• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.123-139
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• 2019

The Korean NPL market was formed by the government and foreign capital shortly after the 1997 IMF crisis. However, this market is short-lived, as the bad debt has started to increase after the global financial crisis in 2009 due to the real economic recession. NPL has become a major investment in the market in recent years when the domestic capital market's investment capital began to enter the NPL market in earnest. Although the domestic NPL market has received considerable attention due to the overheating of the NPL market in recent years, research on the NPL market has been abrupt since the history of capital market investment in the domestic NPL market is short. In addition, decision-making through more scientific and systematic analysis is required due to the decline in profitability and the price fluctuation due to the fluctuation of the real estate business. In this study, we propose a prediction model that can determine the achievement of the benchmark yield by using the NPL market related data in accordance with the market demand. In order to build the model, we used Korean NPL data from December 2013 to December 2017 for about 4 years. The total number of things data was 2291. As independent variables, only the variables related to the dependent variable were selected for the 11 variables that indicate the characteristics of the real estate. In order to select the variables, one to one t-test and logistic regression stepwise and decision tree were performed. Seven independent variables (purchase year, SPC (Special Purpose Company), municipality, appraisal value, purchase cost, OPB (Outstanding Principle Balance), HP (Holding Period)). The dependent variable is a bivariate variable that indicates whether the benchmark rate is reached. This is because the accuracy of the model predicting the binomial variables is higher than the model predicting the continuous variables, and the accuracy of these models is directly related to the effectiveness of the model. In addition, in the case of a special purpose company, whether or not to purchase the property is the main concern. Therefore, whether or not to achieve a certain level of return is enough to make a decision. For the dependent variable, we constructed and compared the predictive model by calculating the dependent variable by adjusting the numerical value to ascertain whether 12%, which is the standard rate of return used in the industry, is a meaningful reference value. As a result, it was found that the hit ratio average of the predictive model constructed using the dependent variable calculated by the 12% standard rate of return was the best at 64.60%. In order to propose an optimal prediction model based on the determined dependent variables and 7 independent variables, we construct a prediction model by applying the five methodologies of discriminant analysis, logistic regression analysis, decision tree, artificial neural network, and genetic algorithm linear model we tried to compare them. To do this, 10 sets of training data and testing data were extracted using 10 fold validation method. After building the model using this data, the hit ratio of each set was averaged and the performance was compared. As a result, the hit ratio average of prediction models constructed by using discriminant analysis, logistic regression model, decision tree, artificial neural network, and genetic algorithm linear model were 64.40%, 65.12%, 63.54%, 67.40%, and 60.51%, respectively. It was confirmed that the model using the artificial neural network is the best. Through this study, it is proved that it is effective to utilize 7 independent variables and artificial neural network prediction model in the future NPL market. The proposed model predicts that the 12% return of new things will be achieved beforehand, which will help the special purpose companies make investment decisions. Furthermore, we anticipate that the NPL market will be liquidated as the transaction proceeds at an appropriate price.

• Journal of Intelligence and Information Systems
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• v.20 no.3
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• pp.109-131
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• 2014

