• The Korean Journal of Applied Statistics
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• v.31 no.4
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• pp.447-461
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• 2018

Domestic film industry sales are increasing every year. Theaters are the primary sales channels for movies and the number of audiences using the theater affects additional selling rights. Therefore, the number of audiences using the theater is an important factor directly linked to movie industry sales. In this paper we consider a hybrid model that combines a multiple linear regression model and the Bass model to predict the audience numbers for a specific day. By combining the two models, the predictive value of the regression analysis was corrected to that of the Bass model. In the analysis, three films with different release dates were used. All subset regression method is used to generate all possible combinations and 5-fold cross validation to estimate the model 5 times. In this case, the predicted value is obtained from the model with the smallest root mean square error and then combined with the predicted value of the Bass model to obtain the final predicted value. With the existence of past data, it was confirmed that the weight of the Bass model increases and the compensation is added to the predicted value.

• Child Health Nursing Research
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• v.12 no.4
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• pp.514-522
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• 2006

Purpose: The purpose of this study was to identify the factors of child behavioral problems and construct a descriptive model that explains child behavioral problems for school age children. Method: The participants in the study were 586 4th, 5th, 6th graders and their mothers. The children attended 8 elementary schools located in Taejon city and their mothers. The tools used in this study was the Mother's Child Raising Behavior Scale by Park, Seong-Yeon and Yi, Sook(1990). To measure child's self esteem, the Self Esteem Scale by Kim(1987) was used; child perceived social support was measured with the Social Support Evaluation Scale by Dubow and Ullman(1989), and childhood behavioral problems were measured with the Korean standardized of version of the Korean-Child Behavior Checklist(K-CBCL)(1997). Descriptive statistics and linear structural relationship(LISREL) modeling were used to analyze the data. SAS and LISREL 8.12a programs were used. Results: The overall fit of the hypothetical model to the data was good $>X^2=103.07(p=0.00)$, GFI=0.96, AGFI=0.94, RMSR=0.04, RMSEA=0.07, NFI=0.94, NNFI=0.95< Maternal child raising behaviors(T=2.21) and child perceived social support(T=10.29) had a significant, direct effect on a child's self esteem. Maternal child raising behaviors(T=-3.87), and child self esteem(T=-2.04) and had a significant total effect on child behavioral problems. These variables accounted for 63% of the variance of the child behavioral problems in the school age children. Conclusion: These finding have provided support for maternal child raising behaviors, child perceived social support, and child self esteem as predictive variables of behavioral problems in school age children.

• Journal of Korean Academy of Nursing
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• v.26 no.2
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• pp.320-336
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• 1996

Health promoting behaviors of an individual are affected by various variables. Recently, there has been a growing concern over important health problems of the middle aged women. Physiological changes in the middle aged women and their responsibility for family care can result in physical and psychological burden experienced by middle aged women. This study was designed to test Pender's model and thus purpose a model that explains health promoting behaviors among middle-aged women in Korea. The hypothetical model was developed based on the Pender's health promoting model and the findings from past studies on women's health. Data were collected by self-reported questionnaires from 863 women living in Seoul, between 20th, April and 15th, July 1995. Data were analyzed using descriptive statistics and correlation analysis. The Linear Structural Relationship(LISREL) modeling process was used to find the best fit model which assumes causal relationships among variables. The results are as follows : 1. The Overall fit of the hypothetical model to the data was good expect chi-square value（GFI＝.96, AGFI＝.91, RMR＝.04）. 2. Paths of the model were modified by considering both its theoretical implication and statistical significance of the parameter estimates. Compared to the hypothetical model, the revised model has become parsimonious and had a better fit to the data expect chi-square value（GFI＝.95, AFGI＝ .92. RMR＝.04）. 3. Some of modifying factors, especially age, occupation, educational levels and body mass index (BMI) are revealed significant effects on health promoting behaviors. 4. Some of cognitive-perceptual factors, especially internal health locus of control, self-efficacy and perceptive health status are revealed significant effects on health promoting behaviors. 5. All predictive variables of health promoting behaviors, especially age, occupation, educational levels, body mass index(BMI), internal health locus of control, self-efficacy ＆ perceptive health status are explained 20.0％ of the total variance in the model.

• The Journal of the Acoustical Society of Korea
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• v.13 no.6
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• pp.5-14
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• 1994

This paper is about an experiment of speaker-independent automatic Korean spoken words recognition using Multi-Layered Perceptron and Error Back-propagation algorithm. The object words are 50 citynames of D.D.D local numbers. 43 of those are 2 syllables and the rest 7 are 3 syllables. The words were not segmented into syllables or phonemes, and some feature components extracted from the words in equal gap were applied to the neural network. That led independent result on the speech duration, and the PARCOR coefficients calculated from the frames using linear predictive analysis were employed as feature components. This paper tried to find out the optimum conditions through 4 differerent experiments which are comparison between total and pre-classified training, dependency of recognition rate on the number of frames and PAROCR order, recognition change due to the number of neurons in the hidden layer, and the comparison of the output pattern composition method of output neurons. As a result, the recognition rate of $89.6\%$ is obtaimed through the research.

