• IE interfaces
• /
• v.16 no.4
• /
• pp.485-495
• /
• 2003

This paper reports on the application of the integration of mathematical programming model and database in a decision support system (DSS) for the planning of the multi-reservoir operating system. The DSS is based on a multi-objective, mixed-integer goal programming (MIGP) model, which can generate efficient solutions via the weighted-sums method (WSM). The major concern of this study is seamless, efficient integration between the mathematical model and the database, because there are significant differences in structure and content between the data for a mathematical model and the data for a conventional database application. In order to load the external optimization results on the database, we developed a systematic way of naming variable/constraint so that a rapid identification of variables/constraints is possible. An efficient database structure for planning of the multi-reservoir operating system is presented by taking advantage of the naming convention of the variable/constraint.

• Journal of the Korean Society of Industry Convergence
• /
• v.25 no.1
• /
• pp.121-129
• /
• 2022

With the recent increase in international trade volume the trade volume through ships is also continuously increasing. The treatment of ballast water goes through the following five steps, samples are taken and analyzed at each step, and samples are obtained using a suction pump. These suction pumps have low efficiency and thus need to be improved. In this study, it is to optimize the form of the impeller which affects directly improvements of performance to determine the capacity of suction pump and to fulfill the purpose of this research. To do it, we have carried out parametric design as an input variable, geometric form for the impeller. By conducting the flow analysis for the optimum form, it has confirmed the value of improved results and achieved the purpose to study in this paper. It has selected the necessary parameter for optimizing the form of the pump impeller and analyzed the property using experiment design. And it can reduce the factor of parameter for local optimization from findings to analyze the property of form parameter. To perform MOGA(Multi-Objective Genetic Algorithm) it has generated response surface using parameters for local optimization and conducts the optimization using multi-objective genetic algorithm. with created experiment cases, it has performed the computational fluid dynamics with model applying the optimized impeller form and checked that the capacity of the pump was improved. It could verify the validity concerning the improvement of pump efficiency, via optimization of pump impeller form which is suggested in this study.

• Journal of Broadcast Engineering
• /
• v.16 no.6
• /
• pp.1009-1017
• /
• 2011

Fast generation of digital hologram is of importance for real-time holography broadcasting. In this paper, we propose such a method that parallelizes the Computer-Generated Holography (CGH) algorithm for digital hologram generation and make it faster using Multi Graphic Processing Unit (Multi-GPU) with help of the Compute Unified Device Architecture (CUDA) and the Open Multi-Processing (OpenMP). In addition, we propose optimization methods such as fixation variable, vectorization, and loop unrolling for making the CGH algorithm much faster. Experimental results show that our method is about 9,700 times faster than a CPU-based one.

• Composites Research
• /
• v.25 no.6
• /
• pp.230-235
• /
• 2012

This paper presents the vibration characteristics and the optimization using the orthogonal array about applied composite optical structure of an aircraft. To acquire the vibration characteristics for stable line of sight, modal analysis are performed by using multi-body program ADAMS. And to optimize optical structure, for design variables were selected, larger-the-better characteristics were considered using results of S/N ratio and orthogonal array $L_9(3^4)$. When bearing constraints are selected, radial, axial and moment stiffness value are used to analysis for optimization until now. But B.S.R which is non-dimensional parameter is proposed, structures including bearings can be used for optimization. And then having a result of lager-the-better, the optimized values of each design variable were successfully suggested.

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.12 no.7
• /
• pp.542-549
• /
• 2002

Computer simulation is essential to design the suspension elements of railway vehicle. By computer simulation, engineers can assess the feasibility of given design variables and chance them to get a bettor design. Even though commercial simulation codes are used, the computational time and cost remains non-trivial. Therefore, malty researchers have used a mesa model made by sampling data through simulation. In this paper, four mesa-models for each index group such as ride comfort, derailment Quotient, unloading radio and stability index, are constructed by use of neural network. After these meta models are constructed, multi-objective optimization are achieved by using the differential evolution. This paper shows that the optimization of design variables using the neural network model is very efficient to solve the complex optimization Problem.

• Proceedings of the KIEE Conference
• /
• 2007.07a
• /
• pp.138-139
• /
• 2007

This research provides machine designers with some intuition to consider both, magnetic and heat transfer effects. A topological multi-objective function includes magnetic energy and heat inflow rate to the system, which equals to the total heat dissipation by conduction and convection. For the thermal field regarding the heat inflow, introduced as a reaction force, topology design sensitivity is derived by employing discrete equations. The adjoint variable method is used to avoid numerous sensitivity evaluations. As a numerical example, a C-core design excited by winding current demonstrates the strength of the multi-physical approach.

