• 한국작물학회지
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• 제50권4호
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• pp.268-275
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• 2005

Early predictions of crop yields call provide information to producers to take advantages of opportunities into market places, to assess national food security, and to provide early food shortage warning. The objectives of this study were to identify the most useful parameters for estimating yields and to compare two model selection methods for finding the 'best' model developed by multiple linear regression. This research was conducted in two 65ha corn/soybean rotation fields located in east central South Dakota. Data used to develop models were small temporal variability information (STVI: elevation, apparent electrical conductivity $(EC_a)$, slope), large temporal variability information (LTVI : inorganic N, Olsen P, soil moisture), and remote sensing information (green, red, and NIR bands and normalized difference vegetation index (NDVI), green normalized difference vegetation index (GDVI)). Second order Akaike's Information Criterion (AICc) and Stepwise multiple regression were used to develop the best-fitting equations in each system (information groups). The models with $\Delta_i\leq2$ were selected and 22 and 37 models were selected at Moody and Brookings, respectively. Based on the results, the most useful variables to estimate corn yield were different in each field. Elevation and $EC_a$ were consistently the most useful variables in both fields and most of the systems. Model selection was different in each field. Different number of variables were selected in different fields. These results might be contributed to different landscapes and management histories of the study fields. The most common variables selected by AICc and Stepwise were different. In validation, Stepwise was slightly better than AICc at Moody and at Brookings AICc was slightly better than Stepwise. Results suggest that the Alec approach can be used to identify the most useful information and select the 'best' yield models for production fields.

• 대한간호학회지
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• 제51권3호
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• pp.363-379
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• 2021

Purpose: The purpose of this study was to evaluate the validity and reliability of the Korean version of Person-Centered Practice Inventory-Staff (PCPI-S) for nurses. Methods: The English PCPI-S was translated into Korean with forward and backward translation. Data were collected from 338 nurses at one general hospital in Korea. Construct validity was evaluated with confirmatory factor analysis, convergent validity, and discriminant validity. Known-group validity was also evaluated. Cronbach's α was used to assess the reliability. Results: The PCPI-S Korean version consisted of 51 items in three areas: prerequisites, the care environment, and person-centered process. The comparative fit index (CFI) and values of person-centered care process were improved after engagement and having sympathetic presence items were combined as one component. The construct validity of PCPI-S Korean version was verified using four-factor structures (.05 < RMSEA < .10, AGFI > .70, CFI > .70, and AIC). The convergent validity and discriminant validity of the entire PCPI-S question were verified using a two-factor structures (AVE > .50, construct reliability > .70). There was an acceptable known-group validity with a significant correlation between the PCPI-S level and the degree of person-centered care awareness and education. Internal consistency was reliable with Cronbach's α .95. Conclusion: The Korean version of PCPI-S is valid and reliable. It can be used as a standardized Korean version of person-centered care measurement tool. Abbreviation: RMSEA = root mean square error of approximation; AGFI = adjusted goodness of fit index; AIC = Akaike information criterion; AVE = average variance extracted.

• Smart Structures and Systems
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• 제17권6호
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• pp.1087-1105
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• 2016

Traffic load and volume is one of the most important physical quantities for bridge safety evaluation and maintenance strategies formulation. This paper aims to conduct the statistical analysis of traffic volume information and the multimodal modeling of gross vehicle weight (GVW) based on the monitoring data obtained from the weigh-in-motion (WIM) system instrumented on the arch Jiubao Bridge located in Hangzhou, China. A genetic algorithm (GA)-based mixture parameter estimation approach is developed for derivation of the unknown mixture parameters in mixed distribution models. The statistical analysis of one-year WIM data is firstly performed according to the vehicle type, single axle weight, and GVW. The probability density function (PDF) and cumulative distribution function (CDF) of the GVW data of selected vehicle types are then formulated by use of three kinds of finite mixed distributions (normal, lognormal and Weibull). The mixture parameters are determined by use of the proposed GA-based method. The results indicate that the stochastic properties of the GVW data acquired from the field-instrumented WIM sensors are effectively characterized by the method of finite mixture distributions in conjunction with the proposed GA-based mixture parameter identification algorithm. Moreover, it is revealed that the Weibull mixture distribution is relatively superior in modeling of the WIM data on the basis of the calculated Akaike's information criterion (AIC) values.