As the demand of nuclear power plant equipment is continuously growing worldwide, the importance of handling nuclear strategic materials is also increasing. While the number of cases submitted for the exports of nuclear-power commodity and technology is dramatically increasing, preadjudication (or prescreening to be simple) of strategic materials has been done so far by experts of a long-time experience and extensive field knowledge. However, there is severe shortage of experts in this domain, not to mention that it takes a long time to develop an expert. Because human experts must manually evaluate all the documents submitted for export permission, the current practice of nuclear material export is neither time-efficient nor cost-effective. Toward alleviating the problem of relying on costly human experts only, our research proposes a new system designed to help field experts make their decisions more effectively and efficiently. The proposed system is built upon case-based reasoning, which in essence extracts key features from the existing cases, compares the features with the features of a new case, and derives a solution for the new case by referencing similar cases and their solutions. Our research proposes a framework of case-based reasoning system, designs a case-based reasoning system for the control of nuclear material exports, and evaluates the performance of alternative keyword extraction methods (full automatic, full manual, and semi-automatic). A keyword extraction method is an essential component of the case-based reasoning system as it is used to extract key features of the cases. The full automatic method was conducted using TF-IDF, which is a widely used de facto standard method for representative keyword extraction in text mining. TF (Term Frequency) is based on the frequency count of the term within a document, showing how important the term is within a document while IDF (Inverted Document Frequency) is based on the infrequency of the term within a document set, showing how uniquely the term represents the document. The results show that the semi-automatic approach, which is based on the collaboration of machine and human, is the most effective solution regardless of whether the human is a field expert or a student who majors in nuclear engineering. Moreover, we propose a new approach of computing nuclear document similarity along with a new framework of document analysis. The proposed algorithm of nuclear document similarity considers both document-to-document similarity (${\alpha}$) and document-to-nuclear system similarity (${\beta}$), in order to derive the final score (${\gamma}$) for the decision of whether the presented case is of strategic material or not. The final score (${\gamma}$) represents a document similarity between the past cases and the new case. The score is induced by not only exploiting conventional TF-IDF, but utilizing a nuclear system similarity score, which takes the context of nuclear system domain into account. Finally, the system retrieves top-3 documents stored in the case base that are considered as the most similar cases with regard to the new case, and provides them with the degree of credibility. With this final score and the credibility score, it becomes easier for a user to see which documents in the case base are more worthy of looking up so that the user can make a proper decision with relatively lower cost. The evaluation of the system has been conducted by developing a prototype and testing with field data. The system workflows and outcomes have been verified by the field experts. This research is expected to contribute the growth of knowledge service industry by proposing a new system that can effectively reduce the burden of relying on costly human experts for the export control of nuclear materials and that can be considered as a meaningful example of knowledge service application.

• Korean Journal of Poultry Science
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• v.18 no.1
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• pp.43-55
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• 1991

This experiment was conducted to study the sparing effects of animal proteins on weight gain, nutrients utilizability and economic analysis of broiler. Experiment diet contained different ratio of animal and plant protein and were supplemented different levels of methionine and lysine for the respective protein levels. A total of 264 broiler chicks were fed four diets of control, $T_1$, $T_2$ and $T_3$ for 42 days from April 7, 1990 to May 19, 1990. Dietary protein levels of control, $T_1$, $T_2$ and $T_3$ for starter and finisher were 20~18, 18~16, 18~16 and 22~20%, respectively. Methionine and lysine levels of control, $T_1$, $T_2$ and $T_3$ were 0.4~1.1, 0.44~1.21, 0.48~1.32 and 0.48~1.32% for starter diet, respectively, and were 0.32~0.90. 0.35~0.99, 0.38~1.08 and 0.38~1.08% for finisher diet, respectively. The results obtained were summarized as follows. 1. The birds fed control diet gained most for overall period. $T_3$ treatment which was high in protein, methionine and lysine levels gained most for finisher period. 2 The birds fed control diet consumed most feed, and the birds fed T$_3$ diet consumed least feed. for overall period. Feed conversion during 1~4 weeks was better in $T_1$ (1.51) and $T_2$ (1.53) than in control (1.61) and $T_3$ (1.63) . During 4~6weeks, feed conversion was better in $T_3$ (1.37) and control(1.58) than T, (2.05) and T, (2.16) (P＜0.01) 3. Dry matter, crude fiber and NFE utilizability were increased for 1~4 weeks and decreased for 4~6 weeks as methionine and lysine levels increased and crude protein utilizability tended to be increased as protein levels increased. 4. Abdominal fat content was lowest in bird fed control diet and was high in birds fed low protein diet Carcass percentage was highest at control and the abdominal fat content was higher in bird fed lower protein diet than bird fed other protein diets(P＜0.05). 5. Feed cost per kg weight gain was lowest at $T_3$ which contained more soybean oil meal than other feeds and next was control. According to the results of this experiment, it was revealed that optimum protein, methionine and lysine levels for starter and finisher broiler diet were 20~18, 0.4~0.32 and 1.1~0.9%, respectively.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.149-162
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• 2014

