The Optimum design of golf shaft is carried out using explicit time integration FEM analysis and mathematical programming technique. The 3 dimensional motions of the grip and the body of the player are measured using motion sensor and used to analyze the force and torque which act on the body. It is also used for the input of FEM, and shaft is divided into several segment, and bending rigidity and torsional rigidity of each segment is taken to be the design variables. The speed of club head and the load to the player's body are taken as objective function to maximize. Pareto solution is also introduced to solve the double objective function.

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To reduce vehicle body weight, a new concept is shown here as follows: For body panels, truss core panel can be used in such way that the conventional panel is used for outer panel and truss core panel is used for inner panel. Cylindrical Origami Structure using Reversed Spiral Model is used for side member which is expected to absorb dynamic energy in head-on collision, To realize this concept, optimization analysis is the key technology in such that the key technology is the topology optimization for reduction of the weight of truss core panel and it is the response surface optimization for raising the reaction force of Cylindrical Origami Structure.

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In most structural optimization, the stress level based on the linear structural analysis is used as objective function or constraints. In this paper new structural optimization concept is proposed, that maximize lifetime period of structure based on the crack propagation analysis using X-FEM. In the optimization, period until structure became unstable is used as objective function to maximize, and volume of material is constrained. The shape of the structure is changed using basis vector method. In the example, it is shown that the optimized shape based on the von-Mises stress level and the optimized shapes based on the crack propagation analysis are quite different

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Flapping flight mechanisms of insects and birds are known to have high flight performance. However they are very complex and hard to understand all of them. Nowadays, an inch-size flying robot so called MAV (Micro Air Vehicle) has been attracting much attention. In this size, flapping wing is expected to have better performance than conventional fixed and rotational wing. For flapping MAV, it is necessary to clarify relationship between wing motion and fluid force. In this research we perform computational simulation on several wing motions and analyze flight performance using ADVENTURE_DecisionMaker. As the result of analysis, it is clarified that flight performance is decided by not an individual parameter but some special combinations of motion parameters.

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Our previous design optimization of a steam turbine stator blade had lack in the capability of searching optimal solutions, due to low accuracy of response surfaces for the objective functions. This paper starts from a further discussion on the previous optimization results. It finds out that the airfoil representation method used in the previous optimization cannot assure the uniqueness to critical parameters, which are sensitive to the objective functions. Then, this study discusses and modifies a choice of airfoil representation methods. The present optimization using the modified method for airfoil representation realizes successful improvement in the response surface accuracy, and can obtain better solutions than the previous optimization.

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To obtain the optimal shape of a 3D object minimizing the fluid traction, an adjoint variable method based on the variational principle is formulated and applied to the finite element method. The optimality condition of the present method consists of the state equations, the adjoint equations, and the sensitivity equations. In high Reynold's number cases, shape optimization methods are demanded that the initial shape be sufficiently close to the optimal shape and that Korman vortices not be present in the computational domain. Therefore, these methods were geneally applied to the steady state of the flows. In the present paper, the 3D adjoint variable method used to decrease the traction force of an object in unsteady flow is formulated by using FEM. The particularity of this method resides in the fact that both the start of the test time and the end of the test time in the optimization are determined by the stationary condition of the Lagrange function. The state variable is calculated from the start of the test time to the end of the test time in forward time and this data is saved, while the adjoint variable is calculated in backward time by using the saved data. The algorithm of the method is implemented using HEC-MW. By using the prepared algorithm, robust convergence of the cost function can be attained. This robustness makes possible the shape optimization even under unsteady flow containing Karman vortices.

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This paper discusses a method to find a collision-free path of industrial robot manipulators in a known environment where collision detection between manipulators and obstacles is necessary. The configuration of robot manipulators is represented based on the concept of configuration space(C-space) whose coordinates correspond to degrees of freedom of manipulators. Geometric criteria of collision between three dimensional manipulators and obstacles are formulated and estimated in each region of segmented C-space by using interval analysis. Based on this estimation, the shortest path from given starting point to end point in C-space is obtained by Dijkstra algorithm through the series only of the feasible regions where it is guaranteed that manipulators do not collide with obstacles. Then the joint trajectories are determined such that the total traveling time is minimized under physical constraints. Validation of this algorithm is tested by a numerical example.

