• Proceedings of the Korean Institute of Surface Engineering Conference
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• 2017.05a
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• pp.122-122
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• 2017

인산-망간 화성피막의 경우 양질의 피막층을 형성하기 위하여 표면조정제를 사용하고 있으며, 화성피막 직전에 표면 조정제 처리를 하여 피막 결정의 미세, 치밀, 균일하게 하는 동시에 피막의 화성시간을 단축하고 있다. 본 연구는 표면조정제의 입자 사이즈에 따른 화성피막 입자 사이즈 변화 및 물리적인 특성 향상을 확인하였다. 하지금속 소재로는 기계구조용 탄소강재(SM45C)을 $50{\times}50{\times}3mm$로 제작하였고, 전처리 공정으로는 탈지 ${\rightarrow}$ 에칭 ${\rightarrow}$ 디스머트 후 표면조정제 입자 사이즈별로 표면조정 후, 화성피막 처리를 하였으며 각 조건에 따른 피막 층의 미세조직은 SEM을 사용하여 관찰하였고, 윤활성은 내마모시험기(Ball on disc)를 사용하여 마찰계수 측정을 통해 확인하였으며, 내식성은 5% NaCl 염수분무를 실시하여 적청 발생 면적으로 측정하였다. 표면조정제의 입자 사이즈는 4종류로 세분화하여 표면조정 후 화성피막 처리하였으며, 표면조정제의 입자 사이즈를 미세화함에 따라 화성피막의 입자 사이즈가 미세, 균일해지고 피막의 치밀도가 향상됨을 확인할 수 있었다. 표면조정제의 미분화는 소재 표면에 작고 치밀한 결정(활성점)을 만들며, 표면조정제의 입자 사이즈가 작아질수록 이러한 활성점의 크기가 미세해지고 화성피막의 입자 사이즈 또한 미세화 시키는 역할을 하는 것을 확인 할 수 있었다. 이처럼 표면 조정제의 입자 사이즈에 따른 화성피막 입자 사이즈 및 물성변화는 SEM, 내마모시험 및 내식성 시험을 통하여 확인할 수 있었다. 즉, 표면조정제의 입자 사이즈가 미세해질수록, 화성피막의 입자사이즈가 미세화되었고, 윤활성 및 내식성이 향상되는 것을 확인 할 수 있었다.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.967-970
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• 2001

본 논문에서는 적응 필터의 계수 갱신에서 가변 블록을 사용하는 방법을 제안하였다. 데이터 블록의 길이는 MSE 학습곡선의 시정수에 비례하도록 하였다. 이 방법에서는 적응 필터가 정상상태로 접근함에 따라 스텝사이즈를 조정하여 필터계수 갱신의 횟수를 줄일 수 있다. 제안한 방법의 유용성을 입증하기 위한 컴퓨터모의 실험을 통해 기존의 최적 스텝사이즈 수열을 이용한 알고리듬과 가변 스텝사이즈 알고리듬과 성능을 비교하였다. 그리고 MSE 의 초기값을 최소화하는 최적 초기 스텝사이즈를 유도하였다. 유도된 최적 스텝사이즈를 가변 스텝사이즈 알고리듬에 적용, 그 성능을 평가 하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.1267-1268
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• 2013

온라인 패션 쇼핑몰을 이용하는 소비자가 늘어나면서 소비자의 불만 사항으로 상품의 불량상태, 사이즈 불일치, 배송 및 환불 지연, 품절 및 입고 지연, 색상 불일치, 입어보지 못하는 불편함 등이 있는데, 그 중에 하나인 사이즈 불일치와 입어보지 못하는 불편함을 가상의 모델로 대체하여 옷을 입어 사용자가 확인하는 어플리케이션이다. 본 논문에서는 사용자의 키와 몸무게, 사이즈를 입력하고 사람의 표준적인 몸으로 사이즈에 맞는 옷을 입혀 사용자가 옷을 사기 전 사이즈를 확인 할 수 있도록 도움을 주는 어플리케이션이다.

• Journal of IKEEE
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• v.6 no.2 s.11
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• pp.111-118
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• 2002

In this paper, we present a variable block method to reduce additive computational requirements in determining step-size of variable step-size LMS (VS-LMS) algorithms. The block length is inversely proportional to the changing of step-size in VS-LMS algorithm. The technique reduces computational requirements of the conventional VS-LMS algorithms without a degradation of performance in convergence rate and steady state error. And a method for deriving initial step-size, when the input is zero mean, white Gaussian sequence, is proposed. For demonstrating the good performances of the proposed method, simulation results are compared with the conventional variable step-size algorithms in convergence speed and computational requirements.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.561-566
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• 2021

