본 연구의 목적은 적응신경망퍼지추론시스템(ANFIS)과 회귀분석을 활용하여 7가지 역학적 특성치를 갖는 면직물의 시각적 질감을 해석하고 두 가지 방법을 비교하는 것이다. AMFIS는 퍼지 소속 함수와 신경망 구조를 갖는 것으로 인간의 비선형적 감성예측에 유용한 도구이다. 상관관계 및 회귀 분석의 통계분석은 7가지 역학적 특성치가 주관적 질감과 선형의 관계가 있음을 나타내었지만 설명력이 높지 않았고, 선형 이외의 관련성과 변수들 간의 상호작용을 표현하기 어려운 문제가 있었다. 통계분석과 비교하여, ANFIS는 변수들 간의 비선형적인 관련성과 상호작용을 가시적으로 보여주는데 설명력 있는 유용한 도구였으나, 입력 변수 중 출력 변수에 영향력이 있는 변수를 변별하지 못하여, 생성된 규칙의 수가 복잡한 문제가 있었다. 따라서 ANFIS의 해석이 단순하고 의미있는 모델을 구성하기 위해서는 영향력 있는 출력 변수를 추출하고 나머지 변수를 유사하게 통제하는 실험 모델의 구성이 필요하다.

Many studies have shown that the initial median frequency (MDF) and slope correlate with the muscle fiber composition. This study tested the hypothesis that the initial MDF and slope are fixed, regardless of the interval at which data are collected. MDF data using moving fast Fourier transformation of EMG signals, following local fatigue induced by isotonic exercise, were obtained. An inverse FFT was used to eliminate noise, and characteristic decreasing regression lines were obtained. The regression analysis was done in three different periods, the first one third, first half, and full period, looking at variance in the initial MDF, slope, and fatigue index. Data from surface EMG signals during fatiguing isotonic exercise of the biceps brachii and vastus lateralis in 20 normal subjects were collected. The loads tested were 30% and 60% maximum voluntary contraction (MVC) in the biceps brachii and 40% and 80% MVC in the vastus lateralis. The rate was 25 flexions per minute. There were no significant differences in the initial MDF or slope during the early or full periods of the regression, but there was a significant difference in the fatigue index. Therefore, to observe the change in the initial MDF and slope of the MDF regression line during isotonic exercise, this study suggest that only the early interval need to be observed.

모형저항시험 자료를 토대로 선형요소들에 대한 통계 회귀계수를 만들고, 이를 이용하여 새로운 어선의 기본설계 단계에서 유효마력을 추정할 수 있는 방법을 정립하였다. 이러한 방법을 Chine Line이 있는 소형 표준어선 4척과 Round Bottom형 표준어선 2척에 적용하여 유효마력을 추정하였으며, 이를 실험값과 비교하였다. 이 6척 어선의 경우 전속도 범위에서 추정치와 실험치가 잘 일치하고 있는 것과 근접한 것으로 나타났다. 그러므로 작성된 수치계산법은 어선의 유효마력추정에 매우 유용하게 쓰일 수 있음이 입증되었다. 따라서 제안된 방법을 어선설계에 적용하면, 직접적으로는 저항성능을 개선할 수 있을 뿐 아니라, 설계공수단축, 원가절감 등에 기여할 수 있을 것이다.

Purpose- This paper aims to examines the various factors Influencing Sales of Infant Formula Milk in China under the Covid-19 Pandemic, and base on this analysis, to present some measures for the sellers and consumers. Design/methodology- The 846 number of comments for 84 bands form e-commerce platform was taken as the index reflecting consumers’ purchasing behavior, and the properties of various types of infant formula milk (commodity name, gross weight and milk source) were taken as the independent variables to analyze the influencing factors of purchasing behavior of infant formula milk by multiple regression method. Findings- The important factors influencing the purchase behavior of infant formula milk are that product name, source of milk, domestic or imported products, applicable age, and packing unit. And then Consumers tend to buy 200-500yuan barrels of imported infant formula milk. Originality/value – The originality of this study can be found in its context, that is, under the Covid-19 Pandemic. At present, China has achieved the best epidemic control effects, which creates a great opportunity for Chinese brand infant formula milk to go abroad and realize internationalization in the future.

