• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.2
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• pp.301-307
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• 2021

In addition to the revival of the Internet of Things, embedded systems, which are at the core of the Internet of Things, require intelligent control as things change. Embedded systems, however, are heavily constrained by resources such as hardware, memory, time and power. When changes are needed to firmware in an embedded system, flash Memory must be initialized and the entire firmware must be uploaded again. Therefore, it is time- and energy-efficient in that areas that do not need to be modified must also be initialized and rewritten. In this paper, we propose how to upload firmware in installments to each sector of flash memory so that only firmware can be replace the firmware in the parts that need to be modified when the firmware needs to be modified. In this paper, the proposed method was evaluated using real target board, and as a result, the time was reduced by about half.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.1
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• pp.181-186
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• 2022

The world's electric automobile sector has shifted beyond technological environmental changes to a stage that has an impact on the market environment. And automakers are shifting from the existing strategy of "technological advantage → brand enhancement → sales expansion of existing internal combustion engine vehicles" to the expansion of the electric automobile market itself, which is to enhance market competitiveness. In addition, competition in the electric automotive parts market is expected to intensify due to the expansion of the business areas of existing parts makers and the entry of new companies, and development cooperation is expected to actively proceed to improve the efficiency of major eco-friendly parts. Along with this prospect, electric vehicles are expected to change the overall structure of the automobile industry, the overall growth of the electric vehicle value chain such as batteries, power trains (motors, power management and control systems), electric vehicle production, and charging infrastructure Is expected. Therefore, in this thesis, in order to cultivate a variety of high-quality human resources that companies want to keep pace with the changing automobile industry, we study a professional manpower training program that leads the growth engine of the electric automobile industry.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.1-19
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• 2018

Since stock movements forecasting is an important issue both academically and practically, studies related to stock price prediction have been actively conducted. The stock price forecasting research is classified into structured data and unstructured data, and it is divided into technical analysis, fundamental analysis and media effect analysis in detail. In the big data era, research on stock price prediction combining big data is actively underway. Based on a large number of data, stock prediction research mainly focuses on machine learning techniques. Especially, research methods that combine the effects of media are attracting attention recently, among which researches that analyze online news and utilize online news to forecast stock prices are becoming main. Previous studies predicting stock prices through online news are mostly sentiment analysis of news, making different corpus for each company, and making a dictionary that predicts stock prices by recording responses according to the past stock price. Therefore, existing studies have examined the impact of online news on individual companies. For example, stock movements of Samsung Electronics are predicted with only online news of Samsung Electronics. In addition, a method of considering influences among highly relevant companies has also been studied recently. For example, stock movements of Samsung Electronics are predicted with news of Samsung Electronics and a highly related company like LG Electronics.These previous studies examine the effects of news of industrial sector with homogeneity on the individual company. In the previous studies, homogeneous industries are classified according to the Global Industrial Classification Standard. In other words, the existing studies were analyzed under the assumption that industries divided into Global Industrial Classification Standard have homogeneity. However, existing studies have limitations in that they do not take into account influential companies with high relevance or reflect the existence of heterogeneity within the same Global Industrial Classification Standard sectors. As a result of our examining the various sectors, it can be seen that there are sectors that show the industrial sectors are not a homogeneous group. To overcome these limitations of existing studies that do not reflect heterogeneity, our study suggests a methodology that reflects the heterogeneous effects of the industrial sector that affect the stock price by applying k-means clustering. Multiple Kernel Learning is mainly used to integrate data with various characteristics. Multiple Kernel Learning has several kernels, each of which receives and predicts different data. To incorporate effects of target firm and its relevant firms simultaneously, we used Multiple Kernel Learning. Each kernel was assigned to predict stock prices with variables of financial news of the industrial group divided by the target firm, K-means cluster analysis. In order to prove that the suggested methodology is appropriate, experiments were conducted through three years of online news and stock prices. The results of this study are as follows. (1) We confirmed that the information of the industrial sectors related to target company also contains meaningful information to predict stock movements of target company and confirmed that machine learning algorithm has better predictive power when considering the news of the relevant companies and target company's news together. (2) It is important to predict stock movements with varying number of clusters according to the level of homogeneity in the industrial sector. In other words, when stock prices are homogeneous in industrial sectors, it is important to use relational effect at the level of industry group without analyzing clusters or to use it in small number of clusters. When the stock price is heterogeneous in industry group, it is important to cluster them into groups. This study has a contribution that we testified firms classified as Global Industrial Classification Standard have heterogeneity and suggested it is necessary to define the relevance through machine learning and statistical analysis methodology rather than simply defining it in the Global Industrial Classification Standard. It has also contribution that we proved the efficiency of the prediction model reflecting heterogeneity.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.43 no.12
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• pp.1097-1107
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• 2015

