• KIPS Transactions on Software and Data Engineering
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• v.12 no.4
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• pp.173-178
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• 2023

As Korean literature spreads around the world, its position in the overseas publishing market has become important. As demand in the overseas publishing market continues to grow, it is essential to predict future book sales and analyze the characteristics of books that have been highly favored by overseas readers in the past. In this study, we proposed ensemble learning based prediction model and analyzed characteristics of the cumulative sales of more than 5,000 copies classified as good sellers published overseas over the past 5 years. We applied the five ensemble learning models, i.e., XGBoost, Gradient Boosting, Adaboost, LightGBM, and Random Forest, and compared them with other machine learning algorithms, i.e., Support Vector Machine, Logistic Regression, and Deep Learning. Our experimental results showed that the ensemble algorithm outperforms other approaches in troubleshooting imbalanced data. In particular, the LightGBM model obtained an AUC value of 99.86% which is the best prediction performance. Among the features used for prediction, the most important feature is the author's number of overseas publications, and the second important feature is publication in countries with the largest publication market size. The number of evaluation participants is also an important feature. In addition, text mining was performed on the four book reviews that sold the most among good-selling books. Many reviews were interested in stories, characters, and writers and it seems that support for translation is needed as many of the keywords of "translation" appear in low-rated reviews.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.3
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• pp.337-352
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• 2023

In general, Rainfall-Runoff model parameter set is optimized using the entire data to calculate unique parameter set. However, Korea has a large precipitation deviation according to the season, and it is expected to even worsen due to climate change. Therefore, the need for hydrological data considering seasonal characteristics. In this study, we conducted regional sensitivity analysis(RSA) using the conceptual Rainfall-Runoff model, GR4J aimed at the Soyanggang dam basin, and clustered combining the RSA results with hydrometeorological data using Self-Organizing map(SOM). In order to consider the climate characteristics in parameter estimation, the data was divided based on clustering, and a calibration approach of the Rainfall-Runoff model was developed by comparing the objective functions of the Global Optimization method. The performance of calibration was evaluated by statistical techniques. As a result, it was confirmed that the model performance during the Cold period(November~April) with a relatively low flow rate was improved. This is expected to improve the performance and predictability of the hydrological model for areas that have a large precipitation deviation such as Monsoon climate.

• Resources Recycling
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• v.32 no.2
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• pp.12-18
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• 2023

In 2018, the demand for silver (referred to as Ag) in the electrical and electronics sector was 249 million tons. The demand stood at 81 million tons in the solar module production sector. Currently, due to the rapid increase in solar module installation, the demand for silver is increasing drastically in Korea. However, Korea's natural metal resources and reserves are insufficient in comparison to their consumption, and the domestic silver ore self-sufficiency rate was as low as 2.2% as of 2021. This implies that a recycling technology is necessary to recover valuable metal resources contained in the waste plating solution generated in the metal industry. Therefore, this study compared and analyzed, the results of the impact evaluation through life cycle assessment according to an improvement in the process of recovery of valuable metals in the waste plating solution. The process improvement resulted in reducing GWP (Global Warming Potential) and ADP(Abiotic Depletion Potential) by 50% and 67%, respectively. The GWP of electricity and industrial water was reduced by 98% and 93%, respectively, which significantly contributed to the minimization of energy and water consumption. Thus, the improvement in recycling technology has a high potential to reduce chemical and energy use and improve resource productivity in the urban mining industry.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.285-301
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• 2023

Due to the increasing demand and importance of non-face-to-face education, open online learning platforms are getting interests both domestically and internationally. These platforms exhibit different characteristics from online courses by universities and other educational institutions. In particular, students engaged in these platforms can receive more learner autonomy, and the development of tools to assist learning is required. From the past, researchers have attempted to utilize process mining to understand realistic study behaviors and derive learning patterns. However, it has a deficiency to employ it to the open online learning platforms. Moreover, existing research has primarily focused on the process model perspective, including process model discovery, but lacks a method for the process pattern and instance perspectives. In this study, we propose a method to identify learning patterns within an open online learning platform using process mining techniques. To achieve this, we suggest three different viewpoints, e.g., model-level, variant-level, and instance-level, to comprehend the learning patterns, and various techniques are employed, such as process discovery, conformance checking, autoencoder-based clustering, and predictive approaches. To validate this method, we collected a learning log of machine learning-related courses on a domestic open education platform. The results unveiled a spaghetti-like process model that can be differentiated into a standard learning pattern and three abnormal patterns. Furthermore, as a result of deriving a pattern classification model, our model achieved a high accuracy of 0.86 when predicting the pattern of instances based on the initial 30% of the entire flow. This study contributes to systematically analyze learners' patterns using process mining.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.401-401
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• 2020

