• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.4
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• pp.418-427
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• 2017

In Korea, interest in basic research is growing, in order to ensure the sustainable competitiveness of the main industries and to support world-class technology that can create high added value in the future. However, companies are known to encounter various problems when attempting to market products based on the results of basic research, which is known to be due to their lack of experience of commercialization and related activities. Therefore, in this study, we tried to analyze the effect of the technology commercialization activity stemming from basic science and technology research on the business performance. The technology commercialization activities are divided into experience of commercialization, complete charge department, and consulting on technology commercialization and we developed an analytical model that (distinguishes between?) the technology innovation activities and technology innovation capabilities and analyzed their impact on the business performance. As a result, the importance of technology commercialization activities was confirmed by the fact that it had a positive effect on the business performance, while the technological innovation activity was found to positively affect the management performance, demonstrating that it plays a strategic role in companies. Finally, it was found that the technology innovation capacity partially influences the management performance and that it is necessary to establish a strategic research and development infrastructure.

• Korean Chemical Engineering Research
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• v.59 no.2
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• pp.191-199
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• 2021

A distillation column, which is a main facility of the chemical process, separates the desired product from a mixture by using the difference of boiling points. The distillation process requires the optimization and the prediction of operation because it consumes much energy. The target process of this study is difficult to operate efficiently because the composition of feed flow is not steady according to the supplier. To deal with this problem, we could develop a data-driven model to predict operating conditions. However, data preprocessing is essential to improve the predictive performance of the model because the raw data contains outlier and noise. In this study, after optimizing the predictive model based long-short term memory (LSTM) and Random forest (RF), we used a low-pass filter and one-class support vector machine for data preprocessing and compared predictive performance according to the method and range of the preprocessing. The performance of the predictive model and the effect of the preprocessing is compared by using R2 and RMSE. In the case of LSTM, R2 increased from 0.791 to 0.977 by 23.5%, and RMSE decreased from 0.132 to 0.029 by 78.0%. In the case of RF, R2 increased from 0.767 to 0.938 by 22.3%, and RMSE decreased from 0.140 to 0.050 by 64.3%.

• Genomics & Informatics
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• v.15 no.4
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• pp.156-161
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• 2017

Transcriptome analysis has been widely used to make biomarker panels to diagnose cancers. In breast cancer, the age of the patient has been known to be associated with clinical features. As clinical transcriptome data have accumulated significantly, we classified all human genes based on age-specific differential expression between normal and breast cancer cells using public data. We retrieved the values for gene expression levels in breast cancer and matched normal cells from The Cancer Genome Atlas. We divided genes into two classes by paired t test without considering age in the first classification. We carried out a secondary classification of genes for each class into eight groups, based on the patterns of the p-values, which were calculated for each of the three age groups we defined. Through this two-step classification, gene expression was eventually grouped into 16 classes. We showed that this classification method could be applied to establish a more accurate prediction model to diagnose breast cancer by comparing the performance of prediction models with different combinations of genes. We expect that our scheme of classification could be used for other types of cancer data.

• Journal of Information Processing Systems
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• v.17 no.5
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• pp.947-959
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• 2021

Aiming at the problem of low accuracy in the water boundary automatic extraction of islands from GF-2 remote sensing image with high resolution in three bands, new water edges automatic extraction method in island based on GF-2 remote sensing images, genetic algorithm (GA) method, is proposed in this paper. Firstly, the GA-OTSU threshold segmentation algorithm based on the combination of GA and the maximal inter-class variance method (OTSU) was used to segment the island in GF-2 remote sensing image after pre-processing. Then, the morphological closed operation was used to fill in the holes in the segmented binary image, and the boundary was extracted by the Sobel edge detection operator to obtain the water edge. The experimental results showed that the proposed method was better than the contrast methods in both the segmentation performance and the accuracy of water boundary extraction in island from GF-2 remote sensing images.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.11
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• pp.1720-1728
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• 2022

