• Journal of Digital Contents Society
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• v.16 no.4
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• pp.555-564
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• 2015

Since the size of the construction project has become massive, complicated and specialized, the use for a substantial amount of information provided from diverse participants is considered important. Schedule information obtained from a variety of sources is key during the construction project. Misunderstanding of schedule management information causes delay of construction period and low quality of construction. Currently, interest in BIM (Building Information Modeling) that produces the necessary data for the entire life cycle of the building process is rising. The study is to construct the BIM-base-information system in order to systematically manage schedule information of the construction work. For this purpose, the study established a BIM-base-schedule-management-business process and drew a classification system for the work for schedule-information construction. The study also drew information that can be extracted from the BIM model among properties required to build certain information. The schedule is made upon consideration of information needed for schedule management, and information required to schedule a timeline of the construction project by process is established.

• Journal of Korean Society for Atmospheric Environment
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• v.33 no.6
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• pp.593-604
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• 2017

The suspended particulate matter(PM) was measured in subway tunnel of Seoul Line 1 to 9 in order to evaluate the pollution degree and characteristics of the PM in the subway tunnel. Also, to analyze the effect of outdoor aerosol concentration on the PM concentration of subway tunnels, the ambient PM concentration around the subway station was extracted by spatial analysis using $PM_{10}$ data of Seoul air pollution monitoring network. Finally, in order to understand pollution pattern in the Seoul subway tunnels, cluster analysis was performed based on input data set such as PM levels in tunnel, tunnel depth, length, curvature radius, outdoor ambient air pollution levels and so on. The average concentration of $PM_{10}$, $PM_{2.5}$, and $PM_1$ on subway tunnels were $98.0{\pm}37.4$, $78.4{\pm}28.7$, and $56.9{\pm}19.2{\mu}g/m^3$, respectively. As a result of the cluster analysis, tunnels from Seoul subway Line-1 to Line-9 were classified into five classes, and the concentrations and physical properties of the tunnels were compared. This study can provide a method to reduce PM concentration in tunnel for each pollution pattern and provide basic information about air quality control in Seoul subway tunnel.

• The Korean Journal of Community Living Science
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• v.23 no.2
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• pp.103-116
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• 2012

The purpose of this study was to investigate the trend of academic journals of institutional foodservice published from 2005 to 2009. This study was conducted by content analysis. We collected 322 articles including the subject of institutional foodservice Industry published from January 1, 2005 to December 31, 2009. Classification based on content analysis was conducted based on research method, statistical analysis, survey area, institution and location, sample type and research subject. First, in case of the research method, primary field survey/monitoring showed the highest rate. In addition, statistical analysis was as follows. Frequency/descriptive analysis were used as the highest rate. Survey area was a high percentage in case of Seoul/Incheon/Gyeong-gi province. In case of the institution and location, school/university showed the highest rate. In the sample type, foodservice employee/dietitian/nutrition teacher showed the highest rate. In this study, the most important research subjects were classified study subjects into seven by taking advantage of the previous studies. The greatest numerical study in seven study subjects was 'service quality and customers'(28.9%), and the following subjects were 'foodservice operation'(26.4%), 'hygiene, security and microbiology' (15.8%), 'organization and human resource' (15.5%). But it is noteworthy that 'marketing and strategic management'(9.6%) and 'education and training'(3.1%) of lower research results in this study are also important fields in institutional foodservice industry. Moreover, the study of such subjects is considered more necessary in the future.

• KIEAE Journal
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• v.12 no.6
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• pp.77-84
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• 2012

The current architectural design of unit modular has been based on 2D of CAD program, so unit modular character which needs unit information management, as a dried-member system, has no effect on design process. The purpose of this study is We have developed a suitable BIM design process, according to various works of construction, then tried to contribute to supply and activation of the urban-life-housing based on unit modular. The BIM modeling process based on unit modular has been in order of unit combination with preparing manual classification, and, it has been constructed, at construction site, from housing foundation to roof finish by Bottom-up method. At a manufacturing factory, it has been produced in order of 1) grouping materials and parts, 2) fabricating unit boxes, and 3) interference examination of unit boxes, and each order has been classified as housing structure, architecture, plumbing process separately. At a construction site, the fabrication has been done in order of, like as a real housing construction scenario, 1) RC foundation work 2) unit module job-site-fabrication work, 3) roof truss work, 4) plumbing and HVAC work, and 5) housing interior finish work. After modeling process, the interference examination on each work of construction has finally completed modeling. The Unit modular utilizing BIM modeling can make easy housing maintenance through systematic control with preparing manual of unit module information, and securing accurate and speedy construction information. And it will promote design credibility and create maximum effect of unit modular construction method, such as construction period reduction and upgrade of construction quality, etc., through the computer simulation as real as construction environment in cyber space, and with the interfering examination.

• Journal of Korean Academy of Nursing
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• v.43 no.1
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• pp.1-10
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• 2013

Purpose: The purpose of this study was to develop a prediction model for the characteristics of older adults with depression using the decision tree method. Methods: A large dataset from the 2008 Korean Elderly Survey was used and data of 14,970 elderly people were analyzed. Target variable was depression and 53 input variables were general characteristics, family & social relationship, economic status, health status, health behavior, functional status, leisure & social activity, quality of life, and living environment. Data were analyzed by decision tree analysis, a data mining technique using SPSS Window 19.0 and Clementine 12.0 programs. Results: The decision trees were classified into five different rules to define the characteristics of older adults with depression. Classification & Regression Tree (C&RT) showed the best prediction with an accuracy of 80.81% among data mining models. Factors in the rules were life satisfaction, nutritional status, daily activity difficulty due to pain, functional limitation for basic or instrumental daily activities, number of chronic diseases and daily activity difficulty due to disease. Conclusion: The different rules classified by the decision tree model in this study should contribute as baseline data for discovering informative knowledge and developing interventions tailored to these individual characteristics.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.4
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• pp.785-794
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• 2019