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This paper describes the lace angle dependence on the side force coefficient and the trade-off condition between Pareto optimal solutions on the fluctuating punted kick by using Self-Organizing Maps. It was found that the side force depends on the position of the lace since the flow around the ball, which is affected by the lace and the 4 seams of the ball, is asymmetric. Therefore, it is considered that a punted kick rotating at lower spin rates fluctuates in the lateral direction during the flight. In the optimization study, it was assumed that there were five objective functions and nine control parameters. Four of 5 objective functions are concerned with the fluctuation in the forward and the lateral directions, and the fifth objective function is the hang time. The relationship between five objective functions and nine control parameters was visualized by using Self-Organizing Maps. It was found that there was a trade-off between the hang time and the fluctuation.

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A minimum time navigation strategy and its practical implementation of an AUV in current disturbances are addressed. The navigation realized by our approach is defined as quasi-optimal navigation. Reactive and interactive cooperation between optimal guidance and tracking control is the foundation of quasi-optimal navigation. To achieve the optimal guidance, a numerical procedure to obtain its solution is presented. Developed solution procedure never fails to derive optimal vehicle headings for minimum time navigation, if only flow distribution in navigation region is deterministic. Quasi-optimal strategy is especially useful in case of environmental uncertainties or obstacles in navigation region. Simplicity is another notable characteristic of the quasi-optimal strategy, compared to the optimal one. Simulations of quasi-optimal navigations are conducted based on the dynamics of an AUV "r2D4". The r2D4 is a deep ocean exploring AUV, developed by IIS, the University of Tokyo.

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If a biped robot has no sensors, it would slip or can not walk. However, it is worth pointing out, some biped robots can walk without sensors. They have only servo actuators to move their body or legs. In such a case, walking biped robots are affected external factors that are friction or restitution between bottoms of its feet and floors. The floor's surface, location or materials change these factors. Thus, biped robots can walk if they use the gait for them including their factors. In this paper, we study to optimize the gait for biped robots by using robust optimization considered random values as floor's friction and restitution. In addition, the generated gait is simple for the small and low cost robot.

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This paper describes robustness and reliability based optimization considering practicality. Monte Carlo is well known as estimation method of robustness and reliability. However it is difficult to use in actual design field, because it needs many calculations for steady value. Therefore the number of calculation that Monte Carlo requires to get steady value is found out. Then this method is compared with other methods on accuracy that need less number of calculations. Those methods are FOSM, Linear model MC for robustness, and FORM, SORM, FORM+ILHS for reliability. (All methods are available in PIDO tool OPTIMUS.) Finally an optimization example is explained by applying efficient method in OPTIMUS.

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A numerical method based on the semidefinite program is proposed for computing confidential bounds for the dynamic steady-state responses of a damped structure subjected to uncertain driving loads. We assume that amplitudes of harmonic driving loads obey a non-probabilistic uncertainty model. Semidefinite programming problems are formulated for finding confidential bounds for various characteristic amounts of dynamic steady-state response, including the modulus and phase angle of the complex amplitude of the displacement and stress. Numerical examples demonstrate that sufficiently tight bounds can be obtained.

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This research applies SLSV (Single-Loop-Single-Variable) method to reliability-based topology optimization (RBTO) for frame structures to improve computational efficiency. The design problem is formulated to minimize the structural volume of frame structure in terms of cross-sectional area of each frame element under the two reliability constraints. The two mode reliability criteria consists of the mean compliance and mean eifenfrequency criterion under variations on applied loads and nonstructural mass. Through numerical examples, higher computational efficiency and accuracy of reliability approximation by the SLSV method are demonstrated in comparison with those by the conventional double loop method that mode reliabilities are evaluated by the first order reliability method (FORM). Additionally, the importance of normalization of the limit state functions that are directly applied as constraint conditions in the SLSV method is also demonstrated.

