• Title/Summary/Keyword: Learning-by-Investment

Search Result 144, Processing Time 0.026 seconds

A Study on Determinants of e-Learning Acceptance Intention: Focused on Service Convenience (e-Learning 수용의도의 결정요인에 관한 연구:서비스 편의성을 중심으로)

  • Lee, Seong Ho
    • Journal of Information Technology Services
    • /
    • v.12 no.4
    • /
    • pp.59-75
    • /
    • 2013
  • As education environment is changing rapidly and competition of education industry is more intensive, the importance of service view about education is increasing as a differential competitive advantage. This study attempted to investigate the impact of service convenience as a different competitive advantage on e-learning acceptance by using TAM. The purpose of this study is to examine how five-dimensional service convenience constructs(decision convenience, access convenience, transaction convenience, benefit convenience, post-benefit convenience) affect consumers' perceived usefulness, attitude and usage intention. For this study, data were gathered from respondents who bought or used e-learning services and analyzed by structural equation model. Among the five-dimensional service convenience constructs, two constructs(benefit convenience, post-benefit convenience) affected consumers' positive perceived usefulness, attitude and usage intention about e-learning service. The results show that management and investment to improve benefit and post-benefit service convenience make consumers' positive attitude and usage intention about e-learning service.

Prediction of Electric Power on Distribution Line Using Machine Learning and Actual Data Considering Distribution Plan (배전계획을 고려한 실데이터 및 기계학습 기반의 배전선로 부하예측 기법에 대한 연구)

  • Kim, Junhyuk;Lee, Byung-Sung
    • KEPCO Journal on Electric Power and Energy
    • /
    • v.7 no.1
    • /
    • pp.171-177
    • /
    • 2021
  • In terms of distribution planning, accurate electric load prediction is one of the most important factors. The future load prediction has manually been performed by calculating the maximum electric load considering loads transfer/switching and multiplying it with the load increase rate. In here, the risk of human error is inherent and thus an automated maximum electric load forecasting system is required. Although there are many existing methods and techniques to predict future electric loads, such as regression analysis, many of them have limitations in reflecting the nonlinear characteristics of the electric load and the complexity due to Photovoltaics (PVs), Electric Vehicles (EVs), and etc. This study, therefore, proposes a method of predicting future electric loads on distribution lines by using Machine Learning (ML) method that can reflect the characteristics of these nonlinearities. In addition, predictive models were developed based on actual data collected at KEPCO's existing distribution lines and the adequacy of developed models was verified as well. Also, as the distribution planning has a direct bearing on the investment, and amount of investment has a direct bearing on the maximum electric load, various baseline such as maximum, lowest, median value that can assesses the adequacy and accuracy of proposed ML based electric load prediction methods were suggested.

Dynamic Asset Allocation by Applying Regime Detection Analysis (Regime 탐지 분석을 이용한 동적 자산 배분 기법)

  • Kim, Woo Chang
    • Journal of Korean Institute of Industrial Engineers
    • /
    • v.38 no.4
    • /
    • pp.258-261
    • /
    • 2012
  • In this paper, I propose a new asset allocation framework to cope with the dynamic nature of the financial market. The investment performance can be much improved by protecting the capital from the market crashes, and such crashes can be pre-identified with high probabilities by regime detection analysis via a specialized unsupervised machine learning technique.

Learning-to-export Effect as a Response to Export Opportunities: Micro-evidence from Korean Manufacturing

  • HAHN, CHIN HEE;CHOI, YONG-SEOK
    • KDI Journal of Economic Policy
    • /
    • v.43 no.4
    • /
    • pp.1-21
    • /
    • 2021
  • This paper aims to investigate whether there is empirical evidence supporting the learning-to-export hypothesis, which has received little attention in the literature. By taking full advantage of plant-product level data from Korea during 1990-1998, we find some evidence for the learning-to-export effect, especially for the innovated product varieties with delayed exporters: their productivity, together with research and development and investment activity, was superior to their matched sample. On the other hand, this learning-to-export effect was not significantly pronounced for industries protected by import tariffs. Thus, our empirical findings suggest that it would be desirable to implement certain policy tools to promote the learning-to-export effect, whereas tariff protection is not justifiable for that purpose.

