With the increasing availability of precipitation radar data from space,enhancement of the resolution of spaceborne precipitation observations is important,particularly for hazard prediction and climate modeling at lo...With the increasing availability of precipitation radar data from space,enhancement of the resolution of spaceborne precipitation observations is important,particularly for hazard prediction and climate modeling at local scales relevant to extreme precipitation intensities and gradients.In this paper,the statistical characteristics of radar precipitation reflectivity data are studied and modeled using a hidden Markov tree(HMT)in the wavelet domain.Then,a high-resolution interpolation algorithm is proposed for spaceborne radar reflectivity using the HMT model as prior information.Owing to the small and transient storm elements embedded in the larger and slowly varying elements,the radar precipitation data exhibit distinct multiscale statistical properties,including a non-Gaussian structure and scale-to-scale dependency.An HMT model can capture well the statistical properties of radar precipitation,where the wavelet coefficients in each sub-band are characterized as a Gaussian mixture model(GMM),and the wavelet coefficients from the coarse scale to fine scale are described using a multiscale Markov process.The state probabilities of the GMM are determined using the expectation maximization method,and other parameters,for instance,the variance decay parameters in the HMT model are learned and estimated from high-resolution ground radar reflectivity images.Using the prior model,the wavelet coefficients at finer scales are estimated using local Wiener filtering.The interpolation algorithm is validated using data from the precipitation radar onboard the Tropical Rainfall Measurement Mission satellite,and the reconstructed results are found to be able to enhance the spatial resolution while optimally reproducing the local extremes and gradients.展开更多
【目的】为提升混合储能系统(hybrid energy storage system,HESS)辅助火电机组响应自动发电控制(automatic generation control,AGC)指令时的调节性能,提出一种基于随机模型预测控制(stochastic model predictive control,SMPC)的火储...【目的】为提升混合储能系统(hybrid energy storage system,HESS)辅助火电机组响应自动发电控制(automatic generation control,AGC)指令时的调节性能,提出一种基于随机模型预测控制(stochastic model predictive control,SMPC)的火储联合功率分配策略。【方法】首先,针对包括功率型储能钛酸锂电池与能量型储能磷酸铁锂电池构成的HESS系统,提出基于马尔科夫概率矩阵构建未来时段火电机组响应AGC指令的HESS功率需求模型,并引入自适应机制实时动态修正状态转移概率,以提升AGC指令波动下的预测精度;其次,提出一种基于概率阈值与分层抽样相结合的场景树生成方法用于将自适应马尔科夫模型输出的概率分布转化为可用于优化的有限场景集合,描述多场景下功率需求预测的不确定性;最后,在上述框架基础上构建随机预测控制器,实现火电机组和HESS的功率最优分配。【结果】仿真实验表明,所提策略在调节性能上优于不考虑功率预测的传统联合调频策略以及未引入动态修正的由静态转移概率矩阵构建的SMPC策略,其性能指标Kp分别提升14.1%和7.5%。【结论】该策略有效提升了火电机组与HESS的协同调节性能,具有较强的应用潜力。未来可以进一步优化模型,提升其在实际应用中的鲁棒性和适应性,推动该技术的实际落地。展开更多
There were various conventional modeling techniques with varied semantics for system reliability assessment, such as fault trees(FT), Markov process(MP), and Petri nets. However, it is strenuous to construct and to ma...There were various conventional modeling techniques with varied semantics for system reliability assessment, such as fault trees(FT), Markov process(MP), and Petri nets. However, it is strenuous to construct and to maintain models utilizing these formalisms throughout the life cycle of system under development. This paper proposes a unified formal modeling language to build a general reliability model. The method eliminates the gap between the actual system and reliability model and shows details of the system clearly. Furthermore,the model could be transformed into FT and MP through specific rules defined by a formal language to assess system-level reliability.展开更多
Fault tree analysis is an effective method for predicting the reliability of a system. It gives a pictorial representation and logical framework for analyzing the reliability. Also, it has been used for a long time as...Fault tree analysis is an effective method for predicting the reliability of a system. It gives a pictorial representation and logical framework for analyzing the reliability. Also, it has been used for a long time as an effective method for the quantitative and qualitative analysis of the failure modes of critical systems. In this paper, we propose a new general coverage model （GCM） based on hardware independent faults. Using this model, an effective software tool can be constructed to detect, locate and recover fault from the faulty system. This model can be applied to identify the key component that can cause the failure of the system using failure mode effect analysis （FMEA）.展开更多
