• 한국지하수토양환경학회:학술대회논문집
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• 한국지하수토양환경학회 2003년도 추계학술발표회
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• pp.565-569
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• 2003

One-year-long groundwater-level data have been collected from 18 wells in Cheon-an area. The result of barometric efficiency, autocorrelation, cross-correlation and statistical distribution evaluated from the measurement data shows that groundwater-level measurements from observation wells are the principal source of information about aquifer characteristics. Data from WA-2 has high barometric efficiency as well as steady decreasing auto-correlation coefficient, which means nonleaky confined aquifer, Most aquifers in this study show the unconfined properties so that barometric efficiencies are mostly low and the coefficients of cross-correlation between groundwater-level and precipitation are commonly high. This study showed that the long-term groundwater-level monitoring data without artificial stress such as pumping would give accurate information about aquifer characteristics.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2007년도 정기 학술대회 논문집
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• pp.43-48
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• 2007

In this study, an ANN-based damage detection algorithm using acceleration signals is developed for alarming locations of damage in beam-type structures. A new ANN-algorithm using output-only acceleration responses is designed for damage detection in real time. The cross-covariance of two acceleration signals measured at two different locations is selected as the feature representing the structural condition. Neural networks are trained for potential loading patterns and damage scenarios of the target structure for which its actual loadings are unknown. The feasibility and practicality of the proposed method are evaluated from laboratory-model tests on free-free beams for which accelerations were measured before and after several damage cases.

• Journal of the Korean Data and Information Science Society
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• 제27권4호
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• pp.1059-1066
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• 2016

In this paper we propose a robust version of varying coefficient models, which is based on the regularized regression with L1 regularization. We use the iteratively reweighted least squares procedure to solve L1 regularized objective function of varying coefficient model in locally weighted regression form. It provides the efficient computation of coefficient function estimates and the variable selection for given value of smoothing variable. We present the generalized cross validation function and Akaike information type criterion for the model selection. Applications of the proposed model are illustrated through the artificial examples and the real example of predicting the effect of the input variables and the smoothing variable on the output.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2017년도 학술발표회
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• pp.170-170
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• 2017

본 연구의 목표는 극한 지역의 대비 시스템을 구축하기 위하여 인공 신경망(Artificial Neural Networks)을 이용하여 보다 관측하기 쉬운 기상 인자들로부터 적설량을 실시간 측정 가능성을 제시하는 것이다. 본 연구에서 사용한 데이터베이스는 기상청의 기상자료개방포털에서 사람이 직접 측정한 종관기상관측의 자료다. 이 중에서 일최대 기온, 일최저 기온, 일평균 기온, 강수량을 사용하여 오차를 줄여나가는 최적화방법으로 인공 신경망 시스템을 설계하였다. 설계된 시스템으로 500회 시뮬레이션한 연구 결과는 상관계수가 적설량 측정에 대한 인공 신경망의 크기(노드의 개수)와 관계없이 평균적으로 0.8627인 것을 보여준다. 추가적으로 보조 입력 값인 고도를 사용한 결과, 성능은 좋아졌지만 상관계수의 차이는 평균 0.0044로 미세했다. 또한 Cross-Validation을 통해 기존의 보간법인 Kriging기법과 비교하여 미 관측 지역에서 인공 신경망(ANNs) 사용이 Kriging기법 보다 우수하다는 것을 2차원 Regression's map을 통해 나타냈다. 마지막으로 오차가 크게 발생했을 경우 보안할 수 있는 확률적인 방안을 제시하였다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2022년도 춘계학술발표대회
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• pp.264-267
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• 2022

Recently, federated learning (FL) has increased prominence as a viable approach for enhancing user privacy and data security by allowing collaborative multi-party model learning without exchanging sensitive data. Despite this, most present FL systems still depend on a centralized aggregator to generate a global model by gathering all submitted models from users, which could expose user privacy and the risk of various threats from malicious users. To solve these issues, we suggested a safe FL framework that employs differential privacy to counter membership inference attacks during the collaborative FL model training process and empowers blockchain to replace the centralized aggregator server.

• Toxicological Research
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• 제16권1호
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• pp.17-25
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• 2000

Chitosan cross-linked collagen-glycosaminoglyan (CCGM) is an artificial skin substitute made to form a sponge like dimensional matrix. It can be used to facilitate reconstruction of dermal tissue when applied on large wounds such as severe burns. In order to study the toxicological effects of CCGM the cytotoxicity, local irritation and skin sensitization test were carried out according to the standards of ISO 10993. In the cytotoxicity test utilizing LDH and MTT test, both the CCGM and its extract had no toxicity of Balb/c 3T3 cells. The local irritatioin test on rabbit skin demonstrated that CCGM did not promote any harmful when directly applied on skin. In addition, it did not elicit any allergic reaction in the guinea pig maximization test. Based on these results, it is suggested that CCGM is a material without cytotoxicity, local irritation and allergenicity.

• 대한조선학회논문집
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• 제44권6호
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• pp.579-589
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• 2007

Contrary to the conventional single-point measuring devices such as LDV, pitot-tube, hot-wire, etc., it would be possible to measure instantaneously 3-D flow fields with a stereoscopic PIV system. In this paper, we present an analysis algorithm for a stereoscopic PIV system using the normalized cross-correlation (NCC) and a 3-D calibration based reconstruction method. The evaluation method based on NCC is one of the most accurate correlation-based methods. We validated the developed algorithm through a benchmarking comparison with 3-D artificial SPIV images and calibration target images.

• 한국지구물리탐사학회:학술대회논문집
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• 한국지구물리탐사학회 2007년도 공동학술대회 논문집
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• pp.185-190
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• 2007

In this study, we developed the 2.5D EM modeling and inversion algorithm for cross-hole source and receiver geometry. Considering the cross-hole environment, we use a VMD (vertical magnetic dipole) as a source and vertical magnetic fields as a measuring data. Developed inversion algorithm is tested for the isolated block model which has a conductive and a resistivity anomaly respectively. For the conductive anomaly, its size and resistivity are inverted well on the inversion results, while for the resistive anomaly, the location of anomalous block is shown on the inverted section, but its values are far from the exact value. Furthermore, artificial conductive anomalies are shown around the resistive anomalous zone. If we consider the inversion artifact shown in the test inversion of restive block, it is almost impossible to image the resistive zone. However, the main target of EM tomography in the engineering problem is conductive target such as fault zone, and contaminated zone etc., EM tomography algorithm can be used for detecting the anomalous zone.

• 대한의용생체공학회:의공학회지
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• 제10권2호
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• pp.89-90
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• 1989

To develop an artificial bone substitute that is gradually degraded and replaced by the regenerated natural bone, the authors designed and produced a composite that is consisted of calcium phosphate and collagen. Human umbilical cord origin pepsin treated type I atelocollagen was used as the structural matrix, by which sintered or non-sintered carbonate apatite was encapsulated to form an inorganic-organic composite. With cross linking atelocollagen by UV ray irradiation, the resistance to both compressive and tensile strength was increased. Collagen degradation by the collagenase induced collagenolysis was also decreased.

• Journal of the Korean Data and Information Science Society
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• 제25권6호
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• pp.1539-1547
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• 2014

In this paper we apply the autoregressive process to the nonlinear quantile regression in order to infer nonlinear quantile regression models for the autocorrelated data. We propose a kernel method for the autoregressive data which estimates the nonlinear quantile regression function by kernel machines. Artificial and real examples are provided to indicate the usefulness of the proposed method for the estimation of quantile regression function in the presence of autocorrelation between data.