An Empirical Investigation of the Impact of Customer Learning on Customer Experience in the Context of Knowledge Product Use

  • KIM, Yong Jin;YIM, Myung-Seong
    • The Journal of Asian Finance, Economics and Business
    • /
    • v.7 no.12
    • /
    • pp.969-976
    • /
    • 2020
  • The role of customers has changed from that of passive users to value co-creators. Therefore, it is important to understand how customer learning takes place and how it affects customer experiences with services and products. However, while past studies insist on the importance of the issues in designing customer experiences, they do not empirically address these issues. This study investigates the support processes for customer learning, and their impact on customer learning, which in turn influences customer experience. To test the hypotheses, we employed the survey method. Target informants were the actual users of Apple iPods. A total of 200 survey questionnaires were distributed and 146 were collected. Among these, seven erroneous responses were excluded, leaving 139 usable ones. The proposed model was empirically analyzed using the Covariance-based SEM (Structural Equation Modelling) technique. The findings of this study suggest that, among the three support processes in customer learning, learning-by-doing support and learning-by-investment support positively affect customer learning, which influences customer experience. This study contributes to the literature by identifying different types of support for different kinds of customer learning processes and by empirically testing the impact of the support for the process on customer learning, and in turn, its impact on customer experience.

Motives, Strategies and Patterns of Foreign Direct Investment : The Case of Japanese and Korean Firms

  • Park, Kang-H.;Lim, Yong-Taek
    • International Commerce and Information Review
    • /
    • v.7 no.4
    • /
    • pp.387-407
    • /
    • 2005
  • This paper is to study globalization motives and strategies of Japanese and Korean industries by analyzing the causes and patterns of foreign direct investment (FDI) of the firms of the two countries during the 1980s and 1990s. First we develop a FDI function from the profit maximizing model of firms. Then we use regression analysis to determine internally driving-out factors and externally-inducing factors. Japanese FDI strategy has gone through three different stages; from natural resource-seeking investment in the 1950s and 1960s to market-expansion investment in the 1970s and 1980s and to a combination of cost-reducing (low-cost labor-seeking) investment and market-penetrating investment in the 1990s. On the other hand, Korean FDI behavior has gone through four different stages; from the learning stage with small investments in the 1970s, to natural resource-seeking investment in the early and mid 1980s, to the growth stage in the late 1980s and the early 1990s, to the maturity stage of the mid and late 1990s. The last two stages were characterized by a combination of cost-reducing investment and market-seeking investment. As a late comer, Korea began its FDI two decades later than Japan, but caught up the patterns of Japanese FDI by the mid 1990s and is in a competing position with Japan. Our findings show that both Japanese FDI and Korean FDI in Asia and other developing countries tendto be in labor-intensive sectors where their firms are losing their comparative advantages at home. The main motive for FDI into these regions is low-cost resource seeking. On the other hand, both Japanese FDI and Korean FDI in the U.S. and Europe tend to be knowledge-intensive sectors where Japanese and Korean firms attempt to internalize transaction and information costs by globalizing its production. The main motive for FDI into these regions is market-seeking. Firms in both countries have increased their investments in Mexico and Western and Eastern Europe in order to penetrate large economic blocs such as the EU and NAFTA area. Korean firms are more aggressive in expanding into new and untested markets than are their counterpart in Japan. Evidence of this can be seen in the scarcity of Japanese FDI and abundance of Korean FDI in Eastern Europe and China.

  • PDF

Development of Comparative Verification System for Reliability Evaluation of Distribution Line Load Prediction Model (배전 선로 부하예측 모델의 신뢰성 평가를 위한 비교 검증 시스템)

