• 제목/요약/키워드: Multiple-Linear-Regression

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HOS 특징 벡터를 이용한 장애 음성 분류 성능의 향상 (Performance Improvement of Classification Between Pathological and Normal Voice Using HOS Parameter)

  • 이지연;정상배;최흥식;한민수
    • 대한음성학회지:말소리
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    • 제66호
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    • pp.61-72
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    • 2008
  • This paper proposes a method to improve pathological and normal voice classification performance by combining multiple features such as auditory-based and higher-order features. Their performances are measured by Gaussian mixture models (GMMs) and linear discriminant analysis (LDA). The combination of multiple features proposed by the frame-based LDA method is shown to be an effective method for pathological and normal voice classification, with a 87.0% classification rate. This is a noticeable improvement of 17.72% compared to the MFCC-based GMM algorithm in terms of error reduction.

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다중 반응표면분석에서의 최적화 문제에 관한 연구 (A Study on Simultaneous Optimization of Multiple Response Surfaces)

  • 유정빈
    • 품질경영학회지
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    • 제23권3호
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    • pp.84-92
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    • 1995
  • A method is proposed for the simultaneous optimization of several response functions that depend on the same set of controllable variables and are adequately represented by a response surface model (polynomial regression model) with the same degree and with constraint that the individual responses have the target values. First, the multiple responses data are checked for linear dependencies among the responses by eigenvalue analysis. Thus a set of responses with no linear functional relationships is used in developing a function that measures the distance estimated responses from the target values. We choose the optimal condition that minimizes this measure. Also, under the different degree of importance two step procedures are proposed.

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Understanding the Relationship between Construction Workers' Psychological Conditions and Safety Factors

  • Lim, Soram;Chi, Seokho
    • 국제학술발표논문집
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    • The 6th International Conference on Construction Engineering and Project Management
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    • pp.138-141
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    • 2015
  • The South Korean construction industry has shown a high proportion of industrial accidents (approximately 28% of whole injuries) and the continuously increasing accident rate. Although many safety research emphasized that the 3E (Enforcement, Education, and Engineering) approach is a potential solution to enhance workplace safety, there should be benefits to consider psychological (i.e., Emotional) effects on the safety performance since most construction works are human-oriented. Thus, understanding construction workers' psychological conditions can be a priority. This research studied the relationships between psychological conditions-which cover stress, personal temperament, emotional disturbance, and drinking habit-and specific safety-related factors including safety motivation and knowledge, and safety performance of individual workers at a construction site. This study conducted a survey of 430 respondents and analyzed the data with the multiple linear regressions. The results imply persistence, trait anxiety, and problem-focused coping style are the critical factors that should be controlled for enhancing jobsite safety. Finally, the research outcomes could be applied to build a strategic safety management plan for a construction manager.

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오리사 바닥재의 수분 증발량 평가 (Assessment of Evaporation Rates from Litter of Duck House)

  • 이상연;이인복;김락우;여욱현;데카노 크리스티나;김준규;최영배;박유미;정효혁
    • 한국농공학회논문집
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    • 제61권5호
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    • pp.101-108
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    • 2019
  • The domestic duck industry is the sixth-largest among the livestock industries. However, 34.3% of duck houses were the duck houses arbitrarily converted from plastic greenhouses. This type of duck house was difficult to properly manage internal air temperature and humidity environment. Humidity environment inside duck houses is an important factor that directly affects the productivity and disease occurrence of the duck. Although the humidity environments of litters (bedding materials) affect directly the inside environment of duck houses, there are only few studies related to humidity environment of litters. In this study, evaporation rates from litters were evaluated according to air temperature, relative humidity, water contents of litters, and wind speed. The experimental chamber was made to measure evaporation rates from litters. Temperature and humidity controlled chamber was utilized during the conduct of the laboratory experiments. Using the measured data, a multi linear regression analysis was carried out to derive the calculation formula of evaporation rates from litters. In order to improve the accuracy of the multi linear regression model, the partial vapor pressure directly related to evaporation was also considered. Variance inflation factors of air temperature, relative humidity, partial vapor pressure, water contents of litters, and wind speed were calculated to identify multicollinearity problem. The Multiple $R^2$ and adjusted-$R^2$ of regression model were calculated at 0.76 and 0.71, respectively. Therefore, the regression models were developed in this study can be used to estimate evaporation rates from the litter of duck houses.

