• E2M - 전기 전자와 첨단 소재
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• 제9권5호
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• pp.498-505
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• 1996

In the present work, we have developed the simulator to optimize the process conditions of the AR(antireflection) and HR(high-reflection) coatings for the high power laser diode. The simulator can run on the PC. After making the simple optical model, we establish the Maxwell equations for the model by the operator conversion. By using the Mathematica, we derive a matrix for the multilayer system by applying the equations to the model and optimize the AR and HR coating process conditions by obtaining the reflection rate from the matrix. We also prove the validity of the simulator by comparing the simulation with the characteristics of the laser diode which is AR and HR coated according to the optimized conditions.

• Environmental Engineering Research
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• 제24권4호
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• pp.559-565
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• 2019

In order to alleviate the environmental pressure caused by production process of thermal power plants, the application of cleaner production is imperative. To estimate the implementation effects of cleaner production in thermal plants and optimize the strategy duly, it is of great significance to take a comprehensive evaluation for sustainable development. In this paper, a hybrid model that integrated the analytic hierarchy process (AHP) with least squares support vector machine (LSSVM) algorithm optimized by grid search (GS) algorithm is proposed. Based on the establishment of the evaluation index system, AHP is employed to pre-process the data and GS is introduced to optimize the parameters in LSSVM, which can avoid the randomness and inaccuracy of parameters' setting. The results demonstrate that the combined model is able to be employed in the comprehensive evaluation of the cleaner production in the thermal power plants.

• Wind and Structures
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• 제28권3호
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• pp.191-201
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• 2019

In this paper, the aerodynamic analysis of a vertical axis wind turbine was carried out by CFD approach to optimize the turbine performance. To perform numerical simulation, SST-Transition turbulence model was used, which demonstrated more precise results compared to non-transition models. A parametric study was conducted to optimize the VAWT performance based on the selected model. The investigation of pitch angle changes showed that the highest power produced by the turbine occurs at $2^{\circ}$ angle. Considering the effect of the rotor's arm junction to the airfoil showed that by increasing the distance of the junction from the edge of the airfoil from 25 cm to 40 cm, the power of the turbine increases by 60%. However, further increase in this distance results in power decrease. Based on the proposed numerical model, a case study was conducted to consider the installation of four VAWTs in the southwest corner of the medical science building at TMU campus with a height of 42m. The results of the simulation showed that 8.27 MWh energy is obtainable annually.

• Corrosion Science and Technology
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• 제21권2호
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• pp.121-129
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• 2022

A study on water-based epoxy coated on mild steel using the electroplating method was conducted to optimize the process parameters for dry film thickness to achieve superior paint quality at optimal cost in an automotive plant. The regression model was used to adjust various parameters such as electrode voltage, bath temperature, processing time, non-volatile matter, and surface area to optimize the dry film thickness. The average dry film thickness computed using the model was in the range of 15 - 35 ㎛. The error in the computed dry film thickness with reference to the experimentally measured dry film thickness value was - 0.5809%, which was well within the acceptable limits of all paint shop standards. Our study showed that the dry film thickness on mild steel was more sensitive to electrode voltage and bath temperature than processing time. Further, the presence of non-volatile matter was found to have the maximum impact on dry film thickness.

• 시스템엔지니어링학술지
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• 제19권1호
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• pp.91-106
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• 2023

Multidimensional efforts such as budgeting, organizing, and institutionalizing are being carried out for the adoption of defense AI. However, there is little interest in eliminating duplication of defense resources that may occur during the AI adoption. In this study, we propose a theoretical conceptual model to optimize duplication of AI technology that may occur during the AI adoption in the vast defense field. For a systematic approach, the JCA of the US DoD and system abstraction method are applied, and the IMO logical structure is used to decompose AI requirements and identify duplication. As a result of analyzing the effectiveness of our conceptual model through six example defense AI requirements, it was found that the amount of requirements of data and AI technologies could be reduced by up to 41.7% and 70%, respectively, and estimated costs could be reduced by up to 35.5%.

