• 한국환경보건학회지
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• 제48권3호
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• pp.151-158
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• 2022

Background: Chemical emissions in the environment have rapidly increased with the accelerated industrialization taking place in recent decades. Residents of industrial complexes are concerned about the health risks posed by chemical exposure. Objectives: This study was performed to suggest modeling methods that take into account multimedia and multi-pathways in human exposure and risk assessment. Methods: The concentration of benzene emitted at industrial complexes in Daesan, South Korea and the exposure of local residents was estimated using the Caltox model. The amount of human exposure based on inhalation rate was stochastically predicted for various activity stages such as resting, normal walking, and fast walking. Results: The coefficient of determination (R²) for the CalTOX model efficiency was 0.9676 and the root-mean-square error (RMSE) was 0.0035, indicating good agreement between predictions and measurements. However, the efficiency index (EI) appeared to be a negative value at -1094.4997. This can be explained as the atmospheric concentration being calculated only from the emissions from industrial facilities in the study area. In the human exposure assessment, the higher the inhalation rate percentile value, the higher the inhalation rate and lifetime average daily dose (LADD) at each activity step. Conclusions: Prediction using the Caltox model might be appropriate for comparing with actual measurements. The LADD of females was higher ratio with an increase in inhalation rate than those of males. This finding would imply that females may be more susceptible to benzene as their inhalation rate increases.

• 대한원격탐사학회지
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• 제36권4호
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• pp.573-586
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• 2020

PM (particulate matter) is of interest to everyone because it can have adverse effects on human health by the infiltration from respiratory to internal organs. To date, many studies have made efforts for the prediction of PM10 and PM2.5 concentrations. Unlike previous studies, we conducted the prediction of tomorrow's PM10 concentration for the Air Korea stations using Chinese PM10 data in addition to the satellite AOD and weather variables. We constructed 230,639 matchups from the raw data over 3 million and built an RF (random forest) model from the matchups to cope with the complexity and nonlinearity. The validation statistics from the blind test showed excellent accuracy with the RMSE (root mean square error) of 9.905 ㎍/㎥ and the CC (correlation coefficient) of 0.918. Moreover, our prediction model showed a stable performance without the dependency on seasons or the degree of PM10 concentration. However, part of coastal areas had a relatively low accuracy, which implies that a dedicated model for coastal areas will be necessary. Additional input variables such as wind direction, precipitation, and air stability should also be incorporated into the prediction model as future work.

• 디지털콘텐츠학회 논문지
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• 제19권9호
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• pp.1779-1785
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• 2018

본 연구에서는 사물인터넷 기술을 이용하는 스마트 웨어러블 기기의 상황인식 기능을 향상시키기 위하여 센서부의 이벤트 데이터에 대한 오차 보정 방안을 제안하였다. 스마트 기기를 통한 상황인식에서 기기의 특성상 필수적인 상황 정보 센싱을 함에 있어서 오차가 불가피하게 발생하고, 이는 예측 성능을 저하시키는 요인이 된다. 이러한 문제를 해결하기 위하여 본 연구에서는 칼만필터의 오류보정 알고리즘을 적용하여 스마트기기의 3축 가속도 센서에서 입수되는 신호 값을 보정하였다. 결과적으로 시계열 데이터를 이루는 3축 가속도 센서가 감지하여 보고하는 데이터에 대한 처리 과정에서 발생하는 오차를 칼만필터를 통하여 효과적으로 제거할 수 있었다. 이 연구가 차후 개발되어질 실시간 상황인지 시스템의 성능을 향상시켜 줄 수 있을 것이라 기대한다.

• Geomechanics and Engineering
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• 제30권1호
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• pp.75-91
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• 2022

This paper aims to show how to use several Machine Learning (ML) methods to estimate the TBM penetration rate systematically (TBM-PR). To this end, 1125 datasets including uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), punch slope index (PSI), distance between the planes of weakness (DPW), orientation of discontinuities (alpha angle-α), rock fracture class (RFC), and actual/measured TBM-PRs were established. To evaluate the ML methods' ability to perform, the 5-fold cross-validation was taken into consideration. Eventually, comparing the ML outcomes and the TBM monitoring data indicated that the ML methods have a very good potential ability in the prediction of TBM-PR. However, the long short-term memory model with a correlation coefficient of 0.9932 and a route mean square error of 2.68E-6 outperformed the remaining six ML algorithms. The backward selection method showed that PSI and RFC were more and less significant parameters on the TBM-PR compared to the others.

