Objective To investigate the human body’s complex system,and classify and characterize the human body’s health states with“a comprehensive integrated method from qualitative to quantitative”.Methods This paper int...Objective To investigate the human body’s complex system,and classify and characterize the human body’s health states with“a comprehensive integrated method from qualitative to quantitative”.Methods This paper introduces the concept of“order parameters”and proposes a method for establishing an order parameter model of gas discharge visualization(GDV)based on the principle of“mastering both permanence and change(MBPC)”.The method involved the fol-lowing three steps.First,average luminous intensity(I)and average area(S)of the GDV im-ages were calculated to construct the phase space,and the score of the health questionnaire was calculated as the health deviation index(H).Second,the k-means++clustering method was employed to identify subclasses with the same health characteristics based on the data samples,and to statistically determine the symptom-specific frequencies of the subclasses.Third,the distance(d)between each sample and the“ideal health state”,which determined in the phase space of each subclass,was calculated as an order parameter describing the health imbalance,and a linear mapping was established between the d and the H.Further,the health implications of GDV signals were explored by analyzing subclass symptom profiles.We also compare the mean square error(MSE)with classification methods based on age,gen-der,and body mass index(BMI)indices to verify that the phase space possesses the ability to portray the health status of the human body.Results This study preliminarily tested the reliability of the order parameter model on data samples provided by 20 participants.Based on the discovered linear law,the current model can use d calculated by measuring the GDV signal to predict H(R^(2)>0.77).Combined with the symptom profiles of the subclasses,we explain the classification basis of the phase space based on the pattern identification.Compared with common classification methods based on age,gender,BMI,etc.,the MSE of phase space-based classification was reduced by an order of magnitude.Conclusion In this study,the GDV order parameter model based on MBPC can identify sub-classes and characterize individual health levels,and explore the TCM health meanings of the GDV signals by using subjective-objective methods,which holds significance for establishing mathematical models from TCM diagnosis principles to interpret human body signals.展开更多
Marine ecosystem dynamic models(MEDMs) are important tools for the simulation and prediction of marine ecosystems. This article summarizes the methods and strategies used for the improvement and assessment of MEDM ski...Marine ecosystem dynamic models(MEDMs) are important tools for the simulation and prediction of marine ecosystems. This article summarizes the methods and strategies used for the improvement and assessment of MEDM skill, and it attempts to establish a technical framework to inspire further ideas concerning MEDM skill improvement. The skill of MEDMs can be improved by parameter optimization(PO), which is an important step in model calibration. An effi cient approach to solve the problem of PO constrained by MEDMs is the global treatment of both sensitivity analysis and PO. Model validation is an essential step following PO, which validates the effi ciency of model calibration by analyzing and estimating the goodness-of-fi t of the optimized model. Additionally, by focusing on the degree of impact of various factors on model skill, model uncertainty analysis can supply model users with a quantitative assessment of model confi dence. Research on MEDMs is ongoing; however, improvement in model skill still lacks global treatments and its assessment is not integrated. Thus, the predictive performance of MEDMs is not strong and model uncertainties lack quantitative descriptions, limiting their application. Therefore, a large number of case studies concerning model skill should be performed to promote the development of a scientifi c and normative technical framework for the improvement of MEDM skill.展开更多
In this paper, MLINEX loss function was considered to solve the problem of high premium in credibility models. The Bayes premium and credibility premium were obtained under MLINEX loss function by using a symmetric qu...In this paper, MLINEX loss function was considered to solve the problem of high premium in credibility models. The Bayes premium and credibility premium were obtained under MLINEX loss function by using a symmetric quadratic loss function. A credibility model with multiple contracts was established and the corresponding credibility estimator was derived under MLINEX loss function. For this model the estimations of the structure parameters and a numerical example were also given.展开更多
