EGR 장착 스파크 점화 LPG 엔진의 성능 및 배기특성

조윤호(Yoonho Cho),구준모(Junemo Koo),장진영(Jinyoung Jang),배충식(Choongsik Bae) 한국자동차공학회 2002 한국 자동차공학회논문집 Vol.10 No.1

가솔린엔진 대상 성능시험시의 노킹보정률을 사용한 엔진 수정토크의 편차개선

조윤호(Yoonho Cho),김용옥(Yongok Kim),이춘우(Chunwoo Lee),김우태(Wootai Kim) 한국자동차공학회 2006 한국자동차공학회 춘 추계 학술대회 논문집 Vol.- No.-

Recent trends of development in small size gasoline engines are both to have higher compression ratio for the purpose of improved fuel consumption and to advance spark timing up to DBL in a low to mid engine speed region for a good acceleration performance of vehicles. However, there occurs the deviation of corrected engine torque results during engine performance test on dynamometer because test conditions influence the onset of knock. Therefore, this research shows the test deviation of corrected engine torque decreases when knock correction rate is used.

청평-양평 지역에 분포하는 근청석 편마암의 백립암상 변성작용과 P-T 진화 경로

조윤호(Yoonho Cho),조문섭(Moonsup Cho),이승렬(Seung Ryeol Lee) 한국암석학회 1996 암석학회지 Vol.5 No.1

신상품 추천을 위한 사회연결망분석의 활용

조윤호(Yoonho Cho),방정혜(Jounghae Bang) 한국지능정보시스템학회 2009 지능정보연구 Vol.15 No.4

Collaborative Filtering is one of the most used recommender systems. However, basically it cannot be used to recommend new products to customers because it finds products only based on the purchasing history of each customer. In order to cope with this shortcoming, many researchers have proposed the hybrid recommender system, which is a combination of collaborative filtering and content?based filtering. Content?based filtering recommends the products whose attributes are similar to those of the products that the target customers prefer. However, the hybrid method is used only for the limited categories of products such as music and movie, which are the products whose attributes are easily extracted. Therefore it is essential to find a more effective approach to recommend to customers new products in any category. In this study, we propose a new recommendation method which applies centrality concept widely used to analyze the relational and structural characteristics in social network analysis. The new products are recommended to the customers who are highly likely to buy the products, based on the analysis of the relationships among products by using centrality. The recommendation process consists of following four steps; purchase similarity analysis, product network construction, centrality analysis, and new product recommendation. In order to evaluate the performance of this proposed method, sales data from H department store, one of the well?known department stores in Korea, is used.

사회연결망분석과 인공신경망을 이용한 추천시스템 성능 예측

조윤호(Yoonho Cho),김인환(Inhwan Kim) 한국지능정보시스템학회 2010 지능정보연구 Vol.16 No.4

협업필터링 추천은 다양한 분야에서 활용되고 있지만 트랜잭션 데이터의 성격에 따라 추천 성능에 현저한 차이를 보이고 있다. 기존 연구에서는 이러한 추천 성능의 차이가 나타나는 이유에 대한 설명을 구체적으로 제시하지 못하고 있고 이에 따라 추천 성능의 예측 또한 연구된 바가 없다. 본 연구는 사회네트워크분석과 인공신경망 모형을 이용하여 협업필터링 추천시스템의 성능을 예측하고자 한다. 본 연구의 목적을 달성하기 위해 국내 백화점의 트랜잭션 데이터를 기반으로 형성되는 고객간 사회 네트워크의 구조적 지표를 측정한 후 이를 기반으로 인공신경망 모형을 구축하고 검증한다. 본 연구는 협업필터링 추천 성능을 예측할 수 있는 새로운 모형을 제시하였다는 점에서 그 의의가 있으며 이를 통해 기업들의 협업필터링 추천시스템 도입에 대한 의사결정에 도움을 줄 수 있을 것으로 기대된다.

