• Journal of Intelligence and Information Systems
• /
• v.23 no.4
• /
• pp.127-146
• /
• 2017

Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.

• Economic and Environmental Geology
• /
• v.57 no.3
• /
• pp.281-292
• /
• 2024

North Korea relies heavily on coal as the primary energy source, playing an important role in all energy demand sectors except for the transportation sector. Approximately half of the total electricity is generated through coal-fired power plants, and coal is used to produce heat and power for all industrial facilities. Furthermore, coal has been a significant contributor to earning foreign currency through long-term exports to China. Nevertheless, since the 1980s, indiscriminate mining activities have led to rapid depletion of coal production in most coal mines. Aging mine facilities, lack of investment in new equipment, shortages of fuel and electricity, difficulties in material supply, and frequent damage from flooding have collectively contributed to a noticeable decline in coal production since the late 1980s. North Korea's coal deposits are distributed in various geological formations from the Proterozoic to the Cenozoic, but the most critical coal-bearing formations are Ripsok and Sadong formations distributed in the Pyeongnam Basin of the Late Paleozoic from Carboniferous to Permian, which are called as Pyeongnam North and South Coal Fields. Over 90% of North Korea's coal is produced in these coal fields. The classification of coal in North Korea differs from the international classification based on coalification (peat, lignite, sub-bituminous coal, bituminous coal, and anthracite). North Korean classification based on industrial aspect is classified into bituminous coal, anthracite, and low-grade coal (Chomuyeontan). Based on the energy factor, it is classified into high-calorie coal, medium calorie coal, and low-calorie coal. In North Korea, the term "Chomuyeontan" refers to a type of coal that is not classified globally and is unique to North Korea. It is a low-grade coal exclusively used in North Korea and is not found or used in any other country worldwide. This article compares North Korea's coal classification and the international coal classification of coal and provides insights into the geological characteristics, reserves, utilization, and research trends of North Korean coal resources. This study could serve as a guide for preparing scientific and industrial agendas related to coal collaboration between North Korea and South Korea.

• Journal of Intelligence and Information Systems
• /
• v.27 no.2
• /
• pp.33-54
• /
• 2021

With the fourth industrial revolution and the arrival of the New Normal era due to Corona, the importance of Non-contact technologies such as artificial intelligence and big data research has been increasing. Convergent research is being conducted in earnest to keep up with these research trends, but not many studies have been conducted in the area of nuclear research using artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. This study was conducted to confirm the applicability of data science analysis techniques to the field of nuclear research. Furthermore, the study of identifying trends in nuclear spent fuel recognition is critical in terms of being able to determine directions to nuclear industry policies and respond in advance to changes in industrial policies. For those reasons, this study conducted a media trend analysis of pyroprocessing, a spent nuclear fuel treatment technology. We objectively analyze changes in media perception of spent nuclear fuel dry treatment techniques by applying text mining analysis techniques. Text data specializing in Naver's web news articles, including the keywords "Pyroprocessing" and "Sodium Cooled Reactor," were collected through Python code to identify changes in perception over time. The analysis period was set from 2007 to 2020, when the first article was published, and detailed and multi-layered analysis of text data was carried out through analysis methods such as word cloud writing based on frequency analysis, TF-IDF and degree centrality calculation. Analysis of the frequency of the keyword showed that there was a change in media perception of spent nuclear fuel dry treatment technology in the mid-2010s, which was influenced by the Gyeongju earthquake in 2016 and the implementation of the new government's energy conversion policy in 2017. Therefore, trend analysis was conducted based on the corresponding time period, and word frequency analysis, TF-IDF, degree centrality values, and semantic network graphs were derived. Studies show that before the 2010s, media perception of spent nuclear fuel dry treatment technology was diplomatic and positive. However, over time, the frequency of keywords such as "safety", "reexamination", "disposal", and "disassembly" has increased, indicating that the sustainability of spent nuclear fuel dry treatment technology is being seriously considered. It was confirmed that social awareness also changed as spent nuclear fuel dry treatment technology, which was recognized as a political and diplomatic technology, became ambiguous due to changes in domestic policy. This means that domestic policy changes such as nuclear power policy have a greater impact on media perceptions than issues of "spent nuclear fuel processing technology" itself. This seems to be because nuclear policy is a socially more discussed and public-friendly topic than spent nuclear fuel. Therefore, in order to improve social awareness of spent nuclear fuel processing technology, it would be necessary to provide sufficient information about this, and linking it to nuclear policy issues would also be a good idea. In addition, the study highlighted the importance of social science research in nuclear power. It is necessary to apply the social sciences sector widely to the nuclear engineering sector, and considering national policy changes, we could confirm that the nuclear industry would be sustainable. However, this study has limitations that it has applied big data analysis methods only to detailed research areas such as "Pyroprocessing," a spent nuclear fuel dry processing technology. Furthermore, there was no clear basis for the cause of the change in social perception, and only news articles were analyzed to determine social perception. Considering future comments, it is expected that more reliable results will be produced and efficiently used in the field of nuclear policy research if a media trend analysis study on nuclear power is conducted. Recently, the development of uncontact-related technologies such as artificial intelligence and big data research is accelerating in the wake of the recent arrival of the New Normal era caused by corona. Convergence research is being conducted in earnest in various research fields to follow these research trends, but not many studies have been conducted in the nuclear field with artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. The academic significance of this study is that it was possible to confirm the applicability of data science analysis technology in the field of nuclear research. Furthermore, due to the impact of current government energy policies such as nuclear power plant reductions, re-evaluation of spent fuel treatment technology research is undertaken, and key keyword analysis in the field can contribute to future research orientation. It is important to consider the views of others outside, not just the safety technology and engineering integrity of nuclear power, and further reconsider whether it is appropriate to discuss nuclear engineering technology internally. In addition, if multidisciplinary research on nuclear power is carried out, reasonable alternatives can be prepared to maintain the nuclear industry.