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In this paper, we compared efficiency of Stochastic optimization (SO) with other optimization methods, i.e., Genetic algorithm (GA) and Differential evolution (DE). For the index of efficiency, we compared function call numbers until we could obtain an approximate solution of some famous test functions. The results showed that SO could obtain approximate solutions inefficiently than DE and effectively than GA. The results also showed that SO is an optimization method that is easy to deal with, because SO needed not adjust parameters for each problem unlike GA and DE.

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In recent years, optimization methods for planning, operating, and controlling systems have been important with enlargement and complication of systems. Especially, it is important to develop effective algorithms for large scale combinatorial optimization problems. It is experientially known that Proximate Optimality Principle (POP) - good solutions possess some similar structure -holds in most of the engineering combinatorial optimization problems. This paper proposes an algorithm - Multi-Point Tabu Search - that considers POP. The proposed algorithm is applied to some typical combinatorial optimization problems in order to verify the performance of the proposed algorithm.

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This paper deals with Evolutionary Algorithms that have been researched to solve multi-peak problem's global optimal solution. These algorithms have problem to require a large amount of computational cost to solve the optimal solution steadily. Authors developed new method that is Adaptive Plan system with Genetic Algorithm (APGA) for the solution of various multi-peak problems. This is a combination method of global search ability of GA and the Adaptive Plan with excellent local search ability. In this paper, the neighborhood range control is introduced into the APGA. This is changed neighborhood range to fit by each individual's situation. Benchmark functions examines to APGA. The performance of APGA is discussed.

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Genetic Programming (GP) is one of evolutionary computation methods and GP can handle structural data such as tree structure, and graph structure. One of the problems of GP is surplus chromosome growth which is called a bloat. In this paper, searching mechanism of GP is discussed. Through the numerical examples of two types of Symbolic Regression problems, the performance of crossover and influence of mutation rate were researched. In these researches, the influences of tree depth and mutation rate on search solution were discussed. As result, it was found out that GP doesn't search interpolation by crossover in complex problem.

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This paper describes Self-Organizing Maps for Genetic Algorithms (SOM-GA), which is the combinational algorithm of Genetic Algorithms (GA) and Self-Organizing Maps (SOM). In the algorithm, the whole population is divided into sub-populations by using SOM clustering. Real-coded genetic algorithm (RCGA) is applied in the sub-populations. The algorithm is applied to the solution search of Rastrigin function. Comparing SOM-GA with RCGA, we notice that the present algorithm has much better search performance than the RCGA. Besides, the discussion on the map-size of SOM indicates that the map-size affects the search performance and the CPU time.

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Recently, several heuristic computational methods have been developed for complex real problems. Especially, PMBGAs using statistical information of the good search points have been attracted attention by many researchers and several types of PMBGAs are proposed. In this paper, a novel real-coded probabilistic model-building Genetic Algorithms (PMBGAs) which is called Multiple Neighborhoods Sampling (MNS) is proposed. PMBGAs have a issue that search points converge, while the MNS has a characteristic that search points are maintained different diversities. In the MNS, a new searching point is generated using the neighborhood area. When the neighborhood is narrow, a new search point is generated for the local search. On the other hand, when the neighborhood is wide, a new search point is generated for global search. The MNS has multiple neighborhoods whose width is different respectively. Therefore, search points do not converge and the MNS can run efficiently solution search. Through the standard test functions, the effectiveness of the MNS is examined. The results describe that the MNS shows the better performance than Simple GA.

