• Safety and Health at Work
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• 제12권3호
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• pp.296-303
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• 2021

Background: Rapid population aging in developed countries has resulted in the working-age population increasingly being tasked with the provision of informal care. Methods: An educational intervention was delivered to 21 carer-employees employed at a Canadian University. Work role function, job security, schedule control, work-family conflict, familywork conflict, and supervisor and coworker support were measured as part of an aggregated workplace experience score. This score was used to measure changes pre/post intervention and at a follow-up period approximately 12 months post intervention. Three random intercept models were created via linear mixed modeling to illustrate changes in participants' workplace experience across time. Results: All three models reported statistically significant random and fixed effects intercepts, with a positive coefficient of change. Conclusion: This suggests that the intervention demonstrated an improvement of the workplace experience score for participants over time, with the association particularly strong immediately after intervention.

• 지능정보연구
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• 제24권4호
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• pp.1-32
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• 2018

본 연구는 경제적으로 국내에 큰 영향을 주었던 글로벌 금융위기를 기반으로 총 10년의 연간 기업데이터를 이용한다. 먼저 시대 변화 흐름에 일관성있는 부도 모형을 구축하는 것을 목표로 금융위기 이전(2000~2006년)의 데이터를 학습한다. 이후 매개 변수 튜닝을 통해 금융위기 기간이 포함(2007~2008년)된 유효성 검증 데이터가 학습데이터의 결과와 비슷한 양상을 보이고, 우수한 예측력을 가지도록 조정한다. 이후 학습 및 유효성 검증 데이터를 통합(2000~2008년)하여 유효성 검증 때와 같은 매개변수를 적용하여 모형을 재구축하고, 결과적으로 최종 학습된 모형을 기반으로 시험 데이터(2009년) 결과를 바탕으로 딥러닝 시계열 알고리즘 기반의 기업부도예측 모형이 유용함을 검증한다. 부도에 대한 정의는 Lee(2015) 연구와 동일하게 기업의 상장폐지 사유들 중 실적이 부진했던 경우를 부도로 선정한다. 독립변수의 경우, 기존 선행연구에서 이용되었던 재무비율 변수를 비롯한 기타 재무정보를 포함한다. 이후 최적의 변수군을 선별하는 방식으로 다변량 판별분석, 로짓 모형, 그리고 Lasso 회귀분석 모형을 이용한다. 기업부도예측 모형 방법론으로는 Altman(1968)이 제시했던 다중판별분석 모형, Ohlson(1980)이 제시한 로짓모형, 그리고 비시계열 기계학습 기반 부도예측모형과 딥러닝 시계열 알고리즘을 이용한다. 기업 데이터의 경우, '비선형적인 변수들', 변수들의 '다중 공선성 문제', 그리고 '데이터 수 부족'이란 한계점이 존재한다. 이에 로짓 모형은 '비선형성'을, Lasso 회귀분석 모형은 '다중 공선성 문제'를 해결하고, 가변적인 데이터 생성 방식을 이용하는 딥러닝 시계열 알고리즘을 접목함으로서 데이터 수가 부족한 점을 보완하여 연구를 진행한다. 현 정부를 비롯한 해외 정부에서는 4차 산업혁명을 통해 국가 및 사회의 시스템, 일상생활 전반을 아우르기 위해 힘쓰고 있다. 즉, 현재는 다양한 산업에 이르러 빅데이터를 이용한 딥러닝 연구가 활발히 진행되고 있지만, 금융 산업을 위한 연구분야는 아직도 미비하다. 따라서 이 연구는 기업 부도에 관하여 딥러닝 시계열 알고리즘 분석을 진행한 초기 논문으로서, 금융 데이터와 딥러닝 시계열 알고리즘을 접목한 연구를 시작하는 비 전공자에게 비교분석 자료로 쓰이기를 바란다.

