• Knowledge Management Research
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• v.22 no.4
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• pp.71-85
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• 2021

The purpose of this study is to find out which artificial intelligence methodology is most suitable for creating a foreign exchange rate prediction model using the indicators of bond market and interest rate market. KTBs and MSBs, which are representative products of the Korea bond market, are sold on a large scale when a risk aversion occurs, and in such cases, the USD/KRW exchange rate often rises. When USD liquidity problems occur in the onshore Korean market, the KRW Cross-Currency Swap price in the interest rate market falls, then it plays as a signal to buy USD/KRW in the foreign exchange market. Considering that the price and movement of products traded in the bond market and interest rate market directly or indirectly affect the foreign exchange market, it may be regarded that there is a close and complementary relationship among the three markets. There have been studies that reveal the relationship and correlation between the bond market, interest rate market, and foreign exchange market, but many exchange rate prediction studies in the past have mainly focused on studies based on macroeconomic indicators such as GDP, current account surplus/deficit, and inflation while active research to predict the exchange rate of the foreign exchange market using artificial intelligence based on the bond market and interest rate market indicators has not been conducted yet. This study uses the bond market and interest rate market indicator, runs artificial neural network suitable for nonlinear data analysis, logistic regression suitable for linear data analysis, and decision tree suitable for nonlinear & linear data analysis, and proves that the artificial neural network is the most suitable methodology for predicting the foreign exchange rates which are nonlinear and times series data. Beyond revealing the simple correlation between the bond market, interest rate market, and foreign exchange market, capturing the trading signals between the three markets to reveal the active correlation and prove the mutual organic movement is not only to provide foreign exchange market traders with a new trading model but also to be expected to contribute to increasing the efficiency and the knowledge management of the entire financial market.

• 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.

• Korean Chemical Engineering Research
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• v.55 no.3
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• pp.322-331
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• 2017

District heating was first introduced in Korea in 1985. As the service life of the underground thermal piping network has increased for more than 30 years, the maintenance of the underground thermal pipe has become an important issue. A variety of complex technologies are required for periodic inspection and operation management for the maintenance of the aged thermal piping network. Especially, it is required to develop a model that can be used for decision making in order to derive optimal maintenance and replacement point from the economic viewpoint in the field. In this study, the analysis was carried out based on the repair history and accident data at the operation of the thermal pipe network of five districts in the Korea District Heating Corporation. A failure probability model was developed by introducing statistical techniques of qualitative analysis and binomial logistic regression analysis. As a result of qualitative analysis of maintenance history and accident data, the most important cause of pipeline damage was construction erosion, corrosion of pipe and bad material accounted for about 82%. In the statistical model analysis, by setting the separation point of the classification to 0.25, the accuracy of the thermal pipe breakage and non-breakage classification improved to 73.5%. In order to establish the failure probability model, the fitness of the model was verified through the Hosmer and Lemeshow test, the independent test of the independent variables, and the Chi-Square test of the model. According to the results of analysis of the risk of thermal pipe network damage, the highest probability of failure was analyzed as the thermal pipeline constructed by the F construction company in the reducer pipe of less than 250mm, which is more than 10 years on the Seoul area motorway in winter. The results of this study can be used to prioritize maintenance, preventive inspection, and replacement of thermal piping systems. In addition, it will be possible to reduce the frequency of thermal pipeline damage and to use it more aggressively to manage thermal piping network by establishing and coping with accident prevention plan in advance such as inspection and maintenance.

• Korean Journal of Construction Engineering and Management
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• v.11 no.6
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• pp.100-110
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• 2010

The main roots of CM service contracts include existing customer repurchases and those made by new customers by existing ones. The study on customers and loyalty can be factors to strengthen CM's competitiveness. However, there have been little attempt to study customer satisfaction and customer loyalty. Construction Management (CM), the advanced construction management method, was introduced 15 years ago in the mid 1990's in the domestic market. The aim of this research is to build a model that can predict customer loyalty based on how much customers are satisfied with CM service. To measure customer satisfaction and loyalty, this research surveyed 135 decision-makers who have experienced CM services. Customer satisfaction was tested and analyzed according to different phases: planning, designing, procurement, construction, and post construction. Referral intention was tested based on NPS theory. Customer types were divided into detractors, passively satisfied and promoters according to the tested measurement and multinomial logistic regression between the satisfaction by construction phases and customer types. This research resulted to a model that can predict customer types: detractors, passively satisfied and promoters, which were determined according to satisfaction level. The initial planning phase also revealed which factor is most influential for a customer to become promoter. These results can be used to acquire customer loyalty by managing the satisfaction of customers through a project under an Internet-based environment. Such can provide the needed information in quickly exploring positive and negative word-of-mouth feedbacks.