항만의 주요 정책 및 향후 운영계획 수립 시 정확한 물동량 예측에 관한 연구는 매우 중요하며 이러한 중요성으로 인해 관련 연구가 활발히 수행되고 있다. 본 논문에서는 국내 최대 석탄 및 철광석 처리 항만인 광양항을 대상으로 단계적 회귀분석과 인공신경망모형을 활용하여 모형간 예측력을 비교하였다. 2009년 1월부터 2019년 1월까지 총 121개월의 월별자료를 활용하였으며 석탄 및 철광석 물동량에 영향을 주는 요인을 선정하여 공급관련요인과 시장·경제관련요인으로 분류하였다. 단계적 회귀분석 결과, 광양항 석탄 물동량 예측모형의 경우, 입항선박 톤수, 석탄가격 및 대미환율이 최종변수로 선정되었고 철광석 물동량 예측모형의 경우, 입항선박 톤수, 철광석가격이 최종변수로 선정되었다. 인공신경망모형의 경우, 모델 성능에 영향을 미치는 다양한 Hyper-parameters를 조정하며 최적 모델을 선정하는 시행착오법을 사용하였다. 분석결과 인공신경망모형이 단계적 회귀분석에 비해 우수한 예측성능을 나타내었으며 예측 모형별 예측값과 실측값을 그래프 상 비교 시에도 인공신경망모형이 단계적 회귀분석에 비해 고·저점을 유사하게 나타냈다.

The study investigates the relationship between the inflation rate and economic growth to find out the optimal inflation threshold for economic growth. Therefore, this study applied an ordinary least square model (OLS) and the ordinal regression model, and collected the time-series data from 1996 to 2017 to test the relationship between inflation and economic growth in the short-term and long-term. The sample fits the model and is statistically significant. The study showed that 96.6% of correlation between inflation rate and economic growth are close and 4.5% of optimal inflation threshold is appropriate for economic growth. It finds that the optimal inflation threshold is base to perform economic growth, besides the inflation rate is positively related to economic growth. The results support the monetary policy appropriately. This study identifies issues for Government to consider: have a comprehensive solution among macroeconomic policies, monetary policy, fiscal policy and other policies to control and maintain the inflation and stimulate growth; have appropriate policies to regulate inflation to stimulate economic growth over the long term; set a priority goal for sustainable economic growth; not pursue economic growth by maintaining the inflation rate in the long term, but take appropriate measures to stabilize the inflation at the optimal inflation threshold.

The purpose of this study is to investigate the determinants of the capital structure of firms operating in a developing economy, Pakistan. The quantile regression method is applied on a sample of 183 non-financial companies listed on the Pakistan Stock Exchange during the period of 2008-2017. Specifically, the empirical analysis focuses on changes in the coefficients of the determinants according to the leverage ratio quantiles of the examined listed firms. The findings show that the capital structure of Pakistan listed firms differs between firms in different quantiles of leverage. These differences are significant with the sign of explanatory variables changes with the level of leverage. The research result found tangibility, profitability and age to be positively related to leverage among listed firms in Pakistan. However, size, liquidity and non-debt tax shield (NDTS) are negatively related to leverage. A firm’s growth and risk are found to be insignificant predictors of capital structure in Pakistan listed firms. Moreover, the study also found a significant impact of industry characteristic on leverage. The findings of this study indicate that an individual firm’s finance policy needs to be responsive to the firm’s characteristics and should match with the different borrowing requirements of listed firms.

This study aims to investigate the interplay between education and local roads on Foreign Direct Investment (FDI) in the Philippines, using economic growth as an instrument. The study used the quantitative research design applying both descriptive and inferential statistics. A combination of Two Stage Least Square Regression Model and three approaches in Panel Regression Model such as Pooled Least Square, Fixed Effect Model, and Random Effect Model were utilized in order to study the effects of education and local roads on foreign direct investment of the Philippines. Based on Fixed Effect regression results, higher education graduates and local road investments, as conditioned by economic growth, were significant factors in order to increase the foreign direct investment in the Philippines. Accordingly, a unit increase in higher education graduates, as conditioned by economic growth, leads to 8.758 unit increases in the foreign direct investment. While, a unit increased in local road investments, as conditioned by economic growth, leads to a 0.002 decrease in foreign direct investment. The regression results of the study suggest that the Foreign Direct Investment in the regions such as CAR, I, II, IV-B, V, VIII, IX, X, XI, XII, XIII, and ARMM are higher compared to Region IV-A.