• Journal of Korea Water Resources Association
• /
• v.42 no.12
• /
• pp.1029-1038
• /
• 2009

Recently, it is expected that small hydropower (SHP) could potentially provide sufficient amounts of alternative energy in Korea where there is an abundance of potential sites and where social efforts are being made to reduce the emissions of green house gases. In the past, the resources survey for SHP development has been carried out using onsite surveys and paper maps, which incurred a great deal of time and cost. Furthermore, the tools for decision making such as determining development priorities or evaluating feasibility have been considered only economic aspect and focused on the performance characteristics of power generation. However, as the concept of sustainable development has been being advanced in recent years, especially focused on human-social, environmental and ecological in addition to economical sector; the consideration of these multiple criteria has become essential for sustainable SHP development. This study aims to propose the spatial multi-criteria decision making (MCDM) methodology for determining priorities among a number of locations on the planning stage of SHP development using AHP and GIS. The proposed methodology is applied for determining development priorities among the SHP locations in Cho River basin and this study presents the detailed spatial information data and the results of development priorities. As a fundamental work, this study will be beneficial to the future activation of SHP development and will help the decision making in evaluating the feasibility of SHP development.

• Journal of the Korean Society for Railway
• /
• v.15 no.4
• /
• pp.408-412
• /
• 2012

Recently, new climate change mechanism after 2020 year has been accepted with the parties, and so government is pushing ahead the GHG reduction policies to achieve the effective results. Especially, it is essential to enhance the role of railroad in the public traffic system as well as to develop new cars with high energy efficiency for the GHG reduction of transportation sector. Thus, the calculation method of GHG emission of railroad should be established to manage the emission continuously. In this study, the calculation method of GHG emission of railroad was defined with Tier level considering its emission sources to refer to 2006 IPCC guideline for national GHG inventories. Also, the GHG emission of railroad at Tier 1 level was investigated using the activity data related to the amount of diesel and electricity consumption from 2008 to 2010. As a result, total GHG emission in 2010 was about 2,060 thousands ton CO2e, which have 73% of electricity and 27% of diesel. In future, the plans on the GHG reduction of railroad will be accomplished by the analysis of the detailed trends on the basis of the emission management of Tier 3 level under operating patterns. Therefore, it is important to develop the specific GHG emission factors of railroad in advance.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.33 no.1
• /
• pp.297-304
• /
• 2013

Due to its excessive $CO_2$ emissions, road transport sector becomes a target for emission reduction strategies. Although precise and reliable emissions inventories are necessary for evaluating plans and strategies, developing the region-wide inventory is a difficult task mainly because of a lack of data including travel patterns and modal volumes in the reginonal context. Most existing inventory methodologies employ fuel sale data within the target region, which ignores actual regional traffic patterns and thus not suited to its geographical context. To overcome these problems, this study develops region-wide $CO_2$ emissions inventory methodology by utilizing the Korea Transport DB (KTDB). KTDB provides a number of useful information and data, such as road network with which one can identify in and out trips over the entire region, traffic volumes of various modes, distance of travel, travel speed and so on. A model of equations that allow the computation of volume of $CO_2$ emitting from the road transport activities within the target region is developed. Using the model, numerical analyses are performed for the case of Busan Metropolitan City to demonstrate the applicability of the developed model. This study is indeed exploratory in the sense that using the existing data, it develops the $CO_2$ emissions inventory methodology which can produce better results than those from conventional fuel sales methodology. This study also suggests further reresarch directions to develop more refined methodologies in region-wide basis.