General VTOL aircraft uses gas turbine engine which has high power to weight ratio. However, in the VTOL UAV in small sector, the gas turbine as a prime mover is not adequate because of the limitation of the high fuel consumption ratio of the gas turbine. In this research, The Series Hybrid-Electric Propulsion System(SHEPS) has been proposed and technology survey & comparison analysis has conducted to constitute propulsion system for engine, electric motor and battery. To achieve this object a 65kg-class P-UAV from "Company I" was used. And to estimate the validity of power control algorithm and developed power management control, Matlab/simulink$^{(R)}$ has been used for the simulation. As a result, the developed algorithm worked comparatively well and the research has predicted that SHEPS was satisfied enough for 7 hour of endurance for mission profile.

• Journal of Energy Engineering
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• v.24 no.2
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• pp.129-143
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• 2015

Increasing concerns on climate change and energy security accelerated policies to reduce green-house gas emission, especially from the transportation sector. Electric vehicle (EV) has been on the spotlight to deal with such environmental issue and V2G (Vehicle-to-Grid) technology began to draw attentions as an alternative to reduce ownership costs while contributing to an efficient and decentralized power grid. This study conducts a scenario analysis on total cost of ownership of EV under V2G scheme and compare with non-V2G EV and Internal Combustion Engine (ICE) vehicle. As result, V2G service is expected to provide an annual average profit of $210 to EV users willing to reverse flow its residual power in the battery. The profit from V2G service leaves a margin of $4,530 over operational lifetime, compared with $2,420 cost of charge for non-V2G EV. In summary, total cost of ownership of V2G-capable EV was 6.2% less than non-V2G EV and 10.2% higher than ICE vehicle. The results confirm a comparative economic advantage of operating EV under V2G scheme. Increased number of EVs with V2G service has shown to provide positive effects to power industry for valley filling in load distribution, thus, favorably increasing the overall economic feasibility.

• Plant Journal
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• v.10 no.2
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• pp.28-37
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• 2014

Recently, it is considered that environment and energy are critical issues all over the world. In power generation sector in Korea, almost power stations are constructed and operated as cogeneration plants in conformity with this trend. KDHC(Korea District Heating Corporation) goes one step further adopting renewable energy technology like heat pump using wasted heat for energy-saving and environment improvement. This study investigates the performance characteristics by the location of waste heat recovery heat pumps of 5 Gcal/h capacity in 150 MW-class Gwang-gyo cogeneration plant using commercial software 'THERMOFLEX'. Prior to analysis, the simulations are performed with actual operation data, and then the validation of simulations is verified by checking the error within 2%. After verification, the simulations are carried out with 3 locations and the effect on electrical power output and heat output is analyzed. As a result, overall efficiency of cogeneration plant is the highest in the case of heat pump located before DH(District Heating) Heater because of the largest increase of heat output despite of decrease of electrical power output.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.1
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• pp.92-111
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• 2021

As fine dust negatively affects disease, industry and economy, the people are sensitive to fine dust. Therefore, if the occurrence of fine dust can be predicted, countermeasures can be prepared in advance, which can be helpful for life and economy. Fine dust is affected by the weather and the degree of concentration of fine dust emission sources. The industrial sector has the largest amount of fine dust emissions, and in industrial complexes, factories emit a lot of fine dust as fine dust emission sources. This study targets regions with old industrial complexes in local cities. The purpose of this study is to explore the factors that cause fine dust and develop a predictive model that can predict the occurrence of fine dust. weather data and fine dust data were used, and variables that influence the generation of fine dust were extracted through multiple regression analysis. Based on the results of multiple regression analysis, a model with high predictive power was extracted by learning with a machine learning regression learner model. The performance of the model was confirmed using test data. As a result, the models with high predictive power were linear regression model, Gaussian process regression model, and support vector machine. The proportion of training data and predictive power were not proportional. In addition, the average value of the difference between the predicted value and the measured value was not large, but when the measured value was high, the predictive power was decreased. The results of this study can be developed as a more systematic and precise fine dust prediction service by combining meteorological data and urban big data through local government data hubs. Lastly, it will be an opportunity to promote the development of smart industrial complexes.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.4
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• pp.985-994
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• 2016