The study aims to investigate a close relation between macro and non-macro variables on stock performance of tourism companies in Korea. The sample used in this study includes monthly data from January 2001 to December 2018. The stock price index of the tourism companies as a dependent variable are obtained from Sejoong, HanaTour, and RedcapTour as three leading Korean tourism companies that have been listed on the Korea Stock Exchange. This study assesses the tourism stock performance using the quantile regression approach. This study also investigates whether global crisis events as the Iraq War and the global financial crisis as non-macro variables have a significant effect on the stock performance of tourism companies in Korea. The results show that the oil prices, exchange rate and industrial production have negative coefficients on stock prices of tourism companies, while the effects of tourist expenditure and consumer price index are positive and significant. We estimate the result of quantile regression that non-macro determinants have statistically a significant and negative effect on tourism stock performance because the global crisis could threaten traveler’s safety and economy. Overall, empirical results suggest that the effects of macro and non-macro variables are statistically asymmetric and highly related to tourism stock performance.

This research is a case study of underwater object tracking based on real-time recurrent regression networks (Re3). Re3 has the concept of generic object tracking. Because of these characteristics, it is very effective to apply this model to unclear underwater sonar images. The model also an pursues object tracking method, thus it solves the problem of calculating load that may be limited when object detection models are used, unlike the tracking models. The model is also highly intuitive, so it has excellent continuity of tracking even if the object being tracked temporarily becomes partially occluded or faded. There are 4 types of the dataset using multi-beam sonar images: including (a) dummy object floated at the testbed; (b) dummy object settled at the bottom of the sea; (c) tire object settled at the bottom of the testbed; (d) multi-objects settled at the bottom of the testbed. For this study, the experiments were conducted to obtain underwater sonar images from the sea and underwater testbed, and the validity of using noisy underwater sonar images was tested to be able to track objects robustly.

In this study, the water quality components (pH, BOD, COD, TOC, SS, DO, TP) and the water quality, observed for 10 years (2008~2017) in the five tributaries of the Nakdong River with the highest flow rates, were analyzed. Monthly levels of the water quality components were estimated and regression functions were used to quantitatively explain the changes in the BOD and COD components, with respect to the TOC components. The results of analyzing the water quality levels in terms of the living environmental standards show that the lowest water quality was observed midstream (ST-3) and the highest water quality was observed upstream (ST-1 and ST-2). The regression function was estimated to be a linear function in all five tributaries, and the goodness of fit of the function was high upstream (ST-2), midstream (ST-3), and downstream (ST-4). According to the regression analysis using the observation data from 2008~2017, we found that the consumption of dissolved oxygen increased with an increase in organic matter in the major tributaries of the Nakdong River.

본 연구의 목적은 기상자료(강수량, 최고기온, 최저기온, 평균기온, 평균풍속) 기반의 다중선형 회귀모형을 개발하여 농업용저수지 저수율을 예측 하는 것이다. 나이브 베이즈 분류를 활용하여 전국 1,559개의 저수지를 지리형태학적 제원(유효저수량, 수혜면적, 유역면적, 위도, 경도 및 한발빈도)을 기준으로 30개 군집으로 분류하였다. 각 군집별로, 기상청 기상자료와 한국농어촌공사 저수지 저수율의 13년(2002~2014) 자료를 활용하여 월별 회귀모형을 유도하였다. 저수율의 회귀모형은 결정계수(R2)가 0.76, Nash-Sutcliffe efficiency (NSE)가 0.73, 평균제곱근오차가 8.33%로 나타났다. 회귀모형은 2년(2015~2016) 기간의 기상청 3개월 기상전망자료인 GloSea5 (GS5)를 사용하여 평가되었다. 현재저수율과 평년저수율에 의해 산정되는 저수지 가뭄지수(Reservoir Drought Index, RDI)에 의한 ROC (Receiver Operating Characteristics) 분석의 적중률은 관측값을 이용한 회귀식에서 0.80과 GS5를 이용한 회귀식에서 0.73으로 나타났다. 본 연구의 결과를 이용해 미래 저수율을 전망하여 안정적인 미래 농업용수 공급에 대한 의사결정 자료로 사용할 수 있을 것이다.

This study analyzes the error factors of cable tension estimation method using linear regression and suggests the upper and lower limits of tension error. The tension estimation method using linear regression is affected by errors such as boundary condition, natural frequency, cable length, and mass density. The boundary condition error analyzes the error rate for the case where the error occurs the greatest. The error of mass density and length is analytically derived, and the error of measurement natural frequency is analyzed by Monte Carlo simulation. Among these errors, the boundary condition error and the measurement natural frequency error are expected to be able to present the upper and lower limits of the error that can be caused to the estimated tension by the corresponding error factor.

