• Geomechanics and Engineering
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• v.37 no.3
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• pp.223-241
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• 2024

Evaluating the performance of Tunnel Boring Machines (TBMs) stands as a pivotal juncture in the domain of hard rock mechanized tunneling, essential for achieving both a dependable construction timeline and utilization rate. In this investigation, three advanced artificial neural networks namely, gated recurrent unit (GRU), back propagation neural network (BPNN), and simple recurrent neural network (SRNN) were crafted to prognosticate TBM-rate of penetration (ROP). Drawing from a dataset comprising 1125 data points amassed during the construction of the Alborze Service Tunnel, the study commenced. Initially, five geomechanical parameters were scrutinized for their impact on TBM-ROP efficiency. Subsequent statistical analyses narrowed down the effective parameters to three, including uniaxial compressive strength (UCS), peak slope index (PSI), and Brazilian tensile strength (BTS). Among the methodologies employed, GRU emerged as the most robust model, demonstrating exceptional predictive prowess for TBM-ROP with staggering accuracy metrics on the testing subset (R² = 0.87, NRMSE = 6.76E-04, MAD = 2.85E-05). The proposed models present viable solutions for analogous ground and TBM tunneling scenarios, particularly beneficial in routes predominantly composed of volcanic and sedimentary rock formations. Leveraging forecasted parameters holds the promise of enhancing both machine efficiency and construction safety within TBM tunneling endeavors.

• Information Systems Review
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• v.22 no.3
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• pp.15-30
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• 2020

In the real estate industry, the latest changes in the Fourth Industrial Revolution, such as big data analytics, machine learning, and VR (virtual reality), combine to bring about industry change. Proptech is a new term combining properties and technology. This study aims to derive and analyze from a comprehensive perspective the quality factors (systems, services, interfaces, information) for mobile real estate brokerage services that are well known and used in the domestic market. The surveys in this study were conducted online and offline and a total of 161 samples were used for statistical analysis. As a result, all hypotheses were approved to except system quality and service quality. The results show that the domestic proptech companies who are mostly focused on real estate brokerage services, peer-to-peer lending, advertising platforms and apartments need to grow in various fields of proptech business of other countries including Europe, USA and China.

• Journal of Technologic Dentistry
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• v.46 no.2
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• pp.36-41
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• 2024

Purpose: This study aimed to generate virtual mandibular left first molar teeth using deep convolutional generative adversarial networks (DCGANs) and analyze their matching accuracy with actual tooth morphology to propose a new paradigm for using medical data. Methods: Occlusal surface images of the mandibular left first molar scanned using a dental model scanner were analyzed using DCGANs. Overall, 100 training sets comprising 50 original and 50 background-removed images were created, thus generating 1,000 virtual teeth. These virtual teeth were classified based on the number of cusps and occlusal surface ratio, and subsequently, were analyzed for consistency by expert dental technicians over three rounds of examination. Statistical analysis was conducted using IBM SPSS Statistics ver. 23.0 (IBM), including intraclass correlation coefficient for intrarater reliability, one-way ANOVA, and Tukey's post-hoc analysis. Results: Virtual mandibular left first molars exhibited high consistency in the occlusal surface ratio but varied in other criteria. Moreover, consistency was the highest in the occlusal buccal lingual criteria at 91.9%, whereas discrepancies were observed most in the occusal buccal cusp criteria at 85.5%. Significant differences were observed among all groups (p<0.05). Conclusion: Based on the classification of the virtually generated left mandibular first molar according to several criteria, DCGANs can generate virtual data highly similar to real data. Thus, subsequent research in the dental field, including the development of improved neural network structures, is necessary.

• The Korean Journal of Applied Statistics
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• v.37 no.1
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• pp.13-38
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• 2024

As economic uncertainties have increased recently due to COVID-19, there is a growing need to quickly grasp private consumption trends that directly reflect the economic situation of private economic entities. This study proposes a method of estimating private consumption in real-time by comprehensively utilizing big data as well as existing macroeconomic indicators. In particular, it is intended to improve the accuracy of private consumption estimation by comparing and analyzing various machine learning methods that are capable of fitting ultra-high-dimensional big data. As a result of the empirical analysis, it has been demonstrated that when the number of covariates including big data is large, variables can be selected in advance and used for model fit to improve private consumption prediction performance. In addition, as the inclusion of big data greatly improves the predictive performance of private consumption after COVID-19, the benefit of big data that reflects new information in a timely manner has been shown to increase when economic uncertainty is high.

