• Structural Engineering and Mechanics
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• v.68 no.6
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• pp.691-700
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• 2018

Ground granulated blast furnace slag (GGBS) is a by product obtained from iron and steel industries, useful in the design and development of high quality cement paste/mortar and concrete. This paper investigates the applicability of relevance vector machine (RVM) based regression model to predict the compressive strength of various GGBS based concrete mixes. Compressive strength data for various GGBS based concrete mixes has been obtained by considering the effect of water binder ratio and steel fibres. RVM is a machine learning technique which employs Bayesian inference to obtain parsimonious solutions for regression and classification. The RVM is an extension of support vector machine which couples probabilistic classification and regression. RVM is established based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. Compressive strength model has been developed by using MATLAB software for training and prediction. About 70% of the data has been used for development of RVM model and 30% of the data is used for validation. The predicted compressive strength for GGBS based concrete mixes is found to be in very good agreement with those of the corresponding experimental observations.

• Journal of Korea Multimedia Society
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• v.22 no.8
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• pp.832-843
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• 2019

Prostate cancer is a high-risk with a high incidence and is a disease that occurs only in men. Accurate diagnosis of cancer is necessary as the incidence of cancer patients is increasing. Prostate cancer is also a disease that is difficult to predict progress, so it is necessary to predict in advance through prognosis. Therefore, in this paper, grade classification is attempted based on texture feature extraction. There are two main methods of classification: Uses One-way Analysis of Variance (ANOVA) to determine whether texture features are significant values, compares them with all texture features and then uses only one classification i.e. Benign versus. The second method consisted of more detailed classifications without using ANOVA for better analysis between different grades. Results of both these methods are compared and analyzed through the machine learning models such as Support Vector Machine and K-Nearest Neighbor. The accuracy of Benign versus Grade 4&5 using the second method with the best results was 90.0 percentage.

• Journal of the Korean Society of Radiology
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• v.17 no.3
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• pp.297-303
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• 2023

Pediatric Computed Tomography (CT) examinations can often result in exam failures or the need for frequent retests due to the difficulty of cooperation from young patients. Deep Learning Image Reconstruction (DLIR) methods offer the potential to obtain diagnostically valuable images while reducing the retest rate in CT examinations of pediatric patients with high radiation sensitivity. In this study, we investigated the possibility of applying DLIR to reduce artifacts caused by respiration or motion and obtain clinically useful images in pediatric chest CT examinations. Retrospective analysis was conducted on chest CT examination data of 43 children under the age of 7 from P Hospital in Gyeongsangnam-do. The images reconstructed using Filtered Back Projection (FBP), Adaptive Statistical Iterative Reconstruction (ASIR-50), and the deep learning algorithm TrueFidelity-Middle (TF-M) were compared. Regions of interest (ROI) were drawn on the right ascending aorta (AA) and back muscle (BM) in contrast-enhanced chest images, and noise (standard deviation, SD) was measured using Hounsfield units (HU) in each image. Statistical analysis was performed using SPSS (ver. 22.0), analyzing the mean values of the three measurements with one-way analysis of variance (ANOVA). The results showed that the SD values for AA were FBP=25.65±3.75, ASIR-50=19.08±3.93, and TF-M=17.05±4.45 (F=66.72, p=0.00), while the SD values for BM were FBP=26.64±3.81, ASIR-50=19.19±3.37, and TF-M=19.87±4.25 (F=49.54, p=0.00). Post-hoc tests revealed significant differences among the three groups. DLIR using TF-M demonstrated significantly lower noise values compared to conventional reconstruction methods. Therefore, the application of the deep learning algorithm TrueFidelity-Middle (TF-M) is expected to be clinically valuable in pediatric chest CT examinations by reducing the degradation of image quality caused by respiration or motion.

• Physical Therapy Korea
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• v.11 no.3
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• pp.11-18
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• 2004

This study is aimed at investigating the influence of different quantitative knowledge of results on the measurement error during lumbar proprioceptive sensation training. Twenty-eight healthy adult men participated and subjects were randomly assigned into four different feedback groups(100% relative frequency with an angle feedback, 50% relative frequency with an angle feedback, 100% relative frequency with a length feedback, 50% relative frequency with a length feedback). An electrogoniometer was used to determine performance error in an angle, and the Schober test with measurement tape was used to determine performance error in a length. Each subject was asked to maintain an upright position with both eyes closed and both upper limbs stabilized on their pelvis. Lumbar vertebrae flexion was maintained at $30^{\circ}$ for three seconds. Different verbal knowledge of results was provided in four groups. After lumbar flexion was performed, knowledge of results was offered immediately. The resting period between the sessions per block was five seconds. Training consisted of 6 blocks, 10 sessions per one block, with a resting period of one minute. A resting period of five minutes was provided between 3 blocks and 4 blocks. A retention test was performed between 10 minutes and 24 hours later following the training block without providing knowledge of results. To determine the training effects, a two-way analysis of variance and a one-way analysis of variance were used with SPSS Ver. 10.0. A level of significance was set at .05. A significant block effect was shown for the acquisition phase (p<.05), and a significant feedback effect was shown in the immediate retention phase (p>.05). There was a significant feedback effect in the delayed retention phase (p<.05), and a significant block effect in the first acquisition phase and the last retention phase (p<.05). In conclusion, it is determined that a 50% relative frequency with a length feedback is the most efficient feedback among different feedback types.

