Despite the maturity of ensemble numerical weather prediction(NWP),the resulting forecasts are still,more often than not,under-dispersed.As such,forecast calibration tools have become popular.Among those tools,quantil...Despite the maturity of ensemble numerical weather prediction(NWP),the resulting forecasts are still,more often than not,under-dispersed.As such,forecast calibration tools have become popular.Among those tools,quantile regression(QR)is highly competitive in terms of both flexibility and predictive performance.Nevertheless,a long-standing problem of QR is quantile crossing,which greatly limits the interpretability of QR-calibrated forecasts.On this point,this study proposes a non-crossing quantile regression neural network(NCQRNN),for calibrating ensemble NWP forecasts into a set of reliable quantile forecasts without crossing.The overarching design principle of NCQRNN is to add on top of the conventional QRNN structure another hidden layer,which imposes a non-decreasing mapping between the combined output from nodes of the last hidden layer to the nodes of the output layer,through a triangular weight matrix with positive entries.The empirical part of the work considers a solar irradiance case study,in which four years of ensemble irradiance forecasts at seven locations,issued by the European Centre for Medium-Range Weather Forecasts,are calibrated via NCQRNN,as well as via an eclectic mix of benchmarking models,ranging from the naïve climatology to the state-of-the-art deep-learning and other non-crossing models.Formal and stringent forecast verification suggests that the forecasts post-processed via NCQRNN attain the maximum sharpness subject to calibration,amongst all competitors.Furthermore,the proposed conception to resolve quantile crossing is remarkably simple yet general,and thus has broad applicability as it can be integrated with many shallow-and deep-learning-based neural networks.展开更多
Matrix method is being proposed for qualitative evaluation of the reliability of technical systems on a finite set of structural elements. We are introducing the criteria for qualitative assessment of the reliability ...Matrix method is being proposed for qualitative evaluation of the reliability of technical systems on a finite set of structural elements. We are introducing the criteria for qualitative assessment of the reliability in the form of structural reliability of the system as the probability of the troubleproof state of this system and the significancy of the individual elements in ensuring the structural reliability of the system as a general aggregate of conditional probabilities, which compose two (2 × 2) matrices of significancy for each element. We are using chain diagrams for solving the combinatronic problems and matrices for algorithmization of calculating procedures.展开更多
采用可靠性框图或故障树建模基础上,基于最小路集和最小割集不交化计算系统可靠度方程,提出了给定可靠度的寿命下限评定的 Monte Carlo 仿真方法,通过应用举例说明了评定过程。针对大型复杂系统,解决了直接由可靠性框图或故障树,评定给...采用可靠性框图或故障树建模基础上,基于最小路集和最小割集不交化计算系统可靠度方程,提出了给定可靠度的寿命下限评定的 Monte Carlo 仿真方法,通过应用举例说明了评定过程。针对大型复杂系统,解决了直接由可靠性框图或故障树,评定给定可靠度的寿命下限问题。所提出的方法便于工程应用。展开更多
Recently, the physics-of-failure(PoF) method has been more and more popular in engineering to understand the failure mechanisms(FMs) of products.However, due to the lack of system modeling methods and problem-solving ...Recently, the physics-of-failure(PoF) method has been more and more popular in engineering to understand the failure mechanisms(FMs) of products.However, due to the lack of system modeling methods and problem-solving algorithms,the information of FMs cannot be used to evaluate system reliability.This paper presents a system reliability evaluation method with failure mechanism tree(FMT) considering physical dependency(PDEP) such as competition, trigger, acceleration, inhibition, damage accumulation, and parameter combination.And the binary decision diagram(BDD) analytical algorithm is developed to establish a system reliability model.The operation rules of ite operators for generating BDD are discussed.The flow chart of system reliability evaluation method based on FMT and BDD is proposed.The proposed method is applied in the case of an electronic controller drive unit.Results show that the method is effective to evaluate system reliability from the perspective of FM.展开更多
基金supported by the National Natural Science Foundation of China (Project No.42375192)the China Meteorological Administration Climate Change Special Program (CMA-CCSP+1 种基金Project No.QBZ202315)support by the Vector Stiftung through the Young Investigator Group"Artificial Intelligence for Probabilistic Weather Forecasting."
文摘Despite the maturity of ensemble numerical weather prediction(NWP),the resulting forecasts are still,more often than not,under-dispersed.As such,forecast calibration tools have become popular.Among those tools,quantile regression(QR)is highly competitive in terms of both flexibility and predictive performance.Nevertheless,a long-standing problem of QR is quantile crossing,which greatly limits the interpretability of QR-calibrated forecasts.On this point,this study proposes a non-crossing quantile regression neural network(NCQRNN),for calibrating ensemble NWP forecasts into a set of reliable quantile forecasts without crossing.The overarching design principle of NCQRNN is to add on top of the conventional QRNN structure another hidden layer,which imposes a non-decreasing mapping between the combined output from nodes of the last hidden layer to the nodes of the output layer,through a triangular weight matrix with positive entries.The empirical part of the work considers a solar irradiance case study,in which four years of ensemble irradiance forecasts at seven locations,issued by the European Centre for Medium-Range Weather Forecasts,are calibrated via NCQRNN,as well as via an eclectic mix of benchmarking models,ranging from the naïve climatology to the state-of-the-art deep-learning and other non-crossing models.Formal and stringent forecast verification suggests that the forecasts post-processed via NCQRNN attain the maximum sharpness subject to calibration,amongst all competitors.Furthermore,the proposed conception to resolve quantile crossing is remarkably simple yet general,and thus has broad applicability as it can be integrated with many shallow-and deep-learning-based neural networks.
文摘Matrix method is being proposed for qualitative evaluation of the reliability of technical systems on a finite set of structural elements. We are introducing the criteria for qualitative assessment of the reliability in the form of structural reliability of the system as the probability of the troubleproof state of this system and the significancy of the individual elements in ensuring the structural reliability of the system as a general aggregate of conditional probabilities, which compose two (2 × 2) matrices of significancy for each element. We are using chain diagrams for solving the combinatronic problems and matrices for algorithmization of calculating procedures.
基金supported by the National Natural Science Foundation of China (6150301462073009)。
文摘Recently, the physics-of-failure(PoF) method has been more and more popular in engineering to understand the failure mechanisms(FMs) of products.However, due to the lack of system modeling methods and problem-solving algorithms,the information of FMs cannot be used to evaluate system reliability.This paper presents a system reliability evaluation method with failure mechanism tree(FMT) considering physical dependency(PDEP) such as competition, trigger, acceleration, inhibition, damage accumulation, and parameter combination.And the binary decision diagram(BDD) analytical algorithm is developed to establish a system reliability model.The operation rules of ite operators for generating BDD are discussed.The flow chart of system reliability evaluation method based on FMT and BDD is proposed.The proposed method is applied in the case of an electronic controller drive unit.Results show that the method is effective to evaluate system reliability from the perspective of FM.