• 대한교통학회지
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• 제25권5호
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• pp.135-148
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• 2007

교통사고의 특성과 사고에 대한 지속시간 사이의 관계에 대한 이해는 사고의 효과적인 대응과 사고에 의한 혼잡을 감소시키는데 핵심 요소가 된다. 때문에 본 연구의 목적은 AFT metric 모형을 적용한 사고 지속시간을 분석하는 것이다. 비록 로그 로지스틱 및 로그 정규 AFT 모형이 통계적 이론과 기존 연구 사례를 기반으로 선정되었으나, 로그 로지스틱 모형이 보다 우수하게 추정되었다. AFT 모형은 예측 목적으로도 널리 사용되기 때문에, 추정된 모형은 사고 발생시 사고 관련 기본 정보 접수 즉시 고속도에서의 사고 지속시간 예측에 사용될 수 있다. 결과적으로, 예측된 사고 지속시간 정보는 사고를 처리하기 위한 제반 의사 결정에 도움을 줄 뿐 아니라 교통 혼잡의 감소 및 추가 사상자의 감소로 그 효과가 이어질 것으로 판단된다.

• 한국측량학회지
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• 제35권1호
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• pp.31-39
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• 2017

The goal of this study was to efficiently analyze the relationships of the number of thefts with related factors, considering the spatial patterns of theft crimes. Theft crime data for a 5-year period (2009-2013) were collected from Haeundae Police Station. A logarithmic transformation was performed to ensure an effective statistical analysis and the number of theft crimes was used as the dependent variable. Related factors were selected through a literature review and divided into social, environmental, and defensive factors. Seven factors, were selected as independent variables: the numbers of foreigners, aged persons, single households, companies, entertainment venues, community security centers, and CCTV (Closed-Circuit Television) systems. OLS (Ordinary Least Squares) and GWR (Geographically Weighted Regression) were used to analyze the relationship between the dependent variable and independent variables. In the GWR results, each independent variable had regression coefficients that differed by location over the study area. The GWR model calculated local values for, and could explain the relationships between, variables more efficiently than the OLS model. Additionally, the adjusted R square value of the GWR model was 10% higher than that of the OLS model, and the GWR model produced a AICc (Corrected Akaike Information Criterion) value that was lower by 230, as well as lower Moran's I values. From these results, it was concluded that the GWR model was more robust in explaining the relationship between the number of thefts and the factors related to theft crime.

• 대한기계학회논문집A
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• 제38권6호
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• pp.649-653
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• 2014

대부분의 신뢰성 기반 최적설계는 최적설계 과정에서 설계점이 이동함에도 불구하고 설계변수의 불확실성은 변하지 않는다. 하지만 실제 문제에서 설계변수의 값에 따라 불확실성이 변하는 경우가 있다. 예를 들어 철판의 두께가 설계변수이고, 철판의 제작공차가 불확실성인 경우, 철판의 두께에 따라 철판의 제작공차가 달라지기 때문에 설계변수의 값에 따라 그 값의 불확실성이 변한다. 본 연구에서는 설계변수의 값에 따라 불확실성이 변하는 것을 변동 불확실성으로 정의하고, 이를 신뢰성 기반 최적설계에 적용하는 변동 불확실성을 고려한 신뢰성 기반 최적설계 기법을 제안한다. 수학예제에서 변동 불확실성을 고려하지 않은 신뢰성 기반 최적설계와 변동 불확실성을 고려한 신뢰성 기반 최적설계의 비교를 통해 제안한 방법의 필요성을 확인한다. 또한 엔진 크래들의 변동 불확실성을 고려한 신뢰성 기반 최적설계를 통해 제안한 방법의 유용성을 확인한다.

• Structural Engineering and Mechanics
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• 제63권6호
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• pp.809-824
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• 2017

The accurate evaluation of wind characteristics and wind-induced structural responses during a typhoon is of significant importance for bridge design and safety assessment. This paper presents an expectation maximization (EM) algorithm-based angular-linear approach for probabilistic modeling of field-measured wind characteristics. The proposed method has been applied to model the wind speed and direction data during typhoons recorded by the structural health monitoring (SHM) system instrumented on the arch Jiubao Bridge located in Hangzhou, China. In the summer of 2015, three typhoons, i.e., Typhoon Chan-hom, Typhoon Soudelor and Typhoon Goni, made landfall in the east of China and then struck the Jiubao Bridge. By analyzing the wind monitoring data such as the wind speed and direction measured by three anemometers during typhoons, the wind characteristics during typhoons are derived, including the average wind speed and direction, turbulence intensity, gust factor, turbulence integral scale, and power spectral density (PSD). An EM algorithm-based angular-linear modeling approach is proposed for modeling the joint distribution of the wind speed and direction. For the marginal distribution of the wind speed, the finite mixture of two-parameter Weibull distribution is employed, and the finite mixture of von Mises distribution is used to represent the wind direction. The parameters of each distribution model are estimated by use of the EM algorithm, and the optimal model is determined by the values of $R^2$ statistic and the Akaike's information criterion (AIC). The results indicate that the stochastic properties of the wind field around the bridge site during typhoons are effectively characterized by the proposed EM algorithm-based angular-linear modeling approach. The formulated joint distribution of the wind speed and direction can serve as a solid foundation for the purpose of accurately evaluating the typhoon-induced fatigue damage of long-span bridges.