아동의 다중지능과 학습의 정의적 요인의 관계 (Relationships Between Multiple Intelligences and Affective Factors in Children's Learning)

  • 정혜영;이경화
    • 아동학회지
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    • 제28권5호
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    • pp.253-267
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    • 2007
  • This study examined the relationships between multiple intelligences as cognitive factors and affective factors of learning motivation and academic self-concept. The data were collected from 276 4th grade elementary school students and analyzed by correlation, multi-variate analysis, and step-wise multiple regression. Results were that (1) multiple intelligences, learning motivation, and academic self-concept had statistically significant correlations among themselves. Multi-variate analysis showed that intra-personal intelligence explained 58.6% of the linear combination of learning motivation and academic self-concept. (2) Intra-personal intelligence explained 29% to 58% of learning motivation and its sub-factors of achievement motivation, internal locus of control, self-efficacy, and self-regulation. (3) Intra-personal intelligence, logical-mathematical intelligence, musical intelligence, and inter-personal intelligence were explanatory variables for academic self-concept and its sub-factors.

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기상자료기반 다중선형회귀분석에 의한 농업용 저수지 월단위 저수율 예측 및 저수지 가뭄지수(RDI) 추정 (Forecasting Monthly Agricultural Reservoir Storage and Estimation of Reservoir Drought Index (RDI) Using Meteorological Data Based Multiple Linear Regression Analysis)

  • 이지완;김진욱;정충길;김성준
    • 한국지리정보학회지
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    • 제21권3호
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    • pp.19-34
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    • 2018
  • 본 논문의 목적은 농업용 저수지 저수율 계측자료와 기상인자와의 다중선형회귀분석을 통해 저수율 예측 월단위 회귀식을 산정하는데 있다. 2002년부터 2016년까지의 한국농어촌공사 저수지 3,067개에 대한 저수율 관측자료와 기상청 63개 지점 관측자료를 수집하여 저수율 예측 다중선형 회귀식을 도출하였으며, 개발된 월별 회귀식에 대한 $R^2$는 0.51~0.95로 분석되었다. 또한 회귀식의 적용성 평가를 위해 9개 대표저수지에 대해 관측값과 비교한 $R^2$는 0.44~0.81로 나타났다. 회귀식을 이용하여 평년(1976-2005) 대비 저수지 가뭄지수(Reservoir Drought Index, RDI)를 산정하여 ROC 분석을 수행한 결과, 극심한 가뭄의 경우 2년(2015~2016) 평균 적중률은 0.64로 겨울의 적중률이 0.70으로 가장 높았고, 여름의 적중률이 0.58로 가장 낮게 나타났으며, 봄과 가을의 적중률은 각각 0.59, 0.68로 분석되었다. 본 연구에서 도출한 회귀식은 가용한 관측자료 및 1~3개월의 장기 기상전망자료 기반의 월단위 저수율 전망자료 생산이 가능하므로, 이를 기반으로 농업가뭄 전망정보의 생산이 가능할 것으로 판단된다.

영역별 수직 투영 히스토그램 매칭 및 선형 회귀모델 기반의 차량 운행 영상의 안정화 기술 개발 (Regional Projection Histogram Matching and Linear Regression based Video Stabilization for a Moving Vehicle)

  • 허유정;최민국;이현규;이상철
    • 방송공학회논문지
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    • 제19권6호
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    • pp.798-809
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    • 2014
  • 본 논문에서는 블랙박스 혹은 운전석에 장착된 카메라로부터 얻어진 차량 영상에 대한 영역별 수직 투영 히스토그램 매칭 및 선형 회귀분석 모델을 활용한 강건한 차량 운행 동영상의 안정화 기법을 제안한다. 동영상 안정화 기법은 영상의 흔들림 보정 뿐 아니라 동영상 내 강건한 특징점 추적 및 매칭을 위한 이전의 전처리 과정으로 활용된다. 일반적으로 촬영 과정에서 많은 떨림이 포함될 수 있는 야외 CCTV 영상이나 손으로 들고 촬영된 동영상에 대한 흔들림 보정 등에 적용되고 있으나 영상 내 특징점이 지속적으로 변하고 영상의 변화 정도가 매우 심한 차량 운행 동영상에서는 적용된 사례가 드물다. 본 연구에서는 일반적인 비디오 안정화 기술이 적용되기 어려운 차량 운행 동영상에 대하여 흔들림 보정을 위한 동영상 안정화 기법을 제안한다. 제안된 기법은 입력 영상에 대한 영역별 수직 투영 히스토그램 매칭을 수행하고 선형 회귀모델을 통해 영상에 나타나는 수직 및 회전 이동 변환을 선형 근사하여 시간 영역상에서의 입력 영상에 대한 안정화를 수행한다. 제안 방법의 검증을 위해 블랙박스로 촬영된 동영상에 동영상 안정화 기술을 적용하였으며, 운행 중 불규칙한 노면으로 인한 영상의 흔들림이 효과적으로 제거되는 것을 확인할 수 있었다.

