• International journal of advanced smart convergence
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• v.12 no.4
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• pp.202-207
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• 2023

In this paper, we discuss the design and implementation of a recommendation platform actually built in the field. We survey deep learning-based recommendation models that are effective in reflecting individual user characteristics. The recently proposed RNN-based sequential recommendation models reflect individual user characteristics well. The recommendation platform we proposed has an architecture that can collect, store, and process big data from a company's commercial services. Our recommendation platform provides service providers with intuitive tools to evaluate and apply timely optimized recommendation models. In the model evaluation we performed, RNN-based sequential recommendation models showed high scores.

• Annual Conference on Human and Language Technology
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• 2017.10a
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• pp.67-70
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• 2017

상호(商號)란 상인이나 회사가 영업 활동을 위해 자기를 표시하는데 쓰는 명칭을 말한다. 일반적으로 국내 기업의 상호 표기법은 한글과 로마자를 혼용함으로 상호 검색 시스템에서 단어 불일치 문제를 발생시킨다. 본 연구에서는 이러한 단어 불일치 문제를 해결하기 위해 Sequence-to-sequence 모델을 이용하여 로마자 상호를 이에 대응하는 한글 상호로 변환하고 그 후보들을 생성하는 시스템을 제안한다. 실험 결과 본 연구에서 구축한 시스템은 57.82%의 단어 정확도, 90.73%의 자소 정확도를 보였다.

• 한국어정보학회:학술대회논문집
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• 2017.10a
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• pp.67-70
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• 2017

상호(商號)란 상인이나 회사가 영업 활동을 위해 자기를 표시하는데 쓰는 명칭을 말한다. 일반적으로 국내 기업의 상호 표기법은 한글과 로마자를 혼용함으로 상호 검색 시스템에서 단어 불일치 문제를 발생시킨다. 본 연구에서는 이러한 단어 불일치 문제를 해결하기 위해 Sequence-to-sequence 모델을 이용하여 로마자 상호를 이에 대응하는 한글 상호로 변환하고 그 후보들을 생성하는 시스템을 제안한다. 실험 결과 본 연구에서 구축한 시스템은 57.82%의 단어 정확도, 90.73%의 자소 정확도를 보였다.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.29 no.2
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• pp.84-93
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• 2021

Airline schedule is the most basic data for flight operations and has significant importance to an airline's management. It is crucial to know the airline's current schedule status in order to effectively manage the company and to be prepared for abnormal situations. In this study, machine learning techniques were applied to actual schedule data to examine the possibility of whether the airline's fleet state could be artificially learned without prior information. Given that the schedule is in categorical form, One Hot Encoding was applied and t-SNE was used to reduce the dimension of the data and visualize them to gain insights into the airline's overall fleet status. Interesting results were discovered from the experiments where the initial findings are expected to contribute to the fields of airline schedule health monitoring, anomaly detection, and disruption management.

• Annual Conference on Human and Language Technology
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• 2019.10a
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• pp.236-238
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• 2019

신경망 기계 번역(Neural Machine Translation)은 주로 지도 학습(Supervised learning)을 이용한 End-to-end 방식의 연구가 이루어지고 있다. 그러나 지도 학습 방법은 데이터가 부족한 경우에는 낮은 성능을 보이기 때문에 BERT와 같은 대량의 단일 언어 데이터로 사전학습(Pre-training)을 한 후에 미세조정(Finetuning)을 하는 Transfer learning 방법이 자연어 처리 분야에서 주로 연구되고 있다. 최근에 발표된 MASS 모델은 언어 생성 작업을 위한 사전학습 방법을 통해 기계 번역과 문서 요약에서 높은 성능을 보였다. 본 논문에서는 영어-한국어 기계 번역 성능 향상을 위해 MASS 모델을 신경망 기계 번역에 적용하였다. 실험 결과 MASS 모델을 이용한 영어-한국어 기계 번역 모델의 성능이 기존 모델들보다 좋은 성능을 보였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2809-2821
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• 2023

Effective recommendation of similar business groups is a critical factor in obtaining market information for companies. In this study, we propose a novel method for enhancing similar business group recommendation by incorporating derivative criteria and web crawling. We use employment announcements, employment incentives, and corporate vocational training information to derive additional criteria for similar business group selection. Web crawling is employed to collect data related to the derived criteria from 'credit jobs' and 'worknet' sites. We compare the efficiency of different datasets and machine learning methods, including XGBoost, LGBM, Adaboost, Linear Regression, K-NN, and SVM. The proposed model extracts derivatives that reflect the financial and scale characteristics of the company, which are then incorporated into a new set of recommendation criteria. Similar business groups are selected using a Euclidean distance-based model. Our experimental results show that the proposed method improves the accuracy of similar business group recommendation. Overall, this study demonstrates the potential of incorporating derivative criteria and web crawling to enhance similar business group recommendation and obtain market information more efficiently.