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Interactive genetic algorithms(iGAs) are methods that acquire and analyse user preference based on user's subjective evaluation. IGA has been applied to various unimodal problems, such as parameter setting of a hearing aid and fashion design. On the other hand, the goal of this study is achieving iGA which also corresponds to multimodal preferences with equivalent fitness at the peaks. For example, when users select products on shopping sites, they have several types of preference trends at the same time. In this case, reflecting all the trends in product presentation leads to increased sales and consumer satisfaction. The dependency among design variables should be also considered toward each trend of preference. In this study, a new offspring generation method that enables efficient search even if user preference is multimodal and there are dependencies among design variables is proposed. The proposed method is capable to detect a multimodal preference using clustering, and spawn off-spring based on probabilistic model eliminating the dependencies among design variables by principal component analysis. The results of experiments indicate that the proposed method can generate offspring corresponding to multimodal preferences with equivalent fitness at the peaks and dependencies among design variables. However, there are few subjects who have dependencies within their preferences.

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In this paper, interpolation and extrapolation search algorithms for the multi point SA were developed and the discussed in the continuous problems. In the multi point search, the next new searching point is determined from the information of the old searching points or is determined randomly. The former method is also classified into two categories; interpolation search and extrapolation search. To perform the effective search in the continuous problems, interpolation and extrapolation searches should be discussed carefully and should design good algorithms. This paper proposed the definition of the neighborhood of solutions. Then, the algorithms of interpolation and extrapolation searches were proposed. These two searches are dynamically changed. The effectiveness of the proposed methods was discussed through the numerical test function. From these results, it was described that the interpolation search increases the local search ability and the extrapolation search increases the global search ability.

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By focusing on the two basic search functions, diversification and intensification, the origin of the success of finite-time optimization by simulated annealing (SA) is investigated on the traveling salesman problems. Two and one additional experiments are designed with the help of the methods devised for the studies on liquid and glass. The present experimental analyses show the existence of effective temperature again; in the search process of the Metropolis algorithm running at this temperature, a successive interbasin transition in a downward direction effectively lasts until the end of observation, that is, a good intensification characteristic appears on the observation time scale. In the optimization process of SA, this effective relaxation dynamics and the resulting good performance are not only dependent on but also sensitive to the search around the effective temperature. This influential temperature is determined from the temperature dependence of the Deborah number, which is used to identify glass transition.

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A gradient-based method for global optimization of nonlinear programming problems, named "Modal Trimming Method," has been proposed. It has been shown that the capability of global search is because the renewal of the values of variables based on an extended Newton-Raphson method creates a chaotic behavior. In some cases, however, this method also creates a zigzag behavior, which may result in the deterioration of the capability of global search. In this paper, a method of searching for feasible solutions toward the Newton direction is proposed to avoid the zigzag behavior and enhance the capability of global search. In addition, the modal trimming method with this strategy is applied to some test problems, and its validity is ascertained.

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When simple genetic algorithm (SGA) is applied to solve structural topology optimization problem checkerboard-like material distribution and disconnection phenomena often occur. To solve these problems, we introduce a stress-based crossover operator (SX) in this paper. Comparisons of SGA, ESO and SX are performed to experiment the effectiveness of SX. Generation alternation models are adopted to speed up the convergence speed. Different elements are discussed to study how the elements affect the resulting topology. At last three mapping methods are used to discuss the stress dependency of SX.

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An optimization approach is presented for generating a truss structure which has specified equilibrium path. Such a specified path can be achieved utilizing elastic deformation of a structure. Adequate flexibility is allocated to a structure generated through the optimization procedure. Two optimization problems are formulated. One is minimization of total structural volume. The other is minimization of weighted sum of the error of the equilibrium path from the target path. Several numerical examples are demonstrated to validate the proposed method.

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Under the trends of multi-disciplinary design optimization, modeling techniques and computational algorithms that are configured by following complicatedness of problems have been promoted and gathered much attentions. However, their applicability has been limited within continuously defined problems, while layout optimization is typically complicated but combinatorial. Under this contrast, this paper proposes a hierarchical optimization method for layout design. In the method, a rectangular packing layout problem is hierarchically decomposed into the top-level layout and a series of sub-level layout, those partial layout problems are represented with sequence pair and optimized by genetic algorithms, and they are coordinated through mutual exchange of sets of Pareto optimal solutions explored in the lower levels and ranges of desirable aspect ratio. The validity and effectiveness of the method is investigated through its application to an example problem.

