• Journal of Intelligence and Information Systems
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• v.14 no.4
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• pp.89-101
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• 2008

The most used tool for quality control is control chart in manufacturing industry. But it has limitations at current situation where most of manufacturing facilities are automated and several manufacturing processes have interdependent relationship such as CCM assembly line. To Solve problems, we propose quality management system based on data mining that are consisted of monitoring system where it monitors flows of processes at single window and feature extraction system where it predicts the yield of final product and identifies which processes have impact on the quality of final product. The quality management system uses decision tree, neural network, self-organizing map for data mining. We hope that the proposed system can help manufacturing process to produce stable quality of products and provides engineers useful information such as the predicted yield for current status, identification of causal processes for lots of abnormality.

• Information Systems Review
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• v.12 no.2
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• pp.75-88
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• 2010

This paper proposed a method that segmented customers utilizing SOM(Self-organizing Map) and predicted the customers' response of a marketing promotion for each customer's segments. Our proposed method focused on predicting the response of customers dividing into customers' segment whereas most studies have predicted the response of customers all at once. We deployed logistic regression, neural networks, and support vector machines to predict customers' response that is a kind of dichotomous classification while the integrated approach was utilized to improve the performance of the prediction model. Sample data including 45 variables regarding demographic data about 600 customers, transaction data, and promotion activities were applied to the proposed method presenting classification matrix and the comparative analyses of each data mining techniques. We could draw some significant promotion strategies for segmented customers applying our proposed method to sample data.

• Korean Journal of Ecology and Environment
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• v.47 no.3
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• pp.146-159
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• 2014

In this study, we evaluated the temporal changes of water quality in the 90 reservoirs in Korea and the relationships between water quality and their environmental factors in the reservoirs for effective management of reservoirs. The majority of study reservoirs were categorized as the eutrophic state based on Carlson's trophic index. Among 90 reservoirs, more than 55.0% were nutrient-rich based on $TSI_{TP}$ in each month, where more than 50.0% were nutrient-rich based on $TSI_{Chl-a}$ from June to November. Seasonal Mann-Kendall test was used to analyze temporal variation of water quality in the selected 60 reservoirs using monthly data from 2004 to 2008. The results showed that 27 (45.0%) reservoirs showed the improvement of water quality based on TP and Chl-a concentrations, while 14 (23.3%) and 11 (18.3%) reservoirs displayed the degradation of water quality based on TP and Chl-a concentrations, respectively. Meanwhile, a self-organizing map classified the study reservoirs into five groups based on differences of hydrogeomorphology (altitude, catchment area, bank height, lake age, etc.). Physicochemical factors and land use/cover types showed clear differences among groups. Finally, hydrogeomorphology of reservoirs were related to water quality, indicating that the hydrogeomorphological characters strongly affect water quality of reservoirs.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.24 no.2
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• pp.141-148
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• 2011

This study presents the optimal grouping technique of columns which groups together columns of similar shortening trends to improve the efficiency of column shortening compensation. Here, Kohonen's self-organizing feature map which can classify patterns of input data by itself with unsupervised learning was used as the optimal grouping algorithm. The Kohonen network applied in this study is composed of two input neurons and variable output neurons, here the number of output neuron is equal to the column groups to be classified. In input neurons the normalized mean and standard deviation of shortening of each columns are inputted and in the output neurons the classified column groups are presented. The applicability of the proposed algorithm was evaluated by applying it to the two buildings where column shortening analyses had already been performed. The proposed algorithm was able to classify columns with similar shortening trends as one group, and from this we were able to ascertain the field-applicability of the proposed algorithm as the optimal grouping of column shortening.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.5
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• pp.432-441
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• 2002

Though many services offer useful information on internet, computer users are not so familiar with such services that they need an assistant system to use the services easily In the case of web sites, for example, the operators answer the users e-mail questions, but the increasing number of users makes it hard to answer the questions efficiently. In this paper, we propose an assistant system which responds to the users questions automatically and helps them browse the Hanmail Net FAQ (Frequently Asked Question) conceptually. This system uses two-level self-organizing map (SOM): the keyword clustering SOM and document classification SOM. The keyword clustering SOM reduces a variable length question to a normalized vector and the document classification SOM classifies the question into an answer class. Experiments on the 2,206 e-mail question data collected for a month from the Hanmail net show that this system is able to find the correct answers with the recognition rate of 95% and also the browsing based on the map is conceptual and efficient.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.5
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• pp.34-45
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• 1997

In this paper, we present hardware implemcntation of self-organizing feature map (SOFM) neural networkwith constant adaptation gain and binary reinforcement function on FPGA. Whereas a tnme-varyingadaptation gain is used in the conventional SOFM, the proposed SOFM has a time-invariant adaptationgain and adds a binary reinforcement function in order to compensate for the lowered abilityof SOFM due to the constant adaptation gain. Since the proposed algorithm has no multiplication operation.it is much easier to implement than the original SOFM. Since a unit neuron is composed of 1adde $r_tracter and 2 adders, its structure is simple, and thus the number of neurons fabricated onFPGA is expected to he large. In addition, a few control signal: ;:rp sufficient for controlling !he neurons.Experimental results show that each componeni ot thi inipiemented neural network operates correctlyand the whole system also works well.stem also works well.