Landfill and incineration tax was introduced in 2018 to reduce waste and promote recycling. However, there is a debate about tax rate. An analysis of the external effects of waste-treatment facilities is necessary, but first, an analysis of direct costs (construction, operation) is compulsory and must be conducted precisely. This study analyzed factors that affect operating cost. Ultimately, an estimation of annual operating cost was achieved by applying a multiple regression analysis to the previously-recorded data from 33 incineration facilities and 199 reclamation facilities. The results showed that incineration operating cost is affected by capacity, capacity utilization rate, and use of electricity. Annual landfill amount, area, and leachate treatment affect landfill operating cost, as well. The coefficient of determination is 0.6 or higher. Significance and collinearity between independent variables is at an acceptable level.

수공구조물 설계시 실측 유량의 자료 부족으로 홍수량의 빈도해석 결과보다는 강우자료를 수집하여 강우-유출 관계에 따라 산정된 설계강우량을 이용하여 특정 빈도에 해당하는 설계 홍수량을 사용하는 것이 일반적이다. 과거에는 첨두유량 산정을 위하여 합리식과 같은 경험식을 이용하였으나 지속기간이 장기화됨에 따라 실제 사상과는 다른 유출양상이 나타나게 되므로 확률강우량 시간분포의 정확성이 중요하게 되었다. 현재 실무에서는 설계강우량의 시간분포 방법으로 Huff의 4분위 방법 중 3분위를 사용하고 있으며 분위별 곡선에 대한 회귀식은 지속기간 전반에 걸쳐 정확도가 높은 이유로 6차식을 적용하고 있다. 그러나 통계 모델링에서는 간결함의 원리에 따라 회귀식이 간결할 필요가 있으며, 통계적 유의수준에 기초하여 회귀계수를 결정할 필요가 있다. 따라서 본 연구에서는 기상청 관할 69개 강우관측지점을 대상으로 설계강우량의 시간분포 방법으로 사용되고 있는 Huff 4분위 방법의 시간분포 회귀식에 대한 유의성 검정을 실시하였다. 기상청 관할 69개 강우관측지점의 Huff 4분위 방법의 시간분포 회귀식의 유의성 검정결과 대부분의 지점에서 4차식까지 회귀계수가 유의한 것으로 나타나 통계학적으로 Huff의 4분위 방법의 시간분포 회귀식은 4차까지만 고려하여도 무방한 것으로 분석되었다.

This study used a quantile regression model and a non-homogeneous regression model to calibrate probabilistic forecasts of wind speed. These techniques were applied to the forecasts of wind speed over Pyeongchang area using 51-member European Centre for Medium-Range Weather Forecast (ECMWF). Reliability analysis was carried out by using rank histogram to identify the statistical consistency of ensemble forecasts and corresponding observations. The performances were evaluated by rank histogram, mean absolute error, root mean square error and continuous ranked probability score. The results showed that the forecasts of quantile regression and non-homogeneous regression models performed better than the raw ensemble forecasts. However, the differences of prediction skills between quantile regression and nonhomogeneous regression models were insignificant.

NRCS-CN 방법을 이용해서 유출량을 결정하는 과정에 가장 큰 영향을 주는 변수는 유출곡선지수와 초기손실률이다. 수자원 실무에서 유출곡선 지수와 초기손실률은 대부분 미국 National Engineering Handbook의 지침에 따라 결정하지만, 우리나라 유역의 토양 및 수문학적 특성은 미국과 다르기 때문에 유출량을 과대 또는 과소 산정하게 된다. 따라서 본 연구에서는 우리나라 유역특성에 적합한 유효우량을 산정하기 위하여 점근유 출곡선지수법을 이용하여 총 8개의 유효우량 산정 모형(M1~M8)을 제시하였다. RSR (RMSE-observations standard deviation ratio)과 NSE (Nash-Sutcliffe Efficiency)의 두 가지 지표를 이용하여 모형을 평가하였으며, 그 결과 계측 유역에서는 M6 (평균 RSR: 0.76, 평균 NSE: 0.39), 미계측 유역에서는 M7 (평균 RSR: 0.82, 평균 NSE: 0.31)이 최적의 모형으로 선정되었다. 또한 대부분의 유역에서 기존에 사용되고 있던 초기손 실률 0.20보다 더 작은 값(0.01-0.10)을 적용할 때 더 좋은 결과를 나타내는 것을 확인하였다.

In this paper, we used a nonhomogeneous Gaussian regression model (NGR) as the postprocessing techniques to calibrate probabilistic forecasts that take the form of probability density functions for temperature. We also performed the alternative implementation techniques of NGR, which are stationspecific ensemble model output statistics (EMOS) model. These techniques were applied to forecast temperature over Pyeongchang area using 24-member Ensemble Prediction System for Global (EPSG). The results showed that the station-specific EMOS model performed better than the raw ensemble and EMOS model.