• Journal of forensic and investigative science
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• v.1 no.2
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• pp.5-19
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• 2006

Data mining is an information extraction activity to discover hidden facts contained in databases. Using a combination of machine learning, statistical analysis, modeling techniques and database technology, data mining finds patterns and subtle relationships in data and infers rules that allow the prediction of future results. Typical applications include market segmentation, customer profiling, fraud detection, evaluation of retail promotions, and credit risk analysis. Law enforcement agencies deal with mass data to investigate the crime and its amount is increasing due to the development of processing the data by using computer. Now new challenge to discover knowledge in that data is confronted to us. It can be applied in criminal investigation to find offenders by analysis of complex and relational data structures and free texts using their criminal records or statement texts. This study was aimed to evaluate possibile application of data mining and its limitation in practical criminal investigation. Clustering of the criminal cases will be possible in habitual crimes such as fraud and burglary when using data mining to identify the crime pattern. Neural network modelling, one of tools in data mining, can be applied to differentiating suspect's photograph or handwriting with that of convict or criminal profiling. A case study of in practical insurance fraud showed that data mining was useful in organized crimes such as gang, terrorism and money laundering. But the products of data mining in criminal investigation should be cautious for evaluating because data mining just offer a clue instead of conclusion. The legal regulation is needed to control the abuse of law enforcement agencies and to protect personal privacy or human rights.

• Knowledge Management Research
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• v.21 no.3
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• pp.45-65
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• 2020

It is desirable to invest in wine that increases its value, but wine investment itself is unfamiliar in Korea. Also, the process itself is unreasonable, and information is often forged, because pricing in the wine market is done by a small number of people. With the right solution, however, the wine market can be a desirable investment destination in that the longer one invests, the higher one can expect. Also, it is expected that the domestic wine consumption market will expand through the steady increase in domestic wine imports. This study presents the consortium block chain framework for revitalizing the wine market and enhancing transparency as the "right solution" of the nation's wine investment market. Blockchain governance can compensate for the shortcomings of the wine market because it guarantees desirable decision-making rights and accountability. Because the data stored in the block chain can be checked by consumers, it reduces the likelihood of counterfeit wine appearing and complements the process of unreasonably priced. In addition, digitization of assets resolves low cash liquidity and saves money and time throughout the supply chain through smart contracts, lowering entry barriers to wine investment. In particular, if the governance of the block chain is composed of 'chateau-distributor-investor' through consortium blockchains, it can create a desirable wine market. The production process is stored in the block chain to secure production costs, set a reasonable launch price, and efficiently operate the distribution system by storing the distribution process in the block chain, and forecast the amount of orders for futures trading. Finally, investors make rational decisions by viewing all of these data. The study presented a new perspective on alternative investment in that ownership can be treated like a share. We also look forward to the simplification of food import procedures and the formation of trust within the wine industry by presenting a framework for wine-owned sales. In future studies, we would like to expand the framework to study the areas to be applied.

• Journal of Korean Home Economics Education Association
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• v.30 no.1
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• pp.99-114
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• 2018

Textbooks are one of the most crucial variables that determine the quality of education and one of the most influential sources for education. Textbooks should be designed with inquiring tendencies so as to help students to practice self-directed learning. inquiring textbooks can not only intrigue students but also enhance the quality of class. Here, this author is going to analyze the inquiring tendencies of a core construct, 'management', in 12 different types of high school Technology & Home Economics textbooks reflecting the 2015 revised national curriculum based on the Romey method. According to the analysis results, only one type of textbook shows inquiring tendencies in the text, which means that the text is more oriented to delivering knowledge rather than to pursuing students' inquiry learning. Because analysis indexes for figures and diagrams fall under the range of desirable inquiring tendencies in 10 types of textbooks, the figures and diagrams presented in Home Economics textbooks are intended to induce particular learning activity. Learning activity indexes reveal 9 types of textbooks' inquiring tendencies, which means that Home Economics textbooks present a reasonable amount of learning activity in general in order to intrigue students and stimulate their inquiring mind. In terms of evaluation indexes, only 2 types of textbooks show inquiring tendencies. According to the results of examining each individual textbook, some of the Home Economics textbooks reflecting the 2015 revised national curriculum include inquiring elements in the text, figures and diagrams, activities, and evaluation all; however, some still need improvements. Regarding how to better the Home Economics textbooks, this researcher will suggest the following implications and complements. Home Economics textbooks should include inquiring elements to induce interactions properly in the class, and when they write the textbooks, the text, figures and diagrams, activities, and evaluation should be designed balancedly, and the questions need to be expressed in a way to develop students' inquiring mind.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1361-1371
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• 2021