• Journal of Practical Engineering Education
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• v.6 no.2
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• pp.71-76
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• 2014

Improving lecture quality is very necessary for the service quality of education in universities, enterprises and education institutes. The student lecture evaluation survey data is a good tool for measuring lecture quality and have been often analyzed by simple statistical methods. This study presents an intelligent lecture quality improvement method that can improve student's overall satisfaction and performance by analyzing student lecture evaluation survey data. The method uses SOFM (Self-Organizing Feature Map) neural network and C4.5 to find the patterns in student's satisfaction and performance more correctly and then decide what to change in the lecture for the improvement of student's satisfaction and performance. We apply the proposed method to an enterprise lecture in Korea. We can find that it can improve the quality of an enterprise lecture by changing total lecture time, lecture material and organization of lecture schedule to be necessary improvements.

• The Journal of Korean Institute of Information Technology
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• v.17 no.4
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• pp.109-117
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• 2019

In modern society, due to various accidents and crime threats committed to an unspecified number of people, individual security awareness is increasing throughout society and various surveillance techniques are being actively studied. Still, there is a decline in robustness due to many problems, requiring higher reliability monitoring techniques. Thus, this paper suggests a real-time change detection technique to complement the low robustness problem in various environments and dynamic/static change detection and to solve the cost efficiency problem. We used the Self-Organizing Map (SOM) applied as a data clustering technique to implement change detection, and we were able to confirm the superiority of noise robustness and abnormal detection judgment compared to the detection technique applied to the existing image surveillance system through simulation in the indoor office environment.

• Atmosphere
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• v.28 no.3
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• pp.305-315
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• 2018

This study investigates the characteristic spatial patterns and dynamic processes associated with the summertime extreme temperature events in South Korea during the last 20 years (1995~2014) using Self-Organizing Map (SOM). The classified SOM patterns commonly have high temperature and anticyclonic circulation anomalies over South Korea. The two major teleconnection patterns are identified: one is from the subtropical western North Pacific (WNP) affecting to the north and the other is from the North Atlantic (NA) affecting downstream region. The meridional teleconnection pattern is related to the forcing of positive sea surface temperature (SST) anomaly over the WNP. The northward propagating Rossby wave generates the East Asia-Pacific (EAP) pattern to form an anticyclonic circulation anomaly over South Korea. On the other hand, NA SST anomalies generate an eastward Rossby wave train across the Eurasian continent, leading to the development of an anticyclonic circulation anomaly over South Korea. The EAP pattern occurs more frequently in July and August, whereas the midlatitude teleconnection pattern associated with NA SST anomalies develops more frequently in early summer (June).

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.65-82
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• 2021

Stock market investors are generally split into foreign investors, institutional investors, and individual investors. Compared to individual investor groups, professional investor groups such as foreign investors have an advantage in information and financial power and, as a result, foreign investors are known to show good investment performance among market participants. The purpose of this study is to propose an investment strategy that combines investor-specific transaction information and machine learning, and to analyze the portfolio investment performance of the proposed model using actual stock price and investor-specific transaction data. The Korea Exchange offers daily information on the volume of purchase and sale of each investor to securities firms. We developed a data collection program in C# programming language using an API provided by Daishin Securities Cybosplus, and collected 151 out of 200 KOSPI stocks with daily opening price, closing price and investor-specific net purchase data from January 2, 2007 to July 31, 2017. The self-organizing map model is an artificial neural network that performs clustering by unsupervised learning and has been introduced by Teuvo Kohonen since 1984. We implement competition among intra-surface artificial neurons, and all connections are non-recursive artificial neural networks that go from bottom to top. It can also be expanded to multiple layers, although many fault layers are commonly used. Linear functions are used by active functions of artificial nerve cells, and learning rules use Instar rules as well as general competitive learning. The core of the backpropagation model is the model that performs classification by supervised learning as an artificial neural network. We grouped and transformed investor-specific transaction volume data to learn backpropagation models through the self-organizing map model of artificial neural networks. As a result of the estimation of verification data through training, the portfolios were rebalanced monthly. For performance analysis, a passive portfolio was designated and the KOSPI 200 and KOSPI index returns for proxies on market returns were also obtained. Performance analysis was conducted using the equally-weighted portfolio return, compound interest rate, annual return, Maximum Draw Down, standard deviation, and Sharpe Ratio. Buy and hold returns of the top 10 market capitalization stocks are designated as a benchmark. Buy and hold strategy is the best strategy under the efficient market hypothesis. The prediction rate of learning data using backpropagation model was significantly high at 96.61%, while the prediction rate of verification data was also relatively high in the results of the 57.1% verification data. The performance evaluation of self-organizing map grouping can be determined as a result of a backpropagation model. This is because if the grouping results of the self-organizing map model had been poor, the learning results of the backpropagation model would have been poor. In this way, the performance assessment of machine learning is judged to be better learned than previous studies. Our portfolio doubled the return on the benchmark and performed better than the market returns on the KOSPI and KOSPI 200 indexes. In contrast to the benchmark, the MDD and standard deviation for portfolio risk indicators also showed better results. The Sharpe Ratio performed higher than benchmarks and stock market indexes. Through this, we presented the direction of portfolio composition program using machine learning and investor-specific transaction information and showed that it can be used to develop programs for real stock investment. The return is the result of monthly portfolio composition and asset rebalancing to the same proportion. Better outcomes are predicted when forming a monthly portfolio if the system is enforced by rebalancing the suggested stocks continuously without selling and re-buying it. Therefore, real transactions appear to be relevant.