• Journal of The Korean Association For Science Education
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• v.21 no.1
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• pp.13-21
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• 2001

This study examined the influences of project activities based on multiple intelligences to science achievement of elementary school children. The proportions of variance of science achievement explained by General Intelligence(GI) and Multiple Intelligences(MI) were analyzed, then the influences of project activities, which used various aspects of MI were investigated. Two classes of grade 5 at Pusan in Korea were selected for the study. On the basis of science achievement of prior term, the subjects were classified into upper-, average-, and lower-achievement groups. GI and MI were measured for each child, and the relationships of these measures with prior science achievement were analyzed using multiple regression analyses. In order to investigate the effects of the project activities on science achievement, the classes were divided into the control and experimental groups, which the former group learned science topics using the traditional teaching and learning method and the latter group performed the projects about the same topics using their own multiple intelligences. Then, their achievements were analyzed by ANOVA. Results showed that the proportion of variance explained by MI was higher about two times than that of explained by GI. Project activities contributed to the improvement of science achievement of average and upper achievers, however, in the case of under achievers, this effect was not statistically significant.

• The Journal of Bigdata
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• v.4 no.2
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• pp.1-12
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• 2019

As Artificial Intelligence (AI) technology develops, it is applied to various fields such as image, voice, and text. AI has shown fine results in certain areas. Researchers have tried to predict the stock market by utilizing artificial intelligence as well. Predicting the stock market is known as one of the difficult problems since the stock market is affected by various factors such as economy and politics. In the field of AI, there are attempts to predict the ups and downs of stock price by studying stock price patterns using various machine learning techniques. This study suggest a way of predicting stock price patterns based on the Convolutional Neural Network(CNN) among machine learning techniques. CNN uses neural networks to classify images by extracting features from images through convolutional layers. Therefore, this study tries to classify candlestick images made by stock data in order to predict patterns. This study has two objectives. The first one referred as Case 1 is to predict the patterns with the images made by the same-day stock price data. The second one referred as Case 2 is to predict the next day stock price patterns with the images produced by the daily stock price data. In Case 1, data augmentation methods - random modification and Gaussian noise - are applied to generate more training data, and the generated images are put into the model to fit. Given that deep learning requires a large amount of data, this study suggests a method of data augmentation for candlestick images. Also, this study compares the accuracies of the images with Gaussian noise and different classification problems. All data in this study is collected through OpenAPI provided by DaiShin Securities. Case 1 has five different labels depending on patterns. The patterns are up with up closing, up with down closing, down with up closing, down with down closing, and staying. The images in Case 1 are created by removing the last candle(-1candle), the last two candles(-2candles), and the last three candles(-3candles) from 60 minutes, 30 minutes, 10 minutes, and 5 minutes candle charts. 60 minutes candle chart means one candle in the image has 60 minutes of information containing an open price, high price, low price, close price. Case 2 has two labels that are up and down. This study for Case 2 has generated for 60 minutes, 30 minutes, 10 minutes, and 5minutes candle charts without removing any candle. Considering the stock data, moving the candles in the images is suggested, instead of existing data augmentation techniques. How much the candles are moved is defined as the modified value. The average difference of closing prices between candles was 0.0029. Therefore, in this study, 0.003, 0.002, 0.001, 0.00025 are used for the modified value. The number of images was doubled after data augmentation. When it comes to Gaussian Noise, the mean value was 0, and the value of variance was 0.01. For both Case 1 and Case 2, the model is based on VGG-Net16 that has 16 layers. As a result, 10 minutes -1candle showed the best accuracy among 60 minutes, 30 minutes, 10 minutes, 5minutes candle charts. Thus, 10 minutes images were utilized for the rest of the experiment in Case 1. The three candles removed from the images were selected for data augmentation and application of Gaussian noise. 10 minutes -3candle resulted in 79.72% accuracy. The accuracy of the images with 0.00025 modified value and 100% changed candles was 79.92%. Applying Gaussian noise helped the accuracy to be 80.98%. According to the outcomes of Case 2, 60minutes candle charts could predict patterns of tomorrow by 82.60%. To sum up, this study is expected to contribute to further studies on the prediction of stock price patterns using images. This research provides a possible method for data augmentation of stock data.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.6
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• pp.295-303
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• 2016

