In this paper, three problems from the field of reliability engineering are considered. The first problem is a nonlinear constraint optimization problem. The problem is to determine the minimum cost of a life support system in a space capsule subject to the constraints on reliability of the system. The objective is to find the minimum cost of the system as well as maximum reliability. The second problem is to determine the minimum cost of a complex bridge network system with constraints on system reliability. The objective is to minimize the cost and maximize reliability at the same time. The third problem is a discrete optimization problem. This problem is to determine the optimal number of redundancies in a multistage mixed system so that the reliability of the system can be maximized. Three cases of this problem are considered. All the problems are solved using Self Organizing Migrating Genetic Algorithm (C-SOMGA) which is a recently published algorithm for obtaining the global optimal solution of constrained optimization problems. C- SOMGA is a hybridized genetic algorithm inspired by the features of Self Organizing Migrating Algorithm (SOMA) as well as Simple binary GA. The results obtained by C-SOMGA are compared with the existing published results in order to exhibit the roboutness of C-SOMGA for solving reliability engineering problems.

In this paper, an inverse eigenvalue problem of constructing the mass and spring matrices of the mass-spring system from the information of frequency(eigenvalue) and mode(eigenvector) is considered. The solvability of the problem is discussed. Furthermore numerical algorithms and some numerical experiments are given with applications to mass-spring vibrating system.

The harmonic projection method can be used to find interior eigenpairs of large matrices. Given a target point or shift t to which the needed interior eigenvalues are close, the desired interior eigenpairs are the eigenvalues nearest t and the associated eigenvectors. In this paper, we present a new algorithm, which is called weighted harmonic projection algorithm for computing the eigenvalues of a nonsymmetric matrix. The implementation of the algorithm has been tested by numerical examples, the results show that the algorithm converges fast and works with high accuracy

CAD of woven textile structures has received much attention in the technical textiles community. Research and development has concentrated on the simple 2D weaves and some of the 3D weaves such as angle-interlock, orthogonal, multi-layer and hollow woven structures. This paper addresses the void in the literature with respect to the more advanced and complex nodal structure and the CAD of 3D woven nodal textile structures. Innovative use of a k-ary tree data structure is used to keep track of nodal connectivity and parent-child hierarchical transformation relationships. In addition, the k-ary tree allows the design of more complex nodal configurations beyond simple ‘T’ and ‘K’ shapes due to the inherent hierarchical nature of the tree data structure. The described nodal CAD software progresses beyond the frequently encountered simple 2D interfaces of many existing textile CAD systems. 3D interfaces are used to allow the user to interactively create and edit almost any nodal configuration limited only by the weaving width of the employed loom. Novel and robust algorithms and modelling techniques are described to automate the nodal CAD process: 3D geometry specification, flattening and segmentation of nodal geometry, design paper mapping and weave architecture allocation to include: automatic computation of strut weaves, strut composite weaves and semi- automatic computation of stitching points. 3D nodal CAD has been implemented in C++ as part of the ongoing WeaveStudio CAD/CAM software system.

The  conventional  closed-loop  power  control  in  cellular  code  division  multiple  access  systems can only achieve limited performance due to its inability to track channel variations quickly. In this paper, we present  a  new  power  control  algorithm  which  is  able  to  increase  the  speed  of  convergence  to  track  the changes in radio channel efficiently. Simulation results show that it outperforms the conventional algorithms.

This paper presents a novel algorithm for rate-complexity scalable multi-view image coding using an adaptive disparity-compensated (DC) wavelet lifting scheme. First, image regions of multi-view images are prioritized by counting matching points. The proposed algorithm selects either Haar, 5/3, or our proposed multiple picture reference DC wavelet lifting adaptively. The selection criterion is based on the bit budget constraint, the complexity budget constraint, and the priorities of image regions. Then, the low-pass and high-pass subbands, obtained from the DC wavelet lifting, are further decomposed by a spatial wavelet transform. The resulting wavelet coefficients are entropy encoded with the SPIHT codec. Experimental results show that the proposed algorithm provides an efficient adaptive framework for multi-view image coding.

We study computability of the solution operators of the initial-value problem for the nonlinear fourth-Schrödinger equationwith both high-order dispersion term. The computayions are performed on Type- 2 turing machines.

This paper proposes a new method of face image feature extraction, namely, the fuzzy 2DFLD (F2DFLD) based on the 2D fisher discriminant criterion and fuzzy set theory. In the proposed method, we calculate membership degree matrix by FKNN, then we incorporate the membership degree into the definition of the between-class scatter matrix and within-class scatter matrix and get the fuzzy between-class scatter matrix and fuzzy within-class scatter matrix. Experiments on the Yale, ORL and FERET face databases show that the new method can work well