• The Journal of Society for e-Business Studies
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• v.11 no.3
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• pp.53-71
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• 2006

In order to cope with the rapidly changing product development environment, individual manufacturing enterprises are forced to collaborate with each other through establishing a virtual organization. In collaboration, designated organizations work together for mutual gain based on product data interoperability. However, product data interoperability is not fully facilitated due to semantic inconsistency among product data models of individual enterprises. In order to overcome the semantic inconsistency problem, this paper proposes a reference ontology, Reference Domain Ontology(RDO), and a methodology for product data interoperability with semantic consistency using RDO. RDO describes semantics of product data model and metamodel for all application domains in a virtual organization. Using RDO, application domains in a virtual organization can easily understand the product data models of others. RDO is agile and temporal such that it is created with the formation of a virtual organization, copes with changes of the organization, and disappears with the vanishment of the organization. RDO is built by a hybrid approach of top-down using a upper ontology and bottom-up based on the merging of ontologies of application domains in a virtual organization. With this methodology, every domain in a virtual organization can achieve product data model interoperability without model transformation.

• The Journal of Society for e-Business Studies
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• v.24 no.4
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• pp.119-133
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• 2019

Recently, the importance prediction of photovoltaic power (PV) is considered as an essential function for scheduling adjustments, deciding on storage size, and overall planning for stable operation of PV facility systems. In particular, since most of PV power is generated in peak time, PV power prediction in a peak time is required for the PV system operators that enable to maximize revenue and sustainable electricity quantity. Moreover, Prediction of the PV power output in peak time without meteorological information such as solar radiation, cloudiness, the temperature is considered a challenging problem because it has limitations that the PV power was predicted by using predicted uncertain meteorological information in a wide range of areas in previous studies. Therefore, this paper proposes the LSTM (Long-Short Term Memory) based the PV power prediction model only using the meteorological, seasonal, and the before the obtained PV power before peak time. In this paper, the experiment results based on the proposed model using the real-world data shows the superior performance, which showed a positive impact on improving the PV power in a peak time forecast performance targeted in this study.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.711-719
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• 2024

This study examined the policy of opening up the Chinese A-share market and its performance in four aspects: institutional investors system, cross-trading system with overseas stock markets, inclusion of A-shares into global indices, and establishment of a new board. Then, the impact of these policies on the Stock Index was empirically analyzed, and it was confirmed that institutional investors system such as QFII and RQFII, cross-trading system with overseas stock markets such as Shanghai-Hong Kong Stock Connect and Shenzhen-Hong Kong Stock Connect, inclusion of A-shares into global indices such as the MSCI EM index and FTSE Russell index, and the establishment of a new board of the Science Innovation Board all had statistically significant positive impacts on the stock index. Based on the results of these analysis, we conclude that China should further expand its stock market opening to the outside world, that mutual efforts are needed to alleviate political conflicts and improve understanding, and that easing industry regulations, including real estate, will help China's economic recovery and foreigners' investment in the A-share market.

• Journal of the Korean Operations Research and Management Science Society
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• v.40 no.3
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• pp.73-95
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• 2015

Firms have reexamined and restructured their supply chain based on a long-term and partnership perspective as a firm's competitive advantage increasingly relies on its supply chain capability. A number of scholar works has provided evidence to support the positive effects of supply chain collaboration; however, the relationship between collaboration and performance is still inconclusive. This study refuses to have blinded faith on supply chain collaboration, but rather this paper suggests that the contribution of collaboration to supply chain performance improvement can be limited and vary along the contextual characteristics of a buyer-supplier relationship. Moreover, we argue that the relationship between supply chain collaboration and performance can be curvilinear. This paper provides and test hypotheses regarding the relationship between supply chain collaboration and performance. By using data of the Manufacturing Panel Survey (MPS), this study empirically validates the hypotheses. Overall, the results of the study support our hypotheses about a limited contribution of supply chain collaboration to manufacturing performance, which is opposite to a conventional expectation. Particularly, the effects of supply chain collaboration differ depending on the dimensions of performance such as customer satisfaction, quality, cost, delivery, and flexibility as well as the dependency in the buyer-supplier relationship. Moreover, the results of the study indicate that supply chain collaboration and performance may have curvilinear relationships in a certain context. Through a comprehensive model and empirical evidence, this study presents a better understanding of supply chain collaboration and provokes an open discussion about the effects of collaboration. This study also provides insightful implications for managers of buyers as well as suppliers who wish to foster stronger supply chain performance via a deep buyer-supplier relationship and collaboration.

• Journal of Korean Society of Rural Planning
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• v.21 no.2
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• pp.91-102
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• 2015