Course guidance is a mentoring process which is performed before students register for coming classes. The course guidance plays a very important role to students in checking degree audits of students and mentoring classes which will be taken in coming semester. Also, it is intimately involved with a graduation assessment or a completion of ABEEK certification. Currently, course guidance is manually performed by some advisers at most of universities in Korea because they have no electronic systems for the course guidance. By the lack of the systems, the advisers should analyze each degree audit of students and curriculum information of their own departments. This process often causes the human error during the course guidance process due to the complexity of the process. The electronic system thus is essential to avoid the human error for the course guidance. If the relation data model-based system is applied to the mentoring process, then the problems in manual way can be solved. However, the relational data model-based systems have some limitations. Curriculums of a department and certification systems can be changed depending on a new policy of a university or surrounding environments. If the curriculums and the systems are changed, a scheme of the existing system should be changed in accordance with the variations. It is also not sufficient to provide semantic search due to the difficulty of extracting semantic relationships between subjects. In this paper, we model a course mentoring ontology based on the analysis of a curriculum of computer science department, a structure of degree audit, and ABEEK certification. Ontology-based course guidance system is also proposed to overcome the limitation of the existing methods and to provide the effectiveness of course mentoring process for both of advisors and students. In the proposed system, all data of the system consists of ontology instances. To create ontology instances, ontology population module is developed by using JENA framework which is for building semantic web and linked data applications. In the ontology population module, the mapping rules to connect parts of degree audit to certain parts of course mentoring ontology are designed. All ontology instances are generated based on degree audits of students who participate in course mentoring test. The generated instances are saved to JENA TDB as a triple repository after an inference process using JENA inference engine. A user interface for course guidance is implemented by using Java and JENA framework. Once a advisor or a student input student's information such as student name and student number at an information request form in user interface, the proposed system provides mentoring results based on a degree audit of current student and rules to check scores for each part of a curriculum such as special cultural subject, major subject, and MSC subject containing math and basic science. Recall and precision are used to evaluate the performance of the proposed system. The recall is used to check that the proposed system retrieves all relevant subjects. The precision is used to check whether the retrieved subjects are relevant to the mentoring results. An officer of computer science department attends the verification on the results derived from the proposed system. Experimental results using real data of the participating students show that the proposed course guidance system based on course mentoring ontology provides correct course mentoring results to students at all times. Advisors can also reduce their time cost to analyze a degree audit of corresponding student and to calculate each score for the each part. As a result, the proposed system based on ontology techniques solves the difficulty of mentoring methods in manual way and the proposed system derive correct mentoring results as human conduct.

• Korean Journal of Heritage: History & Science
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• v.57 no.1
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• pp.82-108
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• 2024

With the transition of archaeological recording method's transition from analog to digital, the 3D scanning technology has been actively adopted within the field. Research on the digital archaeological digital data gathered from 3D scanning and photogrammetry is continuously being conducted. However, due to cost and manpower issues, most buried cultural heritage organizations are hesitating to adopt such digital technology. This paper aims to present a digital recording method of relics utilizing open-source software and photogrammetry technology, which is believed to be the most efficient method among 3D scanning methods. The digital recording process of relics consists of three stages: acquiring a 3D model, creating a joining map with the edited 3D model, and creating an digital drawing. In order to enhance the accessibility, this method only utilizes open-source software throughout the entire process. The results of this study confirms that in terms of quantitative evaluation, the deviation of numerical measurement between the actual artifact and the 3D model was minimal. In addition, the results of quantitative quality analysis from the open-source software and the commercial software showed high similarity. However, the data processing time was overwhelmingly fast for commercial software, which is believed to be a result of high computational speed from the improved algorithm. In qualitative evaluation, some differences in mesh and texture quality occurred. In the 3D model generated by opensource software, following problems occurred: noise on the mesh surface, harsh surface of the mesh, and difficulty in confirming the production marks of relics and the expression of patterns. However, some of the open source software did generate the quality comparable to that of commercial software in quantitative and qualitative evaluations. Open-source software for editing 3D models was able to not only post-process, match, and merge the 3D model, but also scale adjustment, join surface production, and render image necessary for the actual measurement of relics. The final completed drawing was tracked by the CAD program, which is also an open-source software. In archaeological research, photogrammetry is very applicable to various processes, including excavation, writing reports, and research on numerical data from 3D models. With the breakthrough development of computer vision, the types of open-source software have been diversified and the performance has significantly improved. With the high accessibility to such digital technology, the acquisition of 3D model data in archaeology will be used as basic data for preservation and active research of cultural heritage.

