• Economic Analysis
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• v.27 no.4
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• pp.1-42
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• 2021

The outlook for Korea's consumer price inflation rate has a profound impact not only on the Bank of Korea's operation of the inflation target system but also on the overall economy, including the bond market and private consumption and investment. This study presents the prediction results of consumer price inflation in Korea for the next three years. To this end, first, model selection is performed based on the out-of-sample predictive power of autoregressive distributed lag (ADL) models, AR models, small-scale vector autoregressive (VAR) models, and large-scale VAR models. Since there are many potential predictors of inflation, a Bayesian variable selection technique was introduced for 12 macro variables, and a precise tuning process was performed to improve predictive power. In the case of the VAR model, the Minnesota prior distribution was applied to solve the dimensional curse problem. Looking at the results of long-term and short-term out-of-sample predictions for the last five years, the ADL model was generally superior to other competing models in both point and distribution prediction. As a result of forecasting through the combination of predictions from the above models, the inflation rate is expected to maintain the current level of around 2% until the second half of 2022, and is expected to drop to around 1% from the first half of 2023.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10b
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• pp.1649-1654
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• 1991

In 1995 the VSOP satellite, which is called MUSES-B in Japan, will be launched under the VLBI Space Observatory Programme(VSOP) promoted by ISAS(Institute of Space and Astronautical Science) of Japan. We are now developing the GPS Receiver(GPSR) and On-board Orbit Determination System. This paper describes the GPS(Global Positioning System), VSOP, GPSR(GPS Receiver system) configuration and the results of the GPS system analysis. The GPSR consists of three GPS antennas and 5 channel receiver package. In the receiver package, there are two 16 bits microprocessing units. The power consumption is 25 Watts in average and the weight is 8.5 kg. Three GPS antennas on board enable GPSR to receive GPS signals from any NAVSTARs(GPS satellites) which are visible. NAVSATR's visibility is described as follows. The VSOP satellite flies from 1, 000 km to 20, 000 km in height on the elliptical orbit around the earth. On the other hand, the orbit of NAVSTARs are nearly circular and about 20, 000 km in height. GPSR can't receive the GPS signals near the apogee, because NAVSTARs transmit the GPS signals through the NAVSTAR's narrow beam antennas directed toward the earth. However near the perigee, GPSR can receive from 12 to 15 GPS signals. More than 4 GPS signals can be received for 40 minutes, which are related to GDOP(Geometric Dillusion Of Precision of selected NAVSTARs). Because there are a lot of visible NAVSTARs, GDOP is small near the perigee. This is a favorqble condition for GPSR. Orbit determination system onboard VSOP satellite consists of a Kalman filter and a precise orbit propagator. Near the perigee, the Kalman filter can eliminate the orbit propagation error using the observed data by GPSR. Except a perigee, precise onboard orbit propagator propagates the orbit, taking into account accelerations such as gravities of the earth, the sun, the moon, and other acceleration caused by the solar pressure. But there remain some amount of calculation and integration errors. When VSOP satellite returns to the perigee, the Kalman filter eliminates the error of the orbit determined by the propagator. After the error is eliminated, VSOP satellite flies out towards an apogee again. The analysis of the orbit determination is performed by the covariance analysis method. Number of the states of the onboard filter is 8. As for a true model, we assume that it is based on the actual error dynamics that include the Selective Availability of GPS called 'SA', having 17 states. Analytical results for position and velocity are tabulated and illustrated, in the sequel. These show that the position and the velocity error are about 40 m and 0.008 m/sec at the perigee, and are about 110 m and 0.012 m/sec at the apogee, respectively.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.5
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• pp.1099-1112
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• 2018

The field of post-quantum cryptography (PQC) is an active area of research as cryptographers look for public-key cryptosystems that can resist quantum adversaries. Among those categories in PQC, code-based cryptosystem provides high security along with efficiency. Recent works on code-based cryptosystems focus on the side-channel resistant implementation since previous works have indicated the possible side-channel vulnerabilities on existing algorithms. In this paper, we recovered the secret key in HyMES(Hybrid McEliece Scheme) using a single power consumption trace. HyMES is a variant of McEliece cryptosystem that provides smaller keys and faster encryption and decryption speed. During the decryption, the algorithm computes the parity-check matrix which is required when computing the syndrome. We analyzed HyMES using the fact that the joint distributions of nonlinear functions used in this process depend on the secret key. To the best of our knowledge, we were the first to propose the side-channel analysis based on joint distributions of leakages on public-key cryptosystem.