  • Lee, Haesung;Lee, Byung-Sung;Moon, Sang-Keun;Kim, Junhyuk;Lee, Hyeseon
    • KEPCO Journal on Electric Power and Energy
    • /
    • v.7 no.1
    • /
    • pp.115-123
    • /
    • 2021
  • Through machine learning-based load prediction, it is possible to prevent excessive power generation or unnecessary economic investment by estimating the appropriate amount of facility investment in consideration of the load that will increase in the future or providing basic data for policy establishment to distribute the maximum load. However, in order to secure the reliability of the developed load prediction model in the field, the performance comparison verification between the distribution line load prediction models must be preceded, but a comparative performance verification system between the distribution line load prediction models has not yet been established. As a result, it is not possible to accurately determine the performance excellence of the load prediction model because it is not possible to easily determine the likelihood between the load prediction models. In this paper, we developed a reliability verification system for load prediction models including a method of comparing and verifying the performance reliability between machine learning-based load prediction models that were not previously considered, verification process, and verification result visualization methods. Through the developed load prediction model reliability verification system, the objectivity of the load prediction model performance verification can be improved, and the field application utilization of an excellent load prediction model can be increased.

Current Status of Automatic Fish Measurement (어류의 외부형질 측정 자동화 개발 현황)

  • Yi, Myunggi
    • Korean Journal of Fisheries and Aquatic Sciences
    • /
    • v.55 no.5
    • /
    • pp.638-644
    • /
    • 2022
  • The measurement of morphological features is essential in aquaculture, fish industry and the management of fishery resources. The measurement of fish requires a large investment of manpower and time. To save time and labor for fish measurement, automated and reliable measurement methods have been developed. Automation was achieved by applying computer vision and machine learning techniques. Recently, machine learning methods based on deep learning have been used for most automatic fish measurement studies. Here, we review the current status of automatic fish measurement with traditional computer vision methods and deep learning-based methods.

Black-Litterman Portfolio with K-shape Clustering (K-shape 군집화 기반 블랙-리터만 포트폴리오 구성)

  • Yeji Kim;Poongjin Cho
    • Journal of Korean Society of Industrial and Systems Engineering
    • /
    • v.46 no.4
    • /
    • pp.63-73
    • /
    • 2023
  • This study explores modern portfolio theory by integrating the Black-Litterman portfolio with time-series clustering, specificially emphasizing K-shape clustering methodology. K-shape clustering enables grouping time-series data effectively, enhancing the ability to plan and manage investments in stock markets when combined with the Black-Litterman portfolio. Based on the patterns of stock markets, the objective is to understand the relationship between past market data and planning future investment strategies through backtesting. Additionally, by examining diverse learning and investment periods, it is identified optimal strategies to boost portfolio returns while efficiently managing associated risks. For comparative analysis, traditional Markowitz portfolio is also assessed in conjunction with clustering techniques utilizing K-Means and K-Means with Dynamic Time Warping. It is suggested that the combination of K-shape and the Black-Litterman model significantly enhances portfolio optimization in the stock market, providing valuable insights for making stable portfolio investment decisions. The achieved sharpe ratio of 0.722 indicates a significantly higher performance when compared to other benchmarks, underlining the effectiveness of the K-shape and Black-Litterman integration in portfolio optimization.

A System for Determining the Growth Stage of Fruit Tree Using a Deep Learning-Based Object Detection Model (딥러닝 기반의 객체 탐지 모델을 활용한 과수 생육 단계 판별 시스템)

  • Bang, Ji-Hyeon;Park, Jun;Park, Sung-Wook;Kim, Jun-Yung;Jung, Se-Hoon;Sim, Chun-Bo
    • Smart Media Journal
    • /
    • v.11 no.4
    • /
    • pp.9-18
    • /
    • 2022
  • Recently, research and system using AI is rapidly increasing in various fields. Smart farm using artificial intelligence and information communication technology is also being studied in agriculture. In addition, data-based precision agriculture is being commercialized by convergence various advanced technology such as autonomous driving, satellites, and big data. In Korea, the number of commercialization cases of facility agriculture among smart agriculture is increasing. However, research and investment are being biased in the field of facility agriculture. The gap between research and investment in facility agriculture and open-air agriculture continues to increase. The fields of fruit trees and plant factories have low research and investment. There is a problem that the big data collection and utilization system is insufficient. In this paper, we are proposed the system for determining the fruit tree growth stage using a deep learning-based object detection model. The system was proposed as a hybrid app for use in agricultural sites. In addition, we are implemented an object detection function for the fruit tree growth stage determine.