The industrial 4.0 era has been opened with the development of artificial intelligence technology, and the realization of smart farms incorporating ICT technology is receiving great attention in the livestock industry. Among them, the quality management technology of livestock products and livestock operations incorporating computer vision-based artificial intelligence technology represent key technologies. However, the insufficient number of livestock image data for artificial intelligence model training and the severely unbalanced ratio of labels for recognizing a specific defective state are major obstacles to the related research and technology development. To overcome these problems, in this study, combining oversampling and adversarial case generation techniques is proposed as a method necessary to effectively utilizing small data labels for successful defect detection. In addition, experiments comparing performance and time cost of the applicable techniques were conducted. Through experiments, we confirm the validity of the proposed methods and draw utilization strategies from the study results.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.29-45
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• 2015

Response modeling is a well-known research issue for those who have tried to get more superior performance in the capability of predicting the customers' response for the marketing promotion. The response model for customers would reduce the marketing cost by identifying prospective customers from very large customer database and predicting the purchasing intention of the selected customers while the promotion which is derived from an undifferentiated marketing strategy results in unnecessary cost. In addition, the big data environment has accelerated developing the response model with data mining techniques such as CBR, neural networks and support vector machines. And CBR is one of the most major tools in business because it is known as simple and robust to apply to the response model. However, CBR is an attractive data mining technique for data mining applications in business even though it hasn't shown high performance compared to other machine learning techniques. Thus many studies have tried to improve CBR and utilized in business data mining with the enhanced algorithms or the support of other techniques such as genetic algorithm, decision tree and AHP (Analytic Process Hierarchy). Ahn and Kim(2008) utilized logit, neural networks, CBR to predict that which customers would purchase the items promoted by marketing department and tried to optimized the number of k for k-nearest neighbor with genetic algorithm for the purpose of improving the performance of the integrated model. Hong and Park(2009) noted that the integrated approach with CBR for logit, neural networks, and Support Vector Machine (SVM) showed more improved prediction ability for response of customers to marketing promotion than each data mining models such as logit, neural networks, and SVM. This paper presented an approach to predict customers' response of marketing promotion with Case Based Reasoning. The proposed model was developed by applying different weights to each feature. We deployed logit model with a database including the promotion and the purchasing data of bath soap. After that, the coefficients were used to give different weights of CBR. We analyzed the performance of proposed weighted CBR based model compared to neural networks and pure CBR based model empirically and found that the proposed weighted CBR based model showed more superior performance than pure CBR model. Imbalanced data is a common problem to build data mining model to classify a class with real data such as bankruptcy prediction, intrusion detection, fraud detection, churn management, and response modeling. Imbalanced data means that the number of instance in one class is remarkably small or large compared to the number of instance in other classes. The classification model such as response modeling has a lot of trouble to recognize the pattern from data through learning because the model tends to ignore a small number of classes while classifying a large number of classes correctly. To resolve the problem caused from imbalanced data distribution, sampling method is one of the most representative approach. The sampling method could be categorized to under sampling and over sampling. However, CBR is not sensitive to data distribution because it doesn't learn from data unlike machine learning algorithm. In this study, we investigated the robustness of our proposed model while changing the ratio of response customers and nonresponse customers to the promotion program because the response customers for the suggested promotion is always a small part of nonresponse customers in the real world. We simulated the proposed model 100 times to validate the robustness with different ratio of response customers to response customers under the imbalanced data distribution. Finally, we found that our proposed CBR based model showed superior performance than compared models under the imbalanced data sets. Our study is expected to improve the performance of response model for the promotion program with CBR under imbalanced data distribution in the real world.

• Journal of the Korean Data and Information Science Society
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• v.23 no.1
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• pp.143-150
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• 2012

Based on the student evaluation of teaching from 'A' university where students are graded by relative performance, we conducted a research on the effect of course characteristics in student evaluation of teaching. The results show that the score of student evaluation of teaching seems to be higher for those classes with more proportion of male students, higher grades, and smaller class sizes. From these results, we suggest an evaluation model which can control the effect of grade and sex. Also we illustrate the performance of the evaluation model by using a case study.

