• Magazine of the Korean Society of Agricultural Engineers
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• v.38 no.4
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• pp.110-124
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• 1996

This study was conducted to simulate long seres of synthetic monthly flows by multi-season first order Markov model with selection of best fitting frequency distribution, harmonic synthetic and harmonic regression models and to make a comparison of statistical parameters between observes and synthetic flows of five watersheds in Geum river system. The results obtained through this study can be summarized as follow. 1. Both gamma and two parameter lognormal distributions were found to be suitable ones for monthly flows in all watersheds by Kolmogorov-Smirnov test. 2. It was found that arithmetic mean values of synthetic monthly flows simulated by multi-season first order Markov model with gamma distribution are much closer to the results of the observed data in comparison with those of the other models in the applied watersheds. 3. The coefficients of variation, index of fluctuation for monthly flows simulated by multi-season first order Markov model with gamma distribution are appeared closer to those of the observed data in comparison with those of the other models in Geum river system. 4. Synthetic monthly flows were simulated over 100 years by multi-season first order Markov model with gamma distribution which is acknowledged as a suitable simulation modal in this study.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.5 s.311
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• pp.37-44
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• 2006

In this paper, a new version of the ICM method(MICM, modified ICM) in which the contextual information is modelled by Markov random fields (MRF) is introduced. To extract the feature, a new local MRF model with a fitting block neighbourhood is proposed. This model selects contextual information not only from the relative intensity levels but also from the geometrically directional position of neighbouring cliques. Feature extraction depends on each block's contribution to the local variance. They discriminates it into several regions, for example context and background. Boundaries between these regions are also distinctive. The proposed algerian performs segmentation using directional block fitting procedure which confines merging to spatially adjacent elements and generates a partition such that pixels in unified cluster have a homogeneous intensity level. From experiment with ink rubbed copy images(Takbon, 拓本), this method is determined to be quite effective for feature identification. In particular, the new algorithm preserves the details of the images well without over- and under-smoothing problem occurring in general iterated conditional modes (ICM). And also, it may be noted that this method is applicable to the handwriting recognition.

• 한국방재학회:학술대회논문집
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• 2008.02a
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• pp.249-252
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• 2008

The solutions are expressed in terms of a double integral transformation over wavenumber and frequency. The complete solution is considered in such a full wave theory approach. This method can handle a larger number of plane layers. Therefore, the result of FK method is very similar to real data. Using the models that were modified in velocity and Q value with depth by iterative process from a model (Kang and Park, 2006) and considered as one of the best models in Korean Peninsula, the synthetic data are simulated for explosions and earthquakes of North Korea. This study notes that the wave shape of the synthetic data is very dependent on Q value, velocities, and thickness of sedimentary layers. Comparing between the real and the synthetic, fitting well in arrival time of first arrival and wave shape causes us to arrive at an indication that the model is very close representation of upper crustal structure and simulations are well done in amplitude fitting and in identification of phases of local and regional waves.

• The Korean Journal of Applied Statistics
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• v.36 no.5
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• pp.473-488
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• 2023

Gangwon-do is one of Korea's most popular tourist destinations, with varying tourism demands and trends across its subregions. It is crucial to identify the characteristics of tourism in each area and compare the tourism patterns over time to devise policies that revitalize tourism in each local government and promote balanced development across regions. In this paper, we classify the regions in Gangwon-do based on tourism data from the last four years and analyze the tourism pattern of each region using the non-Euclidean additive model proposed by Jeon et al. (2021). The model incorporates the proportions of visitors by age groups and the proportions of navigation searches by destination types as two covariates, and the proportions of tourism expenditure types as a response variable. We estimate the model using the smooth-backfitting method and coordinate-wise bandwidth selection. The results are visualized in ternary plots, and changes in tourism patterns over time are analyzed by comparing the ratios of prediction errors to fitting errors.

• Journal of the Korean Society of Safety
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• v.23 no.6
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• pp.51-56
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• 2008

As RFID/USN technology is used in the latest industry trend, the information analysis paradigm shifts to intelligence service environment. The intelligent service includes autonomic operation, which select activity by defining itself to the status of industry facilities. Furthermore, information analysis based on IT used to frequently data mining for detecting the meaning information and deriving new pattern. This paper suggest self-classifying of context-aware by applying data mining in gas facilities for serving the intelligent gas safety management. We modify data algorithm for fitting the domain of gas safety, construct context-aware model by using the proposed algorithm, and demonstrate our method. As the accuracy of our model is improved over 90%, the our approach can apply to intelligent gas safety management based on RFID/USN environments.