• Korean Journal of Community Nutrition
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• v.21 no.3
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• pp.284-292
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• 2016

Objectives: This study compared the differences of postmenopausal women's bone mineral density in relation to the degree of obesity, metabolism index and dietary factors that affect bone mineral density. Methods: The subjects included in the study are 39 postmenopausal women of normal weight with body mass index less than $25kg/m^2$ and 32 postmenopausal who are obese. Anthropometry and biochemical analysis were performed and nutrient intakes and DQI-I were assessed. Results: Normal weight women were $56.03{\pm}3.76years$ old and obese women were $58.09{\pm}5.13years$ old and there was no significant difference in age between the two groups. The T-score of bone mineral density was $0.03{\pm}1.06$ in normal weight women and $-0.60{\pm}1.47$ in obese women and this was significantly different between the two groups (p<0.05). Blood Leptin concentration was significantly lower in normal weight women ($6.09{\pm}3.37ng/mL$) compared to obese women in ($9.01{\pm}4.99ng/mL$) (p<0.05). The total score of diet quality index-international was $70.41{\pm}9.34$ in normal weight women and $64.93{\pm}7.82$ in obese women (p<0.05). T-score of bone mineral density showed negative correlations with percentage of body fat (r = -0.233, p=0.05), BMI (r = -0.197, p=0.017), triglyceride (r = -0.281, p=0.020) and leptin (r = -0.308, p=0.011). The results of multiple regression analysis performed as the method of entry showed that with 22.0% of explanation power, percentage of body fat (${\beta}=-0.048$, p<0.05), triglyceride (${\beta}=-0.005$, p<0.05) and HDL-cholesterol (${\beta}=0.034$, p<0.01), moderation of DQI-I (${\beta}=-0.231$, p<0.05) affected T-score significantly. Conclusions: The results of the study showed that obese women have less bone density than those with normal weight women. In addition, the factor analysis result that affect bone mineral density showed that intake of fat is a very important factor. Therefore, postmenopausal women need to maintain normal weight and manage blood lipid levels within normal range. They also need to take various sources of protein and reduce consumption of empty calorie foods that have high calories, fat, cholesterol and sodium.

• Journal of Energy Engineering
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• v.28 no.4
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• pp.35-41
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• 2019

In order to overcome these disadvantages, the halogen light source, which was previously used as a vehicle fog light, has increased power consumption and a short lifetime, and thus, an automobile light source is gradually being replaced with an LED. However, when the vehicle LED fog light is turned on, there is a disadvantage in reducing the life of the fog lamp due to the high heat generated from the LED. The heat generated by the LED inside the fog lamp is mainly emitted by the heatsink, but most of the remaining heat is released to the outside through convection. When cooling efficiency decreases due to convection, thermal energy generates heat to lenses, reflectors, and bezels, which are the main parts of lamps, or generates high temperatures in LED, thereby shortening the life of LED fog lights. In this study, we tried to improve the heat dissipation performance by convection in addition to the heat dissipation method by heat sink, and to determine the installation location of vents that can discharge the internal air or intake the external air of LED fog lamp for vehicle. Thermal fluid analysis was performed to ensure that the optimal data were reflected in the design. The average velocity of air increased in the order of Case3 and Case2 compared to Case1, which is the existing prototype, and the increase rate of Case3 was relatively higher than that of other cases. This is because the vents installed above and below the fog lamps induce the convective phenomena generated according to the temperature difference, and the heat is efficiently discharged with the increase of the air speed.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.95-112
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• 2021