[가솔린엔진부문] EGR 장착 스파크 점화 LPG 엔진의 성능 및 배기특성

조윤호(Yoonho Cho),구준모(Junemo Koo),김정헌(Jeongheon Kim),김승규(Seunggyu Kim),배충식(Choongsik Bae),오승묵(Seungmook Oh),강건용(Kemyong Kang) 한국자동차공학회 2000 한국자동차공학회 춘 추계 학술대회 논문집 Vol.- No.-

EGR (Exhaust Gas Recirculation) system has been used 10 reduce NO, emissions, improve fuel economy, and decrease thermal loading of engine because it offers the benefits of charge dilution as is the case with a lean bum technique. It is currently used in conventional engines, especially light-duty gasoline and diesel engines for a variety of advantages, and in recent years, it has become as a means of reducing engine-out emissions for heavy-duty vehicles as a consequence of the development of its control schemes as well.<br/> However, the occurrence of excessive cyclic variation with high EGR rates, especially at high load conditions, brings about the undesirable combustion instability within the engine cylinder, which results in the deterioration of both engine performance and emissions. Therefore, in order to avoid the reduction of thermal efficiency and to improve fuel economy, the optimum EGR rate depending on operating conditions of engine, should be derived effectively.<br/> An experimental study was conducted to investigate the effects of EGR on performance and emission characteristics of a spark-ignition LPG fuelled engine, and the feasibility of an enhanced methodology, such as a cooled EGR system.<br/>

Applying Centrality Analysis to Solve the Cold-Start and Sparsity Problems in Collaborative Filtering

Yoonho Cho(조윤호),Jounghae Bang(방정혜) 한국지능정보시스템학회 2011 지능정보연구 Vol.17 No.3

Collaborative Filtering (CF) suffers from two major problems:sparsity and cold-start recommendation. This paper focuses on the cold-start problem for new customers with no purchase records and the sparsity problem for the customers with very few purchase records. For the purpose, we propose a method for the new customer recommendation by using a combined measure based on three well-used centrality measures to identify the customers who are most likely to become neighbors of the new customer. To alleviate the sparsity problem, we also propose a hybrid approach that applies our method to customers with very few purchase records and CF to the other customers with sufficient purchases. To evaluate the effectiveness of our method, we have conducted several experiments using a data set from a department store in Korea. The experiment results show that the combination of two measures makes better recommendations than not only a single measure but also the best-seller-based method and that the performance is improved when applying the hybrid approach.

가솔린엔진 대상 성능시험시의 노킹보정률을 사용한 엔진 수정토크의 편차개선

조윤호(Yoonho Cho),김우태(Wootai Kim),배충식(Choongsik Bae) 한국자동차공학회 2008 한국 자동차공학회논문집 Vol.16 No.4

경영분석지표와 의사결정나무기법을 이용한 유상증자 예측모형 개발

김명균(Myeong-Kyun Kim),조윤호(Yoonho Cho) 한국지능정보시스템학회 2012 지능정보연구 Vol.18 No.4

This study focuses on predicting which firms will increase capital by issuing new stocks in the near future. Many stakeholders, including banks, credit rating agencies and investors, performs a variety of analyses for firms’ growth, profitability, stability, activity, productivity, etc., and regularly report the firms’ financial analysis indices. In the paper, we develop predictive models for rights issues using these financial analysis indices and data mining techniques. This study approaches to building the predictive models from the perspective of two different analyses. The first is the analysis period. We divide the analysis period into before and after the IMF financial crisis, and examine whether there is the difference between the two periods. The second is the prediction time. In order to predict when firms increase capital by issuing new stocks, the prediction time is categorized as one year, two years and three years later. Therefore Total six prediction models are developed and analyzed. In this paper, we employ the decision tree technique to build the prediction models for rights issues. The decision tree is the most widely used prediction method which builds decision trees to label or categorize cases into a set of known classes. In contrast to neural networks, logistic regression and SVM, decision tree techniques are well suited for high-dimensional applications and have strong explanation capabilities. There are well-known decision tree induction algorithms such as CHAID, CART, QUEST, C5.0, etc. Among them, we use C5.0 algorithm which is the most recently developed algorithm and yields performance better than other algorithms. We obtained data for the rights issue and financial analysis from TS2000 of Korea Listed Companies Association. A record of financial analysis data is consisted of 89 variables which include 9 growth indices, 30 profitability indices, 23 stability indices, 6 activity indices and 8 productivity indices. For the model building and test, we used 10,925 financial analysis data of total 658 listed firms. PASW Modeler 13 was used to build C5.0 decision trees for the six prediction models. Total 84 variables among financial analysis data are selected as the input variables of each model, and the rights issue status (issued or not issued) is defined as the output variable. To develop prediction models using C5.0 node (Node Options: Output type = Rule set, Use boosting = false, Cross-validate = false, Mode = Simple, Favor = Generality), we used 60% of data for model building and 40% of data for model test. The results of experimental analysis show that the prediction accuracies of data after the IMF financial crisis (59.04% to 60.43%) are about 10 percent higher than ones before IMF financial crisis (68.78% to 71.41%). These results indicate that since the IMF financial crisis, the reliability of financial analysis indices has increased and the firm intention of rights issue has been more obvious. The experiment results also show that the stability-related indices have a major impact on conducting rights issue in the case of short-term prediction. On the other hand, the long-term prediction of conducting rights issue is affected by financial analysis indices on profitability, stability, activity and productivity. All the prediction models include the industry code as one of significant variables. This means that companies in different types of industries show their different types of patterns for rights issue. We conclude that it is desirable for stakeholders to take into account stability-related indices and more various financial analysis indices for short-term prediction and long-term prediction, respectively. The current study has several limitations. First, we need to compare the differences in accuracy by using different data mining techniques such as neural networks, logistic regression and SVM. Second, we are required to develop and to evaluate new prediction model

