• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.5
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• pp.574-583
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• 2021

Predicting shipping markets is an important issue. Such predictions form the basis for decisions on investment methods, fleet formation methods, freight rates, etc., which greatly affect the profits and survival of a company. To this end, in this study, we propose a shipping freight rate prediction model for container ships using gated recurrent units (GRUs) and long short-term memory structure. The target of our freight rate prediction is the China Container Freight Index (CCFI), and CCFI data from March 2003 to May 2020 were used for training. The CCFI after June 2020 was first predicted according to each model and then compared and analyzed with the actual CCFI. For the experimental model, a total of six models were designed according to the hyperparameter settings. Additionally, the ARIMA model was included in the experiment for performance comparison with the traditional analysis method. The optimal model was selected based on two evaluation methods. The first evaluation method selects the model with the smallest average value of the root mean square error (RMSE) obtained by repeating each model 10 times. The second method selects the model with the lowest RMSE in all experiments. The experimental results revealed not only the improved accuracy of the deep learning model compared to the traditional time series prediction model, ARIMA, but also the contribution in enhancing the risk management ability of freight fluctuations through deep learning models. On the contrary, in the event of sudden changes in freight owing to the effects of external factors such as the Covid-19 pandemic, the accuracy of the forecasting model reduced. The GRU1 model recorded the lowest RMSE (69.55, 49.35) in both evaluation methods, and it was selected as the optimal model.

• The Korean Journal of Archival Studies
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• no.29
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• pp.225-268
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• 2011

The general public are interested in the politics and form public opinion and keep in check the government for true democracy. The general public have the right to be furnished information from the government. And the government should enact the Freedom of Information Act to provide the public's right to know. At the same time, the government should enact the Data Protection Act to provide the public's right to privacy. There is a friction between the Freedom of Information Act and the Data Protection Act. It's hard to maintain the proper balance between the Freedom of information Act and the Data Protection Act, but many countries try to do so. The UK enacted the Data Protection Act 1998(DPA), which entered into force on 2000, to comply with EU Directive 1995. The Freedom of Information Act 2000(FOI), which came fully into force on 2005, was passed in 2000. The FOI imposes significant duties and responsibilities on public authorities to give access to the information they hold. The purpose of this study is to consider the provisions of the personal data in FOI and DPA. Besides this, it identifies the complaint cases on public authorities about the disclosure and exemption of the personal data in comparison with the acts. If information is the personal data of the person making the request, it will disclose under the DPA. If information is the personal data of a third party, it will disclose under the FOI. These acts interact each other to make up for the weak points in the other to make a proper application of the act on public authorities. This study may have any limitation in making a comparative study of the disclosure and exemption of the personal data in Korea. But it is expected to provide a basis for understanding the disclosure and exemption of the personal data in the UK.

• Korean Journal of Construction Engineering and Management
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• v.19 no.2
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• pp.107-116
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• 2018

Since 2010, Korea's overseas construction orders have seen dramatic fluctuations. I propose causes and remedies for the industry as a whole. Orders have recorded an annual average of $63.8 billion dollars from 2011 to 2014, reaching its highest at $71.6 billion dollars(2010) which marked the peak of Korea's overseas construction. However, due to a decline in international oil prices, starting in the last half of 2014, Korea's overseas construction orders have followed suit recording $46.1 billion dollar in 2014, $28.2 billion dollars in 2016, and $29.0 billion dollars in 2017. Facing uncertainty in Korea's overseas construction market, caused by continued slow growth of the global economy, Korean EPC contractors are at a critical point in regards to their award-winning capabilities. Together with declining oil prices, the challenges have never been bigger. To mitigate the challenges, I would suggest policy direction as a way to grow and develop the overseas construction industry. Proper counterplans are needed to foster Korea's overseas construction industry. Forecasting total order amount for overseas construction projects is essencial. Analyzing contract award & tender structure and its changing trends in both overseas and world construction markets should also be included. Korea has great potential and global competitiveness. These measures will serve to enhance Korea's overall export strategy in uncertain overseas markets and global economy.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.4
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• pp.456-462
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• 2011