• 대한교통학회지
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• 제18권1호
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• pp.87-97
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• 2000

Signal optimization model is divided bandwidth-maximizing model and delay-minimizing model. Bandwidth-maximizing model express model formulation as MILP(Mixed Integer Linear Programming) and delay-minimizing model like TRANSYT-7F use "hill climbing" a1gorithm to optimize signal times. This study Proposed optimization model using genetic algorithm one of evolution algorithm breaking from existing optimization model This Proposed model were tested by several scenarios and evaluated through NETSIM with TRANSYT-7F\`s outputs. The result showed capability that can obtain superior solution.

• Design & Manufacturing
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• 제18권2호
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• pp.64-73
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• 2024

The purpose of this paper is to introduce a fusion method that combines the design of experiments (DOE) and machine learning to optimize the bias of plastic products. The study focuses on the plastic motor housing used in automobiles, which is manufactured through plastic injection molding. Achieving optimal molding for the motor housing involves the optimization of various molding conditions, including injection pressure, injection time, holding pressure, mold temperature, and cooling time. Failure to optimize these conditions can lead to increased product deformation. To minimize the deformation of the motor housing, the widely used Taguchi method, which is one of the design of experiment techniques, was employed to identify the injection molding conditions that affect deformation. Machine learning was then applied to various models based on the identified molding conditions. Among the models, the Random Forest model emerged as the most effective in predicting deformation amounts. The validity of the Random Forest model was also confirmed through verification. The verification results demonstrated the excellent prediction accuracy of the trained Random Forest model. By utilizing the validated model, molding conditions that minimize deformation were determined. Implementation of these optimal molding conditions led to a reduction of approximately 5.3% in deformation compared to the conditions before optimization. It is noteworthy that all injection molding outcomes presented in this paper were obtained through robust injection molding simulations, ensuring both research objectivity and speed.

• 전자공학회논문지A
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• 제30A권3호
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• pp.88-94
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• 1993

In this paper, a simple Ebers-Moll Model for the heterojunction bipolar transistor is presented. Using the model structure for the npn type HBT, the current-voltage characteristics was analyzed. And from the obtained terminal currents, the Ebers-Moll equations were derived. Then substituting the physical parameters for heterojunction to those for homojunction, this model would be used to analyze the characteristics of single and/or duble heterojunction HBT's. And directly relating model parameters to device parameters, it would be also used to optimize the characteristics of HBT's. The simulated results using this model were in good agreement with experimental data.

• 한국생산제조학회지
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• 제26권1호
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• pp.30-35
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• 2017

The major parts of a vacuum cleaner are molded. The vacuum cleaner works in multi-load conditions. Therefore, the designer needs to optimize the structure and injection molding conditions simultaneously. Here, the main factor of design is the rib shape and thickness. The greater the rib thickness, the greater the stiffness of the structure. However, it causes an increase in weight. On the other hand, the lower the rib thickness, the greater the increase in the injection pressure. However, the weight will be reduced. Therefore, the designer needs to optimize the rib shape and thickness for structure stiffness and injection molding. In order to solve this problem, we propose an optimization method using D.O.E and a response surface model, which is a multi-objective optimization method using the multi-objective genetic algorithm.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2006년도 추계학술대회 학술발표 논문집 제16권 제2호
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• pp.285-288
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• 2006

Most previous studies on improving the effectiveness of CBR have focused on the similarity function aspect or optimization of case features and their weights. However, according to some of the prior research, finding the optimal k parameter for the k-nearest neighbor (k-NN) is also crucial for improving the performance of the CBR system. Nonetheless, there have been few attempts to optimize the number of neighbors, especially using artificial intelligence (AI) techniques. In this study, we introduce a genetic algorithm (GA) to optimize the number of neighbors that combine, as well as the weight of each feature. The new model is applied to the real-world case of a major telecommunication company in Korea in order to build the prediction model for the customer profitability level. Experimental results show that our GA-optimized CBR approach outperforms other AI techniques for this mulriclass classification problem.