• 한국시뮬레이션학회논문지
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• 제19권3호
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• pp.127-135
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• 2010

최근의 현대화된 다양한 항해장비들로 인해 선박에 있는 항해사들은 위험상황 인식에 도움이 될 수 있는 정보들을 획득할 수 있게 되었다. 하지만, 이러한 유용한 도구들에도 불구하고 항해사들은 여전히 안전항행을 위한 의사결정에 어려움을 겪고 있는데, 이는 다양한 장비들이 제공하는 선박 내 외 상황에 관한 많은 양의 데이터를 지속적으로 관찰해야 한다는 항해사의 부담과 여러 장비 간 정보의 불일치성 때문이다. 실제로, 많은 해양 사고가 항해사의 부주의에 의해 일어나고 있다는 것이 이미 잘 알려져 있다. 따라서, 항행 상황의 일부 정보만을 제공하는 보조 장비를 넘어서 항해사의 의사결정을 도울 수 있는 지원도구가 요구되고 있다. 시뮬레이션은 의사결정을 지원 할 수 있는 기술 중 하나며, 선박에서의 실시간 주변상황에 대한 종합적인 평가 및 예측 가능한 시스템은 항해사의 안전항행에 대한 의사결정에 도움을 줄 수 있다. 이 논문은 선박을 위한 항행안전정보 시스템에서의 위험 상황 예측을 위한 시뮬레이션 방안에 관한 연구로서, 다양한 지식 베이스 및 이산 사건 시뮬레이션 방식을 활용한 시스템 전체 구성 방법을 제안하고 제한된 항행상황 시나리오에서의 구성 요소들의 예시를 통해 시스템의 타당성을 보인다.

• 산업경영시스템학회지
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• 제46권1호
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• pp.105-111
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• 2023

Recently, research on prediction algorithms using deep learning has been actively conducted. In addition, algorithmic trading (auto-trading) based on predictive power of artificial intelligence is also becoming one of the main investment methods in stock trading field, building its own history. Since the possibility of human error is blocked at source and traded mechanically according to the conditions, it is likely to be more profitable than humans in the long run. In particular, for the virtual currency market at least for now, unlike stocks, it is not possible to evaluate the intrinsic value of each cryptocurrencies. So it is far effective to approach them with technical analysis and cryptocurrency market might be the field that the performance of algorithmic trading can be maximized. Currently, the most commonly used artificial intelligence method for financial time series data analysis and forecasting is Long short-term memory(LSTM). However, even t4he LSTM also has deficiencies which constrain its widespread use. Therefore, many improvements are needed in the design of forecasting and investment algorithms in order to increase its utilization in actual investment situations. Meanwhile, Prophet, an artificial intelligence algorithm developed by Facebook (META) in 2017, is used to predict stock and cryptocurrency prices with high prediction accuracy. In particular, it is evaluated that Prophet predicts the price of virtual currencies better than that of stocks. In this study, we aim to show Prophet's virtual currency price prediction accuracy is higher than existing deep learning-based time series prediction method. In addition, we execute mock investment with Prophet predicted value. Evaluating the final value at the end of the investment, most of tested coins exceeded the initial investment recording a positive profit. In future research, we continue to test other coins to determine whether there is a significant difference in the predictive power by coin and therefore can establish investment strategies.

• 한국통신학회논문지
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• 제36권11C호
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• pp.655-661
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• 2011

다양한 네트워크 환경에서 적응적인 서비스 품질을 제공할 수 있는 H.264 SVC 전송에 대한 연구가 활발하다. 본 논문은 H.264 SVC의 무기준법 객관적 화질 평가 방법으로서, H.264 SVC의 계층성을 이용한 품질 평가 지표를 제안한다. 제안하는 지표는 패킷 손실의 위치에 따라 움직임 벡터, 계층적 예측 구조에 의한 에러 전파 패턴, 양자화 파라미터, 영향을 받은 영상프레임 수 등 에러를 반영한 인지적 화질 평가를 예측한다. 제안하는 품질평가 지표는 사람의 인지적인 영상 품질을 반영한 객관적 지표이며 이 지표를 주관적 화질평가 결과인 DMOS와의 상관관계를 통해 성능을 검증하였다.