In university education, the choice of major class plays an important role in students' careers. However, in line with the changes in the industry, the fields of major subjects by department are diversifying and increasing in number in university education. As a result, students have difficulty to choose and take classes according to their career paths. In general, students choose classes based on experiences such as choices of peers or advice from seniors. This has the advantage of being able to take into account the general situation, but it does not reflect individual tendencies and considerations of existing courses, and has a problem that leads to information inequality that is shared only among specific students. In addition, as non-face-to-face classes have recently been conducted and exchanges between students have decreased, even experience-based decisions have not been made as well. Therefore, this study proposes a recommendation system model that can recommend college major classes suitable for individual characteristics based on data rather than experience. The recommendation system recommends information and content (music, movies, books, images, etc.) that a specific user may be interested in. It is already widely used in services where it is important to consider individual tendencies such as YouTube and Facebook, and you can experience it familiarly in providing personalized services in content services such as over-the-top media services (OTT). Classes are also a kind of content consumption in terms of selecting classes suitable for individuals from a set content list. However, unlike other content consumption, it is characterized by a large influence of selection results. For example, in the case of music and movies, it is usually consumed once and the time required to consume content is short. Therefore, the importance of each item is relatively low, and there is no deep concern in selecting. Major classes usually have a long consumption time because they have to be taken for one semester, and each item has a high importance and requires greater caution in choice because it affects many things such as career and graduation requirements depending on the composition of the selected classes. Depending on the unique characteristics of these major classes, the recommendation system in the education field supports decision-making that reflects individual characteristics that are meaningful and cannot be reflected in experience-based decision-making, even though it has a relatively small number of item ranges. This study aims to realize personalized education and enhance students' educational satisfaction by presenting a recommendation model for university major class. In the model study, class history data of undergraduate students at University from 2015 to 2017 were used, and students and their major names were used as metadata. The class history data is implicit feedback data that only indicates whether content is consumed, not reflecting preferences for classes. Therefore, when we derive embedding vectors that characterize students and classes, their expressive power is low. With these issues in mind, this study proposes a Net-NeuMF model that generates vectors of students, classes through network analysis and utilizes them as input values of the model. The model was based on the structure of NeuMF using one-hot vectors, a representative model using data with implicit feedback. The input vectors of the model are generated to represent the characteristic of students and classes through network analysis. To generate a vector representing a student, each student is set to a node and the edge is designed to connect with a weight if the two students take the same class. Similarly, to generate a vector representing the class, each class was set as a node, and the edge connected if any students had taken the classes in common. Thus, we utilize Node2Vec, a representation learning methodology that quantifies the characteristics of each node. For the evaluation of the model, we used four indicators that are mainly utilized by recommendation systems, and experiments were conducted on three different dimensions to analyze the impact of embedding dimensions on the model. The results show better performance on evaluation metrics regardless of dimension than when using one-hot vectors in existing NeuMF structures. Thus, this work contributes to a network of students (users) and classes (items) to increase expressiveness over existing one-hot embeddings, to match the characteristics of each structure that constitutes the model, and to show better performance on various kinds of evaluation metrics compared to existing methodologies.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.1-27
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• 2017

