Conventional principal component analysis(PCA) can obtain low-dimensional representations of original data space, but the selection of principal components(PCs) based on variance is subjective, which may lead to infor...Conventional principal component analysis(PCA) can obtain low-dimensional representations of original data space, but the selection of principal components(PCs) based on variance is subjective, which may lead to information loss and poor monitoring performance. To address dimension reduction and information preservation simultaneously, this paper proposes a novel PC selection scheme named full variable expression. On the basis of the proposed relevance of variables with each principal component, key principal components can be determined.All the key principal components serve as a low-dimensional representation of the entire original variables, preserving the information of original data space without information loss. A squared Mahalanobis distance, which is introduced as the monitoring statistic, is calculated directly in the key principal component space for fault detection. To test the modeling and monitoring performance of the proposed method, a numerical example and the Tennessee Eastman benchmark are used.展开更多
This paper takes Principal-agent Theory as the basic analysis flame to analyze the modern corporate principal and agent in a state of the two sides in asymmetric information on the basis of self-interest maximization,...This paper takes Principal-agent Theory as the basic analysis flame to analyze the modern corporate principal and agent in a state of the two sides in asymmetric information on the basis of self-interest maximization, and the game strategy which revolves the information disclosure and hideaway to launch, and therefore can get the game way which causes the auditing institution. The equilibrium in game of the information disclosure causes the auditing institution, the expense and cost which the audit profession consumes is the company governs reduces the information not asymmetrical diligently center essential agency costs.展开更多
基金Supported by the National Natural Science Foundation of China(No.61374140)Shanghai Pujiang Program(Project No.12PJ1402200)
文摘Conventional principal component analysis(PCA) can obtain low-dimensional representations of original data space, but the selection of principal components(PCs) based on variance is subjective, which may lead to information loss and poor monitoring performance. To address dimension reduction and information preservation simultaneously, this paper proposes a novel PC selection scheme named full variable expression. On the basis of the proposed relevance of variables with each principal component, key principal components can be determined.All the key principal components serve as a low-dimensional representation of the entire original variables, preserving the information of original data space without information loss. A squared Mahalanobis distance, which is introduced as the monitoring statistic, is calculated directly in the key principal component space for fault detection. To test the modeling and monitoring performance of the proposed method, a numerical example and the Tennessee Eastman benchmark are used.
文摘This paper takes Principal-agent Theory as the basic analysis flame to analyze the modern corporate principal and agent in a state of the two sides in asymmetric information on the basis of self-interest maximization, and the game strategy which revolves the information disclosure and hideaway to launch, and therefore can get the game way which causes the auditing institution. The equilibrium in game of the information disclosure causes the auditing institution, the expense and cost which the audit profession consumes is the company governs reduces the information not asymmetrical diligently center essential agency costs.