• Journal of information and communication convergence engineering
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• 제2권2호
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• pp.110-115
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• 2004

The prosthesis of current commercialized apparatus has considerable problems, requiring improvement. Especially, LLP(Lower Limb Prosthesis)-related problems have improved, but it cannot provide normal walking because, mainly, the gait control of the LLP does not fit with patient's gait manner. To solve this problem, HCI((Human Computer Interaction) that adapts and controls LLP postures according to patient's gait manner more effectively is studied in this research. The proposed control technique has 2 steps： 1) the multilayer neural network forecasts angles of gait of LLP by using the angle of normal side of lower limbs； and 2) the adaptive neural controller manages the postures of the LLP based on the predicted joint angles. According to the experiment data, the prediction error of hip angles was 0.32［deg.］, and the predicted error of knee angles was 0.12［deg.] for the estimated posture angles for the LLP. The performance data was obtained by applying the reference inputs of the LLP controller while walking. Accordingly, the control performance of the hip prosthesis improved by 80% due to the control postures of the LLP using the reference input when comparing with LQR controller.

• 해양환경안전학회지
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• 제24권5호
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• pp.519-526
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• 2018

본 연구에서는 인명구조활동을 지원하기 위한 피난동선예측 알고리즘 개발의 첫 단계로 피난동선예측 알고리즘의 개념을 정립하고 그 타당성을 수치적으로 명확히 제시하였다. 제안하는 알고리즘은 평상시 선박내 모니터링 시스템으로부터 지속적으로 승객이동 데이터를 취득, 분석, 정형화하고, 재난발생시 이 데이터와 예측 툴을 활용해 도출한 승선자의 피난동선예측 정보를 구조자에게 제공하여 인명피해를 최소화시키는 프로세스로 요약할 수 있다. 피난훈련을 통해 피난특성 데이터를 취득하였고 이를 기존 인명피난예측 툴에 입력하여 피난특성을 예측한 결과, 예측 툴의 구조적 원인으로 인해 가시거리가 충분히 확보되고 피난경로를 충분히 숙지한 상황에서의 피난 시나리오(SN1)에서만 신뢰할 만한 예측결과가 도출되었다. 본 연구에서 제안하는 알고리즘의 타당성을 확인하기 위해 타 분야의 예측 툴을 사용하여 피난특성을 예측한 결과, 제안 알고리즘이 구현될 경우 평균피난시간예측값과 피난동선(지점경유)예측값이 각각 0.6 ~ 6.9 %, 0.6 ~ 3.6 % 범위의 오차에서 실측값과 매우 유사한 경향을 보였다. 향후 선내 모니터링 데이터를 분석하고 이를 활용한 예측성능이 우수한 피난동선예측 알고리즘을 개발할 계획이다.

• 한국근적외분광분석학회:학술대회논문집
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• 한국근적외분광분석학회 2001년도 NIR-2001
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• pp.4102-4102
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• 2001

In multivariate analysis, absorbance spectrum is measured over a band of wavelengths. One does not often pay attention to the size of this wavelength band. However, it is desirable that spectrum is measured at only necessary wavelengths as long as the acceptable accuracy of prediction can be met. In this paper, the method of selecting an optimal band of wavelengths based on the loading vector analysis was proposed and applied for determining total protein in human serum using near-infrared transmission spectroscopy and PLSR. Loading vectors in the full spectrum PLSR were used as reference in selecting wavelengths, but only the first loading vector was used since it explains the spectrum best. Absorbance spectra of sera from 97 outpatients were measured at 1530∼1850 nm with an interval of 2 nm. Total protein concentrations of sera were ranged from 5.1 to 7.7 g/㎗. Spectra were measured by Cary 5E spectrophotometer (Varian, Australia). Serum in the 5 mm-pathlength cuvette was put in the sample beam and air in the reference beam. Full spectrum PLSR was applied to determine total protein from sera. Next, the wavelength region of 1672∼1754 nm was selected based on the first loading vector analysis. Standard Error of Cross Validation (SECV) of full spectrum (1530∼l850 nm) PLSR and selected wavelength PLSR (1672∼1754 nm) was respectively 0.28 and 0.27 g/㎗. The prediction accuracy between the two bands was equal. Wavelength selection based on loading vector in PLSR seemed to be simple and robust in comparison to other methods based on correlation plot, regression vector and genetic algorithm. As a reference of wavelength selection for PLSR, the loading vector has the advantage over the correlation plot since the former is based on multivariate model whereas the latter, on univariate model. Wavelength selection by the first loading vector analysis requires shorter computation time than that by genetic algorithm and needs not smoothing.