It is known that the economic sentiment index and macroeconomic indicators are closely related because economic agent's judgment and forecast of the business conditions affect economic fluctuations. For this reason, consumer sentiment or confidence provides steady fodder for business and is treated as an important piece of economic information. In Korea, private consumption accounts and consumer sentiment index highly relevant for both, which is a very important economic indicator for evaluating and forecasting the domestic economic situation. However, despite offering relevant insights into private consumption and GDP, the traditional approach to measuring the consumer confidence based on the survey has several limits. One possible weakness is that it takes considerable time to research, collect, and aggregate the data. If certain urgent issues arise, timely information will not be announced until the end of each month. In addition, the survey only contains information derived from questionnaire items, which means it can be difficult to catch up to the direct effects of newly arising issues. The survey also faces potential declines in response rates and erroneous responses. Therefore, it is necessary to find a way to complement it. For this purpose, we construct and assess an index designed to measure consumer economic sentiment index using sentiment analysis. Unlike the survey-based measures, our index relies on textual analysis to extract sentiment from economic and financial news articles. In particular, text data such as news articles and SNS are timely and cover a wide range of issues; because such sources can quickly capture the economic impact of specific economic issues, they have great potential as economic indicators. There exist two main approaches to the automatic extraction of sentiment from a text, we apply the lexicon-based approach, using sentiment lexicon dictionaries of words annotated with the semantic orientations. In creating the sentiment lexicon dictionaries, we enter the semantic orientation of individual words manually, though we do not attempt a full linguistic analysis (one that involves analysis of word senses or argument structure); this is the limitation of our research and further work in that direction remains possible. In this study, we generate a time series index of economic sentiment in the news. The construction of the index consists of three broad steps: (1) Collecting a large corpus of economic news articles on the web, (2) Applying lexicon-based methods for sentiment analysis of each article to score the article in terms of sentiment orientation (positive, negative and neutral), and (3) Constructing an economic sentiment index of consumers by aggregating monthly time series for each sentiment word. In line with existing scholarly assessments of the relationship between the consumer confidence index and macroeconomic indicators, any new index should be assessed for its usefulness. We examine the new index's usefulness by comparing other economic indicators to the CSI. To check the usefulness of the newly index based on sentiment analysis, trend and cross - correlation analysis are carried out to analyze the relations and lagged structure. Finally, we analyze the forecasting power using the one step ahead of out of sample prediction. As a result, the news sentiment index correlates strongly with related contemporaneous key indicators in almost all experiments. We also find that news sentiment shocks predict future economic activity in most cases. In almost all experiments, the news sentiment index strongly correlates with related contemporaneous key indicators. Furthermore, in most cases, news sentiment shocks predict future economic activity; in head-to-head comparisons, the news sentiment measures outperform survey-based sentiment index as CSI. Policy makers want to understand consumer or public opinions about existing or proposed policies. Such opinions enable relevant government decision-makers to respond quickly to monitor various web media, SNS, or news articles. Textual data, such as news articles and social networks (Twitter, Facebook and blogs) are generated at high-speeds and cover a wide range of issues; because such sources can quickly capture the economic impact of specific economic issues, they have great potential as economic indicators. Although research using unstructured data in economic analysis is in its early stages, but the utilization of data is expected to greatly increase once its usefulness is confirmed.

• 한국균학회소식:학술대회논문집
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• 2018.05a
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• pp.27-27
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• 2018

The production scale of mushroom cultivation in Korea is approximately 600 billion won, which is 1.6% of the Korean gross agricultural output. Annually, ca. 190,000 tons of mushrooms are harvested in Korea. Although the numbers of mushroom farms and cultivators are constantly decreasing, the total mushroom yields are increasing due to the large-scale cultivation facilities and automation. The recent expansion of the well-being trend causes increase in mushroom consumption in Korea: annual per capita consumption of mushroom was 3.9kg ('13) that is a little higher than European's average. Thus the exports of mushrooms, mainly Flammulina velutipes and Pleurotus ostreatus, have been increased since the middle of 2000s. Recently, however, it is slightly reduced. However, Vietnam, Hong Kong, the United States, the Netherlands and continued to export, and the country has increased recently been exported to Australia, Canada, Southeast Asia and so on. Canned foods of Agaricus bisporus was the first exports of the Korean mushroom industry. This business has reached the peak of the sale in 1977-1978. As Korea initiated trade with China in 1980, the international prices of mushrooms were sharply fall that led to shrink the domestic markets. According to the high demand to develop new items to substitute for A. bisporus, oyster mushroom (Pleurotus ostreatus) was received the attention since it seems to suit the taste of Korean consumers. Although log cultivation technique was developed in the early 1970s for oyster mushroom, this method requires a great deal of labor. Thus we developed shelf cultivation technique which is easier to manage and allows the mass production. In this technique, the growing shelf is manly made from fermented rice straw, that is the unique P. ostreatus medium in the world, was used only in South Korea. After then, the use of cotton wastes as an additional material of medium, the productivity. Currently it is developing a standard cultivation techniques and environmental control system that can stably produce mushrooms throughout the year. The increase of oyster mushroom production may activate the domestic market and contribute to the industrial development. In addition, oyster mushroom production technology has a role in forming the basis of the development of bottle cultivation. Developed mushroom cultivation technology using bottles made possible the mass production. In particular, bottle cultivation method using a liquid spawn can be an opportunity to export the F.velutipes and P.eryngii. In addition, the white varieties of F.velutipes were second developed in the world after Japan. We also developed the new A.bisporus cultivar "Sae-ah" that is easy to grown in Korea. To lead the mushroom industry, we will continue to develop the cultivars with an international competitive power and to improve the cultivation techniques. Mushroom research in Korea nowadays focuses on analysis of mushroom genetics in combination with development of new mushroom varieties, mushroom physiology and cultivation. Further studied are environmental factors for cultivation, disease control, development and utilization of mushroom substrate resources, post-harvest management and improvement of marketable traits. Finally, the RDA manages the collection, classification, identification and preservation of mushroom resources. To keep up with the increasing application of biotechnology in agricultural research the genome project of various mushrooms and the draft of the genetic map has just been completed. A broad range of future studies based on this project is anticipated. The mushroom industry in Korea continually grows and its productivity rapidly increases through the development of new mushrooms cultivars and automated plastic bottle cultivation. Consumption of medicinal mushrooms like Ganoderma lucidum and Phellinus linteus is also increasing strongly. Recently, business of edible and medicinal mushrooms was suffering under over-production and problems in distribution. Fortunately, expansion of the mushroom export helped ease the negative effects for the mushroom industry.

