• Journal of the Korea Institute of Building Construction
• /
• v.11 no.3
• /
• pp.238-246
• /
• 2011

Choosing the best-performance materials is a crucial task for the successful completion of a project in the construction field. In general, the process of material selection is performed through the use of information by a highly experienced expert and the purchasing agent, without the assistance of logical decision-making techniques. For this reason, the construction field has considered various artificial intelligence (AI) techniques to support decision systems as their own selection method. This study proposes the application of a systematic and efficient support vector machine (SVM) model to select optimal exterior materials. The dataset of the study is 120 completed construction projects in South Korea. A total of 8 input determinants were identified and verified from the literature review and interviews with experts. Using data classification and normalization, these 120 sets were divided into 3 groups, and then 5 binary classification models were constructed in a one-against-all (OAA) multi classification method. The SVM model, based on the kernel radical basis function, yielded a prediction accuracy rate of 87.5%. This study indicates that the SVM model appears to be feasible as a decision support system for selecting an optimal construction method.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
• /
• 2004.04a
• /
• pp.395-400
• /
• 2004

This study consists of three steps of data modeling procedures. The first step is to identify possible items for the data model based on literature review and expert interviews. The second step is to design delineate possible sub-themes, feature classes, feature types, attributes, attribute domains, and their relationships. These are presented in various UML class diagrams, and each feature type is clearly defined and modeled. The data model also shows geometry objects and their topological relationships in UML diagrams. Finally, a standardized data model has been provided to avoid possible conflicts in the field of geographic and Facility Area, and thus this study and the data model will eventually assist in alleviating efforts to build standardized geographic information databases for Facility Area.

• The Journal of the Convergence on Culture Technology
• /
• v.5 no.4
• /
• pp.413-420
• /
• 2019

The purpose of the present study is to develop a model for predicting hypercholesterolemia using an integrated set of body fat mass variables based on machine learning techniques, beyond the study of the association between body fat mass and hypercholesterolemia. For this study, a total of six models were created using two variable subset selection methods and machine learning algorithms based on the Korea National Health and Nutrition Examination Survey (KNHANES) data. Among the various body fat mass variables, we found that trunk fat mass was the best variable for predicting hypercholesterolemia. Furthermore, we obtained the area under the receiver operating characteristic curve value of 0.739 and the Matthews correlation coefficient value of 0.36 in the model using the correlation-based feature subset selection and naive Bayes algorithm. Our findings are expected to be used as important information in the field of disease prediction in large-scale screening and public health research.

• Journal of the Korean Data and Information Science Society
• /
• v.27 no.6
• /
• pp.1645-1651
• /
• 2016

A lot of data, particularly in the medical field, contain variables that have a measurement error such as blood pressure and body mass index. On the other hand, recently smoothing methods are often used to solve a complex scientific problem. In this paper, we study a Bayesian curve-fitting under functional measurement error model. Especially, we extend our previous model by incorporating covariates free of measurement error. In this paper, we consider penalized splines for non-linear pattern. We employ a hierarchical Bayesian framework based on Markov Chain Monte Carlo methodology for fitting the model and estimating parameters. For application we use the data from the fifth wave (2012) of the Korea National Health and Nutrition Examination Survey data, a national population-based data. To examine the convergence of MCMC sampling, potential scale reduction factors are used and we also confirm a model selection criteria to check the performance.

• Human Ecology Research
• /
• v.60 no.4
• /
• pp.625-643
• /
• 2022

The purpose of this study was to explore the trends and core keywords of the secondary school home economics teacher selection test using content analysis and text network analysis. The sample comprised texts of the secondary school home economics teacher 1st selection test for the 2017-2022 school years. Determination of frequency of occurrence, generation of word clouds, centrality analysis, and topic modeling were performed using NetMiner 4.4. The key results were as follows. First, content analysis revealed that the number of questions and scores for each subject (field) has remained constant since 2020, unlike before 2020. In terms of subjects, most questions focused on 'theory of home economics education', and among the evaluation content elements, the highest percentage of questions asked was for 'home economics teaching·learning methods and practice'. Second, the network of the secondary school home economics teacher selection test covering the 2017-2022 school years has an extremely weak density. For the 2017-2019 school years, 'learning', 'evaluation', 'instruction', and 'method' appeared as important keywords, and 7 topics were extracted. For the 2020-2022 school years, 'evaluation', 'class', 'learning', 'cycle', and 'model' were influential keywords, and five topics were extracted. This study is meaningful in that it attempted a new research method combining content analysis and text network analysis and prepared basic data for the revision of the evaluation area and evaluation content elements of the secondary school home economics teacher selection test.