• Environmental Engineering Research
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• v.23 no.4
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• pp.364-373
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• 2018

The present study assessed the efficient removal of nutrients and Chlorophyll-a (Chl-a) by using methyl esterified sericite (MES) and pine needle extracts (PNE), a low cost and abundant green hybrid material from nature. For this purpose, the optimal conditions were investigated, such as the pH, temperature, MES and PNE ratio, and MES-PNE dose. In addition, a Microcystis aeruginosa control using MES-PNE was also analyzed with various inhibition models. The removal of the nutrient and Chl-a onto MES-PNE was optimized for over 95% removal as follows: 2-2.5 for the MES-PNE ratio, 7-8 pH and a $22-25^{\circ}C$ temperature. In this respect, approximately 1.52-2.20 g/L of MES-PNE was required to remove each 1 g of dry weight/L of Chl-a. Total phosphorus (TP) has a greater influence on the increase in Chl-a than total nitrogen (TN) according to the correlation between TN, TP and Chl-a. Moreover, the Luong model was the best model for fitting the biodegradation kinetics data from Chl-a on MES-PNE from lake water. The novel hybrid material MES-PNE was very effective at removing TN, TP and Chl-a from the lake and can be applied in the field.

• International Journal of Naval Architecture and Ocean Engineering
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• v.12 no.1
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• pp.657-666
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• 2020

A reliable and cost-effective technique for the development of corrosion damage model is introduced to predict nonlinear time-dependent corrosion wastage of steel structures. A detailed explanation on how to propose a generalised mathematical formulation of the corrosion model is investigated in this paper (Part I), and verification and application of the developed method are covered in the following paper (Part II) by adopting corrosion data of a ship's ballast tank structure. In this study, probabilistic approaches including statistical analysis were applied to select the best fit probability density function (PDF) for the measured corrosion data. The sub-parameters of selected PDF, e.g., the largest extreme value distribution consisting of scale, and shape parameters, can be formulated as a function of time using curve fitting method. The proposed technique to formulate the refined time-dependent corrosion wastage model (TDCWM) will be useful for engineers as it provides an easy and accurate prediction of the 1) starting time of corrosion, 2) remaining life of the structure, and 3) nonlinear corrosion damage amount over time. In addition, the obtained outcome can be utilised for the development of simplified engineering software shown in Appendix B.

• Journal of Korea Multimedia Society
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• v.24 no.3
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• pp.382-395
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• 2021

In this study, character recognition using deep learning is performed among the various defects in the PCB, the purpose of which is to check whether the printed characters are printed correctly on top of components, or the incorrect parts are attached. Generally, character recognition may be perceived as not a difficult problem when considering MNIST, but the printed letters on the PCB component data are difficult to collect, and have very high redundancy. So if a deep learning model is trained with original data without any preprocessing, it can lead to over fitting problems. Therefore, this study aims to reduce the redundancy to the smallest dataset that can represent large amounts of data collected in limited production sites, and to create datasets through data enhancement to train a flexible deep learning model can be used in various production sites. Moreover, ResNet model verifies to determine which combination of datasets is the most effective. This study discusses how to reduce and augment data that is constantly occurring in real PCB production lines, and discusses how to select coresets to learn and apply deep learning models in real sites.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.235-238
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• 2007

본 논문에서는 기존의 클러스터 기반 퍼지 모델트리에서 트리의 깊이에 따른 over-fitting으로 인한 훈련 및 검증데이터의 일관성 문제점을 해결하기 위해 상호 노드간의 정보를 고려하는 방법을 제안하고자 한다. 제안된 방법은 우선 입력과 출력변수의 속성을 고려한 퍼지 클러스터링에 의해 중심벡터를 계산한 후, 중심벡터들과 입력 속성간의 소속도를 이용하여 구간 분할된 영역별로 각각의 선형모델을 구축한다. 예측 단계에서는 입력된 데이터가 잎노드에 도달하는 노드간의 중심벡터와 입력 데이터간의 거리값에 따른 소속도를 계산한 후 최종적으로 무게 중심법을 이용하여 출력값을 예측하게 된다. 제안된 방법의 우수성을 보이기 위해 다양한 벤치마크 데이터를 대상을 실험한 결과, 기존의 클러스터 기반 퍼지 모델트리보다 향상된 성능을 보임을 알 수 있었다.

• Proceedings of the International Microelectronics And Packaging Society Conference
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• 2001.11a
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• pp.134-137
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• 2001

The cure kinetics of a cycloalipatic epoxy / anhydride / Co(II) system for a no-flow underfill encapsulant, has been studied by using a differential scanning calorimetry(DSC) under isothermal and dynamic conditions over the temperature range of $160^{\circ}C ~220^{\circ}C$. The kinetic analysis was carried out by fitting dynamic/isothermal heating experimental data to the kinetic expressions to determine the reaction parameters, such as order of reaction and reaction constants. Diffusion-controlled reaction has been observed as the cure conversion increases and successfully analyzed by incorporating the diffusion control term into the rate equation. The prediction of reaction rates by the model equation corresponded well to experimental data at all temperature.