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A Lower and Upper Bound Method for Complex System Reliability Evaluation

Journal of Donghua University(English Edition) 2011年第5期28卷 445-450页

作者：魏展明周凡陈耀武 College of Biomedical Engineering & Instrument Science Zhejiang University

It is hard for the existing methods to obtain the expression of the system reliability for most of the practical complex systems with a large number of components and possible stales. A new regression algorithm based on the lower and upper bounds is presented in this paper, which can obtain the system reliability analytically without concerning the structure of the complex system. The method has been applied to a real system and the reliability results are compared with those acquired by the classical method and the parametric method. The effectiveness and accuracy of the proposed method have been testified.

关键词： reliability lower bound upper bound regression algorithm

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Risk assessment of malignancy in solitary pulmonary nodules in lung computed tomography:a multivariable predictive model study

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Chinese Medical Journal 2021年第14期134卷 1687-1694页

作者： Hai-Yang Liu Xing-Ru Zhao Meng Chi Xiang-Song Cheng Zi-Qi Wang Zhi-Wei Xu Yong-Li Li Rui Yang Yong-Jun Wu Xiao-Ju Zhang Department of Respiratory and Critical Care Medicine Henan Provincial People’s HospitalZhengzhou University People’s HospitalZhengzhouHenan 450000China Henan Joint International Research Laboratory of Diagnosis and Treatment of Pulmonary Nodules Henan Provincial People’s HospitalZhengzhou University People’s HospitalZhengzhouHenan 450000China Department of Medical Imaging Henan Provincial Chest HospitalZhengzhouHenan 450000China Department of Respiratory and Critical Care Medicine Fuwai Central China Cardiovascular HospitalZhengzhouHenan 450000China Department of Radiology Henan Provincial People’s HospitalZhengzhou University People’s HospitalZhengzhouHenan 450000China College of Public Health Zhengzhou UniversityZhengzhouHenan 450000China.

Background:Computed tomography images are easy to misjudge because of their complexity,especially images of solitary pulmonary nodules,of which diagnosis as benign or malignant is extremely important in lung cancer ***,there is an urgent need for a more effective strategy in lung cancer *** our study,we aimed to externally validate and revise the Mayo model,and a new model was ***:A total of 1450 patients from three centers with solitary pulmonary nodules who underwent surgery were included in the study and were divided into training,internal validation,and external validation sets(n=849,365,and 236,respectively).External verification and recalibration of the Mayo model and establishment of new logistic regression model were performed on the training *** performance of each model was evaluated using area under receiver operating characteristic curve(AUC).Finally,the model validation was completed on the validation data ***:The AUC of the Mayo model on the training set was 0.653(95%confidence interval[CI]:0.613–0.694).After re-estimation of the coefficients of all covariates included in the original Mayo model,the revised Mayo model achieved an AUC of 0.671(95%CI:0.635–0.706).We then developed a new model that achieved a higher AUC of 0.891(95%CI:0.865–0.917).It had an AUC of 0.888(95%CI:0.842–0.934)on the internal validation set,which was significantly higher than that of the revised Mayo model(AUC:0.577,95%CI:0.509–0.646)and the Mayo model(AUC:0.609,95%CI,0.544–0.675)(P<0.001).The AUC of the new model was 0.876(95%CI:0.831–0.920)on the external verification set,which was higher than the corresponding value of the Mayo model(AUC:0.705,95%CI:0.639–0.772)and revised Mayo model(AUC:0.706,95%CI:0.640–0.772)(P<0.001).Then the prediction model was presented as a nomogram,which is easier to ***:After external verification and recalibration of the Mayo model,the results show that they are not suitab

关键词： CT image Lung cancer Prediction model Pulmonary nodules regression algorithm

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A Model Average algorithm for Housing Price Forecast with Evaluation Interpretation

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Journal of Quantum Computing 2022年第3期4卷 147-163页

作者： Jintao Fu Yong Zhou Qian Qiu Guangwei Xu Neng Wan School of Computer Science Southwest Petroleum UniversityChengdu610500China Key Laboratory on Aero-Engine Altitude Simulation Technology Sichuan Gas Turbine EstablishmentAECCMianyang621000China Aero Engine Academy of China Aero Engine Corporation of ChinaBeijing101300China

In the field of computer research,the increase of data in result of societal progress has been remarkable,and the management of this data and the analysis of linked businesses have grown in *** are numerous practical uses for the capability to extract key characteristics from secondary property data and utilize these characteristics to forecast home *** regression methods in machine learning to segment the data set,examine the major factors affecting it,and forecast home prices is the most popular method for examining pricing *** is challenging to generate precise forecasts since many of the regression models currently being utilized in research are unable to efficiently collect data on the distinctive elements that correlate y with a high degree of house price *** today’s forecasting studies,ensemble learning is a very prevalent and well-liked study *** regression integration computation of large housing datasets can use a lot of computer resources as well as computation time,and ensemble learning uses more resources and calls for more machine support in integrating diverse *** Average Model suggested in this paper uses the concept of fusion to produce integrated analysis findings from several models,combining the best benefits of separate *** Average Model has a strong applicability in the field of regression prediction and significantly increases computational *** technique is also easier to replicate and very effective in regression *** using regression processing techniques,this work creates an average of different regression models using the AM(Average Model)algorithm in a novel *** evaluating essential models with 90%accuracy,this technique significantly increases the accuracy of house price *** experimental results show that the AM algorithm proposed in this paper has lower prediction error than other comparison algorithms,and the prediction accurac

关键词： Machine learning AM algorithm price forecast regression algorithm Model evaluation

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