The optimum kernel size for error-distribution estimation with given error samples cannot be used in the weight adjustment of minimum Euclidean distance between error distributions (MED) algorithms. In this paper, a new adaptive kernel estimation method for convergence enhancement of MED algorithms is proposed. The proposed method uses the average rate of change in error power with respect to a small interval of the kernel width for weight adjustment of the MED learning algorithm. The proposed kernel adjustment method is applied to experiments in communication channel compensation, and performance improvement is demonstrated. Unlike the conventional method yielding a very small kernel calculated through optimum estimation of error distribution, the proposed method converges to an appropriate kernel size for weight adjustment of the MED algorithm. The experimental results confirm that the proposed kernel estimation method for MED can be considered a method that can solve the sensitivity problem from choosing an appropriate kernel size for the MED algorithm.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.3
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• pp.627-632
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• 2021

ITL (information-theoretic learning) has been applied successfully to adaptive signal processing and machine learning applications, but there are difficulties in deciding the kernel size, which has a great impact on the system performance. The correntropy algorithm, one of the ITL methods, has superior properties of impulsive-noise robustness and channel-distortion compensation. On the other hand, it is also sensitive to the kernel sizes that can lead to system instability. In this paper, considering the sensitivity of the kernel size cubed in the denominator of the cost function slope, a new adaptive kernel estimation method using the rate of change in error power in respect to the kernel size variation is proposed for the correntropy algorithm. In a distortion-compensation experiment for impulsive-noise and multipath-distorted channel, the performance of the proposed kernel-adjusted correntropy algorithm was examined. The proposed method shows a two times faster convergence speed than the conventional algorithm with a fixed kernel size. In addition, the proposed algorithm converged appropriately for kernel sizes ranging from 2.0 to 6.0. Hence, the proposed method has a wide acceptable margin of initial kernel sizes.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.12
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• pp.1199-1203
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• 2010

Data transfer between host system and storage device is based on the data unit called sector, which can be varied depending on computer systems. If NAND flash memory is used as a storage device, the variant sector size can affect storage system performance since its operation is much related to sector size and page size. In this paper, we propose an efficient FTL mapping management scheme to support multiple sector size within one NAND flash memory based storage device, and analyze the performance effect and management overhead. According to the proposed scheme, the management overhead of proposed FTL management is lower than conventional scheme when various sector sizes are configured in computer systems, while performance is less degraded in comparison with single sector size support system.

• Journal of the Korean Data and Information Science Society
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• v.23 no.5
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• pp.949-959
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• 2012

This article is to find the right size of decision trees that performs better for boosting algorithm. First we defined the tree size D as the depth of a decision tree. Then we compared the performance of boosting algorithm with different tree sizes in the experiment. Although it is an usual practice to set the tree size in boosting algorithm to be small, we figured out that the choice of D has a significant influence on the performance of boosting algorithm. Furthermore, we found out that the tree size D need to be sufficiently large for some dataset. The experiment result shows that there exists an optimal D for each dataset and choosing the right size D is important in improving the performance of boosting. We also tried to find the model for estimating the right size D suitable for boosting algorithm, using variables that can explain the nature of a given dataset. The suggested model reveals that the optimal tree size D for a given dataset can be estimated by the error rate of stump tree, the number of classes, the depth of a single tree, and the gini impurity.

• Journal of the Korean Society of Radiology
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• v.15 no.4
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• pp.547-554
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• 2021

In order to quickly and accurately diagnose pneumonia on a chest X-ray image, different batch sizes of 4, 8, 16, and 32 were applied to the same Xception deep learning model, and modeling was performed 3 times, respectively. As a result of the performance evaluation of deep learning modeling, in the case of modeling to which batch size 32 was applied, the results of accuracy, loss function value, mean square error, and learning time per epoch showed the best results. And in the accuracy evaluation of the Test Metric, the modeling applied with batch size 8 showed the best results, and the precision evaluation showed excellent results in all batch sizes. In the recall evaluation, modeling applied with batch size 16 showed the best results, and for F1-score, modeling applied with batch size 16 showed the best results. And the AUC score evaluation was the same for all batch sizes. Based on these results, deep learning modeling with batch size 32 showed high accuracy, stable artificial neural network learning, and excellent speed. It is thought that accurate and rapid lesion detection will be possible if a batch size of 32 is applied in an automatic diagnosis study for feature extraction and classification of pneumonia in chest X-ray images using deep learning in the future.

• The Journal of the Korea Contents Association
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• v.15 no.7
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• pp.500-510
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• 2015

This study examined the effect of face sensitivities on a consumer's choice of luxury product's logo size. To answer the research question, an experimental design was planed and conducted where the stimulus for the experiment comprised two luxury brands, Louisvuitton and Couronne. For each brand, three different sizes of a logo were considered; no logo, logo with small size, and logo with big size. Each product has an identical design, color, and size except for the size of logos. A total of 115 students were enrolled in the experiment to select one of the logo sizes in each brand. Through the data analysis, we found that shame consciousness and consciousness for others among face sensitivities were significantly associated with the choice of a logo size both in Louisvuitton and Couronne. In addition, there was no significant difference in face sensitivities between male and female, but was significant difference in the choice of a logo size between male and female.