• 한국농공학회논문집
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• 제66권1호
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• pp.1-13
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• 2024

This study assessed the efficacy of improving the accuracy of reservoir water level prediction models by employing automated machine learning models and efficient cross-validation methods for time-series data. Considering the inherent complexity and non-linearity of time-series data related to reservoir water levels, we proposed an optimized approach for model selection and training. The performance of twelve models was evaluated for the Obong Reservoir in Gangneung, Gangwon Province, using the TPOT (Tree-based Pipeline Optimization Tool) and four cross-validation methods, which led to the determination of the optimal pipeline model. The pipeline model consisting of Extra Tree, Stacking Ridge Regression, and Simple Ridge Regression showed outstanding predictive performance for both training and test data, with an R² (Coefficient of determination) and NSE (Nash-Sutcliffe Efficiency) exceeding 0.93. On the other hand, for predictions of water levels 12 hours later, the pipeline model selected through time-series split cross-validation accurately captured the change pattern of time-series water level data during the test period, with an NSE exceeding 0.99. The methodology proposed in this study is expected to greatly contribute to the efficient generation of reservoir water level predictions in regions with high rainfall variability.

• 디지털융복합연구
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• 제15권12호
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• pp.333-340
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• 2017

실시간검색어는 지금 바로 이슈가 되는 검색어의 검색 증가율이 단기간에 급상승하는 것을 중심으로 하기 때문에 일정기간 지속적으로 관심도를 유지하고 있는 이슈를 나타내지 못하고 이들이 가까운 미래에 어떤 변화를 보이는지에 대한 것도 알 수 없는 한계를 가지고 있다. 본 논문에서는 이러한 한계를 극복할 수 있도록 일정기간 동안 상위 10위 안에 속한 적이 있는 실시간검색어에 대해 일자별, 시간별 지속성을 평가하여 꾸준히 관심을 받는 검색어를 추출한다. 그런 다음, 이들 중 상위에 속하는 검색어의 관심도가 어떻게 변화하는지를 알 수 있게 하는 시계열 분석과 신경망을 이용하는 방법을 제시하고 이를 통해 도출한 실제 예를 통해 가까운 미래의 변화량을 예측한 결과를 보인다. 일자별로는 시계열 분석을, 시간별로는 인공신경망의 학습을 통해 예측하는 것이 좋은 결과를 보인다는 것을 알 수 있다.

• 한국의류학회지
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• 제21권7호
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• pp.1139-1152
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• 1997

Clothing consumption expenditure (UX) data of Korean consumers during the period of 1965 to 1993 were analyzed by time series analysis technique. According to the results of regression analysis, current income and UX of the year before showed most significant influences on the current UX. This means that the absolute and permanent income hypotheses can be accepted in case of clothing expenditures. However the effect of income decreased as the economy developed. The relative price of clothing had weak or no influence on clothing expenditures. It was also found out that CSX of the year before, the change of income, relative price of clothing ware the factors that affected clothing expenditures. From the estimation of Houthakker-Taylor state adjustment model, a negative stock coefficient was obtained. That is, clothing is subject to an inventor effect and Korean consumers regard clothing as one of the durable goods. To define whether clothing is a "luxury" or a "necessity", income and relative price elasticity of clothing expenditures were estimated. Income elasticity of clothing is slightly below 1.0 in case of national aggregate expenditures, and slightly above 1.0 in case of urban consumers' expenditures. Income elasticity has declined over time. Meanwhile the coefficient of price elasticity is not significant, indicating that the relative price of clothing have little connection with clothing expenditure.lothing expenditure.

• 한국기후변화학회지
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• 제7권1호
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• pp.19-29
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• 2016

In this study, the emission intensity calculation method of GHGs was developed by considering the characteristics of the models and time series. The telematics device was installed on the car (OBD-II) to collect information on the operation conditions from each sample vehicle of public authorities. Based on emission intensity of GHGs, it presented a methodology of quantitative comparison of GHGs emission by vehicles. Collected driving information of vehicle was used for operating characteristics analysis of the target vehicle, and it was confirmed different operating characteristics through comparison of the results and previous study. GHGs emission intensity were analyzed considering characteristics of vehicle type by passenger car, van, cargo, and considering characteristics of the time series by summer, winter, and intermediate. From the analysis result, it was calculated GHGs emission intensity based on mileage ($g\;CO_2\;eq./km$) and operating time ($g\;CO_2\;eq./sec$).