As the ARRA enacted recently in the United States, the interest in EHR systems have been increased in the field of medical industry. The passage of the ARRA presents a program that provides incentives to office-based physicians and hospitals adapting the EHR systems to guarantee interoperability with various medical standards. Thanks to the incentive program, a great number of EHR systems have been developed and lots of office-based physicians and hospitals have adapted the EHR systems certified by CCHIT. Keeping pace with the rapid changes in the market of healthcare, some enterprises try to push in to the United States healthcare market based on the experience acquired by developing EHR systems for hospitals in Korea. However, the developed system must be customized because of the different medical environment between Korea and the United States. In this paper, therefore, we design and develop an integrated EHR platform to guarantee the interoperability between different medical information systems based on medical standard technologies. In the developed platform, an integrated system has been composed by integrating various basic techniques such as data transmission standards and its methods, medical standard terminologies and its usage, and knowledge management for medical decision-making support. Moreover, medical data can be processed electronically by adapting an HL7 interface engine and the terminologies for exchanging medical information and the standardization of medical information. We develop SeniCare, an EHR system for supporting ambulatory care of the office-based physicians, based on the platform, and we verify the usability of the platform by confirming whether SeniCare satisfies the criteria of "meaningful use" issued by CMS or not.

• Journal of Convergence for Information Technology
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• v.11 no.2
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• pp.31-40
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• 2021

Blockchain technology is also actively studied in the real estate transaction field, and real estate transactions have various ways. In this paper, we propose a model that simplifies the authentication procedure of auction systems using Ethereum's Ether to solve the problem of offline court auctions. The proposed model is written in Ethereum's Solidity language, the court registers the sale date and the sale date with the DApp browser, and the bidder accesses the address of the individual's wallet created through Metamask's private key. The bidder then selects the desired sale and enters the bid price amount to participate in the auction. The bidder's record of the highest bid price for the sale he wants is written on the Ethereum test network as a smart contract. and creates a block. Finally, smart contracts written on the network are distributed by the court auction manager to all nodes in the blockchain network, and each node in the blockchain network can be viewed and contract verified. As a result of analyzing the smart contracts of the proposed model and the performance of the system, there are fees incurred due to the creation and use of Ether on platforms using Ethereum, and participation. Ether's changes in value affect the price of the sale, resulting in inconsistent fees in smart contracts each time. However, in future work, we issue our own tokens to solve the market volatility problem and commission problem with the value change of Ether, and refine complex court auction systems.

• Advanced Industrial SCIence
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• v.2 no.3
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• pp.22-28
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• 2023

In this study, we conducted research on the automated trading of U.S. stocks through a volatility strategy using the Fear and Greed index. Volatility in the stock market is a common phenomenon that can lead to fluctuations in stock prices. Investors can capitalize on this volatility by implementing a strategy based on it, involving the buying and selling of stocks based on their expected level of volatility. The goal of this thesis is to investigate the effectiveness of the volatility strategy in generating profits in the stock market.This study employs a quantitative research methodology using secondary data from the stock market. The dataset comprises daily stock prices and daily volatility measures for the S&P 500 index stocks. Over a five-year period spanning from 2016 to 2020, the stocks were listed on the New York Stock Exchange (NYSE). The strategy involves purchasing stocks from the low volatility group and selling stocks from the high volatility group. The results indicate that the volatility strategy yields positive returns, with an average annual return of 9.2%, compared to the benchmark return of 7.5% for the sample period. Furthermore, the findings demonstrate that the strategy outperforms the benchmark return in four out of the five years within the sample period. Particularly noteworthy is the strategy's performance during periods of high market volatility, such as the COVID-19 pandemic in 2020, where it generated a return of 14.6%, as opposed to the benchmark return of 5.5%.

