In order to more effectively assess the health status of a project, the monitoring indices in a project＇s life cycle are divided into quality index, cost index, time index, satisfaction index, and sustainable develop...In order to more effectively assess the health status of a project, the monitoring indices in a project＇s life cycle are divided into quality index, cost index, time index, satisfaction index, and sustainable development index. Based on the feature of qualitative and quantitative indices combining, the PCA-PR （principal component analysis and pattern recognition） model is constructed. The model first analyzes the principal components of the life-cycle indices system constructed above, and picks up those principal component indices that can reflect the health status of a project at any time. Then the pattern recognition model is used to study these principal components, which means that the real time health status of the project can be divided into five lamps from a green lamp to a red one and the health status lamp of the project can be recognized by using the PR model and those principal components. Finally, the process is shown with a real example and a conclusion consistent with the actual situation is drawn. So the validity of the index system and the PCA-PR model can be confirmed.展开更多
In order to directly construct the mapping between multiple state parameters and remaining useful life(RUL),and reduce the interference of random error on prediction accuracy,a RUL prediction model of aeroengine based...In order to directly construct the mapping between multiple state parameters and remaining useful life(RUL),and reduce the interference of random error on prediction accuracy,a RUL prediction model of aeroengine based on principal component analysis(PCA)and one-dimensional convolution neural network(1D-CNN)is proposed in this paper.Firstly,multiple state parameters corresponding to massive cycles of aeroengine are collected and brought into PCA for dimensionality reduction,and principal components are extracted for further time series prediction.Secondly,the 1D-CNN model is constructed to directly study the mapping between principal components and RUL.Multiple convolution and pooling operations are applied for deep feature extraction,and the end-to-end RUL prediction of aeroengine can be realized.Experimental results show that the most effective principal component from the multiple state parameters can be obtained by PCA,and the long time series of multiple state parameters can be directly mapped to RUL by 1D-CNN,so as to improve the efficiency and accuracy of RUL prediction.Compared with other traditional models,the proposed method also has lower prediction error and better robustness.展开更多
Hepatitis C is recognized as a major threat to global public health. The current treatment of patients with chronic hepatitis C is the addition of ribavirin to interferon-based therapy which has limited efficacy, poor...Hepatitis C is recognized as a major threat to global public health. The current treatment of patients with chronic hepatitis C is the addition of ribavirin to interferon-based therapy which has limited efficacy, poor tolerability, and significant expense. New treatment options that are more potent and less toxic are much needed. Moreover, more effective treatment is an urgent priority for those who relapse or do not respond to current regimens. A major obstacle in combating hepatitis C virus （HCV） infection is that the fidelity of the viral replication machinery is notoriously low, thus enabling the virus to quickly develop mutations that resist compounds targeting viral enzymes. Therefore, an approach targeting the host cofactors, which are indispensable for the propagation of viruses, may be an ideal target for the development of antiviral agents because they have a lower rate of mutation than that of the viral genome, as long as they have no side effects to patients. Drugs targeting, for example, receptors of viral entry, host metabolism or nuclear receptors, which are factors required to complete the HCV life cycle, may be more effective in combating the viral infection. Targeting host cofactors of the HCV life cycle is an attractive concept because it imposes a higher genetic barrier for resistance than direct antiviral compounds. However the principle drawback of this strategy is the greater potential for cellular toxicity.展开更多
基金The Social Science Fund of Hebei Province (No.200607011)the Key Science and Technology Project of Hebei Province(No.07213529)
文摘In order to more effectively assess the health status of a project, the monitoring indices in a project＇s life cycle are divided into quality index, cost index, time index, satisfaction index, and sustainable development index. Based on the feature of qualitative and quantitative indices combining, the PCA-PR （principal component analysis and pattern recognition） model is constructed. The model first analyzes the principal components of the life-cycle indices system constructed above, and picks up those principal component indices that can reflect the health status of a project at any time. Then the pattern recognition model is used to study these principal components, which means that the real time health status of the project can be divided into five lamps from a green lamp to a red one and the health status lamp of the project can be recognized by using the PR model and those principal components. Finally, the process is shown with a real example and a conclusion consistent with the actual situation is drawn. So the validity of the index system and the PCA-PR model can be confirmed.
基金supported by Jiangsu Social Science Foundation(No.20GLD008)Science,Technology Projects of Jiangsu Provincial Department of Communications(No.2020Y14)Joint Fund for Civil Aviation Research(No.U1933202)。
文摘In order to directly construct the mapping between multiple state parameters and remaining useful life(RUL),and reduce the interference of random error on prediction accuracy,a RUL prediction model of aeroengine based on principal component analysis(PCA)and one-dimensional convolution neural network(1D-CNN)is proposed in this paper.Firstly,multiple state parameters corresponding to massive cycles of aeroengine are collected and brought into PCA for dimensionality reduction,and principal components are extracted for further time series prediction.Secondly,the 1D-CNN model is constructed to directly study the mapping between principal components and RUL.Multiple convolution and pooling operations are applied for deep feature extraction,and the end-to-end RUL prediction of aeroengine can be realized.Experimental results show that the most effective principal component from the multiple state parameters can be obtained by PCA,and the long time series of multiple state parameters can be directly mapped to RUL by 1D-CNN,so as to improve the efficiency and accuracy of RUL prediction.Compared with other traditional models,the proposed method also has lower prediction error and better robustness.
文摘Hepatitis C is recognized as a major threat to global public health. The current treatment of patients with chronic hepatitis C is the addition of ribavirin to interferon-based therapy which has limited efficacy, poor tolerability, and significant expense. New treatment options that are more potent and less toxic are much needed. Moreover, more effective treatment is an urgent priority for those who relapse or do not respond to current regimens. A major obstacle in combating hepatitis C virus （HCV） infection is that the fidelity of the viral replication machinery is notoriously low, thus enabling the virus to quickly develop mutations that resist compounds targeting viral enzymes. Therefore, an approach targeting the host cofactors, which are indispensable for the propagation of viruses, may be an ideal target for the development of antiviral agents because they have a lower rate of mutation than that of the viral genome, as long as they have no side effects to patients. Drugs targeting, for example, receptors of viral entry, host metabolism or nuclear receptors, which are factors required to complete the HCV life cycle, may be more effective in combating the viral infection. Targeting host cofactors of the HCV life cycle is an attractive concept because it imposes a higher genetic barrier for resistance than direct antiviral compounds. However the principle drawback of this strategy is the greater potential for cellular toxicity.
