This paper presents several adaptive linear predictive coding techniques based upon extension of recursive ladder filters. A 2-D recursive ladder filter is extended to a 3-D case which can adaptively track the variation of both spatial and temporal changes of moving images. Using the 2-D/3-D ladder filter and a previous farme predictor, two types of adaptive predictor-control schemes are proposed in which the prediction error at each pel can be obtained at or close to a minimum level. We also investigate several modifications of the basic encoding methods. Performance of the 2-D/3-D ladder filters, their adaptive control schemes, and variations in coding methods are evaluated by computer simulations on a real sequence and compared to the results of motion compensation and frame differential coders. As a validity test of the ladder filters developed, the error signals for the different predictors are compared and evaluated.

This paper deals with the development of an expert system modeling by constructing a knowledge-based system of the dive recovery for anticrash on the ground or sea during the task of fighter aircraft. In an IBM PC / XT computer, a prototype dive recovery expert system is constructed using mu LISP-86 programming language, and is interconnected to the SAM SUNG RM-501 robot arm to test and simulate this model. The knowledge base of this model is composed of the dive recovery charts and the V-N envelope charts of F-4 D Phantom fighter aircraft. It is shown that the prototype expert system woks well and the feasibility of practical realization is valid.

This thesis presents a model to assign targets and to determine gun ammunitions required for naval surface warfare. Delivery errors of weapon systems and vulnerability of moving targets are analyzed, then probability to kill moving battle sihps is computed. A weapon-target allocation model is proposed by using the Out of Kilter technique. A model to determine ammunitions required for killing moving targets is also designed. The models are evaluated by simulation and sensitivity analysis.

Power algorithms for analysis of variance tests are presented. In experimental design of operational tests and evaluations the selection of design parameters so as to attain an experiment with desired power is a difficult and important problem. An interactive computer program is presented which uses the power algorithms for ANOVA tests and creates graphical presentations which can be used to assist decision makers in statistical design. ANOVA tests and associated parameters (such as sample size, types and levels of treatments, and alpha-level)are examined.

This thesis describes the curve fitting algorithm that the Portable Wear Metal Analyzer used for calculating concentrations in ppm and compares this with some alternative algorithms. Each algorithm considered fits a curve to the three standards used, which were $20\%,\;50\%,\;and\;100\%$ of the full scale for all nine wear metals. APL was used for all programs.

This paper deals with an algorithm for solving nonlinear programming problems with equality constraints. Nonlinear programming problems are transformed into a square sums of nonlinear functions by the Lagrangian multiplier method. And an iteration method minimizing this square sums is suggested and then an algorithm is proposed. Also theoretical basis of the algorithm is presented.

This paper presents an optimization based heuristic algorithm for a tour bus scheduling problem where buses consist of various kinds of sightseeing and commutation services. First, this algorithm transforms the prolem into a vehicle routing problem on whose nodes denote trips and arcs denote connections between trips. Second, a greedy heuritic routing technique is applied to find a good feasible bus-route set. Then the greedy feasible solution is improved by the simplex method using column generation technique. The algorithm provides a better near-optimal solution which gives much reductions in the total tour distance and the number of tour buses.

This paper studies to develop the procedure which is combined by the progressive goals and progressive weights generation method. This procedure minimizes the number of questions the decision maker has to make, and also satisfies the generated minimum goal of each objective function. With the procedure developed, we are able to improve the previous multiple objective linear programming techniques in two points.