• Food Industry
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• s.180
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• pp.27-64
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• 2004

국내 제조업 전체에서 식음료품은 $6.31\%$를 차지하고 있다. 2000년을 기준으로 식품산업의 총매출실적을 보면 32조 3,463억원을 기록하고 있는데, 이 중 식료품은 26조 921억원, 알콜올음료를 포함한 음료품은 6조 2,516억원을 차지하고 있다. 2001년, 음료시장의 규모는 약 2조 7,900억원 정도였다. 빠르게 성장하던 전체 음료시장의 매출은 IMF 이후 약간 주춤하는 경향을 보였지만 지속적으로 매출액이 상승하여 2002년에는 3조 4천억원의 매출을 기록하였다. 이것은 사이다, 후레바, 정통주스, 냉장주스, 스포츠 음료, 두유 및 기능성음료 등이 성장을 주도하였다. 2003년 상반기, 1조 6,500억원으로 전년 동기대비 $3\%$ 감소하였지만 2003년 전체 실적은 대략 지난해와 비슷한 3조 5,000억원 규모로 전망된다. 내수량의 경우 탄산음료와 과즙음료가 감소하는 경향을 나타냈지만, 전체적으로는 증가하였다. 다류시장의 규모는 약 650억원, 캔커피시장의 규모는 2,400억원 정도로 나타났다. 이처럼 음료$\cdot$다류 시장은 전체적으로 약 4조원의 거대시장규모를 형성하고 있으며 내수가 지속적으로 증가되고 있어, 점차 시장규모도 증가할 것으로 전망되고 있다. 최근에는 기능성 음료 및 다류의 시장규모가 전체의 $20\%$에 달할 정도로 신장하였으며, 기능성음료 및 다류의 특허출원도 지속적으로 증가하고 있는 실정이다. 이는 비단 국내에서 뿐만 아니라 세계적인 현상이며 이러한 추세는 앞으로 지속될 것으로 전망되고 있다. 이처럼 기능성음료 및 다류의 성장이 두드러지고 있는 것은 시대가 변하면서 현대인들의 생활방식과 식생활 변화, 질병형태 다양화 등 여러 사회적 여건이 변화되고 있는데 기인하며, 현대인의 건강에 대한 관심의 증대는 음료시장의 쾌속성장과 틈새수요를 지속적으로 창출하고 있다. 현재 세계 건강기능성 식품의 시장 규모는 약 160조원이며, 국내의 경우 약 1조원에 이르는 실정이다. 이 중 기능성음료 시장은 국내에서 약 4천억원 규모로 기능성식품 시장의 $40\%$를 차지하고 있다. 이처럼 기능성음료 시장은 최근 연간 $7\%$ 정도의 빠른 성장률을 보이고 있으며 향후에도 $6.7\%$의 실질 연간 성장률을 지속할 것으로 전망되고 있다. 전해질(이온)음료나 식이섬유를 포함하는 다이어트음료에서 출발한 기능성음료는 숙취해소, 성인병 예방, 스트레스 해소에 이르기까지 각종 질병의 치료 및 예방으로 확대되고 있다. 앞으로는 BT 및 나노 기술 등을 이용한 신기능 소재들에 대한 다각적인 효능의 규명에 따른 음료$\cdot$다류 소재의 개발과 더불어 체질개선, 다이어트, 숙취해소 등의 특정한 기능성과 관련한 음료$\cdot$다류의 특허출원이 계속될 것으로 전망된다. 복잡한 한약의 제조과정을 단순화하여 티백이나 캔 형태로 만든 맛과 기능이 조화된 음료$\cdot$다류 분야의 연구개발이 지속될 것으로 보이며, 시장 규모의 확대와 더불어 기능성과 간편성을 동시에 추구하는 신세대 소비자들의 성향을 겨냥하여 기존의 음료$\cdot$다류 전문 업체 외에도 제약회사 등의 비음료 업체도 다양한 형태의 음료$\cdot$다류 기술개발을 지속할 것으로 예상되고 있다. 특히 새로운 신기능성 소재를 개발하는 것은 고부가가치 기술로써 BT, NT 등의 최첨단 기술의 발전과 도입으로 인해 그동안 수입에 의존하던 기능성 소재들도 국내 기업들의 축전된 기반기술을 통하여 대거 참입할 것으로 보인다. 하지만, 여러 업체의 음료$\cdot$다류시장 진출에 따라 야기될 수 있는 비위생이고 효능이 불확실한 식품의 유통, 업체간의 과열경쟁에 따른 유통질서 문란 등은 모처럼 활기를 되찾은 음료$\cdot$다류 분야의 기술개발을 위축시킬 수도 있으므로 정부차원에서 체계적이고 효율적인 관리가 조속히 이루어져야 할 것이며 이를 제도적으로 뒷받침하기 위한 정책이 수립되고 시행되어야 할 것으로 보인다. 특히, 원료적인 측면에서의 무역 불균형은 반드시 해소되어야 할 부분이다. 대부분이 수입 기능성 원료에 의존하고 있는 현실에서 국내의 BT 기술 확충에 더욱 많은 투자와 노력이 집중되어야 할 것이다. 세계적으로 경쟁이 될 수 있는 기능성 소재의 개발과 이를 통한 신기능 음료의 개발은 단순한 수학적 계산을 넘어서 국가의 기술력을 홍보할수 있는 좋은 계기가 되기 때문이다. 바야흐로 음료$\cdot$다류 분야에 대한 열기가 식품시장을 주도하면서, 시장이 안정적으로 확대되기 위해서는 지속적인 기술개발이 이루어져야 한다는 것은 재론의 여지가 없다고 하겠다.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.137-148
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• 2014