This paper select the escalator with large flow in the station as the object, analysing the correlation of the AFC data of the in and out gates and the passenger flow parameters by passenger flow density and the passi...This paper select the escalator with large flow in the station as the object, analysing the correlation of the AFC data of the in and out gates and the passenger flow parameters by passenger flow density and the passing time acquired and calculated in the waiting area of the prediction escalator to select the gates related to the predicted the escalator. NARX neural network is used to predict the model of the passenger flow parameters of the escalator waiting area based on the related gates＇ AFC data, then a probabilistic neural network model was established by using the AFC data and predicted passenger flow parameters as input and the passenger flow status in the escalator waiting area of subway station as output.The result shows the predicting model can predict the passenger flow status of the escalator waiting area better by the AFC data in the subway station. Research result can provide decision basis for the operation management of the subway station.展开更多
Multivariate recurrent event data arises when study subjects may experience more than one type of recurrent events. In some situations, however, although event times are always observed, event categories may be partia...Multivariate recurrent event data arises when study subjects may experience more than one type of recurrent events. In some situations, however, although event times are always observed, event categories may be partially missing. In this paper, an additive-multiplicative rates model is proposed for the analysis of multivariate recurrent event data when event categories are missing at random. A weighted estimating equations approach is developed for parameter estimation, and the resulting estimators are shown to be consistent and asymptotically normal. In addition, a model-checking technique is presented to assess the adequacy of the model. Simulation studies are conducted to evaluate the finite sample behavior of the proposed estimators, and an application to a platelet transfusion reaction study is provided.展开更多
For the generalized linear model,the authors propose a sequential sampling procedure based on an adaptive shrinkage estimate of parameter.This method can determine a minimum sample size under which effective variables...For the generalized linear model,the authors propose a sequential sampling procedure based on an adaptive shrinkage estimate of parameter.This method can determine a minimum sample size under which effective variables contributing to the model are identified and estimates of regression parameters achieve the required accuracy.The authors prove that the proposed sequential procedure is asymptotically optimal.Numerical simulation studies show that the proposed method can save a large number of samples compared to the traditional sequential approach.展开更多
基金Program of Office of Science and Technology Development,Peking University(3124-2021|-L-w6).
文摘Objective To investigate the human body’s complex system,and classify and characterize the human body’s health states with“a comprehensive integrated method from qualitative to quantitative”.Methods This paper introduces the concept of“order parameters”and proposes a method for establishing an order parameter model of gas discharge visualization(GDV)based on the principle of“mastering both permanence and change(MBPC)”.The method involved the fol-lowing three steps.First,average luminous intensity(I)and average area(S)of the GDV im-ages were calculated to construct the phase space,and the score of the health questionnaire was calculated as the health deviation index(H).Second,the k-means++clustering method was employed to identify subclasses with the same health characteristics based on the data samples,and to statistically determine the symptom-specific frequencies of the subclasses.Third,the distance(d)between each sample and the“ideal health state”,which determined in the phase space of each subclass,was calculated as an order parameter describing the health imbalance,and a linear mapping was established between the d and the H.Further,the health implications of GDV signals were explored by analyzing subclass symptom profiles.We also compare the mean square error(MSE)with classification methods based on age,gen-der,and body mass index(BMI)indices to verify that the phase space possesses the ability to portray the health status of the human body.Results This study preliminarily tested the reliability of the order parameter model on data samples provided by 20 participants.Based on the discovered linear law,the current model can use d calculated by measuring the GDV signal to predict H(R^(2)>0.77).Combined with the symptom profiles of the subclasses,we explain the classification basis of the phase space based on the pattern identification.Compared with common classification methods based on age,gender,BMI,etc.,the MSE of phase space-based classification was reduced by an order of magnitude.Conclusion In this study,the GDV order parameter model based on MBPC can identify sub-classes and characterize individual health levels,and explore the TCM health meanings of the GDV signals by using subjective-objective methods,which holds significance for establishing mathematical models from TCM diagnosis principles to interpret human body signals.