• Journal of Convergence for Information Technology
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• v.11 no.1
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• pp.28-33
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• 2021

Since the stock price is a measure of the future value of the company, when analyzing the stock price, the company's growth potential, such as sales and profits, is considered and invested in stocks. In order to set the criteria for selecting stocks, institutional investors look at current industry trends and macroeconomic indicators, first select relevant fields that can grow, then select related companies, analyze them, set a target price, then buy, and sell when the target price is reached. Stock trading is carried out in the same way. However, general individual investors do not have any knowledge of investment, and invest in items recommended by experts or acquaintances without analysis of financial statements or growth potential of the company, which is lower in terms of return than institutional investors and foreign investors. Therefore, in this study, we propose a research method to select undervalued stocks by analyzing ROE, an indicator that considers the growth potential of a company, such as sales and profits, and predict the stock price flow of the selected stock through deep learning algorithms. This study is conducted to help with investment.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.468-478
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• 2018

The goal of this study is to propose a new directivity for business training based on an analysis of the learner's satisfaction, the cause of the learning effect and the cause of reenrollment in smart learning courses. The data from 878 learners of 11 companies was analyzed by ANOVA and multiple regression analysis and the following results were obtained. First of all, the satisfaction of studying by smart learning showed various results depending on the motivation, process and contents of studying. According to the results, high rates of satisfaction were observed when the people take an active part in studying, as reflected in the frequency and time of studying. Also, when the learning contents were presented in an animated manner, the satisfaction of the students was increased. Second, the motivation of the students to participate in the smart learning and study process was reflected in the frequency, time and quality of their studies, thus confirming the learning effect. Lastly, the satisfaction and effectiveness of studying by smart learning are the causes of reenrollment. Based on the analysis results, it was concluded that the corporation's support and proper compensation are needed to increase the rate of satisfaction and the effectiveness of smart learning from the corporation's perspective. Also, from the viewpoint of the smart learning system operators, it is necessary to find ways of developing the learning contents.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.9 no.6
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• pp.117-128
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• 2014

This is the case study of SATREC INITIATIVE company which is the unique domestic production of commercial satellites. We examined the path and pattern for accumulation of technological capability and technology learning process. This case study show that the process of technological innovation and their influencing factors. First, the technological learning of the satellite industry follows the stage of technological acquisition, absorption, improvement and is embodied by the technological capability. Second, accumulated technological capability of the satellite industry influences the technology innovation. Third, the top management team(TMT) affects the technological learning and technological capability. Fourth, TMT has a moderating role between the technological capability and the performance of technological innovation. Finally, technological innovations in the small and venture business would be the source of technological capability and technological learning. The implications of this study are as follows. TMT has the very important role for the technological innovation and affect the technology development and the production. Also technology-based companies must gain a competitiveness advantage through technological learning and technological innovations for sustainable growth.

• Journal of the Korean association of regional geographers
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• v.8 no.4
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• pp.529-541
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• 2002

This paper aims to provide a context-specific interpretation on the shifting process of in-house R&D spaces in a large Korean firm in the context of rapidly changing markets and technology. Drawing on the case study of LG Electronics Company, one of the Korea's flagship companies, I examine the causes and mechanisms leading to a shift in domestic R&D spaces and the nature of learning processes between R&D teams and between R&D and other organizational units, particularly manufacturing. It appears that the current reshaping processes of domestic R&D spaces in LGE focus more on the clustering of core R&D laboratories than the geographical integration of conception and execution. However, it should not simply be viewed that such a move would be reduced to the linear model of innovation and organizational learning. Instead, it involves the firm-specific mode of regulating organizational competences. As contextual variables to induce such a firm-specific mode of organizational change, I consider the spatial form of organization, the spatial sources of knowledge and learning, and the powers of relational learning that can be made between distanciated actors and teams.