Estimation of Water Quality of Fish Farms using Multivariate Statistical Analysis

  • Ceong, Hee-Taek;Kim, Hae-Ran
    • Journal of information and communication convergence engineering
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    • 제9권4호
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    • pp.475-482
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    • 2011
  • In this research, we have attempted to estimate the water quality of fish farms in terms of parameters such as water temperature, dissolved oxygen, pH, and salinity by employing observational data obtained from a coastal ocean observatory of a national institution located close to the fish farm. We requested and received marine data comprising nine factors including water temperature from Korea Hydrographic and Oceanographic Administration. For verifying our results, we also established an experimental fish farm in which we directly placed the sensor module of an optical mode, YSI-6920V2, used for self-cleaning inside fish tanks and used the data measured and recorded by a environment monitoring system that was communicating serially with the sensor module. We investigated the differences in water temperature and salinity among three areas - Goheung Balpo, Yeosu Odongdo, and the experimental fish farm, Keumho. Water temperature did not exhibit significant differences but there was a difference in salinity (significance <5%). Further, multiple regression analysis was performed to estimate the water quality of the fish farm at Keumho based on the data of Goheung Balpo. The water temperature and dissolved-oxygen estimations had multiple regression linear relationships with coefficients of determination of 98% and 89%, respectively. However, in the case of the pH and salinity estimated using the oceanic environment with nine factors, the adjusted coefficient of determination was very low at less than 10%, and it was therefore difficult to predict the values. We plotted the predicted and measured values by employing the estimated regression equation and found them to fit very well; the values were close to the regression line. We have demonstrated that if statistical model equations that fit well are used, the expense of fish-farm sensor and system installations, maintenances, and repairs, which is a major issue with existing environmental information monitoring systems of marine farming areas, can be reduced, thereby making it easier for fish farmers to monitor aquaculture and mariculture environments.

Prolificacy and Its Relationship with Age, Body Weight, Parity, Previous Litter Size and Body Linear Type Traits in Meat-type Goats

  • Haldar, Avijit;Pal, Prasenjit;Rajesh, M. Datta;Pal, Saumen K.;Majumdar, Debasis;Biswas, Chanchal K.;Pan, Subhransu
    • Asian-Australasian Journal of Animal Sciences
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    • 제27권5호
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    • pp.628-634
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    • 2014
  • Data on age and body weight at breeding, parity, previous litter size, days open and some descriptive body linear traits from 389 meat-type, prolific Black Bengal goats in Tripura State of India, were collected for 3 and 1/2 years (2007 to 2010) and analyzed using logistic regression model. The objectives of the study were i) to evaluate the effect of age and body weight at breeding, parity, previous litter size and days open on litter size of does; and ii) to investigate if body linear type traits influenced litter size in meat-type, prolific goats. The incidence of 68.39% multiple births with a prolificacy rate of 175.07% was recorded. Higher age (>2.69 year), higher parity order (>2.31), more body weight at breeding (>20.5 kg) and larger previous litter size (>1.65) showed an increase likelihood of multiple litter size when compared to single litter size. There was a strong, positive relationship between litter size and various body linear type traits like neck length (>22.78 cm), body length (>54.86 cm), withers height (>48.85 cm), croup height (>50.67 cm), distance between tuber coxae bones (>11.38 cm) and distance between tuber ischii bones (>4.56 cm) for discriminating the goats bearing multiple fetuses from those bearing a single fetus.

Identification of Regression Outliers Based on Clustering of LMS-residual Plots

  • Kim, Bu-Yong;Oh, Mi-Hyun
    • Communications for Statistical Applications and Methods
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    • 제11권3호
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    • pp.485-494
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    • 2004
  • An algorithm is proposed to identify multiple outliers in linear regression. It is based on the clustering of residuals from the least median of squares estimation. A cut-height criterion for the hierarchical cluster tree is suggested, which yields the optimal clustering of the regression outliers. Comparisons of the effectiveness of the procedures are performed on the basis of the classic data and artificial data sets, and it is shown that the proposed algorithm is superior to the one that is based on the least squares estimation. In particular, the algorithm deals very well with the masking and swamping effects while the other does not.