• Research in Community and Public Health Nursing
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• v.10 no.2
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• pp.320-329
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• 1999

This study has been done for the purpose of testing the effect of Health promotion Education on the Health perception and Health behavior performance of Elementary school student's. The collection data has conducted from June 19, 1999 to August 24, 1999. The subjects for this study were sixth grade of 'ㅅ' elementary school. which is located in 'ㄱ' city a chosen one class experimental group(38) and as a control group(38). The study were designed as nonequivalent control group pretest. posttest. follow test design. In pretest, the general characteristics of two groups, health perception and Health performance were measured, the Experimental group was given health promotion Education for a one week after the posttest, and follow test was done Health performance. for eight week's summer vocation. The instrument used for this study were Health perception scale developed by Ware(l979) were modified by Lee(l984) and Health promoting behavior scale developed by Kim(l997) were modified by No Tae Su(l999). The data analysis was done using t-test, $x^2$ -test, ANOVA. and pearson correlation coefficient using SAS/PC program. The result of this study are summarized as follows: l) There is on difference between experimental group and control group 2) The hypothesis is factor's are supported 'The experimental group which was given health promotion education will shows higher health perception and health behavior performance than control group which given that' (meal habit F=6.40 P<.05. mental health F=8.02 P<.01) 3) In health behavior performance, scale the highest domain was mental health, personal hygiene, meal habit Exercise. The following suggestions are made based on the above results: 1) Replication of the research is needed to confirm effects of health perception and Health promotion education including the elementary school students. 2) Elementary school teachers should make an effort to develop of Health perception progress and carry about continue Health promotion education program for profit stage of growth and development for elementary school students.

• Journal of Engineering Education Research
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• v.18 no.5
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• pp.51-58
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• 2015

This research was conducted to assess the effects of an engineering education accreditation program devised by the University of Seoul on higher education outcomes by comparing and analyzing the evaluation results of engineering accredited students (31) and those who are not accredited (47) with the OECD AHELO (Assessment of Higher Education Learning Outcomes) in 2013. The AHELO assessment tool consisted of 25 multiple-choice questions which evaluated generic skill-learning outcomes, also using contextual surveys to establish the students' backgrounds. The results were evaluated statistically. In the results from the multiple-choice exam for generic skill learning outcomes, accredited students scored 1.35 points higher than non-accredited students. Secondly, according to the contextual survey related to students' university education experience, such as lectures, seminars, group projects, and online tutoring, it was found that accredited students were provided more activities in seminars and group projects. Moreover, for class activities, more of these were provided to accredited students, especially in the areas of assortment-structuralization and teamwork-based activities. Thirdly, according to the contextual survey results related to participation in class, specifically regarding asking questions and participating in discussions, interacting with the professor, and opportunities for study time, there were no recognizable differences between accredited and non-accredited students, However, while accredited students at least had opportunities to gain experience in most areas, there were some areas for which education resources were not provided to non-accredited students. Therefore, for the University of Seoul, our results imply that accredited students may show better performance in the areas of academic accomplishment and in their educational environment as compared to non-accredited students. These results demonstrate that the engineering education accreditation program positively contributes to employment competitiveness while also improving the necessary global standards of higher education outcomes.

• Journal of Korea Society of Industrial Information Systems
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• v.12 no.5
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• pp.173-182
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• 2007

e-Learning can be seen as not only one of Internet based information systems which can provide education services, but also one of teaching-learning methods which can implement self-directed learning. This paper tests factors affecting the e-Learning performance based on information systems success model and self-efficacy theory using a field study. Questionnaires are collected from 216 students who are enrolling a e-learning class using online survey. The results are following: first, the service quality and self-efficacy are significant predictors of use intention, but system quality and information quality are not. second, the system quality, information quality, service quality and self-efficacy are significant predictors of user satisfaction. Third, use intention and user satisfaction are found to be a strong predictor of the learning performance.