• Korean Management Science Review
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• v.11 no.3
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• pp.35-46
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• 1994

The primal-dual log barrier code OB1(Optimization with Barriers 1) has been identified the most efficient one in reducing computer time and solving large linear programs. This is a brief survey of OB1's life-time and performance.

• Proceedings of the KWS Conference
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• 2006.10a
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• pp.95-97
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• 2006

• Korean Journal of Optics and Photonics
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• v.34 no.3
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• pp.106-116
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• 2023

In this paper, we designed a coaxial dual camera incorporating two optical systems-one for the visible rays and the other for far-infrared ones-with the aim of capturing images in both wavelength ranges. The far-infrared system, which uses an uncooled detector, has a sensor array of 640×480 pixels. The visible ray system has 1,945×1,097 pixels. The coaxial dual optical system was designed using a hot mirror beam splitter to minimize heat transfer caused by infrared rays in the visible ray optical system. The optimization process revealed that the final version of the dual camera system reached more than 90% of the fusion performance between two separate images from dual systems. Multiple rigorous testing processes confirmed that the coaxial dual camera we designed demonstrates meaningful design efficiency and improved image conformity degree compared to existing dual cameras.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.5
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• pp.361-366
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• 2013

Prediction of process parameters is very important in parameter design. If predictions are fairly accurate, the quality improvement process will be useful to save time and reduce cost. The concept of dual response approach based on response surface methodology has widely been investigated. Dual response approach may take advantages of optimization modeling for finding optimum setting of input factor by separately modeling mean and variance responses. This study proposes an alternative dual response approach based on machine learning techniques instead of statistical analysis tools. A hybrid neural network-genetic algorithm has been proposed for the purpose of parameter design. A neural network is first constructed to model the relationship between responses and input factors. Mean and variance responses correspond to output nodes while input factors are used for input nodes. Using empirical process data, process parameters can be predicted without performing real experimentations. A genetic algorithm is then applied to find the optimum settings of input factors, where the neural network is used to evaluate the mean and variance response. A drug formulation example from pharmaceutical industry has been studied to demonstrate the procedures and applicability of the proposed approach.

• Korean Management Science Review
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• v.29 no.3
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• pp.81-89
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• 2012

Since introduced by Vining and Myers in 1990, the concept of dual response approach based on response surface methodology has widely been investigated and adopted for the purpose of robust design. Separately estimating mean and variance responses, dual response approach may take advantages of optimization modeling for finding optimum settings of input factors. Explicitly assuming functional relationship between responses and input factors, however, it may not work well enough especially when the behavior of responses are poorly represented. A sufficient number of experimentations are required to improve the precision of estimations. This study proposes an alternative to dual response approach in which additional experiments are not required. An artificial neural network has been applied to model relationships between responses and input factors. Mean and variance responses correspond to output nodes while input factors are used for input nodes. Training, validating, and testing a neural network with empirical process data, an artificial data based on the neural network may be generated and used to estimate response functions without performing real experimentations. A drug formulation example from pharmaceutical industry has been investigated to demonstrate the procedures and applicability of the proposed approach.

• Journal of Biomedical Engineering Research
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• v.26 no.1
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• pp.17-22
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• 2005

Small animal PET using a dual layer phoswich detector has been developed to obtain high and uniform spatial resolution. In this study, a simple analytic model to optimize the lengths of a dual layer phoswich detector was derived and validated by Monte Carlo simulation. For a small animal PET scanner with a 10㎝ ring diameter, the optimal length of the phoswich detector consisting of various crystal materials, such as LSO and LuYAP, were calculated analytically and validated using GATE. The detector module consisted of 8×8 arrays of crystals, with each phoswich detector element having a 2㎜×2㎜ sensitive area. The total crystal length was fixed to 20㎜. The optimal lengths of the phoswich detector layers, as functions of the crystal materials and order, conveniently derived by the analytic equation, showed good agreement with those estimated by the time consuming simulation. The simple analytical model can be used for the fast and accurate design of an optimal phoswich detector for small animal PET to achieve high spatial resolution and uniformity.

• Journal of the Semiconductor & Display Technology
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• v.20 no.3
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• pp.178-183
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• 2021

This paper describes a dynamic spectroscopic ellipsometry based on dual-wavelength calibration. DSE provides ellipsometric parameters at rates above 20 Hz, but the interferometer's sensitivity to temperature makes it difficult for that proposed system to maintain stable 𝜟k over long periods of time. To solve this problem, we set up an additional path in the DSE to perform simulations of the polarization phase calibration method using dual wavelengths. Through simulation, we were able to eliminate most of the polarization phase error and maintain a stable 𝜟k in the long-term stability experiment for 10 hours. This is the result that the 𝜟k stability of the proposed system is improved tens of times compared to the existing system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3780-3797
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• 2022

The estimation of radio map (RM) is a fundamental and critical task for the network planning and optimization performance of mobile communication. In this paper, a RM estimation method is proposed based on a deep dual learning structure. This method can simultaneously and accurately reconstruct the urban building map (UBM) and estimate the RM of the whole cell by only part of the measured reference signal receiving power (RSRP). Our proposed method implements UBM reconstruction task and RM estimation task by constructing a dual U-Net-based structure, which is named RadioCycle. RadioCycle jointly trains two symmetric generators of the dual structure. Further, to solve the problem of interference negative transfer in generators trained jointly for two different tasks, RadioCycle introduces a dynamic weighted averaging method to dynamically balance the learning rate of these two generators in the joint training. Eventually, the experiments demonstrate that on the UBM reconstruction task, RadioCycle achieves an F1 score of 0.950, and on the RM estimation task, RadioCycle achieves a root mean square error of 0.069. Therefore, RadioCycle can estimate both the RM and the UBM in a cell with measured RSRP for only 20% of the whole cell.

• 한국신재생에너지학회:학술대회논문집
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• 2005.11a
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• pp.533-536
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• 2005

In this paper, a new drive system(SHS) for hybrid electric vehicle is proposed. As dual rotor hybrid electric vehicle using planetary gearsets, the SHS has the advantages of both series and parallel systems. The output speed and torque of SHS can be determined at specific point regardless of the engine's operating point. When the size of generator which is used in SHS is same as in THS, the SHS has more activities of engine control due to the ability that is operated in lower speed range. To maximize the performance of system, we carried out optimization for the three parameters that are engine, motorl and motor2. As the result of the optimization, we confirmed the SHS is more preferable to THS in fuel consumption and acceleration area.

• Journal of Electrical Engineering and Technology
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• v.13 no.6
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• pp.2511-2520
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• 2018

In this study, Multi-Radial Basis Function Support Vector Machine (Multi-RBF SVM) classifier is introduced based on a composite kernel function. In the proposed multi-RBF support vector machine classifier, the input space is divided into several local subsets considered for extremely nonlinear classification tasks. Each local subset is expressed as nonlinear classification subspace and mapped into feature space by using kernel function. The composite kernel function employs the dual RBF structure. By capturing the nonlinear distribution knowledge of local subsets, the training data is mapped into higher feature space, then Multi-SVM classifier is realized by using the composite kernel function through optimization procedure similar to conventional SVM classifier. The original training data set is partitioned by using some unsupervised learning methods such as clustering methods. In this study, three types of clustering method are considered such as Affinity propagation (AP), Hard C-Mean (HCM) and Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA). Experimental results on benchmark machine learning datasets show that the proposed method improves the classification performance efficiently.