• Transactions of Materials Processing
• /
• v.19 no.2
• /
• pp.132-137
• /
• 2010

In the multi-pass shape drawing process, the appropriate process design is very important to produce sound products. The reduction ratio, die angle, and the intermediate die shape are very important process variable of the multi-pass shape drawing. The aim of this study is the determination of the reduction ratio, die angle, and the intermediate die shape of the 2 pass shape drawing process for producing steering spline shaft. In this study, FE analysis, Taguchi method, and ANN(artificial neural network) were applied to determine the appropriate reduction ratio, die angle, and intermediate die shape. After the determination of the process variables, FE analysis and drawing experiment were performed to evaluate the effectiveness of the determined process variables. The dimensional accuracy of the final drawn spline shaft was evaluated by using 3D surface profiler and 3D laser digitizing system.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.33 no.6
• /
• pp.607-613
• /
• 2009

In this paper, we made a simple paper feeding system which is one of MTS (media transport system) and controllers. The plant has a flexible paper and two driving rollers and two driven rollers. The control system has two conventional PID controllers. Skew angle and feeding speed of MTS deteriorate the quality of feeding system. In order to control a feeding speed and skew of feeding paper, we control rotational velocity of two driving rollers. Therefore, this controller has two inputs and two outputs as MIMO (multi-input and multi-output) system. The control inputs were the feeding speed and the skew displacement of the paper. The control outputs were the rotational velocity to each driving roller. To find appropriate PID gains of two controllers, we proposed an optimization technique. We assume the system variables and performance of a whole system as follows. PID gains of two controllers for skew and feeding speed are system variables. System performance is both skew and feeding speed. We simulates to making mathematical correlation using global Kriging interpolation. To find appropriate value of system variables, optimization method is simulation in sequence as following method. First, the optimization solver simulates with DOE (design of experiment) tables to find correlation equation of both system variable and performances. Then, the solver guesses the appropriate values and simulates if the system variables are appropriate or not. If the result of validation doesn't satisfy the convergence and iteration tolerance, the solver makes a new Kriging models and iterates this sequence until satisfy the tolerances.

• Journal of the Korean Geotechnical Society
• /
• v.25 no.9
• /
• pp.93-100
• /
• 2009

A core procedure of the direct search method used in this study is optimizing a difference between objective function and real displacement and correcting unknown variables. Because the research procedure comes from back-analyzing of the unknown variable of each layer, back-analyzing results need an additional optimization to minimize interferential effects of unknown variables. Therefore, the direct search method Is used to obtain optimized solutions without a partial differentiation of an objective function. The object of this research is developing the back analysis technique for multi-unknown variables by modeling the soil including underground structure Into upper and lower layer. In order to minimize interferent errors, repeated back analysis is performed and applicability on the real tunnel is examined. Consequently, the multi-layer analysis model is more precise in describing the real behavior of underground structure. It shows the validity of back analysis far multi-layer model which is the understructure placed on multi-layer boundaries.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.31 no.1 s.256
• /
• pp.26-35
• /
• 2007

The vehicle of urban transit is a complex system that consists of various electric, electronic, and mechanical equipments, and the maintenance cost of this complex and large-scale system generally occupies sixty percent of the LCC (Life Cycle Cost). For reasonable establishing of maintenance strategies, safety security and cost limitation must be considered at the same time. The concept of system reliability has been introduced and optimized as the key of reasonable maintenance strategies. For optimization, three preceding studies were accomplished; standardizing a maintenance classification, constructing RBD (Reliability Block Diagram) of VVVF (Variable Voltage Variable Frequency) urban transit, and developing a web based reliability evaluation system. Historical maintenance data in terms of reliability index can be derived from the web based reliability evaluation system. In this paper, we propose applying inverse problem analysis method and hybrid neuro-genetic algorithm to system reliability optimization for using historical maintenance data in database of web based system. Feed-forward multi-layer neural networks trained by back propagation are used to find out the relationship between several component reliability (input) and system reliability (output) of structural system. The inverse problem can be formulated by using neural network. One of the neural network training algorithms, the back propagation algorithm, can attain stable and quick convergence during training process. Genetic algorithm is used to find the minimum square error.