• Journal of Bio-Environment Control
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• v.23 no.4
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• pp.337-341
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• 2014

Due to the nature of the ambient air temperature in summer in korea, the growth of crops in greenhouse normally requires cooling and dehumidification. Even though various cooling and dehumidification methods have been presented, there are many obstacles to figure out in practical application such as excessive energy use, cost, and performance. To overcome this problem, the lab scale experiments using lithium bromide(LiBr) solution and cooling coil for dehumidification and cooling in greenhouses were performed. In this study, preliminary experiment of dehumidification and cooling for the greenhouse was done using LiBr solution as the dehumidifying materials, and cooling coil separately and then combined system was tested as well. Hot and humid air was dehumidified from 85% to 70% by passing through a pad soaked with LiBr, and cooled from 308K to 299K through the cooling coil. computational Fluid Dynamics(CFD) analysis and analytical solution were done for the change of air temperature by heat transfer. Simulation results showed that the final air temperature was calculated 299.7K and 299.9K respectively with the deviation of 0.7K comparing the experimental value having good agreement. From this result, LiBr solution with cooling coil system could be applicable in the greenhouse.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.243-264
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• 2018

Although the worldwide battery market is recently spurring the development of lithium secondary battery, lead acid batteries (rechargeable batteries) which have good-performance and can be reused are consumed in a wide range of industry fields. However, lead-acid batteries have a serious problem in that deterioration of a battery makes progress quickly in the presence of that degradation of only one cell among several cells which is packed in a battery begins. To overcome this problem, previous researches have attempted to identify the mechanism of deterioration of a battery in many ways. However, most of previous researches have used data obtained in a laboratory to analyze the mechanism of deterioration of a battery but not used data obtained in a real world. The usage of real data can increase the feasibility and the applicability of the findings of a research. Therefore, this study aims to develop a model which predicts the battery deterioration using data obtained in real world. To this end, we collected data which presents change of battery state by attaching sensors enabling to monitor the battery condition in real time to dozens of golf carts operated in the real golf field. As a result, total 16,883 samples were obtained. And then, we developed a model which predicts a precursor phenomenon representing deterioration of a battery by analyzing the data collected from the sensors using machine learning techniques. As initial independent variables, we used 1) inbound time of a cart, 2) outbound time of a cart, 3) duration(from outbound time to charge time), 4) charge amount, 5) used amount, 6) charge efficiency, 7) lowest temperature of battery cell 1 to 6, 8) lowest voltage of battery cell 1 to 6, 9) highest voltage of battery cell 1 to 6, 10) voltage of battery cell 1 to 6 at the beginning of operation, 11) voltage of battery cell 1 to 6 at the end of charge, 12) used amount of battery cell 1 to 6 during operation, 13) used amount of battery during operation(Max-Min), 14) duration of battery use, and 15) highest current during operation. Since the values of the independent variables, lowest temperature of battery cell 1 to 6, lowest voltage of battery cell 1 to 6, highest voltage of battery cell 1 to 6, voltage of battery cell 1 to 6 at the beginning of operation, voltage of battery cell 1 to 6 at the end of charge, and used amount of battery cell 1 to 6 during operation are similar to that of each battery cell, we conducted principal component analysis using verimax orthogonal rotation in order to mitigate the multiple collinearity problem. According to the results, we made new variables by averaging the values of independent variables clustered together, and used them as final independent variables instead of origin variables, thereby reducing the dimension. We used decision tree, logistic regression, Bayesian network as algorithms for building prediction models. And also, we built prediction models using the bagging of each of them, the boosting of each of them, and RandomForest. Experimental results show that the prediction model using the bagging of decision tree yields the best accuracy of 89.3923%. This study has some limitations in that the additional variables which affect the deterioration of battery such as weather (temperature, humidity) and driving habits, did not considered, therefore, we would like to consider the them in the future research. However, the battery deterioration prediction model proposed in the present study is expected to enable effective and efficient management of battery used in the real filed by dramatically and to reduce the cost caused by not detecting battery deterioration accordingly.