• Journal of Engineering Education Research
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• v.27 no.1
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• pp.3-12
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• 2024

The degree-linked programs for employees, operated by joint training centers in specialized universities that have implemented work-study integrated programs, are educational programs that require an annual government budget of around 80 billion KRW. However, the 70+ universities running these programs face issues such as a decline in academic achievement and an increase in dropout rates among students. In this paper, I conducted multiple regression analysis based on observed and measured information to examine whether the participating students in these programs are achieving an appropriate level of academic performance and to identify the factors that universities need to invest in to achieve that level. To do this, I hypothesized a causal relationship between the university's input factors and students' academic achievement, and used the SPSS program to analyze the statistical data, confirming the validity of the hypothesis. The collected data for the study were obtained through a survey developed using a Likert 4-point scale, which quantified the distribution of grades among students enrolled in IT-related departments offering the degree-linked programs for employees and the emotional contact efforts made by the universities to motivate them for academic success. Particularly, through the results of multiple regression analysis, it was confirmed that these input factors, unlike those for students in general education programs, require more personalized and frequent interactions.

• Geomechanics and Engineering
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• v.36 no.5
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• pp.465-474
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• 2024

Uniaxial compressive strength (UCS) is a critical geomechanical parameter that plays a significant role in the evaluation of rocks. The practice of indirectly estimating said characteristics is widespread due to the challenges associated with obtaining high-quality core samples. The primary aim of this study is to investigate the feasibility of utilizing the gene expression programming (GEP) technique for the purpose of forecasting the UCS for various rock categories, including Schist, Granite, Claystone, Travertine, Sandstone, Slate, Limestone, Marl, and Dolomite, which were sourced from a wide range of quarry sites. The present study utilized a total of 170 datasets, comprising Schmidt hammer (SH), porosity (n), point load index (Is(50)), and P-wave velocity (Vp), as the effective parameters in the model to determine their impact on the UCS. The UCS parameter was computed through the utilization of the GEP model, resulting in the generation of an equation. Subsequently, the efficacy of the GEP model and the resultant equation were assessed using various statistical evaluation metrics to determine their predictive capabilities. The outcomes indicate the prospective capacity of the GEP model and the resultant equation in forecasting the unconfined compressive strength (UCS). The significance of this study lies in its ability to enable geotechnical engineers to make estimations of the UCS of rocks, without the requirement of conducting expensive and time-consuming experimental tests. In particular, a user-friendly program was developed based on the GEP model to enable rapid and very accurate calculation of rock's UCS, doing away with the necessity for costly and time-consuming laboratory experiments.

• Journal of Advanced Navigation Technology
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• v.28 no.2
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• pp.185-192
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• 2024

The aviation has improved safety through technological innovation and strengthened flight safety through safety regulations and supervision by aviation authorities. As the industry's safety approach has evolved into a systematic approach to the aircraft system, airlines have established a safety management system. Technical defects or abnormal data in an aircraft can be warning signs that could lead to an accident, and the risk of an accident can be reduced by identifying and responding to these signs early. Therefore, management of abnormal warning signs is an essential element in promoting data-based decision-making and enhancing the operational efficiency and safety level of airlines. In this study, we present a model to statistically analyze quick access recorder (QAR) flight data in the preliminary analysis stage to analyze the patterns and causes of crab landing events that can lead to runway departures when landing an aircraft, and provide a precursor to a landing event. We aim to identify signs and causes and contribute to increasing the efficiency of safety management.

• Computers and Concrete
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• v.34 no.1
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• pp.93-122
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• 2024

One well-known reason for using Fiber Reinforced Polymer (FRP) composites is to improve concrete strength and strain capacity via external confinement. Hence, various studies have been undertaken to offer a good illustration of the response of FRP-wrapped concrete for practical design intents. However, in such studies, the strength and strain of the confined concrete were predicted using regression analysis based on a limited number of test data. This study presents an approach based on artificial neural networks (ANNs) to develop models to predict the strength and strain at maximum stress enhancement of circular concrete cross-sections confined with different FRP types (Carbone, Glass, Aramid). To achieve this goal, a large test database comprising 493 axial compression experiments on FRP-confined concrete samples was compiled based on an extensive review of the published literature and used to validate the predicted artificial intelligence techniques. The ANN approach is currently thought to be the preferred learning technique because of its strong prediction effectiveness, interpretability, adaptability, and generalization. The accuracy of the developed ANN model for predicting the behavior of FRP-confined concrete is commensurate with the experimental database compiled from published literature. Statistical measures values, which indicate a better fit, were observed in all of the ANN models. Therefore, compared to existing models, it should be highlighted that the newly developed models based on FRP type are remarkably accurate.