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Multiple listening positions are assumed in a sound system such as the car audio system or the BGM system in a retail premise. Thus it is necessary to control the sound field in a room to obtain better frequency responses at multiple listening positions. There are several methods to control the sound field, i.e. spatial distribution of sound pressure in a room. One is manipulating the geometrical conditions like location of acoustic sources or shape of the room itself. The other is using multiple acoustic sources with multiple digital filters. By manipulating the difference in magnitude and phase response between multiple sources with digital filters, the spatial distribution of sound pressure in the room varies owing to the sound wave interference. This paper presents a concrete procedure of acoustic design in a room which is intended to obtain uniform and flat frequency responses at multiple positions by using optimization method.

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In this paper, we propose a new topology optimization method for designing a soundproof structure that is located between an elastic panel and an acoustic cavity. The soundproof structure is assumed to consist of a poroelastic material and an elastic material. In this method, sound pressure levels radiated from an elastic panel and a soundproof structure are minimized. Air contained in the design domain and an elastic material used in the soundproof structure are approximately represented in the expression of Biot's theory that is widely applied for modeling poroelastic material. These unified expressions of the different types of material by Biot's theory allow to apply a material interpolation scheme in the density approach of topology optimization. Several numerical examples are presented to demonstrate that the proposed design method can provide clear configurations of soundproof structures that reduce mean sound pressure levels within the prescribed acoustic domai in the given frequency ranges.

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In this paper, an optimality condition for a sound pressure minimization in an enclosure is considered. The sound pressure in a frequency domain is expressed in scalar product of a structural frequency response function (FRF) and an acoustic FRF. Therefore, the optimality condition is that the sound pressure is zero when structural and acoustic FRF are orthogonal or zero. And to reduce the sound pressure, it is needed to reduce amplitude of the structural and acoustic FRF or a cosine between them. From this viewpoint, a contour plot which shows optimal state of the sound pressure minimization is introduced. And a novel optimization approach based on the optimality condition is proposed.

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This paper proposes an optimum design method of cooling channel layout in a plastic injection molding die based on the evaluation of warpage to produce precise plastic products and molding cycle time to improve productivity. The numerical simulations, such as injection process, cooling process and ejection process, are implemented to calculate the warpage induced by residual stresses and the molding cycle time. A multi-objective optimum design problem is formulated to achieve the warpage reduction and the short molding cycle time, simultaneously. In this formulation, the warpage and the molding cycle time are adopted as objective functions, and the cooling channel arrangement as design variables. The basis vector method is used to change the cooling channel arrangement. Through two-dimensional numerical example, we discuss about the effect of the arrangement and the cross sectional shape of cooling channel for the minimization of warpage and molding cycle time.

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This study focuses up on the optimization analysis of the blow molding and the extrusion molding in the polymer processing. In order to predict the optimal thickness distribution of the preform, the membrane FEM (Finite Element Method) iterative calculations were applied to simulate the blow molding process utilized in the manufacture of the PET bottle. In these iterative calculations, we employed an optimization method based on TEM (Trial and Error Method). This method properly modifies the initial thickness properties of the membrane elements to diminish the difference between the predicted thickness and the optimal value. As a result, the optimal initial condition of the preform to produce the PET bottle with the uniform thickness can be predicted within practical computation time. In addition, we applied the solid FEM simulations to uniform the flow rate in the branched channel used in the extrusion molding. The Thompson's transformation-remeshing rule was employed to modify the FEM meshes. The hybrid optimization method based on TEM and RSM (Response Surface method) including GA (Generic Algorithm) were introduced to reduce the number of times for a try and error calculation in DOE (Design Of Experiment). These optimization analyses provide rational means to assist the mold design and the determination of the optimal process conditions in the polymer processing.