Since the inauguration of Defense Acquisition Program Administration(DAPA) in 2006, the national defense core technology research & development business has not only pertained to the weapons systems development but also to the improvement of the national science & technology capability via the acquisition of cutting-edge technologies. Furthermore, it has been closely related to the promotion of the defense industry and the mutual improvements of defense and civil technologies. The cyber warfare weapon system, a newly added national defense weapon system field since 2015, has become a promising weapon system branch for improving the national defense power as well as the national defense industry as shown in the case of Israel. By utilizing the existing result of the national defense core technology level, in order to establish the direction of technology planning of the cyber warfare weapon system, this paper analyzes the technology level and features of the cyber warfare weapon system in various aspects via comparisons with other weapons systems. The result of these analyses shows that the cyber warfare weapon system possesses a relatively high technology level due to the technology accumulation in the civilian sector while the relatively slow inclusion to the national weapons systems and the lack of the correspondence case regarding aggressive cyber responses in the defense sector yields a relatively low national rank. However, the technological gap between South Korea and the most advanced country in the field of cyber warfare technology is analyzed to be among the lowest, which indicates that with efficient and effective pursuits in terms of pthe weapons systems acquisitions as well as the core technologies research & development business, an outstanding cyber warfare capacity can be obtained in a short time.

• Journal of the Economic Geographical Society of Korea
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• v.19 no.1
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• pp.83-103
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• 2016

This paper examines the characteristics of structural change in the international agro-food trade network with a global trend of the FTA diffusion. By focusing on the centralities and the community structure to identify the agro-food trade network, we use the social network analysis and the trade data contained of 07, 08, 20, and 21 at the 2-digit HS product level among 45 countries over the last 15 years. The main analytical results are as follows: 1) Not only has intra trade network intensified more than inter-regional trade, but also, there is no doubt that inter-regioanl trade by linking has steadily increased. 2) EU countires have the high indices of centrality, which have already been highly integrated. 3) Intraregional agro-food trade network for fresh vegetables and processed food sector in Asia is shown to be strongly integrated. This finding suggests that the processes of economic integration will help strengthen the trade network for agro-food in a culturally homogeneous region. 4) The case of Korea's participation in the RCEP and TPP, Korea's power in the agro-food network tends to be reinforced, especially in the processed food sector. Overall, there is a need for establishment of spatial strategies and policies across the different regions for Mega FTAs.

• Journal of Korean Society for Atmospheric Environment
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• v.26 no.5
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• pp.507-516
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• 2010

Stationary combustion sources such as coal-fired power plants, waste incinerators, industrial manufacturing, etc. are recognized as major sources of mercury emissions. Due to rapid economic growth, zinc production in Korea has increased significantly during the last 30 years. Total zinc production in Korea exceeded 739,000 tons in 2008, and Korea is currently the third largest zinc producing country in the world. Previous studies have revealed that zinc smelting has become one of the largest single sectors of total mercury emissions in the World. However, studies on this sector are very limited, and a large gap in the knowledge regarding emissions from this sector needs to be bridged. In this paper, Hg emission measurements were performed to develop emission factors from zinc smelting process. Stack sampling and analysis were carried out utilizing the Ontario Hydro method and US EPA method 101A. Preliminary data showed that $Hg^0$ concentrations in the flue gas ranged from 4.56 to $9.90\;{\mu}g/m^3$ with an average of $6.40\;{\mu}g/m^3$, Hg(p) concentrations ranged from 0.03 to $0.09\;{\mu}g/m^3$ with an average of $0.04\;{\mu}g/m^3$, and RGM concentrations ranged from 0.23 to $1.17\;{\mu}g/m^3$ with an average of $6.40\;{\mu}g/m^3$. To date, emission factors of 7.5~8.0 g/ton for Europe, North America and Australia, and of 20 or 25 g/ton for Africa, Asia and South America are widely accepted by researchers. In this study, Hg emission factors were estimated using the data measured at the commercial facilities as emissions per ton of zinc product. Emission factors for mercury from zinc smelting pross ranged from 4.32 to 12.96 mg/ton with an average of 8.31 mg/ton. The emission factors that we obtained in this study are relatively low, considering Hg contents in the zinc ores and control technology in use. However, as these values are estimated by limited data of single measurement of each, the emission factor and total emission amount must be updated in future.