• Journal of the Korean Society of Radiology
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• v.12 no.6
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• pp.737-743
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• 2018

Patient exposure dose exposure test, which is one of the items of accuracy control of Computed Tomography, conducts measurements every year based on the installation and operation of special medical equipment under Article 38 of the Medical Law, And keep records. The CT-Dose phantom used for dosimetry can accurately measure doses, but has the disadvantage of high price. Therefore, through this research, the existing CT - Dose phantom was similarly manufactured with a 3D printer and compared with the existing phantom to examine the usefulness. In order to produce the same phantom as the conventional CT-Dose phantom, a 3D printer of the FFF method is used by using a PLA filament, and in order to calculate the CTDIw value, Ion chambers were inserted into the central part and the central part, and measurements were made ten times each. Measurement results The CT-Dose phantom was measured at $30.44{\pm}0.31mGy$ in the periphery, $29.55{\pm}0.34mGy$ CTDIw value was measured at $30.14{\pm}0.30mGy$ in the center, and the phantom fabricated using the 3D printer was measured at the periphery $30.59{\pm}0.18mGy$, the central part was $29.01{\pm}0.04mGy$, and the CTDIw value was measured at $30.06{\pm}0.13mGy$. Analysis using the Mann - Whiteney U-test of the SPSS statistical program showed that there was a statistically significant difference in the result values in the central part, but statistically significant differences were observed between the peripheral part and CTDIw results I did not show. In conclusion, even in the CT-Dose phantom made with a 3D printer, we showed dose measurement performance like existing CT-Dose phantom and confirmed the possibility of low-cost phantom production using 3D printer through this research did it.

• Journal of Bio-Environment Control
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• v.26 no.3
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• pp.181-187
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• 2017

The purpose of this study was to provide basic data for development of environmental design technology for greenhouses constructed in reclaimed lands. The climatic conditions around seven major reclaimed land areas with a plan to install advanced horticultural complexes in Korea were analyzed. The characteristics of natural ventilation and temperature rise through the thermal environment measurement of the greenhouse in Saemangeum were analyzed. The part to be applied to the environmental design of the greenhouses in reclaimed lands were reviewed. Results of comparing the ventilation rate of the greenhouse according to the presence or absence of plants showed the greenhouse with plants had the lower ventilation rate, but the smaller rise of indoor temperature due to the evapotranspiration of plants. In the greenhouse with plants, the number of air changes was in the range of 0.3 to 0.9 volumes/min and the average was 0.7 volumes/min. The rise of indoor temperature relative to outdoor temperature was in the range of 1 to $5^{\circ}C$ and the average $2.5^{\circ}C$. The natural ventilation performance of the experimental greenhouse constructed in the reclaimed land almost satisfied the recommended ventilation rate in summer and the rise of indoor temperature relative to outdoor temperature did not deviate considerably from the cultivation environment of plants. Therefore, it was determined that the greenhouse cultivation in Saemangeum reclaimed land is possible with only natural ventilation systems without cooling facilities. As the reclaimed land is located in the seaside, the wind is stronger than the inland area, and the fog is frequent. This strong wind speed increases the ventilation rate of greenhouses, which is considered to be a factor for reducing the cooling load. In addition, since the fog duration is remarkably longer than that of inland area, the seasonal cooling load is expected to decrease, which is considered to be advantageous in terms of the operation cost of cooling facilities.

• Journal of Korean Society of Environmental Engineers
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• v.28 no.10
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• pp.1016-1023
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• 2006

The water content of dewatered cake produced from belt press dewatering equipment was about 75 wt% which was some high to handle it, so the equipment contained a limit at the economical and environmental aspect. The thermal dewatering equipment built as an alternative to overcome several problems was set up at the sludge treatment field and estimated some feasibility as comparison with the dewatering performance of belt press. First, dewatering properties of waterworks sludge was analyzed by monthly. The sludge of a water shortage season contained a high organic content which led to be difficult to dewater the cake, the other side the sludge of rainwater season was ease to dewater because of low organic content. According to the results to analysis the water content of dewatered cake produced from two equipments on the base of the seasonal dewatering properties, the water content of dewatered cake produced from thermal dewatering for sludge of water shortage season was $41.6{\sim}48.3$ wt% and $71{\sim}84$ wt% from belt press. In the case of rainwater season, the water content of dewatered cake produced from thermal dewatering was $34{\sim}37.7$ wt% and $57{\sim}70$ wt% from belt press. It was understood that thereduction of water content of cake by thermal dewatering was larger than belt press. The economical aspect for two equipments was evaluated on considering the reduction of cake treatment amount as the decrease of water content of cake. When putting the cost index of thermal dewatering into 100, belt press was 121. This meant that thermal dewater was more economical than belt press by about 20% in the side of construction and operation. In conclusion, thermal dewatering equipment was estimated by producing the low water content dewatered cake as well as being operated with low coat.