Markov modeling of HIV/AIDS progression was done under the assumption that the state holding time (waiting time) had a constant hazard. This paper discusses the properties of the hazard function of the Exponential dis...Markov modeling of HIV/AIDS progression was done under the assumption that the state holding time (waiting time) had a constant hazard. This paper discusses the properties of the hazard function of the Exponential distributions and its modifications namely;Parameter proportion hazard (PH) and Accelerated failure time models (AFT) and their effectiveness in modeling the state holding time in Markov modeling of HIV/AIDS progression with and without risk factors. Patients were categorized by gender and age with female gender being the baseline. Data simulated using R software was fitted to each model, and the model parameters were estimated. The estimated P and Z values were then used to test the null hypothesis that the state waiting time data followed an Exponential distribution. Model identification criteria;Akaike information criteria (AIC), Bayesian information criteria (BIC), log-likelihood (LL), and R2 were used to evaluate the performance of the models. For the Survival Regression model, P and Z values supported the non-rejection of the null hypothesis for mixed gender without interaction and supported the rejection of the same for mixed gender with interaction term and males aged 50 - 60 years. Both Parameters supported the non-rejection of the null hypothesis in the rest of the age groups. For Gender male with interaction both P and Z values supported rejection in all the age groups except the age group 20 - 30 years. For Cox Proportional hazard and AFT models, both P and Z values supported the non-rejection of the null hypothesis across all age groups. The P-values for the three models supported different decisions for and against the Null hypothesis with AFT and Cox values supporting similar decisions in most of the age groups. Among the models considered, the regression assumption provided a superior fit based on (AIC), (BIC), (LL), and R2 Model identification criteria. This was particularly evident in age and gender subgroups where the data exhibited non-proportional hazards and violated the assumptions required for the Cox Proportional Hazard model. Moreover, the simplicity of the regression model, along with its ability to capture essential state transitions without over fitting, made it a more appropriate choice.展开更多
基金This study was funded by the National Natural Science Foundation of China(Grant No.41975027)the Natural Science Foundation of Jiangsu Province(Grant No.BK20171457)the National Key R&D Program on Monitoring,Early Warning and Prevention of Major Natural Disasters(Grant No.2017YFC1501401).
文摘With the increasing availability of precipitation radar data from space,enhancement of the resolution of spaceborne precipitation observations is important,particularly for hazard prediction and climate modeling at local scales relevant to extreme precipitation intensities and gradients.In this paper,the statistical characteristics of radar precipitation reflectivity data are studied and modeled using a hidden Markov tree(HMT)in the wavelet domain.Then,a high-resolution interpolation algorithm is proposed for spaceborne radar reflectivity using the HMT model as prior information.Owing to the small and transient storm elements embedded in the larger and slowly varying elements,the radar precipitation data exhibit distinct multiscale statistical properties,including a non-Gaussian structure and scale-to-scale dependency.An HMT model can capture well the statistical properties of radar precipitation,where the wavelet coefficients in each sub-band are characterized as a Gaussian mixture model(GMM),and the wavelet coefficients from the coarse scale to fine scale are described using a multiscale Markov process.The state probabilities of the GMM are determined using the expectation maximization method,and other parameters,for instance,the variance decay parameters in the HMT model are learned and estimated from high-resolution ground radar reflectivity images.Using the prior model,the wavelet coefficients at finer scales are estimated using local Wiener filtering.The interpolation algorithm is validated using data from the precipitation radar onboard the Tropical Rainfall Measurement Mission satellite,and the reconstructed results are found to be able to enhance the spatial resolution while optimally reproducing the local extremes and gradients.