Among smart city services, the crime and disaster prevention sector accounted for the highest 24% in 2018. The most important platform for providing real-time situation information is CCTV (Closed-Circuit Television). Therefore, it is essential to create the actual CCTV surveillance coverage to maximize the usability of CCTV. However, the amount of CCTV installed in Korea exceeds one million units, including those operated by the local government, and manual identification of CCTV coverage is a time-consuming and inefficient process. This study proposed a method to efficiently construct CCTV's actual surveillance coverage and reduce the time required for the decision-maker to manage the situation. For this purpose, first, the exterior orientation parameters and focal lengths of the pre-installed CCTV cameras, which are difficult to access, were calculated using the point cloud data of the MMS (Mobile Mapping System), and the FOV (Field of View) was calculated accordingly. Second, using the FOV result calculated in the first step, CCTV's actual surveillance coverage area was constructed with 1 m, 2 m, 3 m, 5 m, and 10 m grid interval considering the occluded regions caused by the buildings. As a result of applying our approach to 5 CCTV images located in Uljin-gun, Gyeongsnagbuk-do the average re-projection error was about 9.31 pixels. The coordinate difference between calculated CCTV and location obtained from MMS was about 1.688 m on average. When the grid length was 3 m, the surveillance coverage calculated through our research matched the actual surveillance obtained from visual inspection with a minimum of 70.21% to a maximum of 93.82%.

• Journal of the Korean Society of Radiology
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• v.15 no.6
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• pp.803-811
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• 2021

In this study, the exposure amount of IASCC test worker was evaluated by applying the process simulation technology. Using DELMIA Version 5, a commercial process simulation code, IASCC test facility, hot cells, and workers were prepared, and IASCC test activities were implemented, and the cumulative exposure of workers passing through the dose-distributed space could be evaluated through user coding. In order to simulate behavior of workers, human manikins with a degree of freedom of 200 or more imitating the human musculoskeletal system were applied. In order to calculate the worker's exposure, the coordinates, start time, and retention period for each posture were extracted by accessing the sub-information of the human manikin task, and the cumulative exposure was calculated by multiplying the spatial dose value by the posture retention time. The spatial dose for the exposure evaluation was calculated using MCNP6 Version 1.0, and the calculated spatial dose was embedded into the process simulation domain. As a result of comparing and analyzing the results of exposure evaluation by process simulation and typical exposure evaluation, the annual exposure to daily test work in the regular entrance was predicted at similar levels, 0.388 mSv/year and 1.334 mSv/year, respectively. Exposure assessment was also performed on special tasks performed in areas with high spatial doses, and tasks with high exposure could be easily identified, and work improvement plans could be derived intuitively through human manikin posture and spatial dose visualization of the tasks.

• Journal of the Korean earth science society
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• v.43 no.2
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• pp.239-252
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• 2022

Quality control methods for the first G-band vapor radiometer (GVR) mounted on a weather aircraft in Korea were developed using the GVR Precipitable Water Vapor (PWV). The aircraft attitude information (degree of pitch and roll) was applied to quality control to select the shortest vertical path of the GVR beam. In addition, quality control was applied to remove a GVR PWV ≥20 mm. It was found that the difference between the warm load average power and sky load average power converged to near 0 when the GVR PWV increased to 20 mm or higher. This could be due to the high brightness temperature of the substratus and mesoclouds, which was confirmed by the Communication, Ocean and Meteorological Satellite (COMS) data (cloud type, cloud top height, and cloud amount), cloud combination probe (CCP), and precipitation imaging probe (PIP). The GVR PWV before and after the application of quality control on a cloudy day was quantitatively compared with that of a local data assimilation and prediction system (LDAPS). The Root Mean Square Difference (RMSD) decreased from 2.9 to 1.8 mm and the RMSD with Korea Local Analysis and Precipitation System (KLAPS) decreased from 5.4 to 4.3 mm, showing improved accuracy. In addition, the quality control effectiveness of GVR PWV suggested in this study was verified through comparison with the COMS PWV by using the GVR PWV applied with quality control and the dropsonde PWV.

• Korean Journal of Soil Science and Fertilizer
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• v.27 no.3
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• pp.232-237
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• 1994

Field microplot(D 20cm, 1 85cm) experiment filled with Bonryang sandy loam soil(Typic Udifluvents) was conducted to obtain quantitative information on the movement of applied nitrogen under different soil moisture regimes and ladino clover cultivation. Urea applied to the soil was quickly transformed into $NH_4$-N which was slowly to $NO_3$-N which governed the downward movement of inorganic N applied in the soil. Downward movement of inorganic nitrogen was relatively slow in the early growing stages of ladino clover when $NH_4$-N form was the major inorganic nitrogen in the soil. In the later growing stages when $NO_3$-N was the major form, inorganic nitrogen moved rapidly with soil water. Favorable soil moisture condition increased downward movement and plant uptake of inorganic nitrogen. In the non irrigated bare soil 92% of applied nitrogen was leached downwards out of the microplots at the final harvest. Under the non-irrigated condition 57% of applied nitrogen was taken up by plants and 37% of nitrogen remained in the soil 5.5 months after sowing. Nitrogen uptake by plants in the microplots irrigated at 0.2 bar was 4.03g/microplot at the final harvest, which was more than the amount of nitrogen applied.

