends in transportation activities will impact positively in investment in transportation infrastructure. There is limited literature and data-driven analysis about trends in transportation mode. This thesis delves into the operational challenges of vehicle performance management within logistics clusters, a critical aspect of efficient supply chain operations. It aims to address the issues faced by logistics organizations in optimizing their vehicle fleets’ performance, essential for seamless logistics operations. The study’s core design involves the development of a predictive logistics model based on regression, focused on forecasting, and evaluating vehicle performance in logistics clusters. It encompasses a comprehensive literature review, research methodology, data sources, variables, feature engineering, and model training and evaluation and F-test analysis was done to identify and verify the relationships between attributes and the target variable. The findings highlight the model’s efficacy, with a low mean squared error (MSE) value of 3.42, indicating its accuracy in predicting performance metrics. The high R-squared (R2) score of 0.921 emphasizes its ability to capture relationships between input characteristics and performance metrics. The model’s training and testing accuracy further attest to its reliability and generalization capabilities. In interpretation, this research underscores the practical significance of the findings. The regression-based model provides a practical solution for the logistics industry, enabling informed decisions regarding resource allocation, maintenance planning, and delivery route optimization. This contributes to enhanced overall logistics performance and customer service. By addressing performance gaps and embracing modern logistics technologies, the study supports the ongoing evolution of vehicle performance management in logistics clusters, fostering increased competitiveness and sustainability in the logistics sector.
基金supported by the National Natural Science Foundation of China(62033010)Aeronautical Science Foundation of China(2019460T5001)。
文摘It is quite often that the theoretic model used in the Kalman filtering may not be sufficiently accurate for practical applications,due to the fact that the covariances of noises are not exactly known.Our previous work reveals that in such scenario the filter calculated mean square errors(FMSE)and the true mean square errors(TMSE)become inconsistent,while FMSE and TMSE are consistent in the Kalman filter with accurate models.This can lead to low credibility of state estimation regardless of using Kalman filters or adaptive Kalman filters.Obviously,it is important to study the inconsistency issue since it is vital to understand the quantitative influence induced by the inaccurate models.Aiming at this,the concept of credibility is adopted to discuss the inconsistency problem in this paper.In order to formulate the degree of the credibility,a trust factor is constructed based on the FMSE and the TMSE.However,the trust factor can not be directly computed since the TMSE cannot be found for practical applications.Based on the definition of trust factor,the estimation of the trust factor is successfully modified to online estimation of the TMSE.More importantly,a necessary and sufficient condition is found,which turns out to be the basis for better design of Kalman filters with high performance.Accordingly,beyond trust factor estimation with Sage-Husa technique(TFE-SHT),three novel trust factor estimation methods,which are directly numerical solving method(TFE-DNS),the particle swarm optimization method(PSO)and expectation maximization-particle swarm optimization method(EM-PSO)are proposed.The analysis and simulation results both show that the proposed TFE-DNS is better than the TFE-SHT for the case of single unknown noise covariance.Meanwhile,the proposed EMPSO performs completely better than the EM and PSO on the estimation of the credibility degree and state when both noise covariances should be estimated online.
基金supported in part by the National Natural Science Foundation of China(62201447)the Project Supported by Natural Science Basic Research Plan in Shaanxi Province of China(2022JQ-640)。
文摘Despite some efforts and attempts have been made to improve the direction-of-arrival(DOA)estimation performance of the standard Capon beamformer(SCB)in array processing,rigorous statistical performance analyses of these modified Capon estimators are still lacking.This paper studies an improved Capon estimator(ICE)for estimating the DOAs of multiple uncorrelated narrowband signals,where the higherorder inverse(sample)array covariance matrix is used in the Capon-like cost function.By establishing the relationship between this nonparametric estimator and the parametric and classic subspace-based MUSIC(multiple signal classification),it is clarified that as long as the power order of the inverse covariance matrix is increased to reduce the influence of signal subspace components in the ICE,the estimation performance of the ICE becomes equivalent to that of the MUSIC regardless of the signal-to-noise ratio(SNR).Furthermore the statistical performance of the ICE is analyzed,and the large-sample mean-squared-error(MSE)expression of the estimated DOA is derived.Finally the effectiveness and the theoretical analysis of the ICE are substantiated through numerical examples,where the Cramer-Rao lower bound(CRB)is used to evaluate the validity of the derived asymptotic MSE expression.