• Journal of Broadcast Engineering
• /
• v.23 no.4
• /
• pp.536-548
• /
• 2018

Public data has been transformed from provider-oriented information disclosure to a form of personalized information sharing centered on individual citizens since government 3.0. As a result, the government is implementing policies and projects to maximize the value of public data and increase reuse. This study analyzes the issues related to public data in the news and seeks the status of government agencies and government projects by issue. We conducted semantic analysis on domestic online news and public agency bidding information including public data and conducted the work of linking major key words derived with social and economic values inherent in public data. As a result, major issues related to public data were divided into broader access to public data, growth of new technology, cooperation and conflict among stakeholders, and utilization of the private sector, which were closely related to transparency, efficiency, participation, and innovation mechanisms. Also major agencies of four issues include the Ministry of Strategy and Finance and Seoul, Ministry of Culture, Sports and Tourism and Gyeonggi-do, Ministry of Trade, Industry and Energy and Incheon, and Ministry of Land, Infrastructure and Transport and Gyeongsangbuk-do. Most of the issues are being led by the government.

• Journal of the Korean Society of Food Culture
• /
• v.11 no.2
• /
• pp.243-253
• /
• 1996

The purpose of this study was to evaluate the foodservice management practices in child-care centers in order to provide basic information for the development of a model of a centralized food service information center. This approach was achieved using a variety of qualitative and quantitative information including general foodservice management practices and plate waste. A self-completed questionnaire survey of 651 child-care centers in Korea was undertaken and detailed information was carefully collected at 6 representative child-care centers. The results of the empirical survey were as follows: 1. Child-care centers categorized by location were in large cities (59.9%), medium cities (27.6%) and in provincial areas (12.5). 2. Private sector of child-care centers was 46.4% of the total followed by National/public (44.2%) and licensed home day-care programs (9.4%). 3. Total average number of children in child-care centers was $63.3{\pm}43.1$ with a very significant difference (p<0.001) in types of child-care centers. 4. The average space of kitchen and dining room was $5.0{\pm}3.8\;and\;10.8{\pm}11.0$ pyung ($1pyung=3.3058\;cm^2$). 5. The average cost of interim snack in morning and afternoon in child-care centers were $345.9{\pm}459.3$ won and $359.3{\pm}226.6$ won respectively. The average cost of lunch was $644.0{\pm}481.1$ won. There was a significant difference (p<0.001) by types of child-care centers with a highest cost of 863.9 won in licensed home day-care programs. 6. Only a limited number of dietitian were employed, therefore most of food service management practice was not conducted by professional personnel. 7. The result of nutritional analysis of the food revealed that the level of energy and nutrients contained in the food was below the recommeded level (RDA/3).

• Journal of Korean Society of Transportation
• /
• v.32 no.3
• /
• pp.239-247
• /
• 2014

In modern society, the transportation sector is having difficulties with mobility, safety, energy and environment issues so that solutions should be prepared progressively. Recently developed countries are paying greater attention to car-sharing systems as alternatives for relieving problems caused by increasing number of vehicles. Car-sharing systems are believed to keep the number of personal vehicles down and traffic congestion due to the fact that this system induces efficiency of vehicle operation. In this study, authors focused on solving the imbalance problem of car-sharing systems dynamically, to specifically improve one-way car sharing service. Therefore vehicle relocation algorithm was designed for maximizing profit of car sharing operators. For the application of the vehicle relocation algorithm among rental offices, we selected Haeundae-gu, Busan as the area of study. Rental offices have been designated to the zone centroid and we calculated the shortest path between rental offices by using TransCAD. In the study, we adapted MATLAB for the application of vehicle relocation algorithm. As the results, the total vehicle relocation time was estimated as 59.9 minutes. In addition, vehicle relocation algorithm was verified successfully by using Decision Tree method.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.16 no.3
• /
• pp.2249-2254
• /
• 2015

Currently, green house gas(GHG) emissions in korea is aiming for a 30% reduction that it is compared to BAU by 2020. To this end, the government has proceed to a variety of reduction policies in GHG. In particular, GHG reduction effect in the public buildings is being a active discussion. It needs to reduce GHG for energy efficiency improvements in the way that public buildings are operated and maintained by public taxes. In this background, the purpose of this paper is to study environmental values judgement for non-market goods in the residents who use public buildings. The results of study are as follows; Respond to first suggested price was found the higher in price, the lower in willingness to pay(WTP). The result of second suggested price was as the same. Analysis of DBDC CVM revealed that income level shows a positive impact on WTP, but the other variables are irrelevant to WTP. Therefore, the citizen participation of the local population seems absolutely necessary to more effective GHG reduction of public sector in the future.