文摘【目的】为提升混合储能系统(hybrid energy storage system,HESS)辅助火电机组响应自动发电控制(automatic generation control,AGC)指令时的调节性能,提出一种基于随机模型预测控制(stochastic model predictive control,SMPC)的火储联合功率分配策略。【方法】首先,针对包括功率型储能钛酸锂电池与能量型储能磷酸铁锂电池构成的HESS系统,提出基于马尔科夫概率矩阵构建未来时段火电机组响应AGC指令的HESS功率需求模型,并引入自适应机制实时动态修正状态转移概率,以提升AGC指令波动下的预测精度;其次,提出一种基于概率阈值与分层抽样相结合的场景树生成方法用于将自适应马尔科夫模型输出的概率分布转化为可用于优化的有限场景集合,描述多场景下功率需求预测的不确定性;最后,在上述框架基础上构建随机预测控制器,实现火电机组和HESS的功率最优分配。【结果】仿真实验表明,所提策略在调节性能上优于不考虑功率预测的传统联合调频策略以及未引入动态修正的由静态转移概率矩阵构建的SMPC策略,其性能指标Kp分别提升14.1%和7.5%。【结论】该策略有效提升了火电机组与HESS的协同调节性能,具有较强的应用潜力。未来可以进一步优化模型,提升其在实际应用中的鲁棒性和适应性,推动该技术的实际落地。
文摘There were various conventional modeling techniques with varied semantics for system reliability assessment, such as fault trees(FT), Markov process(MP), and Petri nets. However, it is strenuous to construct and to maintain models utilizing these formalisms throughout the life cycle of system under development. This paper proposes a unified formal modeling language to build a general reliability model. The method eliminates the gap between the actual system and reliability model and shows details of the system clearly. Furthermore,the model could be transformed into FT and MP through specific rules defined by a formal language to assess system-level reliability.
文摘Fault tree analysis is an effective method for predicting the reliability of a system. It gives a pictorial representation and logical framework for analyzing the reliability. Also, it has been used for a long time as an effective method for the quantitative and qualitative analysis of the failure modes of critical systems. In this paper, we propose a new general coverage model （GCM） based on hardware independent faults. Using this model, an effective software tool can be constructed to detect, locate and recover fault from the faulty system. This model can be applied to identify the key component that can cause the failure of the system using failure mode effect analysis （FMEA）.
文摘Markov modeling of HIV/AIDS progression was done under the assumption that the state holding time (waiting time) had a constant hazard. This paper discusses the properties of the hazard function of the Exponential distributions and its modifications namely;Parameter proportion hazard (PH) and Accelerated failure time models (AFT) and their effectiveness in modeling the state holding time in Markov modeling of HIV/AIDS progression with and without risk factors. Patients were categorized by gender and age with female gender being the baseline. Data simulated using R software was fitted to each model, and the model parameters were estimated. The estimated P and Z values were then used to test the null hypothesis that the state waiting time data followed an Exponential distribution. Model identification criteria;Akaike information criteria (AIC), Bayesian information criteria (BIC), log-likelihood (LL), and R2 were used to evaluate the performance of the models. For the Survival Regression model, P and Z values supported the non-rejection of the null hypothesis for mixed gender without interaction and supported the rejection of the same for mixed gender with interaction term and males aged 50 - 60 years. Both Parameters supported the non-rejection of the null hypothesis in the rest of the age groups. For Gender male with interaction both P and Z values supported rejection in all the age groups except the age group 20 - 30 years. For Cox Proportional hazard and AFT models, both P and Z values supported the non-rejection of the null hypothesis across all age groups. The P-values for the three models supported different decisions for and against the Null hypothesis with AFT and Cox values supporting similar decisions in most of the age groups. Among the models considered, the regression assumption provided a superior fit based on (AIC), (BIC), (LL), and R2 Model identification criteria. This was particularly evident in age and gender subgroups where the data exhibited non-proportional hazards and violated the assumptions required for the Cox Proportional Hazard model. Moreover, the simplicity of the regression model, along with its ability to capture essential state transitions without over fitting, made it a more appropriate choice.