최근 전 세계적으로 기후변화로 인한 자연재해가 빈번하게 발생하고 있으며, 우리나라 역시 해마다 가뭄과 홍수 등의 피해가 큰 실정이다. 특히 가뭄으로 인한 피해는 농업분야와 직결되어 있으며, 미래식량과 물안보에 영향을 미친다. 최근에는 국내 물관리일원화 정책에 따른 통합물관리 시행으로 수요관리에 의한 물이용 효율성이 물관리 기본원칙으로 포함되어 있어, 농업용수 분야의 물절약 필요성과 중요성은 더욱 증대되고 있는 실정이다. 농업농촌부문 가뭄대응 종합대책의 일환으로 2016년부터 농업용수 이용자 측면에서 물절약 실천을 유도하기 위한 물절약 교육 모델의 개발과 농업인 대상 시범교육이 실시되고 있으나 일부 지역에만 단발성 사업으로 제한적으로 추진되고 있는 실정이다. 따라서 물절약 교육 및 홍보사업을 보다 체계적이고 광법위하게 적용하여 농업 현장에서의 가시적인 물절약 성과를 도출하기 위한 노력이 요구된다. 이에 대한 일환으로 본 연구에서는 물절약 교육 콘텐트 개발 및 현장 교육에 반영하기 위하여 농업인 대상 물절약 의식과 행동실천 여부에 대해 조사를 실시해 보았다. 포항 및 영덕지역의 한국농어촌공사 관할지구 내 농업용수 이용자 중 수리시설감시원(이하 '수감원') 100여명을 대상으로 설문조사로 파악해 보았다. 설문에 참여한 수감원들은 대부분 65세 이상의 고령으로 농업에 오랜 기간 종사한 경험의 소유자로서 소규모 농업경영이 주를 이루었다. 대부분 농사기간동안 물부족 경험이 있었으며, 모내기 및 벼생육기 강우조건에 따라 물부족을 경험한 것으로 파악되었다. 이로 인해 설문 참여자들의 물절약 필요성에 대해서는 높은 공감대를 나타내었으며, 특히 농업인 대상 물절약 교육의 필요성에 대해서 매우 높은 공감대를 나타내었다. 농업인의 물과다 사용 및 물꼬관리 부실 등 필지단위 물관리 부실에 대해서도 상당히 인정하는 편이었으며, 이러한 농업인의 관행적인 물관리 행태에 대해서 변화를 유도할 수 있는 수리계조직 부활을 통한 농업인 물관리 직접 참여 등의 대안이 필요하다는 의견에 대해서도 긍정적이었다. 또한 농업인 용수이용에 대한 비용 부담에 대해서도 다소 긍정적인 의견도 제시되었다. 본 연구 결과로 농업인의 적극적인 물관리 및 물절약 참여를 이끌어 낼 수 있는 실현가능한 관련 제도 마련의 필요성과 체계적이고 지속적인 물절약 교육 및 홍보 정책 추진의 필요성이 제기된다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.1D
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• pp.17-23
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• 2009

Due to the rapid increase of vehicles on the street, Korean society is facing worsening traffic congestions and air pollutions. Also, the oil price pickup has led to increasing need for the use of public transportation. In particular, transfering among public transportation may be a main factor for riders who are commuting for a long distance journey. In order to ensure such connectivity, transfer stations have been actively built in Korea. However, it would be necessary to shift those vehicles, from cars to public transportations by enhancing the users' satisfaction with public transportation through strategies for minimizing the users' waiting cost by building an efficient connective system between transportation modes as well as the preparation of aforementioned transfer stations. Therefore, this study aimed to develop an algorithm for minimizing transferring passengers' waiting costs based on service intervals of linked buses within the transfer facilities. In order to adjust the service interval, we calculated the total costs, involving the wait cost of transfer passengers and bus operation costs, and produced an allocation interval, that would minimize the costs. We selected a KTX departing from Seoul station, and a No. 6014 bus route in Gwangmyeong city where it starts from the Gwangmyeong station in order to for verifying the model. Then, the transfer passengers' total waitting cost was reduced equivalent to the maximum of 212 minutes, and it revealed that the model performed very effectively.