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This paper studies the optimization for inner rib's thickness of the shell structure of aluminum allow hollow extrusion by using genetic algorithm. In the optimization, total mass of the structure is minimized under the constraint conditions with respect to maximum deformations of the structure. The deformation of the structure is evaluated numerically by using the finite element analysis developed for two-dimensional elasticity in the present study. In numerical calculations optimal design solutions are obtained for the structures by specifying the allowable maximum deflections of the structure, and the effect of the rib's thickness is studied from the numerical results obtained.

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Design sensitivity is very important information for design and manufacture of composite material structure. In this paper, all simple supported boundary condition with each edge symmetrical laminated rectangle plate of free vibration, buckling load and static deformation characteristic design sensitivity analysis problem was studied. The theoretical design sensitivity formulation solution was presented. Then the presented theoretical solution was applied to some numerical example problem and the accuracy and effectiveness was verified.

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A new design method is proposed here to maximize the fundamental frequency of laminated composite plates reinforced by curvilinear fibers. Spline functions represent arbitrarily shaped fibers and Ritz solutions generate frequency equations. The optimum curvilinear shapes are searched for the maximum fundamental frequencies using genetic algorithms, and the increments of data points which define the spline shape are used as design variables under the limited range of undulation. Comparison of the present analysis method gives good agreement with the finite element method in terms of natural frequencies and vibration modes, and the optimized results show that the present plate with optimally shaped curvilinear fibers gives higher or equal fundamental frequencies than those of the conventional plate with optimally oriented parallel fibers.

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It is thought that the content and placement of inclusion in composite materials have a great influence on the mechanical characteristic such as strength. However there is no an effective technique to estimate optimum characteristic of all models. In this research, a technique to obtain an optimum composite material composition under a constant content rate is proposed by using Cellular Automaton (CA), Genetic Algorithm (GA) and Finite Element Method (FEM).

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T This paper deals with multiple constraints for dimension and stacking-sequence optimization of a blade-stiffened composite panel. In a previous study, a multiple objective genetic algorithm using a Kriging response surface with a buckling load constraint was the target. The present study focuses on dimension and stacking-sequence optimization with both a buckling load constraint and a fracture constraint. Multiple constraints complicate the process of selecting sampling analyses to improve the Kriging response surface. The proposed method resolves this problem using the most-critical-constraint approach. The new approach is applied to a blade stiffened composite panel and the approach is shown to be efficient.

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The goal of multi-objective optimization problems is to obtain pareto optimal solutions. However, for a designer who handles real world problems, it is important to obtain not only the optimal solutions but also the information of the design variable space. In this paper we propose an optimization method that is capable of obtaining the information of the design variable space. To obtain the information of the design variable space, an uniform global search of the design variable space is necessary. For this reason, a single objective optimization method called DIRECT has attracted much attention in this field. In this paper, we propose a multi-objective DIRECT algorithm called NSDIRECT-GA, which adapts DIRECT to multi-objective optimization and combines it with MOGA. The effectiveness of NSDIRECT-GA was examined through numerical experiments. From the conducted numerical experiments, it was found out that the accuracy and diversity of the obtained solutions are improved, and the design variable space is also searched uniformly and globaly.

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In this paper, we report a new multi-objective evolutionary algorithm: Archive-based Micro Genetic Algorithm (AMGA), which has developed by Engineous Software. Motivation of the development was mainly driven by two aspects: (1) Recent progress of MOEA studies, (2) Accumulated practice in engineering optimization. In the research field of MOEA(Multi Objective Evolutionary Algorithm), a number of excellent algorithms have been arising year by year since the beginning of 2000. And significant amount of application examples have been accumulated so far. This has been enabled positive cycle between algorithm development and filed application, and AMGA is one of such results. In this paper, beginning with descriptions of algorithmic features which characterize AMGA form other MOEAs, we show performance of proposed algorithm through benchmark problems both in mathematical test functions and in engineering problems, and discuss effectiveness of the algorithm.