• Journal of Industrial Technology
• /
• v.28 no.B
• /
• pp.111-116
• /
• 2008

Since the hydraulic fracturing field testing method was introduced first to Korean geotechnical engineers in 1994, there have been lots of progresses in a hardware system as well as an interpretation tool. The hydrofracturing system of first generation was the pipe-line type, and it has been developed to a wire-line system at their second generation. The current up-to-date system is more compact and is able to be operated by all-in-one system. With a progress in a hardware system, the software for analyzing in-situ stress regime has also been progressed. The shut-in pressure, which is the most ambiguous parameter to be obtained from hydrofracturing pressure curves, can now be acquired automatically from the various methods. While the hardware and software for hydrofracturing tests are being developed during the last decade, the author could accumulate the field test results which can cover the almost whole area of South Korea. Currently these field data are used widely in a feasibility study or a preliminary design step for tunnel construction in Korea. Regarding the difficulties in a site selection and a test performance for the in-situ stress measurement at an off-shore area, the in-situ stress regime obtained from the field experiences in the land area can be used indirectly for the design of a sub-sea tunnel. From the hydrofracturing stress measurements, the trend of magnitude and direction of in-situ stress field was shown identically with the geological information in Korea.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.7 no.2
• /
• pp.257-263
• /
• 2006

A case study was investigated the effect of student field practice of employed worker who graduate department of New Materials & Applied Chemistry of Chungwoon University. A question sheet were composed the general information(8 terms), practice data(3 terms), trouble (10 terms) and effect (9 terms). The results of analysis 40 answer sheets among alumni of 2001 - 2004 are summarized as follows ; the problem of student field practice were rare opportunity on selection of field practice institute (enterprise) and non systemic operation of enterprise. The effect of student field practice were the relation of academic curriculum and the assist on the job guidance. The student field practice was connected on the job almost.

• Journal of the Korean Institute of Telematics and Electronics
• /
• v.27 no.5
• /
• pp.747-755
• /
• 1990

In this paper, we presented a method of constructing a multiplier and an inverse element generator over finite field GF(3**m). We proposed the multiplication method using a descending order arithmetics of mod F(X) to perform the multiplication and mod F(X) arithmetics at the same time. The proposed multiplier is composed of following parts. 1) multiplication part, 2) data assortment generation part and 5) multiplication processing part. Also the inverse element generator is constructed with following parts. 1) multiplier, 2) group of output registers Rs, 3) multiplication and cube selection gate Gl, 4) Ri term sequential selection part. 5) cube processing part and 6) descending order mod F(X) generation part. Especially, the proposed multiplier and inverse element generator give regularity, expansibility and modularity of circuit design.

• Journal of Information Management
• /
• v.33 no.2
• /
• pp.33-48
• /
• 2002

This paper describes evaluation methods of serials and also introduces a case study related to the selection and evaluation of serials. A total of 1,291 titles of serials, which were subscription titles of POSTECH in the field of science and technology in 2002, were analyzed. This study employs the analysis of POSTECH SCI Article reference data to suggest the models for evaluating serials and to identify the guideline for journal selection and collection development.

• Journal of Sensor Science and Technology
• /
• v.32 no.5
• /
• pp.313-319
• /
• 2023

In this study, we propose a method to improve the accuracy of acoustic odometry using optimal frame interval selection for Fourier-based image registration. The accuracy of acoustic odometry is related to the phase correlation result of image pairs obtained from the forward-looking sonar (FLS). Phase correlation failure is caused by spurious peaks and high-similarity image pairs that can be prevented by optimal frame interval selection. We proposed a method of selecting the optimal frame interval by analyzing the factors affecting phase correlation. Acoustic odometry error was reduced by selecting the optimal frame interval. The proposed method was verified using field data.