The purposes of this study are to describe the process for the instrument construction and the development of scales on rating college students' adaptability and to analyze the technical qualities of the test. The primary goal of this study is to inform students and institutions what is needed to college student's adjustment process into university and college life. The scales are tested by specialty group and statistical methods, and finally composed of 142 items, which measures 8 scales, the academic integration, the social integration into college, career identity, emotional stability, learning condition's stability, relationship with professors, satisfaction degree of educational service, satisfaction degree of college education. This study analyzed 1,959 students' responses from 4 colleges and universities. This study confirms that the scales which this study developed show high concurrent evidence with the college student's adaptability inventory for Korean university and college students based on various development process, specially rapid great change of college. The result of factor analysis shows the evidence based on internal structures of the scales. The Cronbach's ${\alpha}$ of the subscales is .965, from 742 to .937. The prediction model to determine the possibility of dropout by 7 scales is statistically significant in .05, except learning condition's stability. According to CFA Model, RMSEA= .08~.09. dependence factor variance are explained by this study's CFA model. In conclusion, this study confirms that the scales which this study developed are valid and reliable instrument for Korean university and college students to predict their adaptability to college.

• 대한공업교육학회지
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• v.32 no.2
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• pp.1-22
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• 2007

The purposes of this study were to measure the school-related adjustment level of technical high school students, and to determine the relationship between school-related adjustment and its related variables. The study was carried out through questionnaire survey method. The population sample for the study constituted 553 completed questionnaires from purposive sample of 600 first grade technical high school students. A survey questionnaire was developed by researcher, which consisted of 28 scales. Both descriptive and inferential statistics were employed for data analysis. Major findings of this study were as follows: First, school-related adjustment level of technical high school students was average. In sub-variables of school-related adjustment, 'compliance with the rule' was the highest, and 'relation to teacher' was the lowest. Second, five related variables were found to be a significant relationship with school-related adjustment level of technical high school students. They were 'orientation for freshman', 'student's department hope', 'teacher activity for student learning improvement', 'teacher support for student school life', 'parent's interest about school life'. Third, after multiple regression analysis, the proportion of the variance in school-related adjustment of technical high school students was about 42.2%. School-related adjustment of technical high school students was most explained by 'teacher activity for student learning improvement'.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.10
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• pp.540-548
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• 2018

The purpose of this study was to test the validity and reliability of the Satisfaction with Simulation Experience (SSE) scale for evaluating high-fidelity simulation education for nursing students. Participants were 174 nursing students, seniors enrolled in two colleges in two different regions. Collected data were analyzed using SPSS / WIN 22.0 and tested for construct validity (factor analysis, group comparison test) and reliability (internal consistency). Factor analysis revealed 17 items and 3 factors explaining 71.581% of the variance. Group comparisons showed that satisfaction with simulation training differed significantly across satisfaction to a college life and school record. Internal consistency reliability for all items was .945. For each sub-domain, the reliability coefficient was .929 for 'Debrief', .908 for 'Clinical learning and reflection', and .860 for 'Clinical reasoning'. Nursing students' mean satisfaction with simulation using the high-fidelity simulator was 3.92. Results of this study are expected to be used for evaluating the satisfaction of nursing college students receiving high-fidelity simulation education, and to serve as groundwork for the development and application of nursing simulation education.

• The Journal of the Korea Contents Association
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• v.16 no.1
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• pp.190-203
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• 2016

Based on the premise that a systematic career process was one of the essential elements of successful task performance both for individuals and the organization in the field of social welfare, this study set out to empirically analyze factors influencing the career commitment of social workers at a multidimensional level and provide practical implications for the directionality of career management on the basis of data with theoretical and statistical accuracy. For those purposes, the study collected individual and organizational characteristics data from 787 social workers at 46 agencies through a structured questionnaire and analyzed influential factors through the multilevel analysis technique by taking organizational effects into account. The analysis results show that explanations by the organization characteristics recorded significant 15% in the total variance of career commitment and that its influential factors included such significant variables as the protean career attitude, desire for growth, human network, and self-efficacy at the individual level and also the qualification compensation system at the organizational level. The study then proposed and discussed integrated practice strategies between individuals and agencies as the measures to promote career success through the activation of individual factors based on the consideration of organizational effects such as the application of an employee assistant program, provision of incentives to professional career development, and shift to a learning organization.