• Journal of Mechanical Science and Technology
• /
• 제19권3호
• /
• pp.768-777
• /
• 2005

This paper shows that the performance of a nonlinear fluid engine mount can be improved by an optimal design process. The property of a hydraulic mount with inertia track and decoupler differs according to the disturbance frequency range. Since the excitation amplitude is large at low excitation frequency range and is small at high excitation frequency range, mathematical model of the mount can be divided into two linear models. One is a low frequency model and the other is a high frequency model. The combination of the two models is very useful in the analysis of the mount and is used for the first time in the optimization of an engine mount in this paper. Normally, the design of a fluid mount is based on a trial and error approach in industry because there are many design parameters. In this study, a nonlinear mount was optimized to minimize the transmissibilities of the mount at the notch and the resonance frequencies for low and high-frequency models by a popular optimization technique of sequential quadratic programming (SQP) supported by $MATLAB^{(R)}$subroutine. The results show that the performance of the mount can be greatly improved for the low and high frequencies ranges by the optimization method.

• Journal of Institute of Control, Robotics and Systems
• /
• 제17권8호
• /
• pp.747-752
• /
• 2011

HMMs (Hidden Markov Models) are widely used for biological signal, such as EEG (electroencephalogram) sequence, analysis because of their ability to incorporate sequential information in their structure. A recent trends of research are going after the biological interpretable HMMs, and we need to control the complexity of the HMM so that it has good generalization performance. So, an automatic means of optimizing the structure of HMMs would be highly desirable. In this paper, we described a procedure of classification of motor imagery EEG signals using HMM. The motor imagery related EEG signals recorded from subjects performing left, right hand and foots motor imagery. And the proposed a method that was focus on the validation of the HSA (Harmony Search Algorithm) based optimization for HMM. Harmony search algorithm is sufficiently adaptable to allow incorporation of other techniques. A HMM training strategy using HSA is proposed, and it is tested on finding optimized structure for the pattern recognition of EEG sequence. The proposed HSA-HMM can performs global searching without initial parameter setting, local optima, and solution divergence.

• Journal of the Korean Society for Precision Engineering
• /
• 제30권11호
• /
• pp.1179-1185
• /
• 2013

In this study, the design optimization of the automotive side member is performed to maximize the crash energy absorption efficiency per unit weight. Design parameters which seriously influence on the frontal crash performance are selected through the sensitivity analysis using the Plackett-Burman design method. And also the design variables, which are determined from the sensitivity analysis, are optimized by two methods. One is conventional approximate optimization method which uses the statistical design of experiments (DOE) and response surface method (RSM). The other is a methodology derived from previous work by the authors, which is called sequential design of experiments (SDOE), to reduce a trial and error procedure and to find an appropriate condition for using micro-genetic algorithm. The proposed optimization technique shows that the automotive side member structure can be designed considering the frontal crash performance.

• Journal of the Korean Society for Precision Engineering
• /
• 제19권10호
• /
• pp.120-129
• /
• 2002

An integrated structure and controller design approach for rotating cantilever beam is presented. An optimization method is developed for improving positioning performance considering the elastic deformations during high speed rotation and adopting the beam shape and the control gains as design variables. For this end, a dynamic model is setup by the finite element method according to the shape of the beam. The mass and stiffness of the beam are distributed in such a way that the closed-loop poles of the control system should be located leftmost in the complex s-plane. For optimization method, the simulated annealing method is employed which has higher probability to find the global minimum than the gradient-based down-hill methods. Sequential design and simultaneous design methods are proposed to obtain the optimal shape and controller. Simulations are performed with new designs by the two methods to verify the effectiveness of the approach and the results show that the settling time is improved for point-to-point position controls.

• Journal of Marine Bioscience and Biotechnology
• /
• 제6권1호
• /
• pp.31-40
• /
• 2014

The production of astaxanthin in the microalga Haematococcus pluvialis has been investigated using a sequential methodology based on the application of two types of statistical designs. The employed preliminary experiment was a fractional factorial design $2^6$ in which the factors studied were: excessive irradiance and nitrate starvation, phosphate deficiency, acetate supplementation, salt stress, and elevated temperature. The experimental results indicate that the amount of astaxanthin accumulation in the cells can be enhanced by excessive irradiance and nitrate starvation whereas the other factors tested did not yield any enhancement. In the subsequent experiment, a central composite design was applied with four variables, light intensity, nitrate, phosphate, and acetate, at five levels each. The optimal conditions for the highest astaxanthin production were found to be $1040{\mu}E/(m^2{\cdot}s)$ light intensity, 0.04 g/L nitrate, 0.31 g/L phosphate, 0.05 g/L acetate concentration.