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The objectives of the Multi-Objective Evolutionary Algorithms (MOEA) are summarized as follows: (1) To find the pareto optimal solutions, (2) To find the pareto optimal solutions as diverse as possible. To achieve these objectives by the PSO for the single objective problems, we propose how to define the g-best in the swarm without introducing some new parameters. Additionally, some particles among the non- inferior solutions are also selected as the g-best of the inferior solutions to find the pareto optimal solutions. The absolute distance in the objective space is utilized to select the g-best of the inferior solutions. We also show the geometric interpretation about the movement of particles. The validity of proposed approach is examined through typical numerical examples.

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In multi-objective optimization, it is important to derive solutions with good accuracy, uniform distribution, and broadness. Of these qualities, we focused on accuracy and broadness of the solutions and proposed a search strategy to improve them. Since it is difficult to improve both convergence and broadness of the solutions at the same time in a multi-objective GA search, we considered to converge the solutions first and then broaden them in the proposed search strategy by dividing the search into two search phases. The first phase is to improve convergence of the solutions, and a reference point specified by a decision maker is adopted in this search. In the second phase, the solutions are broadened using the Distributed Cooperation Scheme. From the results of the numerical experiment, we found that the proposed search strategy is capable of deriving broader solutions than conventional multi-objective GA without deterioration of accuracy.

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Structural optimization methods based on the level set method are a new type of structural optimization method where the outlines of target structures can be implicitly represented using the higher order function. But these methods have the problem which does not allow the topological changes increasing the number of holes in the material domain. In this research, we proposed a new topology optimization method based on the level set model and concept of the phase-field theory. In addition, the method is expanded to minimizing the mean compliance problem. Simple example is provided to confirm the usefulness of the proposed structural optimization method.

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Shape optimization problem of a hinged-clamped beam that is subjected to a concentrated load is reconsidered analytically. Previous report had said that the actual most suitable form within the same volume existed only in the case that the load was in a hinged end side of the span. However, it was found after solving the problem by an iterative method which used numerical integration that there existed several local solutions in which the deflection angle were discontinuous at their inner hinge points where their cross sectional areas were zero. In this paper, the characteristics of local solutions are investigated analytically.

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A numerical method is presented for stability analysis of cable-bar structures. An optimization problem is formulated to find the minimum value of the incremental total potential energy that depends on the direction of the incremental displacements. The penalty method with slack variables is used for representing the discontinuity in member stiffness. The tangent stiffness matrix is shifted to be positive definite so that the minimum of its quadratic form is found by the inverse-power method. It is shown in the numerical examples that the minimum value of the incremental potential energy and the associated displacement increments can be found with good accuracy in about 10 steps of iteration.

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The head positioning accuracy of the hard disk drive (HDD) should be improved to meet today's increasing performance demands. Vibration suppression of the arm in the hard disc drive is very important to enhance the servo bandwidth of the head positioning system. In this study, the smart structure is introduced into the hard disk drive to suppress the vibration. It has been expected that technology of smart structure will contribute to the development of small and light-weight mechatronics devices with the required performance. First, modeling of the system is conducted with finite element method (FEM) and modal analysis. Next, the actuator location and the control system are simultaneously optimized using genetic algorithm (GA). Vibration control effect with the proposed vibration control mechanisms has been evaluated by some simulations.

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An inverse dynamics based estimation for human articulatory motor control has been presented. As the plant for this control application, a dynamic model of human articulators consisting of lip soft tissue with related muscles and bones is developed. The continuum of lip soft tissue is represented as a discrete model approximation composed of networked point masses interconnected via viscoelastic elements. By activating an appropriately selected muscle set, the dynamic model performs a mimic of actual human speech motion, called pseudo-speech motion. Pseudo-speech motion is a forward dynamics simulation using the articulator dynamic model. As a mathematical representation of human speech acquisition, simulations of articulatory motor control based on estimation of muscle motor command are presented. Inverse dynamics driven iterative estimation makes the dynamic model put into practice an articulatory motion. The solution of this inverse dynamics problem is a set of activated muscle motor command as the foundation of induced articulatory motion. Correlation between model-based motor command estimations and EMG signals recorded during actual speech motions is discussed.

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