基金Supported by the National Natural Science Foundation of China(Nos.41206111,41206112)
文摘Marine ecosystem dynamic models(MEDMs) are important tools for the simulation and prediction of marine ecosystems. This article summarizes the methods and strategies used for the improvement and assessment of MEDM skill, and it attempts to establish a technical framework to inspire further ideas concerning MEDM skill improvement. The skill of MEDMs can be improved by parameter optimization(PO), which is an important step in model calibration. An effi cient approach to solve the problem of PO constrained by MEDMs is the global treatment of both sensitivity analysis and PO. Model validation is an essential step following PO, which validates the effi ciency of model calibration by analyzing and estimating the goodness-of-fi t of the optimized model. Additionally, by focusing on the degree of impact of various factors on model skill, model uncertainty analysis can supply model users with a quantitative assessment of model confi dence. Research on MEDMs is ongoing; however, improvement in model skill still lacks global treatments and its assessment is not integrated. Thus, the predictive performance of MEDMs is not strong and model uncertainties lack quantitative descriptions, limiting their application. Therefore, a large number of case studies concerning model skill should be performed to promote the development of a scientifi c and normative technical framework for the improvement of MEDM skill.
基金Supported by the National Natural Science Foundation of China（11271189） Supported by the Scientific Research Innovation Project of Jiangsu Province（KYZZ116_0175）
文摘In this paper, MLINEX loss function was considered to solve the problem of high premium in credibility models. The Bayes premium and credibility premium were obtained under MLINEX loss function by using a symmetric quadratic loss function. A credibility model with multiple contracts was established and the corresponding credibility estimator was derived under MLINEX loss function. For this model the estimations of the structure parameters and a numerical example were also given.
文摘This paper select the escalator with large flow in the station as the object, analysing the correlation of the AFC data of the in and out gates and the passenger flow parameters by passenger flow density and the passing time acquired and calculated in the waiting area of the prediction escalator to select the gates related to the predicted the escalator. NARX neural network is used to predict the model of the passenger flow parameters of the escalator waiting area based on the related gates＇ AFC data, then a probabilistic neural network model was established by using the AFC data and predicted passenger flow parameters as input and the passenger flow status in the escalator waiting area of subway station as output.The result shows the predicting model can predict the passenger flow status of the escalator waiting area better by the AFC data in the subway station. Research result can provide decision basis for the operation management of the subway station.
基金supported by National Natural Science Foundation of China(Grant Nos.11231010,11171330 and 11371299)Key Laboratory of Random Complex Structures and Data Science,Chinese Academy of Sciences(Grant No.2008DP173182)+1 种基金Beijing Center for Mathematics and Information Interdisciplinary Sciences,the Research Grant Council of Hong Kong(Grant Nos.504011 and 503513)The Hong Kong Polytechnic University
文摘Multivariate recurrent event data arises when study subjects may experience more than one type of recurrent events. In some situations, however, although event times are always observed, event categories may be partially missing. In this paper, an additive-multiplicative rates model is proposed for the analysis of multivariate recurrent event data when event categories are missing at random. A weighted estimating equations approach is developed for parameter estimation, and the resulting estimators are shown to be consistent and asymptotically normal. In addition, a model-checking technique is presented to assess the adequacy of the model. Simulation studies are conducted to evaluate the finite sample behavior of the proposed estimators, and an application to a platelet transfusion reaction study is provided.
基金supported by the National Natural Science Foundation of China under Grant No.11101396the State Key Program of National Natural Science of China under Grant No.11231010the Fundamental Research Funds for the Central Universities under Grant No.WK2040000010
文摘For the generalized linear model,the authors propose a sequential sampling procedure based on an adaptive shrinkage estimate of parameter.This method can determine a minimum sample size under which effective variables contributing to the model are identified and estimates of regression parameters achieve the required accuracy.The authors prove that the proposed sequential procedure is asymptotically optimal.Numerical simulation studies show that the proposed method can save a large number of samples compared to the traditional sequential approach.
