• Geomechanics and Engineering
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• v.34 no.6
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• pp.697-726
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• 2023

Brittleness as an important property of rock plays a crucial role both in the failure process of intact rock and rock mass response to excavation in engineering geological and geotechnical projects. Generally, rock brittleness indices are calculated from the mechanical properties of rocks such as uniaxial compressive strength, tensile strength and modulus of elasticity. These properties are generally determined from complicated, expensive and time-consuming tests in laboratory. For this reason, in the present research, an attempt has been made to predict the rock brittleness indices from simple, inexpensive, and quick laboratory test results namely dry unit weight, porosity, slake-durability index, P-wave velocity, Schmidt rebound hardness, and point load strength index using multiple linear regression, exponential regression, support vector machine (SVM) with various kernels, generating fuzzy inference system, and regression tree ensemble (RTE) with boosting framework. So, this could be considered as an innovation for the present research. For this purpose, the number of 39 rock samples including five igneous, twenty-six sedimentary, and eight metamorphic were collected from different regions of Iran. Mineralogical, physical and mechanical properties as well as five well known rock brittleness indices (i.e., B₁, B₂, B₃, B₄, and B₅) were measured for the selected rock samples before application of the above-mentioned machine learning techniques. The performance of the developed models was evaluated based on several statistical metrics such as mean square error, relative absolute error, root relative absolute error, determination coefficients, variance account for, mean absolute percentage error and standard deviation of the error. The comparison of the obtained results revealed that among the studied methods, SVM is the most suitable one for predicting B₁, B₂ and B₅, while RTE predicts B₃ and B₄ better than other methods.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.47 no.2
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• pp.1-9
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• 2024

New motor development requires high-speed load testing using dynamo equipment to calculate the efficiency of the motor. Abnormal noise and vibration may occur in the test equipment rotating at high speed due to misalignment of the connecting shaft or looseness of the fixation, which may lead to safety accidents. In this study, three single-axis vibration sensors for X, Y, and Z axes were attached on the surface of the test motor to measure the vibration value of vibration. Analog data collected from these sensors was used in classification models for anomaly detection. Since the classification accuracy was around only 93%, commonly used hyperparameter optimization techniques such as Grid search, Random search, and Bayesian Optimization were applied to increase accuracy. In addition, Response Surface Method based on Design of Experiment was also used for hyperparameter optimization. However, it was found that there were limits to improving accuracy with these methods. The reason is that the sampling data from an analog signal does not reflect the patterns hidden in the signal. Therefore, in order to find pattern information of the sampling data, we obtained descriptive statistics such as mean, variance, skewness, kurtosis, and percentiles of the analog data, and applied them to the classification models. Classification models using descriptive statistics showed excellent performance improvement. The developed model can be used as a monitoring system that detects abnormal conditions of the motor test.

• Journal of the Korea Computer Graphics Society
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• v.30 no.3
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• pp.141-147
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• 2024

Progressive photon mapping is a widely adopted rendering technique that conducts a kernel-density estimation on photons progressively generated from lights. Its hyperparameter, which controls the reduction rate of the density estimation, highly affects the quality of its rendering image due to the bias-variance tradeoff of pixel estimates in photon-mapped results. We can minimize the errors of rendered pixel estimates in progressive photon mapping by estimating the optimal parameters based on gradient-based optimization techniques. To this end, we derived the gradients of pixel estimates with respect to the parameters when performing progressive photon mapping and compared our estimated gradients with finite differences to verify estimated gradients. The gradient estimated in this paper can be applied in an online learning algorithm that simultaneously performs progressive photon mapping and parameter optimization in future work.

• English Language & Literature Teaching
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• v.13 no.3
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• pp.203-226
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• 2007

This paper analyzes the in-service training program for the 1st grade English teacher in the following 7 regions: Taejeon, Seoul, Incheon, Kangwon, Kyeonggi, Chungnam and Chungbuk. It also investigates the actual conditions of the program. The analyses are performed on three categories of subjects: Teaching Profession, Refinement and English Education. The English Education category can be divided into the three aspects: language skills, communicative skills and language learning/teaching skills. Among the 7 regions, subjects under Teaching Profession, Refinement and English Education have a significant (2-3 times +/-) variance in terms of the number and credit hours of the courses. While the Refinement Program is above the standard set by Ministry of Education & Human Resources Development(MEHRD) in some regions, the Teaching Profession Program and the English Education Program is below the standard set by MEHRD in other regions. To overcome the weaknesses of the program, this paper suggests the following: 1) prescriptions for the proportion ratio of each category should be suitably modified. 2) MEHRD should observe and supervise the program of each region. 3) being organized, the program must have two parts as follows: the same mandatory subjects for all regions and optional subjects suited to each region.

• Journal of Families and Better Life
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• v.15 no.2
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• pp.71-71
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• 1997

This study aims ai the extraction of what is universally to be taught in modern Korean Families and its scalization. That is, it attempts to provide the criterion by which we can determine what to teach adolescent in the family, not in society or school. For this purpose, this study firstly reviews the preceeding studies on the subject. As a result of the review, we postulate the hypothetical structure consisting of 11 domains. Secondary, we gather the parent's view on the topic by interviewing 496 parents with teenagers. On the basis of this study, we can construct the questionnaire(Likert scale; 5 point). After we conduct an extensive empirical research(346 parents) in order to generalize 195 items of the workedout questionnaire. We apply factor analysis(principal axis factoring, oblique (promax) rotation) in the verification of the validity. As the consequence, we select 66 items consisting, 10 factors, which explain 68% of common variance. We name the 10 educational factors extracted in the scale as follows ; The Sense of Value, Communal Society, Sex, Esteem for an ancestor & a traditional way of life, Parent-Child Relationship, the Culture life within the family, The Guide of Learning Way, Setting up the way of life, The control of one's life, Friendship. The reliability of the scale is the cronbach =0.91 which turns out to be satisfactory.