• Journal of Korean Society of Environmental Engineers
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• v.33 no.12
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• pp.874-885
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• 2011

In this study, the urban $CO_2$ emission based on energy consumption (Coal, Petroleum, Electricity, and City Gas) in 16 provincial and metropolitan city governments in South Korea was evaluated. For calculation of the urban $CO_2$ emission, direct and indirect emissions were considered. Direct emissions refer to generation of greenhouse gas (GHG) on-site from the energy consumption. Indirect emissions refer to the use of resources or goods that discharge GHG emissions during energy production. The total GHG emission was 497,083 thousand ton $CO_2eq.$ in 2007. In the indirect GHG emission, about 240,388 thousand ton $CO_2eq.$ was occurred, as 48% of total GHG emission. About 256,694 thousand ton $CO_2eq.$ (52% of total GHG emissions) was produced in the direct GHG emission. This amount shows 13% difference with 439,698 thousand ton $CO_2eq.$ which is total national GHG emission data using current calculation method. Local metropolitan governments have to try to get accuracy and reliability for quantifying their GHG emission. Therefore, it is necessary to develop and use Korean emission factors than using the IPCC (Intergovernmental Panel on Climate Change) emission factors. The method considering indirect and direct GHG emission, which is suggested in this study, should be considered and compared with previous studies.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.9
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• pp.351-360
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• 2018

The LWR (Locally Weighted Regression) model, which is traditionally a lazy learning model, is designed to obtain the solution of the prediction according to the input variable, the query point, and it is a kind of the regression equation in the short interval obtained as a result of the learning that gives a higher weight value closer to the query point. We study on an incremental ensemble learning approach for LWR, a form of lazy learning and memory-based learning. The proposed incremental ensemble learning method of LWR is to sequentially generate and integrate LWR models over time using a genetic algorithm to obtain a solution of a specific query point. The weaknesses of existing LWR models are that multiple LWR models can be generated based on the indicator function and data sample selection, and the quality of the predictions can also vary depending on this model. However, no research has been conducted to solve the problem of selection or combination of multiple LWR models. In this study, after generating the initial LWR model according to the indicator function and the sample data set, we iterate evolution learning process to obtain the proper indicator function and assess the LWR models applied to the other sample data sets to overcome the data set bias. We adopt Eager learning method to generate and store LWR model gradually when data is generated for all sections. In order to obtain a prediction solution at a specific point in time, an LWR model is generated based on newly generated data within a predetermined interval and then combined with existing LWR models in a section using a genetic algorithm. The proposed method shows better results than the method of selecting multiple LWR models using the simple average method. The results of this study are compared with the predicted results using multiple regression analysis by applying the real data such as the amount of traffic per hour in a specific area and hourly sales of a resting place of the highway, etc.

• Clinical and Experimental Pediatrics
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• v.45 no.5
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• pp.575-587
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• 2002

Objective : To investigate the understanding of breast-feeding in pregnant woman and the proper way of encouraging breast-feeding. Methods : Each questionnaire included items about demographic characteristics and the understanding of breast-feeding. The questionnaires were filled up by pregnant women visiting obstetric clinics in Seoul and its vicinities, Busan, Choongjoo and Chungjoo from July 2001 to August 2001. One thousand, two hundred ninety questionnaires were analysed by Chi square tests and multiple logistic regressions. Results : The majority of pregnant women(87.4%) planned breast-feeding. Forty three percent of them had plans to breast-feed for 4-6 months. There were no differences in the level of education, the family size and the source of information about breast-feeding in planning to breast-feed (P>0.05). The main reasons for not choosing to breast-feed were returns to work(41.3%), previous failures of breast-feeding(17.4%), concerns about insufficient amount of breast milk(10.9%), breast and nipple problems(10.3%) and maternal illness(9.4%). The average score on the test of the understanding about breast-feeding was 59.7/100. The average scores on the understanding about the methods and advantages of breast-feeding were 45.3/100 and 86.1/100, respectively. The maternal status of employment, previous history of breast-feeding, the time of decision to breastfeed, person advocating breast-feeding and the understanding on the advantages of breast-feeding were significant determinant factors in planning to breast-feed(P<0.05). Conclusion : Pediatricians should take steps to make an effort to increase the breast-feeding rate and to encourage breast-feeding by timely education. Beyond the medical field, political and social supports for breast-feeding are urgently needed.