한국은 2050년까지 주요 선진국 중에서 고령화가 가장 심각한 사회로 전환되게 될 것으로 예상된다. 기대여명의 증가와 저 출산은 고령화를 더욱 악화시키며, 이는 심각한 사회문제로 발전하게 될 것이다. 이와 같은 문제를 해결하기 위해 한국정부는 2008년에 도시지역에는 주택연금제도를 도입하였으며, 2011년에는 세계 최초로 농촌지역을 대상으로 농지연금제도를 도입하였다. 그렇지만 이와 같은 제도는 설계 당시부터 복지상품이라기 보다는 장기적으로 손실과 수익의 균형에 초점을 둔 금융상품으로 개발되어 실질적으로 노인들에게 크게 인기를 얻지 못하였다. 따라서 본 연구는 농지연금제도를 활성화시켜, 농촌노인들에게 보다 더 많은 혜택을 주기 위해 지역 토지시장을 감안하여 지역별 농지가격상승률을 예측하고 연금액을 산출하였다. 또, 지금까지 사용한 년 혹은 분기별 감정가 대신에 월별, 지역별 실거래 가격을 모형에 적용하여 지역토지시장, 고령화 수준 등 지역 여건에 부합하는 연금액을 산출하였다. 할인율자료도 가장 안정적인 3년 만기 국고채 수익률을 활용하여 미래농지가격을 예측하고, 이를 유동화하여 월 생활자금으로 지급되도록 하였다. 특히 농지규모가 가장 많고, 고령화 정도가 심각하여 농지연금의 잠재적 수요가 가장 높을 것으로 예상되는 경상북도와 전라남도를 사례지역으로 선정하고, 이를 전국평균과 비교하여 지역적인 차이도 함께 분석하였다. 이를 위해 농지가격 및 이자율 시계열 자료의 안정성을 검정하고, 장기농지가격을 예측하였다. 이를 활용하여 경북, 전남, 전국의 노인들의 월평균 지급액을 추정하였다. 분석결과 정책의 잠재적 수요가 가장 높은 두 지역이 가장 낮은 금액이 지급되는 것으로 추정되어 이는 또 다른 지역불균형을 초래할 수 있는 것으로 평가되었다.

• Journal of the Korean Society for information Management
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• v.20 no.1
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• pp.301-320
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• 2003

The rapidly increasing use of the Internet and advancement of the communication network, the explosive growth of digital contents from personal home pages to professional information service the emerging file exchange service and the development of hacking techniques . these are some of the trends contributing to the spread of illegal reproduction and distribution of digital contents, thus threatening the exclusive copyrights of the creative works that should be legally protected Accordingly, there is urgent need for a digital copyright management system designed to provide centralized management while playing the role of bridge between the copyright owners and users for smooth trading of the rights to digital contents, reliable billing, security measures, and monitoring of illegal use. Therefore, in this study, I examined the requirements of laws and systems for the introduction of the centralized management system to support smooth distribution of digital contents, and also researched on the current status of domestic and international centralized management system for copyrights. Furthermore, 1 tried to provide basic materials for the standardization of digital contents copyright management information through the examination of the essential elements of the centralized digital contents management such as the system for unique identification the standardization for data elements, and the digital rights management (DHM) .

• Journal of the Korea Society of Computer and Information
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• v.8 no.3
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• pp.113-123
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• 2003

Many researches on the application of neural networks for making financial index prediction have proven their advantages over statistical and other methods. In this paper, a neural network model is proposed for the Buying, Holding or Selling timing prediction in stocks by the price index of stocks by inputting the closing price and volume of dealing in stocks and the technical indexes(MACD, Psychological Line). This model has an equalized multi-layer arithmetic function as well as the time series prediction function of backpropagation neural network algorithm. In the case that the numbers of learning data are unbalanced among the three categories (Buying, Holding or Selling), the neural network with conventional method has the problem that it tries to improve only the prediction accuracy of the most dominant category. Therefore, this paper, after describing the structure, working and learning algorithm of the neural network, shows the equalized multi-layer arithmetic method controlling the numbers of learning data by using information about the importance of each category for improving prediction accuracy of other category. Experimental results show that the financial index prediction using the equalized multi-layer arithmetic neural network has much higher correctness rate than the other conventional models.

• Journal of Digital Convergence
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• v.19 no.6
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• pp.113-132
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• 2021

This study is a new evaluation using the Arrhenius equation, which is known as the chemical reaction rate estimation equation, to evaluate the intrinsic and extrinsic value elements of patents as a model. The performance of the evaluation model was superior to the SVM, Logistic reg. and ANN models that were used as patent evaluation models in prior studies. In addition, there was a strong correlation between the predicted lifespan of the patent and the actual lifespan of the patent. These evaluation models may be used for evaluation purposes only, or if an evaluation is required, including a commercialization entity or technical characteristics.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.1
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• pp.19-28
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• 2022

Recently, cryptocurrency has attracted much attention, and price prediction studies of cryptocurrency have been actively conducted. Especially, efforts to improve the prediction performance by applying the deep learning model are continuing. LSTM (Long Short-Term Memory) model, which shows high performance in time series data among deep learning models, is applied in various views. However, it shows low performance in cryptocurrency price data with high volatility. Although, to solve this problem, new input features were found and study was conducted using them, there is a lack of study on input features that drop predictive performance. Thus, in this paper, we collect the recent trends of six cryptocurrencies including Bitcoin and Ethereum and analyze effects of input features on the cryptocurrency price predictive performance through statistics and deep learning. The results of the experiment showed that cryptocurrency price predictive performance the best when open price, high price, low price, volume and price were combined except for rate of closing price fluctuation.

• The Korean Journal of Applied Statistics
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• v.36 no.1
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• pp.85-100
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• 2023

The stochastic volatility (SV) model is one of the main methods of modeling time-varying volatility. In particular, SV model is actively used in estimation and prediction of financial market volatility and option pricing. This paper attempts to model the time-varying volatility of the bitcoin market price using SV model. Hidden Markov model (HMM) is combined with the SV model to capture characteristics of regime switching of the market. The HMM is useful for recognizing patterns of time series to divide the regime of market volatility. This study estimated the volatility of bitcoin by using data from Upbit, a cryptocurrency trading site, and analyzed it by dividing the volatility regime of the market to improve the performance of the SV model. The MCMC technique is used to estimate the parameters of the SV model, and the performance of the model is verified through evaluation criteria such as MAPE and MSE.