• Asian Pacific Journal of Cancer Prevention
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• 제16권15호
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• pp.6575-6579
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• 2015

Background: Breast cancer (BC) is the most frequent malignancy among females and is a leading cause of death of middle-aged women. Herein, we evaluated baseline characteristics for BC patients and also compared these variables across ealry and late recurrence groups. Materials and Methods: Between 1995 to 2014, among female breast cancer patients referred to our oncology clinic, eighty-six were entered into our study. All had distant metastasis. Early recurrence was defined as initial recurrence within 5 years following curative surgery irrespective of site. Likewise, late recurrence was defined as initial recurrence after 5 years. No recurrence was defined for survivors to a complete minimum of 10 years follow-up. Significant prognostic factors associated with early or late recurrence were selected according to the Akaike Information Criterion. Results: The median follow-up was 9 years (range, 1-18 years). During follow-up period, 51 recurrences occurred (distant metastasis), 31 early and 20 late. According to the site of recurrence, there were 51 distant. In this follow-up period, 19 patients died. Compared with the early recurrence group, the no recurrence group had lower lymph node involvement and more p53 positive lesions but the late recurrence group had lower tumor size. In comparison to no recurrence, p53 (odds ratio [OR] 6.94, 95% CI 1.49-32.16) was a significant prognostic factor for early recurrence within 5 years. Conclusions: Tumor size, p53 and LN metastasis are the most important risk factors for distance recurrence especially in early recurrence and also between of them, p53 is significant prognostic factor for early recurrence.

• 수산해양기술연구
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• 제52권4호
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• pp.281-289
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• 2016

Spinyhead sculpin Dasycottus setiger, a species of cold water fish, is distributed along the eastern coastal waters of Korea. A series of fishing experiments was carried out in the waters near Uljin from June, 2002 to November, 2004, using the experimental monofilament gill nets of different mesh sizes (82.2, 89.4, 104.8, and 120.2 mm) to describe the selectivity of the gill net for the fish. The SELECT (Share Each Length's Catch Total) analysis with maximum likelihood method was applied to fit the different functional models (normal, lognormal, and bi-normal models) for selection curves to the catch data. The bi-normal model with the fixed relative fishing intensity was selected as the best-fit selection curve by AIC (Akaike's Information Criterion) comparison. For the best-fit selection curve, the optimum relative length (the ratio of fish total length to mesh size) with the maximum efficiency and the selection range ($R_{50%,large}-R_{50%,small}$) of 50% retention were obtained as 2.363 and 0.851, respectively. The ratios of body girth to mesh perimeter at 100% retention where the selection curve of each mesh size represented the optimum total length were calculated as the range of 0.86 ~ 0.87.

• Asian-Australasian Journal of Animal Sciences
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• 제12권3호
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• pp.331-335
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• 1999

The present study describes the analysis of the multiphasic growth function (MGF) to body weight in laboratory and wild mice. Three genetic groups of laboratory mice (Mus musculus domesticus) designated $CF_{{\sharp}1}$, C3H/HeNCrj and C57BL/6NCrj, and a genetic group of Yonakuni wild mice (Mus musculus molossinus yonakuni, Yk) were used. Mean body weights of each genetic group-sex subclass from birth to 69 days of age taken at 3-day intervals were analyzed by a monophasic, diphasic and triphasic functions for describing growth patterns. A comparison among the three functions of the MGF was based on the goodness-of-fit criteria: residual standard deviation (RSD), adjusted R-square (Adj $R^2$) and Akaike's information criterion (AIC). Result of this study indicated that body weight averaged heavier for males than for females. Among the four genetic groups within both sexes, $CF_{{\sharp}1}$ showed the highest, subsequent followed by C3H/HeNCrj, C57BL/6NCrj and Yk. Comparison among the three functions revealed that the triphasic function was the best fit to growth data, with the lowest RSD, the highest Adj $R^2$ and the lowest AIC, for the four genetic groups. For the triphasic function, RSD within each genetic group-sex subclass was similar for males and females. Adj $R^2$ was 0.999 for all genetic group-sex subclasses. AIC for laboratory mice males and females ranged from -70.48 to 66.50 and from -92.81 to -68.64, respectively; whereas for Yk wild mice males was -74.29 and females -78.42.