• Journal of the Korean Statistical Society
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• v.22 no.2
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• pp.249-258
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• 1993

It is well known that design matrix X for any factorial design can be represented by a product $X = TX_o$ where T is replication matrix and $X_o$ is the corresponding balanced design matrix. Since $X_o$ consists of regular arrangement of 0's and 1's, we can easily find the spectral decomposition of $X_o',X_o$. Also using this we propose an efficient algorithm for computing the orthogonal projection matrix for a balanced factorial design.

• The Korean Journal of Applied Statistics
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• v.24 no.4
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• pp.647-656
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• 2011

Incidence matrix is a useful tool for presenting incomplete block designs; however, it is inadequate to use only an incidence matrix in examining whether a certain incomplete block design becomes a balanced incomplete block design or not. We can use a structural matrix as a useful tool to show whether a certain incomplete block design becomes a balanced incomplete block design or not. We propose an augmented incidence matrix and ${\lambda}$ matrix as another tools for evaluating incomplete block designs. Through the augmented incidenc matrix and ${\lambda}$ matrix, we can ascertain whether a certain incomplete block design becomes a balance incomplete block design or not.

• Journal of information and communication convergence engineering
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• v.6 no.1
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• pp.105-109
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• 2008

A robust optimal control system design algorithm in active suspension equipment adopting linear matrix inequalities control system design theory is presented. The validity of the linear matrix inequalities robust control system design in active suspension system through the numerical examples is also investigated.

• Journal of applied mathematics & informatics
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• v.8 no.2
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• pp.651-658
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• 2001

The projection matrix plays an important role in the linear model theory. In this paper we derive an algebraic relationship between the projection matrices of submatrices of the design matrix. Using this relationship we can easily obtain the projection matrices of any submatrices of the design matrix. Also we show that every projection matrix can be obtained as a linear combination of Kronecker products of identity matrices and matrices with all elements equal to 1.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.9-12
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• 2003

This paper describes a step-by-step method to minimize design iterations in a process of product design change. In the design process, two components are coupled if a change of a component can require the other components change, and design iterations are generated by the coupling. The design iteration is one of main factors that increase design effort. In this study, three matrices are used to solve the design iteration of automatic transmission lever, Requirement-Engineering matrix, Engineering-Components matrix, and DSM(Design Structure Matrix). Firstly, with the DSM, the product architecture and conceptual design process are proposed from product function analysis. Secondly, with the QFD, the Requirement-Engineering matrix and Engineering-Components matrix present the relationship among customer requirements, engineering issues, and product components. Lastly, the results of the QFD analysis are used in the DSM to solve the component interactions and to provide design

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.898-902
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• 2003

For linear descriptor systems, we consider the $H_{INFTY}$ controller design problem via output feedback. Both static output feedback and dynamic one are discussed. First, in the case of static output feedback, we reduce our control problem to solving a bilinear matrix inequality (BMI) with respect to the controller coefficient matrix, a Lyapunov matrix and a matrix related to the descriptor matrix. Under a matching condition between the descriptor matrix and the measured output matrix (or the control input matrix), we propose setting the Lyapunov matrix in the BMI as being block diagonal appropriately so that the BMI is reduced to LMIs. For fixed-order dynamic $H_{INFTY}$ output feedback, we formulate the control problem equivalently as the one of static output feedback design, and thus the same approach can be applied.

• Proceedings of the Safety Management and Science Conference
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• 2009.04a
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• pp.423-427
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• 2009

This study proposes the generation mechanism of various design matrix using generalized linear model for design of experiment. Design generation method of GLM analysis, factorial design(FD) with center points, ANOVA design with lack-of-fit test, and response surface design are introduced. In central composite(CC) design, orthogonal blocking and fractional factorial design(FFD) are presented. We compare the design of Box-Benhken(BB) and face-centred central compsite design.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.1307-1312
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• 2009

Design is an act based on multidisciplinary information. The involvement of various stakeholders makes it difficult to process, plan, and integrate. Iteration is frequent in most of the engineering design and development projects including construction. Design iterations cause rework, and extra efforts are required to get the optimal sequence and to manage the projects. The simple project management techniques are insufficient to fulfill the requirements of integrated design. This paper entails two things: design structure matrix and design parameters' information based model. The emphasis has been given to optimal sequence and crucial iteration using design structure matrix analysis technique. The design projects have been studied using survey data from industry. The optimal sequence and crucial iterations results have been utilized for proposed model. Model integrates two things: information about produced- required key design parameters and information of design changes during the design process. It will help to get familiar with Design management in order to fulfill contemporary needs.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.550-555
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• 2011

Utilizing construction knowledge and experiences in design phase can reduce change orders and improve productivity in construction phase. To do so, information must be made available to the design team in time. Current approaches for effective utilization of constructability knowledge, however, only focus on the formalization of constructability knowledge such as a checklist, which lacks the consideration of the appropriate use at the proper point in time. The inadequate use of constructability knowledge can result in unnecessary reworks. To deal with this problem, the design team needs to know what constructability knowledge is required for specific design activities in the design process. This paper presents a constructability implementation model using the dependency structure matrix (DSM) that focuses on information flows between design activities and constructability knowledge. For this objective, design activities in the design process are modeled in a matrix form based on their dependency. Then, constructability knowledge, which needs to be considered in the design stage, is mapped into activities and incorporated into the matrix, creating Constructability-DSM (C-DSM). Next, the partitioning algorithm is applied to C-DSM for optimal information flow. The Partitioned C-DSM is then analyzed based on the relationship between activities. Finally, the optimal utilization of construction knowledge in the design process is determined by identifying what constructability knowledge is required for each design activity, and how and when it is reflected to design for constructability. Thus, this research can help provide robust control actions to reduce unnecessary iterative cycles in design process for efficient constructability implementation.

• Communications for Statistical Applications and Methods
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• v.12 no.3
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• pp.653-657
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• 2005

Eigenstructure of a spatial design matrix of Matheron's variogram estimator in $R^1$ is derived. It is shown that the spatial design matrix in $R^1$ with n/2$\le$h < n has a nice spectral decomposition. The mean, variance, and covariance of this estimator are obtained using the eigenvalues of a spatial design matrix. We also found that the lower bound and the upper bound of the normalized Matheron's variogram estimator.