• Journal of Korean Society of Forest Science
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• v.86 no.1
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• pp.87-97
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• 1997

In this research, photo images of uncultivated marginal lands were simulated to visualize alternative land use patterns using image capture technology. Based on an original photos, 3 simulated images were created ; barren condition, aforested condition and shrub-covered condition. The simulated images were then used to evaluate respondents' visual preference(SBE value) and willingness to pay for the agricultural development tax as a hypothetical payment vehicle. The SBE values for barren condition are the lowest, as expected. When original condition is changed to forested or shrubbed, the SBE values are increased significantly. The logistic models for the willingness to pay for the various alternative land uses performed significantly, ${\rho}$ statistics for 6 models ranges from 0.3 to 0.4 and correct percentage for predicted probability are about 75%. Among independent variables, the amount of tax offered is the most influencing factor to predict the probability. Income also shows some relationship with no statistical significance. Other variables behave inconsistently in the model. When SBE and WTP are correlated, rather consistent trends can be observed. With the increase of SBE, WTP predicted by the model increases accordingly. It can be concluded that enhancement of scenic quality of the agricultural lands leads to increase of people's willingness to pay to support the rural environmental conservation.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2004.11a
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• pp.249-258
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• 2004

오늘날 활발하게 이루어지고 있는 유비쿼터스 컴퓨팅 관련 기술 연구는 사용자가 시간과 장소에 구애받지 않고 네트워크에 접근해 다양한 컴퓨터 관련 서비스를 제공 받을 수 있는 방법에 초점을 맞추고 있다. 이 처럼 시간과 공간의 한계를 뛰어 넘은 네트워크로의 자유로운 접근은 일상 생활의 패러다임을 바꾸어 놓게 될 것이다. 유비쿼터스 컴퓨팅 기술을 통해 가장 큰 변화가 일어나는 분야는 일반 가정환경에서 일어나는 인텔리전트 홈 네트워크 (Intelligent Home Network) 라고 할 수 있다. 집에 들어오면, 자동으로 문을 열어주고, 불을 켜주며, 놓쳤던 TV 프로그램을 자동으로 녹화해 놓았다가 원하는 시간에 보여주고, 적당한 시간에 목욕물을 미리 받아준다. 또한 집밖으로 나가기 전, 일기예보에 따라 우산을 챙겨주고, 일정을 확인시켜주며 입고 나갈 옷을 골라줄 수도 있다. 이 모든 일들이 유비쿼터스 컴퓨팅 기술이 가져올 인텔리전트 홈 네트워크의 모습이다. 그러나, 모든 사용자에게 효과적인 서비스를 제공하기 위해서는 홈 네트워크 상의 자원 관리에서 일어날 수 있는 에이전트들간의 자원 접근 권한 충돌을 효율적으로 방지할 수 있는 기술이 필요하다. 유비쿼터스 컴퓨팅 환경에서 자원관리 특성은 점유의 연속성, 자원 사이의 연관성, 그리고 자원과 사용자 사 사이의 연계성의 3 가지 특성을 지니고 있다. 본 논문에서는 유비쿼터스 컴퓨팅 환경에서 일어날 수 있는 자원 충돌 상황을 효율적으로 처리하기 위한 자원 협상 방법을 제안한다. 본 방법은 자원 관리 특성을 바탕으로 시간논리에 기반을 둔 자원 선점과 분배 규칙으로 구성된다.트 시스템은 b-Cart를 기반으로 할 것으로 예측할 수 있다.타났다. 또한, 스네이크의 초기 제어점을 얼굴은 44개, 눈은 16개, 입은 24개로 지정하여 MER추출에 성공한 영상에 대해 스네이크 알고리즘을 수행한 결과, 추출된 영역의 오차율은 각각 2.2%, 2.6%, 2.5%로 나타났다.해서 Template-based reasoning 예를 보인다 본 방법론은 검색노력을 줄이고, 검색에 있어 Feasibility와 Admissibility를 보장한다.매김할 수 있는 중요한 계기가 될 것이다.재무/비재무적 지표를 고려한 인공신경망기법의 예측적중률이 높은 것으로 나타났다. 즉, 로지스틱회귀 분석의 재무적 지표모형은 훈련, 시험용이 84.45%, 85.10%인 반면, 재무/비재무적 지표모형은 84.45%, 85.08%로서 거의 동일한 예측적중률을 가졌으나 인공신경망기법 분석에서는 재무적 지표모형이 92.23%, 85.10%인 반면, 재무/비재무적 지표모형에서는 91.12%, 88.06%로서 향상된 예측적중률을 나타내었다.ting LMS according to increasing the step-size parameter $\mu$ in the experimentally computed. learning curve. Also we find that convergence speed of proposed algorithm is increased by (B+1) time proportional to B which B is the number of recycled data b