基金National Major Science and Technology Project for"Major New Drug Development"(No.2017ZX09301005)。
文摘Objective:To systematically study the key active ingredients of Jiuwei Huaban pill in the treatment of psoriasis and explore its mechanism of action.Methods:The chemical components of Jiuwei Huaban pill were systematically identified by UPLC-QTOF/MSE,and network pharmacology method was used to study the main pharmacodynamic substances of Jiuwei Huaban pill and discuss the mechanism of action.Molecular docking technology was used to verify the binding activity of key components and target proteins.Results:A total of 75 components of Jiuwei Huaban pill were identified,35 of which were key components for psoriasis treatment,including luteolin,baicalin,baicalin,paeoniflorin and geniposide.Enrichment analysis of 30 core target pathways showed that Jiuwei Huaban pill played its role in the treatment of psoriasis from the IL-17 signaling pathway,TNF signaling pathway,PI3K-Akt signaling pathway,Th17 signaling pathway,and MAPK signaling pathway.The key active components in Jiuwei Huaban pill and targets with the highest DGREE value in the"component target disease"network have strong binding activity.Conclusion:The active ingredients and mechanism of action of Jiuwei Huaban pill in the treatment of psoriasis were preliminarily clarified,which provids reference for quality control.
文摘In this paper, the estimators of the scale parameter of the exponential distribution obtained by applying four methods, using complete data, are critically examined and compared. These methods are the Maximum Likelihood Estimator (MLE), the Square-Error Loss Function (BSE), the Entropy Loss Function (BEN) and the Composite LINEX Loss Function (BCL). The performance of these four methods was compared based on three criteria: the Mean Square Error (MSE), the Akaike Information Criterion (AIC), and the Bayesian Information Criterion (BIC). Using Monte Carlo simulation based on relevant samples, the comparisons in this study suggest that the Bayesian method is better than the maximum likelihood estimator with respect to the estimation of the parameter that offers the smallest values of MSE, AIC, and BIC. Confidence intervals were then assessed to test the performance of the methods by comparing the 95% CI and average lengths (AL) for all estimation methods, showing that the Bayesian methods still offer the best performance in terms of generating the smallest ALs.
文摘Local markets in East Africa have been destroyed by raging fires,leading to the loss of life and property in the nearby communities.Electrical circuits,arson,and neglected charcoal stoves are the major causes of these fires.Previous methods,i.e.,satellites,are expensive to maintain and cause unnecessary delays.Also,unit-smoke detectors are highly prone to false alerts.In this paper,an Interval Type-2 TSK fuzzy model for an intelligent lightweight fire intensity detection algorithm with decision-making in low-power devices is proposed using a sparse inference rules approach.A free open–source MATLAB/Simulink fuzzy toolbox integrated into MATLAB 2018a is used to investigate the performance of the Interval Type-2 fuzzy model.Two crisp input parameters,namely:FIT and FIG��are used.Results show that the Interval Type-2 model achieved an accuracy value of FIO�=98.2%,MAE=1.3010,MSE=1.6938 and RMSE=1.3015 using regression analysis.The study shall assist the firefighting personnel in fully understanding and mitigating the current level of fire danger.As a result,the proposed solution can be fully implemented in low-cost,low-power fire detection systems to monitor the state of fire with improved accuracy and reduced false alerts.Through informed decision-making in low-cost fire detection devices,early warning notifications can be provided to aid in the rapid evacuation of people,thereby improving fire safety surveillance,management,and protection for the market community.
文摘Cohesion is an important soil strength parameter for the overall structure and quality of building foundations as well as slope stability. For a civil engineering project, cohesion (c) can be determined directly from mainly unconfined compression tests, direct shear tests, and triaxial tests of soil. However, it’s quite challenging to collect soil samples as there are time and cost constraints, as well as a diversity of soil deposits. Hence, this research aims to demonstrate a simplified method in order to determine the strength parameter of cohesive soil. Here, we propose an alternative solution adopting statistical correlations and machine learning techniques to establish correlations between the liquid limit, plastic limit, moisture content and %fine of soil with the strength parameter. In laboratory testing, these parameters can be obtained easily and these tests are relatively simple, quick to perform and also comparatively inexpensive. Hence, several test results were used from 100 boreholes which were soft soil or silty clay-type soil. Using the collected in-situ and lab test results of soil samples, a Multiple Linear Regression (MLR), Random Forest Regression (RFR) and Machine Learning (ML) model will be developed to establish a relationship between cohesion and the available test results. In order to assess the performances of both models, several performance indicators like: correlation coefficient (R<sup>2</sup>), mean squared error (MSE), root mean square error (RMSE), and mean average error (MAE) will be used. These correlation coefficients will be used to demonstrate the prediction capacity and accuracy of both models. It should be noted that this approach will substitute the conventional testing required for strength parameters, which is both expensive and time-consuming.