• Journal of Forest and Environmental Science
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• 제40권1호
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• pp.15-23
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• 2024

This research aimed to assess the possibility of detecting forest degradation using time-series satellite imagery and three different deep learning-based change detection techniques. The dataset used for the deep learning models was composed of two sets, one based on surface reflectance (SR) spectral information from satellite imagery, combined with Texture Information (GLCM; Gray-Level Co-occurrence Matrix) and terrain information. The deep learning models employed for land cover change detection included image differencing using the Unet semantic segmentation model, multi-encoder Unet model, and multi-encoder Unet++ model. The study found that there was no significant difference in accuracy between the deep learning models for forest degradation detection. Both training and validation accuracies were approx-imately 89% and 92%, respectively. Among the three deep learning models, the multi-encoder Unet model showed the most efficient analysis time and comparable accuracy. Moreover, models that incorporated both texture and gradient information in addition to spectral information were found to have a higher classification accuracy compared to models that used only spectral information. Overall, the accuracy of forest degradation extraction was outstanding, achieving 98%.

• 대한기계학회논문집A
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• 제30권2호
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• pp.194-201
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• 2006

Gait analysis is essential to identify accurate cause and knee condition from patients who display abnormal walking. Traditional linear tools can, however, mask the true structure of motor variability, since biomechanical data from a few strides during the gait have limitation to understanding the system. Therefore, it is necessary to propose a more precise dynamic method. The chaos analysis, a nonlinear technique, focuses on understand how variations in the gait pattern change over time. Eight healthy eight subjects walked on a treadmill for 100 seconds at 60 Hz. Three dimensional walking kinematic data were obtained using two cameras and KWON3D motion analyzer. The largest Lyapunov exponent from the measured knee angular displacement time series was calculated to quantify local stability. This study quantified the variability present in time series generated from gait parameter via chaos analysis. Knee flexion-extension patterns were found to be chaotic. The proposed Lyapunov exponent can be used in rehabilitation and diagnosis of recoverable patients.

• Journal of Information Processing Systems
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• 제16권2호
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• pp.508-521
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• 2020

We compared a novel encoding Lempel-Ziv complexity (ELZC) with three common complexity algorithms i.e., approximate entropy (ApEn), sample entropy (SampEn), and classic Lempel-Ziv complexity (CLZC) so as to determine a satisfied complexity and its corresponding quality indices for assessing quality of multi-lead electrocardiogram (ECG). First, we calculated the aforementioned algorithms on six artificial time series in order to compare their performance in terms of discerning randomness and the inherent irregularity within time series. Then, for analyzing sensitivity of the algorithms to content level of different noises within the ECG, we investigated their change trend in five artificial synthetic noisy ECGs containing different noises at several signal noise ratios. Finally, three quality indices based on the ELZC of the multi-lead ECG were proposed to assess the quality of 862 real 12-lead ECGs from the MIT databases. The results showed the ELZC could discern randomness and the inherent irregularity within six artificial time series, and also reflect content level of different noises within five artificial synthetic ECGs. The results indicated the AUCs of three quality indices of the ELZC had statistical significance (>0.500). The ELZC and its corresponding three indices were more suitable for multi-lead ECG quality assessment than the other three algorithms.

• 설비공학논문집
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• 제19권1호
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• pp.60-67
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• 2007

The purpose of this study is to evaluate indoor air quality at the stage of building construction. To check the IAQ at the stage of construction IAQX simulation program developed by EPA was used and the values of TVOC were analyzed with time-series. The Simulation conditions are as follows. 1) Ventilation rate, 2) Time schedule of works, 3) Material change. Through this simulation, the major factors which affect the IAQ were analyzed and the importance of empirical data about the time-series emission rate of concerned material could be confirmed.