Recommender system has become one of the most important technologies in e-commerce in these days. The ultimate reason to shop online, for many consumers, is to reduce the efforts for information search and purchase. Recommender system is a key technology to serve these needs. Many of the past studies about recommender systems have been devoted to developing and improving recommendation algorithms and collaborative filtering (CF) is known to be the most successful one. Despite its success, however, CF has several shortcomings such as cold-start, sparsity, gray sheep problems. In order to be able to generate recommendations, ordinary CF algorithms require evaluations or preference information directly from users. For new users who do not have any evaluations or preference information, therefore, CF cannot come up with recommendations (Cold-star problem). As the numbers of products and customers increase, the scale of the data increases exponentially and most of the data cells are empty. This sparse dataset makes computation for recommendation extremely hard (Sparsity problem). Since CF is based on the assumption that there are groups of users sharing common preferences or tastes, CF becomes inaccurate if there are many users with rare and unique tastes (Gray sheep problem). This study proposes a new algorithm that utilizes Social Network Analysis (SNA) techniques to resolve the gray sheep problem. We utilize 'degree centrality' in SNA to identify users with unique preferences (gray sheep). Degree centrality in SNA refers to the number of direct links to and from a node. In a network of users who are connected through common preferences or tastes, those with unique tastes have fewer links to other users (nodes) and they are isolated from other users. Therefore, gray sheep can be identified by calculating degree centrality of each node. We divide the dataset into two, gray sheep and others, based on the degree centrality of the users. Then, different similarity measures and recommendation methods are applied to these two datasets. More detail algorithm is as follows: Step 1: Convert the initial data which is a two-mode network (user to item) into an one-mode network (user to user). Step 2: Calculate degree centrality of each node and separate those nodes having degree centrality values lower than the pre-set threshold. The threshold value is determined by simulations such that the accuracy of CF for the remaining dataset is maximized. Step 3: Ordinary CF algorithm is applied to the remaining dataset. Step 4: Since the separated dataset consist of users with unique tastes, an ordinary CF algorithm cannot generate recommendations for them. A 'popular item' method is used to generate recommendations for these users. The F measures of the two datasets are weighted by the numbers of nodes and summed to be used as the final performance metric. In order to test performance improvement by this new algorithm, an empirical study was conducted using a publically available dataset - the MovieLens data by GroupLens research team. We used 100,000 evaluations by 943 users on 1,682 movies. The proposed algorithm was compared with an ordinary CF algorithm utilizing 'Best-N-neighbors' and 'Cosine' similarity method. The empirical results show that F measure was improved about 11% on average when the proposed algorithm was used