• Journal of the Korea Organic Resources Recycling Association
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• v.19 no.1
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• pp.86-92
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• 2011

In order to reduce dependence on the fossil fuels and $CO_2$ gas emission in farming activities, the government has pushed ahead with making the self-sufficiency of farming energy up 40% level in green villages. The objectives of this study are to survey the energy consumption of horticultural facilities or agricultural machineries, and to analyze the reduced $CO_2$ gas emission level from fossil fuel to bio-diesel fuel. For the implement of this study, it is necessary to analyze the energy consumption level in the various sector of farming activities, and available renewable energy sources should be selected. Annual total $CO_2$ gas emission in the tillage farming sector was analyzed as $5,667,258\;t-CO_2$ and that in the horticultural facilities occupied $4,932,607\;t-CO_2$, while the $CO_2$ gas emission level of diesel fuel was $3,105,707\;t-CO_2$, and that of the heavy oil showed $1,370,578\;t-CO_2$. The average $CO_2$ gas emission level of horticultural facilities in the country was analyzed as $29,418\;t-CO_2/ha$. Among the total energy consumption of agricultural machineries, tractor used 284,763kL, power tiller spent 221,314 kL, grain drier consumed 145,524kL and combine tractor expend 72,537kL. From the comparison of $CO_2$ gas emission level between fossil fuel and bio-diesel fuel for the horticultural facilities or agricultural machinery in G-City, Jeonbuk Province, the $CO_2$ gas emission level can be reduced by 7% through replacing the fuel from fossil to biodiesel.

• Journal of Korean Society of Environmental Engineers
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• v.39 no.6
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• pp.356-370
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• 2017

The energy savings and $CO_2$ emission reductions obtainable from the situation that the Smart Grid system (SGs) is assumed to be applied in Korea up to 2030 is quantitatively analyzed with many reported data. For calculation, SGs is divided into five sectors such as Smart Transmission and Distribution (ST&D), Smart Consumer (SC), Smart Electricity Service (SES), Smart Renewable Energy (SRE) and Smart Transportation (ST). Total annual energy savings in 2030 is estimated to be approximately 103,121 GWh and this is 13.1% of total electricity consumption outlook. Based on this value, total amount of reducible $CO_2$ emissions is calculated to 55.38 million $tCO_2$, which is 17.6% of total nation's GHG reduction target. Although the contribution of energy saving due to SGs to total electricity consumption increases as years go by, that of $CO_2$ emission reduction gradually decreases. This might be because that coal fired based power generation is planned to be sharply increased and the rate of $CO_2$ emission reduction scheduled by nation is very fast. The contributable portion of five each sector to total $CO_2$ emission reductions in 2030 is estimated to be 44.37% for SC, 29.16% for SRE, 20.12% for SES, 5.11% for ST&D, and 1.24% for ST.