• The Journal of the Acoustical Society of Korea
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• v.14 no.3
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• pp.49-60
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• 1995

This paper is a study on the design and implementation of the Korean Text-to-Speech system which is used for a small and simple system. In this paper, a parameter synthesis method is chosen for speech syntheiss method, we use PARCOR(PARtial autoCORrelation) coefficient which is one of the LPC analysis. And we use phoneme for synthesis unit which is the basic unit for speech synthesis. We use PARCOR, pitch, amplitude as synthesis parameter of voice, we use residual signal, PARCOR coefficients as synthesis parameter of unvoice. In this paper, we could obtain the 60% intelligibility by using the residual signal as excitation signal of unvoiced sound. The result of synthesis experiment, synthesis of a word unit is available. The controlling of phoneme duration is necessary for synthesizing of a sentence unit. For setting up the synthesis system, PC 486, a 70[Hz]-4.5[KHz] band pass filter for speech input/output, amplifier, and TMS320C30 DSP board was used.

• Journal of the Korea Convergence Society
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• v.6 no.4
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• pp.73-88
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• 2015

Objectives: The purpose of the study was to identify factors influencing on nurses' Terminal Care Attitude. Methods: A sample of convenience of 190 nurses. Instrument included perception of death, death anxiety, terminal care stress, death attitude, burnout, terminal care attitude. Results: A significant positive correlation was found among terminal care attitude, perception of death, terminal care stress. In addition, a significant negative correlation was found among terminal care attitude, death anxiety, death attitude, burnout. Perception of death, death anxiety, terminal care stress & death attitude were significant predictive variables. This variables accounted for 32.7% of the variance in terminal care attitude. Conclusions: Based on the Findings of this study, it can be used to develop educational programs for Terminal Care.

• Journal of Ocean Engineering and Technology
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• v.26 no.4
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• pp.57-63
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• 2012

In this paper, a Bayesian classifier based on PCA (principle component analysis) is proposed to classify underwater transient signals using $16^{th}$ order LPC (linear predictive coding) coefficients as feature vector. The proposed classifier is composed of two steps. The mechanical signals were separated from biological signals in the first step, and then each type of the mechanical signal was recognized in the second step. Three biological transient signals and two mechanical signals were used to conduct experiments. The classification ratios for the feature vectors of biological signals and mechanical signals were 94.75% and 97.23%, respectively, when all 16 order LPC vector were used. In order to determine the effect of underwater noise on the classification performance, underwater ambient noise was added to the test signals and the classification ratio according to SNR (signal-to-noise ratio) was compared by changing dimension of feature vector using PCA. The classification ratios of the biological and mechanical signals under ocean ambient noise at 10dB SNR, were 0.51% and 100% respectively. However, the ratios were changed to 53.07% and 83.14% when the dimension of feature vector was converted to three by applying PCA. For correct, classification, it is required SNR over 10 dB for three dimension feature vector and over 30dB SNR for seven dimension feature vector under ocean ambient noise environment.

• Korean Journal of Soil Science and Fertilizer
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• v.45 no.6
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• pp.920-924
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• 2012

This study was performed to estimate total contents of arsenic (As) by stepwise multiple-regression analysis using chemical properties and extractable contents of metal in paddy soil adjacent to abandoned mines. The soil was collected from paddies near abandoned mines. Soil pH, electrical conductively (EC), organic mater (OM), available phosphorus ($P_2O_5$), and exchangeable cations (Ca, K, Mg, Na) were measured. Total contents of As and extractable contents of metals were analyzed by ICP-OES. From stepwise analysis, it was showed that the contents of extractable As, available phosphorus, extractable Cu, exchangeable K, exchangeable Na, and organic mater significantly influenced the total contents of As in soil (p<0.001). The multiple linear regression models have been established as Log (Total-As) = 0.741 + 0.716 Log (extractable-As) - 0.734 Log (avail-$P_2O_5$) + 0.334 Log (extractable-Cu) + 0.186 Log (exchangeable-K) - 0.593 Log (exchangeable-Na) + 0.558 Log (OM). The estimated value in total contents of As was significantly correlated with the measured value in soil ($R^2$=0.84196, p<0.0001). This predictive model for estimating total As contents in paddy soil will be properly applied to the numerous datasets which were surveyed with extractable heavy metal contents based on Soil Environmental Conservation Act before 2010.

• Journal of Internet of Things and Convergence
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• v.5 no.2
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• pp.55-66
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• 2019

Objectives: The purpose of the study was to identify the factors influencing the nursing students' Clinical Competency. Methods: A sample of convenience was 185 nursing students, and a questionnaire was used to measure their self-leadership, critical thinking disposition, self-directed learning ability, problem solving ability and clinical competency. Results: A significant positive correlation was found among clinical competency, self-leadership, critical thinking disposition, self-directed learning ability and problem solving ability. Grade of which the participant was in, interpersonal relationship, critical thinking disposition, problem solving ability, and self-directed learning ability were significant predictive variables of which accounted for 53% of the variance in clinical competency. Conclusions: The results from this study can be used to develop the programs for improving clinical competency.

• KIISE Transactions on Computing Practices
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• v.23 no.11
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• pp.632-640
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• 2017

Although solar insolation is the weather factor with the greatest influence on power generation in photovoltaic systems, the Meterological Agency does not provide solar insolation data for future dates. Therefore, it is essential to research prediction methods for solar insolation to efficiently manage photovoltaic systems. In this study, we propose a Dynamic Piecewise Prediction Model that can be used to predict solar insolation values for future dates based on information from the weather forecast. To improve the predictive accuracy, we dynamically divide the entire data set based on the sun altitude and cloudiness at the time of prediction. The Dynamic Piecewise Prediction Model is developed by applying a polynomial linear regression algorithm on the divided data set. To verify the performance of our proposed model, we compared our model to previous approaches. The result of the comparison shows that the proposed model is superior to previous approaches in that it produces a lower prediction error.