• Journal of Korea Multimedia Society
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• v.16 no.10
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• pp.1230-1237
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• 2013

Cyber lectures were expected to replace the traditional classroom lecture, yet they were criticized due to the inefficiency. With educational technology supplemented, cyber lectures have improved their efficiency through the coherence of superior planning ability and improvement in computer and network technology. This paper's purpose is to understand the factors that improve educational efficiency of cyber lectures. Cyber lecture for IT subject for software practice is created to test the educational efficiency as lecture on theories will not distinctively show the lecture efficiency. The survey was conducted to students and statistical analysis was done on collected data to analyze the factors that influence educational efficiency of created lecture.

• Journal of Korean Academy of Fundamentals of Nursing
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• v.23 no.1
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• pp.51-60
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• 2016

Purpose: There is increasing attention to smart-learning as a new education paradigm. The purpose of this study was to identify the level of intention to use smart-based Continuing Nurse Education (CNE) and factors influencing intention to use smart-based CNE. Methods: Participants were 486 nurses from 14 organizations, including 12 hospitals, a nurses association, and an office of education. Data were collected from November 5 to 18, 2014 using self-report questionnaires. Data were analyzed using descriptive statistics, t-test, ANOVA, Pearson correlation, and stepwise multiple regression. Results: The mean score for intention to use smart-based CNE was 6.34 out of 10. The factors influencing intention to use smart-based CNE were nursing informatics competency, current unit career, and smartphone addiction. These variables explained 10% of variance in intention to use smart-based CNE. Conclusion: The findings of this study suggest that efforts to enhance the nursing informatics competency of nurses could increase usage rate of smart-based CNE. The CNE policy makers will find this study very useful and the findings of this study will help to provide insight into the best way to develop smart-based CNE.

• Journal of Families and Better Life
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• v.24 no.2 s.80
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• pp.149-163
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• 2006

The purposes of this study were to explore ecological variables that affect family functions and to analyze relative magnitudes of significant predictors. The study employed ecological model. Data were collected from 143 mothers of mildly handicapped children enrolled in integrated kindergartens in Seoul, Korea. The results of the study were as follows : 1. The level of family functions with mildly handicapped children were found to be moderate(M=3.21, SD=.49). 2. The hierarchical regression analysis yielded Model V as the most powerful model, explaining 79%$(Adjusted\;R^2=.787)$ of the variance. 3. The most powerful predictors throughout Model I to V were found to be maternal efficacy $({\beta}=.578,\;p<.001)$, maternal satisfaction with parent-education and counseling programs $({\beta}=.249,\;p<.001)$, husband's helps $({\beta}=.207,\;p<.01)$, and the availability of assistance other than family members $({\beta}=.232,\;p<.05)$ in the order.

• Journal of Korean Academy of Fundamentals of Nursing
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• v.25 no.1
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• pp.11-21
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• 2018

Purpose: The purpose of this study was to translate the Self-Reflection and Insight Scale (SRIS) into Korean and test its validity and reliability. Methods: Participants were 340 nursing students who were recruited from 5 nursing colleges in Korea. Data were collected from November 21 to December 20, 2016 and analyzed using the IBM Statistics SPSS 22.0 and AMOS 23.0 programs. Exploratory factor analysis, Confirmatory factor analysis, and concurrent validity analysis were performed. Results: For the Korean-SRIS (K-SRIS) 4 items were deleted from the original SRIS. The final scale consisted of 16 items which were sorted into- the 2 factors: self-reflection (11 items), insight (5 items). The cumulative percent of variance was 50.91%. The statistically significant correlation between K-SRIS scores and the Reflection in Learning Scale (RLS) support the concurrent validity of the K-SRIS. The reliability of the scale, Cronbach's ${\alpha}$ was .83. Conclusion: The finding indicate that the K-SRIS has validity and reliability. Therefore it can be used for measuring and developing reflection ability in nursing students.

• The Mathematical Education
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• v.57 no.2
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• pp.75-92
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• 2018

Based on Perceptions about Mathematics Teachers (PMT) perceived by high school students, measured by 2189 students from Seoul Educational Longitudinal Study 2014 (SELS 2014), latent profile analysis (LPA) identified five distinct types of student groups (positive, partial positive, middle, negative, extreme negative). These student of positive, middle, and negative groups are positive, moderate and negative perceptions about math teachers. Partial positive group generally had a positive perception about mathematics teachers, extremely negative group was very negative about mathematics teachers. Both of these groups had peculiarly inconsistent trends and several anomalies. The Multinomial logistic regression analyses also indicated that individual factors (gender, major, self-concept, resilience, self-assessment, career maturity), school factors (friendship, relationship with school teachers) and parental factors (academic-relationship, emotional-relationship) were significant predictors of PMT profile groups. The Analysis of variance also indicated that mathematics class (attitude, satisfaction and atmosphere), Mathematics achievement were significant predictors of PMT profile groups. The profiling of perceptions about mathematics teachers resulted in enhanced understanding of the complex range of processes students employed. During mathematics class, implementation of smooth interactions and communications between students and teachers added in the teaching and learning of mathematics.