• The Journal of Bigdata
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• v.6 no.2
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• pp.71-84
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• 2021

Since small and medium-sized enterprises fell short of the securement of technological competitiveness in the field of big data and artificial intelligence (AI) field-core technologies of the Fourth Industrial Revolution, it is important to strengthen the competitiveness of the overall industry through technology commercialization. In this study, we aimed to propose a priority related to technology transfer and commercialization for practical use of public research results. We utilized public research performance information, improving missing values of 6T classification by deep learning model with an ensemble method. Then, we conducted topic modeling to derive the converging fields of big data and AI. We classified the technology fields into four different segments in the technology portfolio based on technology activity and technology efficiency, estimating the potential of technology commercialization for those fields. We proposed a priority of technology commercialization for 10 detailed technology fields that require long-term investment. Through systematic analysis, active utilization of technology, and efficient technology transfer and commercialization can be promoted.

• Asia Marketing Journal
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• v.11 no.3
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• pp.57-89
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• 2009

This study aims to shed light on the new insights on the cross-buying intentions in the banking industry and suggests an integrated model of the cross-buying intentions. Recently with globalization in the financial sector, financial companies are trying to retain current customers and attract new one by developing various financial products. In South Korea, this trend is especially apparent in the banking sector. Cross-selling of various financial products such as beneficiary certificates, bankasurance and etc. is becoming more important in retaining competitive advantage in Korean banking industry. However, there are few studies which are trying to find out the factors affecting cross-buying intentions and explain their interrelationships comprehensively. Based upon the previous studies, this study finds out the factors affecting cross-buying intentions and classifies them into two dimensions: affective and instrumental. Affective dimension includes trust, satisfaction and commitment. Instrumental dimension includes the factors such as geological convenience, one-stop convenience, professionality, and direct mail. The results from this study are as follow. All the factors in the affective dimension(trust, satisfaction and commitment) have significant impacts on cross-buying intentions. Also all the factors in the instrumental dimension(geological convenience, one-stop convenience, professionality, and DM) significantly affect cross-buying intentions. Some implications of this dissertation are as follow; First, this study identifies the antecedents of cross-buying intentions comprehensively. Second, this paper provides practical guidelines for the banks attempting to intensify cross-selling activities. Third, banks need to develop sophisticated plans which can consolidate the emotional ties with customers through positive service experiences as the affective dimension is important in influencing cross-buying intentions. Finally, regarding the instrumental dimesnion, the implications are: 1) Developing various new financial products in addition to traditional product such as deposits and installment savings for improving customer convenience, 2) Enhancing the professionality of employees by strengthening education programs on numbers of financial products, 3) Increasing cross-buying intentions through the DM.

• Information Systems Review
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• v.24 no.3
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• pp.43-68
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• 2022

With the growing demand for online food delivery (OFD) services via Online to Offline (O2O) platforms, it is required for academic researchers to identify the success factors of OFD businesses. In line with this, this research examines the impact of the core service attributes of a restaurant (hygiene, interactivity, trust,and popularity) on business success in the OFD platform context from the perspective of information asymmetry. Furthermore, the moderating effects of hygiene factor between the core service attributes and the success of restaurants are evaluated. We utilize 1,146 restaurants registered on the largest OFD platform in Korea. The results of this study demonstrate that hygiene (certification), trust (franchise), popularity (favorite) factors have positive impacts on the success of OFD businesses. Moreover, we find that franchise restaurants with high response rates to customer reviews and inquiries achieve higher sales when they have hygiene certifications than those without the certification do. The key findings bear significant contributions to prior literature by empirically substantiating the pivotal role of service quality signals in fostering restaurant success on the OFD platforms. In addition, this study provides business implications for restaurants in O2O platform.

• Knowledge Management Research
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• v.25 no.1
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• pp.21-41
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• 2024

The global Industry has been shaped by start-ups that originated with knowledge-based innovative strategies or technologies in the 21st century. Specifically, laboratory start-ups that rely on research papers or patents for new technology development are recognized for their high survival rate and the creation of employment opportunities. Our study concentrated on 'I-Corps', which also introduced in Korea, standing for innovation corps is a laboratory startup program launched in 2011 by the NSF(National Research Foundation) to commercialize R&D results and foster entrepreneurship as part of the policy to build a start-up system at the national innovation level. In this study, we proposed and empirically tested a research model focusing on teams participating in the I-Corps program to determine how startup team diversity, among the team characteristics of laboratory startups, affected patent performance. As a result of the analysis, among the proposed variables, age diversity, educational background diversity, and value diversity had a significant impact on patent performance. The results of this study are expected to further strengthen the theoretical and practical foundations of researchers or practitioners of the I-Corps program, as well as related areas involving technology & laboratory startups, intellectual property and knowledge management fields in the future.