Strong shock may induce complex processes in porous materials. We use the newly developed materialpoint-method to simulate such processes in an HMX-like material. To pick out relevant information, morphological charac...Strong shock may induce complex processes in porous materials. We use the newly developed materialpoint-method to simulate such processes in an HMX-like material. To pick out relevant information, morphological characterization is used to treat with the temperature map. Via the Minkowski funetional analysis the dynamics and thermodynamics of the shock wave reaction on porous HMX-like material are studied. The geometrical and topological properties of the ＂hot-spots＂ are revealed. Numerical results indicate that, shocks in porous materials are not simple jump states as classically viewed, but rather are a complex sequence of compressions and rarefactions. They cover a broad spectrum of states. We can use coarse-grained description to the wave series. A threshold value of temperature presents a Turing pattern dynamical procedure. A higher porosity is generally preferred when the energetic material needs a higher temperature for initiation. The technique of data analysis can be used to other physical quantities, for example, density, particle velocity, some specific stress, etc. From a series of studies along the line, one may get a large quantity of information for desiring the fabrication of material and choosing shock strength according to what needed is scattered or connected ＂hot-spots＂. PACS numbers： 05.70.Ln, 05 Key words： porous material 70.-a, 05.40.-a, 62.50.Ef shock wave, Minkowski functionals展开更多
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
An ever more demanding consumer market and the need for companies to be more competitive have led organizations to try to eliminate waste. This research is a case study which presents a proposal for intervention in or...An ever more demanding consumer market and the need for companies to be more competitive have led organizations to try to eliminate waste. This research is a case study which presents a proposal for intervention in order to improve performance of a pre-cast concrete block factory in outer Goihnia. As a first step, waste in the production process was identified through analysis of data on time involved in each step of the process. Then, applying the concepts of lean production, a list of activities was drawn up with a view to eliminating non-value-added work, identifying waste, decreasing cycle time, streamlining the production process and increasing the flexibility and transparency of the process. From the results, it was possible to identify the sources of waste and provide management with information for strategic decisions about production. Finally, various suggestions were made with a view to eliminating or mitigating bottlenecks in the production process.展开更多
We propose a two-step variable selection procedure for censored quantile regression with high dimensional predictors. To account for censoring data in high dimensional case, we employ effective dimension reduction and...We propose a two-step variable selection procedure for censored quantile regression with high dimensional predictors. To account for censoring data in high dimensional case, we employ effective dimension reduction and the ideas of informative subset idea. Under some regularity conditions, we show that our procedure enjoys the model selection consistency. Simulation study and real data analysis are conducted to evaluate the finite sample performance of the proposed approach.展开更多
The principal component analysis （PCA） is one of the most celebrated methods in analysing multivariate data. An effort of extending PCA is projection pursuit （PP）, a more general class of dimension-reduction techn...The principal component analysis （PCA） is one of the most celebrated methods in analysing multivariate data. An effort of extending PCA is projection pursuit （PP）, a more general class of dimension-reduction techniques. However, the application of this extended procedure is often hampered by its complexity in computation and by lack of some appropriate theory. In this paper, by use of the empirical processes we established a large sample theory for the robust PP estimators of the principal components and dispersion matrix.展开更多
A concept of AHP restraint cone is firstly introduced, and then a DEA model with an AHP restraint cone is imposesed. This model not only remains all the characteristics of the conventional DEA model, but also reflects...A concept of AHP restraint cone is firstly introduced, and then a DEA model with an AHP restraint cone is imposesed. This model not only remains all the characteristics of the conventional DEA model, but also reflects preference of the decision maker. Some relationships between DEA model and AHP method, on the condition that the judgement matrixes in AHP are completely consistent, is discussed in this paper. Finally, a case study is provided to evaluate 20 junior middle schools of a city in China.展开更多
基金Supported by Science Foundations of Laboratory of Computational Physics and China Academy of Engineering Physics under Grant Nos.2009A0102005 and 2009B0101012National Science Foundation of China under Grant Nos.10702010,10775018,and 10604010
文摘Strong shock may induce complex processes in porous materials. We use the newly developed materialpoint-method to simulate such processes in an HMX-like material. To pick out relevant information, morphological characterization is used to treat with the temperature map. Via the Minkowski funetional analysis the dynamics and thermodynamics of the shock wave reaction on porous HMX-like material are studied. The geometrical and topological properties of the ＂hot-spots＂ are revealed. Numerical results indicate that, shocks in porous materials are not simple jump states as classically viewed, but rather are a complex sequence of compressions and rarefactions. They cover a broad spectrum of states. We can use coarse-grained description to the wave series. A threshold value of temperature presents a Turing pattern dynamical procedure. A higher porosity is generally preferred when the energetic material needs a higher temperature for initiation. The technique of data analysis can be used to other physical quantities, for example, density, particle velocity, some specific stress, etc. From a series of studies along the line, one may get a large quantity of information for desiring the fabrication of material and choosing shock strength according to what needed is scattered or connected ＂hot-spots＂. PACS numbers： 05.70.Ln, 05 Key words： porous material 70.-a, 05.40.-a, 62.50.Ef shock wave, Minkowski functionals
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An ever more demanding consumer market and the need for companies to be more competitive have led organizations to try to eliminate waste. This research is a case study which presents a proposal for intervention in order to improve performance of a pre-cast concrete block factory in outer Goihnia. As a first step, waste in the production process was identified through analysis of data on time involved in each step of the process. Then, applying the concepts of lean production, a list of activities was drawn up with a view to eliminating non-value-added work, identifying waste, decreasing cycle time, streamlining the production process and increasing the flexibility and transparency of the process. From the results, it was possible to identify the sources of waste and provide management with information for strategic decisions about production. Finally, various suggestions were made with a view to eliminating or mitigating bottlenecks in the production process.
基金supported by National Natural Science Foundation of China (Grant Nos. 11401383, 11301391 and 11271080)
文摘We propose a two-step variable selection procedure for censored quantile regression with high dimensional predictors. To account for censoring data in high dimensional case, we employ effective dimension reduction and the ideas of informative subset idea. Under some regularity conditions, we show that our procedure enjoys the model selection consistency. Simulation study and real data analysis are conducted to evaluate the finite sample performance of the proposed approach.
基金The researcb was partially supported by the National Natural Science Foundation of China under Grant No.19631040.
文摘The principal component analysis （PCA） is one of the most celebrated methods in analysing multivariate data. An effort of extending PCA is projection pursuit （PP）, a more general class of dimension-reduction techniques. However, the application of this extended procedure is often hampered by its complexity in computation and by lack of some appropriate theory. In this paper, by use of the empirical processes we established a large sample theory for the robust PP estimators of the principal components and dispersion matrix.
文摘A concept of AHP restraint cone is firstly introduced, and then a DEA model with an AHP restraint cone is imposesed. This model not only remains all the characteristics of the conventional DEA model, but also reflects preference of the decision maker. Some relationships between DEA model and AHP method, on the condition that the judgement matrixes in AHP are completely consistent, is discussed in this paper. Finally, a case study is provided to evaluate 20 junior middle schools of a city in China.