• Journal of Korean Academy of Nursing
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• v.13 no.2
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• pp.22-43
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• 1983

1. Need for and Purpose of the Study. There is an increasing demand for curriculum improvement of the Korean Army Nursing Academy (KANA), since it was upgraded into 4-year institution of higher learning from 3-year one. In particular, it is strongly advocated that the KANA needs the outside expertise for its curriculum improvement-namely not only from the internal military view of points but also from the viewpoints of professional educational society, In line with such a necessity for the study, this study was aimed at 1) analyzing the current actual practices of KANA'S curriculum, 2) investigating the desired practices of KANA'S curriculum, and 3) identifying the discrepancy between the actual and desired practices of curriculum. 2. Problems for the Study This study had 4 problems to be answeared as follows: 1) What are the actual curriculum practices of KANA? 2) What are the desired curriculum practices of KANA? 3) How are the extents of perception of actual and desired curriculum different in four groups (student, faculty & administrator, nurse, and medical doctor in militay hospital) ? 4) What are the restraining forces that impede the change from actual to desired curriculum practices? 5) What are the relationships of curriculum component,』 in actual and desired curriculum practices? 3. Methods and Procedures This study was conducted by means of document analysis in addition to literature review and by means of needs assessment questionnaire which was developed by the researcher. The questionnaire included 62 statments with 7 questions for demographic data collection. The needs assessment questionnaire was managed to a total of 243 subjects (100 students, 46 faculty & administrators, 55 nurses, and 42 medical doctors), The collected data were treated using SPSS computer system so as to calculate mean scores, standard deviations, and correlation coefficients. The significance test was made through t-test and one-way ANOVA. The statistical significance level was set at both .05 and .01 level. 4. Major findings The major findings in this study are as follows: 1) The score of desired practices was significantly greater than that of actual practices, representing a strong need for curriculum betterment. 2) There were significant differences in the perceptions of actual practices as well as desired practices among four groups (student, faculty & administrater, nurse, and medical doctor). 3) The most frequently selected restraining forces were army's inherent character, economical limitation, and educational expertise limitations. 4) Such variables as sex, position attachment to the KANA and grade made a statistically significant effect on the perception of desired curriculum practice, while the variables like marrige, position, and military class made it on the perception of actual curriculum practice. 5) The coefficients among the curriculum components were lower in perception of the actual curriculum practices than those in the desired practices. 5. Conclusions The conclusions based on the major findings of this study are as follows: 1) The current curriculum development procedure of the KANA is not consistent with the theoretical frame of systematic development sarategy of curriculum. 2) There are wide conflicts among the groups who are supposed to participate in curriculnm development, concerning the actual and desired practices of KANN'S curriculum. 3) A great deal of need for curriculum improvement for the KANA is clearly felt, and in particular, in the process of teaching and learning. 4) Each component of curriculum is not intergrated into a whole development procedure, being segregated each other. 5) For better curriculum improvement, such restraining forces as financial and professional limitations should be eliminated. 6. Recommendations 1) For Further Research a. There is a need to replicate this study after in-depth statistical analysis of each item of need assessment questionnaire, and with more representative subjects. b. A study should be conducted which. has its focus on the analysis of restraining forces for the change from actual to desired curriculum practices of the KANA. 2) For KANA'S Curriculum Improvement a. There is a need to promote the professional expertise of the participants in curriculum development and the communication among them. b. It is desirable to establish an institution or section of administration, which is soley in charge of curriculum development. c. To better develop KANA's curriculum not only faculty and administrators but also students should be encouraged to participate in development process, while the military medical doctors' participation should be carefully considered.

• Journal of Korean Home Economics Education Association
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• v.19 no.2
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• pp.35-49
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• 2007

The purpose of this research was to analyze the contents of Housing Studies in Home Economics Education of Secondary School published in master's or doctorial theses and journals since 1985. The 24 research papers were analyzed by the type and time of publication, research subjects, data collecting methods, respondents & sample size, and statistical methods. The data were presented by tables with frequencies. The results showed that the most Housing educational research has been done as a type of theses and the number of published papers on Housing has been increased in somewhat. Over time there were two main theme of Housing educational research: general perception of housing and teaching - learning plans. Research methods, respondents and sample size, and statistical methods were obviously differed by the two research theme of Housing educational studies. This research suggested to broaden the research subjects as well as research methods beside to increase the number of studies on Housing area in Home Economics Education.