• International Journal of Control, Automation, and Systems
• /
• 제4권5호
• /
• pp.601-614
• /
• 2006

In this paper, a study on task assignment strategies for a complex real-time network system is presented. Firstly, two task assignment strategies are proposed to improve previous strategies. The proposed strategies assign tasks with meeting end-to-end real-time constraints, and also with optimizing system utilization through period modulation of the tasks. Consequently, the strategies aim at the optimizationto optimize of system performance with while still meeting real-time constraints. The proposed task assignment strategies are devised using the genetic algorithmswith heuristic real-time constraints in the generation of new populations. The strategies are differentiated by the optimization method of the two objectives-meeting end-to-end real-time constraints and optimizing system utilization: the first one has sequential genetic algorithm routines for the objectives, and the second one has one multiple objective genetic algorithm routine to find a Pareto solution. Secondly, the performances of the proposed strategies and a well-known existing task assignment strategy using the BnB(Branch and Bound) optimization are compared with one other through some simulation tests. Through the comparison of the simulation results, the most adequate task assignment strategies are proposed for some as system requirements-: the optimization of system utilization, the maximization of running tasktasks, and the minimization of the number of network node nodesnumber for a network system.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 한국전산구조공학회 2000년도 가을 학술발표회논문집
• /
• pp.141-148
• /
• 2000

In system design, it is not always possible that all decision makers can cooperate fully and thus avoid conflict. They each control a specified subset of design variables and seek to minimize their own cost functions subject to their individual constraints. However, a system management team makes every effort to coordinate multiple disciplines and overcome such noncooperative environment. Although full cooperation is difficult to achieve, noncooperation also should be avoided as possible. Our approach is to predict the results of their cooperation and generate approximate Pareto set for their multiple objectives. The Pareto set can be obtained according to the degree of one's conceding coupling variables in the other's favor. We employ approximation concept for modelling this coordination and the mutiobjective genetic algorithm for exploring the coupling variable space for obtaining an approximate Pareto set. The approximation management concept is also used for improving the accuracy of the Pareto set. The exploration for the coupling variable space is more efficient because of its smaller dimension than the design variable space. Also, our approach doesn't force the disciplines to change their own way of running analysis and synthesis tools. Since the decision making process is not sequential, the required time can be reduced comparing to the existing multidisciplinary optimization techniques. This approach is applied to some mathematical examples and structural optimization problems.

• Korean Journal of Fisheries and Aquatic Sciences
• /
• 제56권1호
• /
• pp.28-32
• /
• 2023

We optimized ultrasound-assisted extraction to improve the extraction efficiency of bioactive compounds from the microalgae Acutodesmus reginae. To optimize this extraction process, we investigated the effects of solvent type, solvent concentration, extraction time, extraction number, and extraction power on the production of lutein, α-carotene, β-carotene, and chlorophylls a, and b. After sequential optimization of these main variables, the maximum amount of each compound was extracted at 30℃ with an ultrasound power of 80 W and using 99.5% methanol. Under these optimum conditions, the amount of lutein, α-carotene, β-carotene, and chlorophylls a, and b, were measured as 10.43, 8.66, 3.76, 15.43, and 6.39 mg/g dry matter respectively.

• Geomechanics and Engineering
• /
• 제34권1호
• /
• pp.29-41
• /
• 2023

The 3D geospatial modeling of geotechnical information can aid in understanding the geotechnical characteristic values of the continuous subsurface at construction sites. In this study, a geostatistical optimization model for the three-dimensional (3D) mapping of subsurface stratification and the SPT-N value based on a trial-and-error rule was developed and applied to a dam emergency spillway site in South Korea. Geospatial database development for a geotechnical investigation, reconstitution of the target grid volume, and detection of outliers in the borehole dataset were implemented prior to the 3D modeling. For the site-specific subsurface stratification of the engineering geo-layer, we developed an integration method for the borehole and geophysical survey datasets based on the geostatistical optimization procedure of ordinary kriging and sequential Gaussian simulation (SGS) by comparing their cross-validation-based prediction residuals. We also developed an optimization technique based on SGS for estimating the 3D geometry of the SPT-N value. This method involves quantitatively testing the reliability of SGS and selecting the realizations with a high estimation accuracy. Boring tests were performed for validation, and the proposed method yielded more accurate prediction results and reproduced the spatial distribution of geotechnical information more effectively than the conventional geostatistical approach.

• Journal of Institute of Control, Robotics and Systems
• /
• 제9권5호
• /
• pp.407-412
• /
• 2003

An effective methodology is .reported for determining the optimal capacity (lot-size) of batch processing and storage networks which include material recycle or reprocessing streams. We assume that any given storage unit can store one material type which can be purchased from suppliers, be internally produced, internally consumed and/or sold to customers. We further assume that a storage unit is connected to all processing stages that use or produce the material to which that storage unit is dedicated. Each processing stage transforms a set of feedstock materials or intermediates into a set of products with constant conversion factors. The objective for optimization is to minimize the total cost composed of raw material procurement, setup and inventory holding costs as well as the capital costs of processing stages and storage units. A novel production and inventory analysis formulation, the PSW(Periodic Square Wave) model, provides useful expressions for the upper/lower bounds and average level of the storage inventory hold-up. The expressions for the Kuhn-Tucker conditions of the optimization problem can be reduced to two subproblems. The first yields analytical solutions for determining batch sizes while the second is a separable concave minimization network flow subproblem whose solution yields the average material flow rates through the networks. For the special case in which the number of storage is equal to the number of process stages and raw materials storage units, a complete analytical solution for average flow rates can be derived. The analytical solution for the multistage, strictly sequential batch-storage network case can also be obtained via this approach. The principal contribution of this study is thus the generalization and the extension to non-sequential networks with recycle streams. An illustrative example is presented to demonstrate the results obtainable using this approach.