인구통계특성 기반 디지털 마케팅을 위한 클릭스트림 빅데이터 마이닝

박지애(Jiae Park),조윤호(Yoonho Cho) 한국지능정보시스템학회 2016 지능정보연구 Vol.22 No.3

The demographics of Internet users are the most basic and important sources for target marketing or personalized advertisements on the digital marketing channels which include email, mobile, and social media. However, it gradually has become difficult to collect the demographics of Internet users because their activities are anonymous in many cases. Although the marketing department is able to get the demographics using online or offline surveys, these approaches are very expensive, long processes, and likely to include false statements. Clickstream data is the recording an Internet user leaves behind while visiting websites. As the user clicks anywhere in the webpage, the activity is logged in semi-structured website log files. Such data allows us to see what pages users visited, how long they stayed there, how often they visited, when they usually visited, which site they prefer, what keywords they used to find the site, whether they purchased any, and so forth. For such a reason, some researchers tried to guess the demographics of Internet users by using their clickstream data. They derived various independent variables likely to be correlated to the demographics. The variables include search keyword, frequency and intensity for time, day and month, variety of websites visited, text information for web pages visited, etc. The demographic attributes to predict are also diverse according to the paper, and cover gender, age, job, location, income, education, marital status, presence of children. A variety of data mining methods, such as LSA, SVM, decision tree, neural network, logistic regression, and k-nearest neighbors, were used for prediction model building. However, this research has not yet identified which data mining method is appropriate to predict each demographic variable. Moreover, it is required to review independent variables studied so far and combine them as needed, and evaluate them for building the best prediction model. The objective of this study is to choose clickstream attributes mostly likely to be correlated to the demographics from the results of previous research, and then to identify which data mining method is fitting to predict each demographic attribute. Among the demographic attributes, this paper focus on predicting gender, age, marital status, residence, and job. And from the results of previous research, 64 clickstream attributes are applied to predict the demographic attributes. The overall process of predictive model building is compose of 4 steps. In the first step, we create user profiles which include 64 clickstream attributes and 5 demographic attributes. The second step performs the dimension reduction of clickstream variables to solve the curse of dimensionality and overfitting problem. We utilize three approaches which are based on decision tree, PCA, and cluster analysis. We build alternative predictive models for each demographic variable in the third step. SVM, neural network, and logistic regression are used for modeling. The last step evaluates the alternative models in view of model accuracy and selects the best model. For the experiments, we used clickstream data which represents 5 demographics and 16,962,705 online activities for 5,000 Internet users. IBM SPSS Modeler 17.0 was used for our prediction process, and the 5-fold cross validation was conducted to enhance the reliability of our experiments. As the experimental results, we can verify that there are a specific data mining method well-suited for each demographic variable. For example, age prediction is best performed when using the decision tree based dimension reduction and neural network whereas the prediction of gender and marital status is the most accurate by applying SVM without dimension reduction. We conclude that the online behaviors of the Internet users, captured from the clickstream data analysis, could be well used to predict their demographics, thereby being utilized to the digital marketing.