• Journal of Electrical Engineering and Technology
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• 제13권6호
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• pp.2262-2267
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• 2018

In electric machine design, there is a large computation cost for finite element analyses (FEA) when analyzing nonlinear characteristics in the machine Therefore, for the optimal design of an electric machine, designers commonly use an optimization algorithm capable of excellent convergence performance. However, robustness consideration, as this factor can guarantee machine performances capabilities within design uncertainties such as the manufacturing tolerance or external perturbations, is essential during the machine design process. Moreover, additional FEA is required to search robust optimum. To address this issue, this paper proposes a computationally efficient robust optimization algorithm. To reduce the computational burden of the FEA, the proposed algorithm employs a useful technique which termed static analysis assisted technique (SAAT). The proposed method is verified via the effective robust optimal design of electric machine to reduce cogging torque at a reasonable computational cost.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2005년도 추계학술대회 및 정기총회
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• pp.38-50
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• 2005

The current Air defense missile, Nike, will be replaced by the Patriot missile in the near future. In this paper, we developed an optimal allocation model for the Patriot missile. In order to formulate the model, we applied a set covering and If model. This model considers not only weapon's characteristics and performances but also the threat of enemy aircrafts and SCUD missiles. When we apply this model, we can find the optimal location of Patriot batteries which maximizes the kill probability of enemy aircrafts and SCUD missiles attacking vital area of our forces. This model can directly be used to the decision making for the optimal military facility allocation.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2008년도 학술발표회 논문집
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• pp.2193-2196
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• 2008

In this study, the new methodology such as support vector machines neural networks model (SVM-NNM) using the statistical learning theory is introduced to forecast flood stage in Nakdong river, Republic of Korea. The SVM-NNM in hydrologic time series forecasting is relatively new, and it is more problematic in comparison with classification. And, the multilayer perceptron neural networks model (MLP-NNM) is introduced as the reference neural networks model to compare the performance of SVM-NNM. And, for the performances of the neural networks models, they are composed of training, cross validation, and testing data, respectively. From this research, we evaluate the impact of the SVM-NNM and the MLP-NNM for the forecasting of the hydrologic time series in Nakdong river. Furthermore, we can suggest the new methodology to forecast the flood stage and construct the optimal forecasting system in Nakdong river, Republic of Korea.

• International Journal of Contents
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• 제7권3호
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• pp.1-5
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• 2011

It has been an interesting challenge to find a good classifier for imbalanced data, since it is pervasive but a difficult problem to solve. However, classifiers developed with the assumption of well-balanced class distributions show poor classification performance for the imbalanced data. Among many approaches to the imbalanced data problems, the algorithmic level approach is attractive because it can be applied to the other approaches such as data level or ensemble approaches. Especially, the error back-propagation algorithm using the target node method, which can change the amount of weight-updating with regards to the target node of each class, attains good performances in the imbalanced data problems. In this paper, we analyze the relationship between two optimal outputs of neural network classifier trained with the target node method. Also, the optimal relationship is compared with those of the other error function methods such as mean-squared error and the n-th order extension of cross-entropy error. The analyses are verified through simulations on a thyroid data set.

• 한국정밀공학회지
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• 제20권9호
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• pp.84-91
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• 2003

This paper concerns on one-DOF non-rotating active magnetic bearing (AMB) system subject to base motion. In such a system, it is desirable to retain the axis within the predetermined air-gap while the base motion forces the axis to deviate from the desired air-gap. Motivated from this, an optimal acceleration feedforward control is proposed to reduce the base motion response without deteriorating other feedback control performances. Experimental results demonstrate that the proposed optimal feedforward control reduces the standard deviation of the air-gap to 29％ that by feedback control alone.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 26-27 Oct. 1990
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• pp.1250-1255
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• 1990

This paper discusses an optimal cyclic scheduling problem for a FMC (Flexible Manufacturing Cell) modeled by a two-machine flowshop with two machining centers with APC's (Automated Pallet Changers), an AGV (Automated Guided Vehicle) and loading and unloading stations. Cyclic production in which similar patterns of production is repeated can significantly reduce the production lead-time and WIP (Work-In-Process) in such flexible, automated system. Thus we want to find an optimal cyclic schedule that minimizes the cycle time in each cycle. However, the existence of APC's as buffer storage for WIP makes the problem intractable (i.e., NP-complete). We propose an practical approximation algorithm that minimizes, instead of each cycle time, its upper bound. Performances of this algorithm are validated by the way of computer simulations.

• 한국자동차공학회논문집
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• 제8권5호
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• pp.138-147
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• 2000

Vehicle suspension systems are parts which effect performances of a vehicle such as ride quality, handing characteristics, straight performance and steering effort etc. Kinematic design is a decision of joints` position for straight performance and steering effort. But, when vehicle is rebounding and bumping, chang of joints` displacement is nonlinear and a surmise of straight performance and steering effort at that joints` position is difficult. So design of suspension systems is done through a inefficient method of tried-and-error depending on designer`s experience. In this paper, kinematic design of suspension systems was done through the optimal design using a genetic algorithm. For this optimal design, the function for quantification of straight performance and steering effort was made, and the kinematic design method of suspension systems having this function as the objective function was suggested.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2003년도 춘계학술대회논문집
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• pp.1165-1169
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• 2003

This paper presents an approach to optimally design the air-bearing surface (ABS) of the optical flying head for near-field recording technology (NFR). NFR is an optical recording technology using very small beam spot size by overcoming the limit of beam diffraction. One of the most Important problems in NFR is a head disk interface (HDI) issue over the recording band during the operation. A multi-criteria optimization problem is formulated to enhance the flying performances over the entire recording band during the steady state. The optimal solution of the slider, whose target flying height is 50 nm, is automatically obtained. The flying height during the steady state operation becomes closer to the target values than those fur the initial one. The pitch and roll angles are also kept within suitable ranges over the recording band. Especially, all of the air-bearing stiffness are drastically increased by the optimized geometry of the air bearing surface.

• Industrial Engineering and Management Systems
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• 제11권1호
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• pp.114-122
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• 2012

This paper considers a non-identical parallel machine scheduling problem with sequence and machine dependent setup times. The objective of this problem is to determine the allocation of jobs and the scheduling of each machine to minimize makespan. A mathematical model for optimal solution is derived. An in-depth analysis of the model shows that it is very complicated and difficult to obtain optimal solutions as the problem size becomes large. Therefore, two meta-heuristics, genetic algorithm (GA) and a new population-based evolutionary meta-heuristic called self-evolution algorithm (SEA), are proposed. The performances of the meta-heuristic algorithms are evaluated through compare with optimal solutions using randomly generated several examples.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권10호
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• pp.4625-4641
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• 2016

The performances of two commonly used feature detectors for wireless energy-harvesting cognitive radio systems are compared with the energy detector under energy causality and collision constraints. The optimal sensing duration is obtained by analyzing the effect of the detection threshold on the average throughput and collision probability. Numerical examples show that the covariance detector has the optimal sensing duration depending on an appropriate choice of the detection threshold, but no optimal sensing duration exists for the ratio of average energy to minimum eigenvalue detector.