Recently Deep Learning technology, one of the fourth industrial revolution technologies, is used to identify the hidden meaning of network data that is difficult to detect in the security arena and to predict attacks. Property and quality analysis of data sources are required before selecting the deep learning algorithm to be used for intrusion detection. This is because it affects the detection method depending on the contamination of the data used for learning. Therefore, the characteristics of the data should be identified and the characteristics selected. In this paper, the characteristics of malware were analyzed using network data set and the effect of each feature on performance was analyzed when the deep learning model was applied. The traffic classification experiment was conducted on the comparison of characteristics according to network characteristics and 96.52% accuracy was classified based on the selected characteristics.

• Journal of Korean Society on Water Environment
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• v.35 no.3
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• pp.271-277
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• 2019

Korean Trophic State Index ($TSI_{KO}$) was developed in 2006, and was composed of COD ($COD_{Mn}$ based on permanganate method), Chlorophyll a (Chl.a) and total phosphorus (TP). However, $COD_{Mn}$ usually represents only 50-60% of total organic matter in stream or lake water due to low oxidizing power of permanganate. This study investigated the relationship between TOC and $COD_{Mn}$ based on the average data for the whole layer in 81 lakes in Korea, during the period 2013-2017. As a result, $COD_{Mn}$ was found to be 1.54 times more than TOC in 66 of the freshwater lakes and 3 brackish lakes (TOC measured using thermo-oxidation method). TOC was about a quarter of $COD_{Mn}$ in 8 coastal lakes (TOC measured using UV-persulfate oxidation method), and it appeared to be underestimated due to chloride interference. Using the data of 69 lakes with exception of 12 brackish lakes, $TSI_{KO}$(TOC) was developed based on the correlation between TOC and $COD_{Mn}$, while $TSI_{KO}$(COD) was replaced with $TSI_{KO}$(TOC). However, for trophic state assessment of brackish lakes, the $TSI_{KO}$(TOC) can only be utilized in case that TOC is measured through thermo-oxidation method. The determination coefficient of $TSI_{KO}$(Chl) to $TSI_{KO}$(COD) in 66 freshwater lakes and 3 brackish lakes was 0.83, while that to $TSI_{KO}$(TOC) was 0.68. This difference could be attributed to the recalcitrant organic part of TOC.

• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.49-56
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• 2023

Machine learning methods diversely applied to the Internet of Things (IoT) field have been successful due to the enhancement of computer processing power. They offer an effective way of detecting malicious intrusions in IoT because of their high-level feature extraction capabilities. In this paper, we proposed a novel feature selection method for malicious intrusion detection in IoT by using an evolutionary technique - Genetic Algorithm (GA) and Machine Learning (ML) algorithms. The proposed model is performing the classification of BoT-IoT dataset to evaluate its quality through the training and testing with classifiers. The data is reduced and several preprocessing steps are applied such as: unnecessary information removal, null value checking, label encoding, standard scaling and data balancing. GA has applied over the preprocessed data, to select the most relevant features and maintain model optimization. The selected features from GA are given to ML classifiers such as Logistic Regression (LR) and Support Vector Machine (SVM) and the results are evaluated using performance evaluation measures including recall, precision and f1-score. Two sets of experiments are conducted, and it is concluded that hyperparameter tuning has a significant consequence on the performance of both ML classifiers. Overall, SVM still remained the best model in both cases and overall results increased.

• Journal of the Korean Society of Food Culture
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• v.22 no.1
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• pp.104-114
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• 2007

This research entailed collecting domestic and overseas research papers on technologies for fermentation of Kimchi, which is widely considered the national dish of Korea, creating a technology classification structure and conducting quantitative analysis on each technology component and schematization. Five research papers were published in domestic journals in 1990. Afterwards, the number increased by six to nine papers a year. There was no clear increase after the year 2000, but an average of around 20 papers have been Published every year, indicating that Kimchi research is now becoming widespread. An analysis on researchers entailed determining the percentage of research papers published by the top ten authors. The percentage was 76% in the early-1990s; 63% in the late-1990s; and 52% in the 2000s, indicating that Kimchi has been more and more widely researched and Kimchi research has become professionalized. Universities were found to be leading the research as 52% of researchers belonged to universities. Another 9% were at research institutions. Analysis of technologies showed that domestic research mainly focused on the Kimchi fermentation process and an additive for the development of new Kimchi ingredients and types, preservation and quality improvements. Most of the research papers published overseas dealt with the functions of bacteria strains isolated from Kimchi; and improvements in the Kimchi fermentation and ripening processes. And most of the research papers have been published in a field of microorganism and biotechnology.

• The Journal of Society for e-Business Studies
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• v.25 no.3
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• pp.1-13
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• 2020

Machine learning refers to a model generation technique that can solve specific problems from the generalization process for given data. In order to generate a high performance model, high quality training data and learning algorithms for generalization process should be prepared. As one way of improving the performance of model to be learned, the Ensemble technique generates multiple models rather than a single model, which includes bagging, boosting, and stacking learning techniques. This paper proposes a new Ensemble technique with multiple stacking that outperforms the conventional stacking technique. The learning structure of multiple stacking ensemble technique is similar to the structure of deep learning, in which each layer is composed of a combination of stacking models, and the number of layers get increased so as to minimize the misclassification rate of each layer. Through experiments using four types of datasets, we have showed that the proposed method outperforms the exiting ones.