• Journal of Digital Convergence
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• v.17 no.3
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• pp.107-117
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• 2019

In the era of SNS, many people relies on it to express their emotions about various kinds of products and services. Therefore, for the companies eagerly seeking to investigate how their products and services are perceived in the market, emotion mining tasks using dataset from SNSs become important much more than ever. Basically, emotion mining is a branch of sentiment analysis which is based on BOW (bag-of-words) and TF-IDF. However, there are few studies on the emotion mining which adopt feature selection (FS) methods to look for optimal set of features ensuring better results. In this sense, this study aims to propose FS methods to conduct emotion mining tasks more effectively with better outcomes. This study uses Twitter and SemEval2007 dataset for the sake of emotion mining experiments. We applied three FS methods such as CFS (Correlation based FS), IG (Information Gain), and ReliefF. Emotion mining results were obtained from applying the selected features to nine classifiers. When applying DT (decision tree) to Tweet dataset, accuracy increases with CFS, IG, and ReliefF methods. When applying LR (logistic regression) to SemEval2007 dataset, accuracy increases with ReliefF method.

• The Journal of the Korea Contents Association
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• v.19 no.5
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• pp.350-362
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• 2019

The purpose of this study was to examine the effects of job characteristics and health on accident experience by analyzing the data of transportation workers according to age. The analysis used data from 'the fourth Korean Working Conditions Survey(KWCS)'. A total of 1,997 transport workers data were finally analyzed, and correlation analysis, crossover analysis and logistic regression analysis were performed. It was confirmed that there was no correlation between the age of the transport workers and the accident experience. In the relationship between the characteristics of transportation workers and the experience of the accident, it was found that, in the case of older workers, there was a significant effect in the order of 'at mistake someone else hurt', 'musculoskeletal problem', 'cardiovascular problem' and 'repetitive movements of hands or arms', the model explaining power was 56.9%(p <.01). In the case of non-older workers, it was found that 'depression and anxiety disorder', 'relationship between job and safety', 'at mistake someone else hurt' and 'labor union', the model explaining power was 21.8%(p <.01). Therefore, in order to promote prevent accidents of transportation workers in future, it is necessary to consider various variables such as health and job characteristics besides age.

• Journal of Convergence for Information Technology
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• v.11 no.8
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• pp.42-52
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• 2021

This study was conducted to identify the influence of the discrimination experienced in daily life and social isolation on depression among older adults living in the community. This study was a secondary analysis of the data of 2017 Living Profiles of Older Adults Survey. The participants was a representative sample among the older adults 65 years and older. Data from 10,041 older adults were analyzed for this study. Hierarchical logistic regression analyses were used. When the discrimination experiences was added in model 1, the likelihood of being depressed was 1.95(1.60-2.36) times higher for those who experienced discrimination comparing with those who didn't experienced discrimination. When the social isolation was added in model 2, the likelihood of being depressed was 1.89(1.55-2.30) times higher in those who experienced discrimination. In addition, as the number of close friends, neighbors, and acquaintances decreased by one, the likelihood of being depressed increased by 1.14 times. Those who were isolated from family, friends, neighbors and acquaintances were 3.90 times more likely to be depressed. Therefore, social efforts are needed to reduce the experience of discrimination. Maintaining a social network or creating a new network will contribute to lowering the level of depression in older adults who have experienced discrimination.

• Information Systems Review
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• v.22 no.3
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• pp.143-156
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• 2020

Price promotion is one of the most frequently marketing strategies with a long history. According to various studies, the effect of price promotion is controversial. Some studies have argued that price promotion has a positive effect, while others have found that it has no effect or rather has a negative effect. This study aims to examine the effect of price promotion in a subscription-based service. First, we check the effect of price promotion on the repurchase of the consumer. And we investigate how this effect varies depending on the characteristics of the consumer. Using the data from one of the music streaming service in South Korea, the effect of consumers' price promotion experience, demographic characteristics, and behavioral characteristics on their repurchase is analyzed through logistic regression analysis. As a result of the study, it is found that consumers' experience of price promotion has a positive effect on repurchase. In addition, the positive effect of price promotion is relatively greater in younger and female consumers. This study has implications in that it not only confirmed the positive effect of price promotion in a subscription-based environment but also empirically confirmed that the characteristics of consumers should be considered when performing price promotion.