• Korean Security Journal
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• no.59
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• pp.37-69
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• 2019

According to viewpoints of researchers and stakeholders, various opinions can be suggested on self-governing police system. Therefore, success of Korean self-governing police system will be defending on how to balance among conflicting values such as Empowerment, Political neutrality, Financial issues, Comprehensive competence in maintaining public safety. Before the launching of self-governing police system nation-wide, the experience of Jeju provincial police will be valuable model case. In specific, enlargement of work scope of self-governing police in Jeju province which has been introduced since last year will be a useful reference. There is more pessimism about self-governing police of Jeju province so far. However, this perspective is mostly based on the issue regarding hardwares such as manpower, equipment, law and organization. Issues regarding softwares such as organizational culture, operation system and work process need more attention to evaluate self-governing police system properly. To mark the first year after enlargement of work scope of Jeju police, this study demonstrate the overall result and implications of self-governing police of Jeju province based on documents, statistics, reports and media reports. In result, several preconditions are needed to implement the self-governing police system nation-wide successfully. 1. Strengthen the link between local government and local police 2. Establish the foundation for collaboration of state and local police 3. Enhance the aspect of citizen autonomy in local level 4. Reinforcing the capability of handling situation of state and local police 5. Invigorating the inter-organizational working group to operate self-governing police system effectively. The self-governing police system is unclosed topic to discuss. After this study, in-depth studies should be followed with more resources. Particularly, additional perspective including redundancy and equity need to be considered regarding self-governing police. By getting with the changes of macroscopic trends - lowbirth and aging, the fourth industrial revolution and possible reunification of north and south Koreas - these studies should suggest the long-term blueprint of self-governing police system of Korea.

• Journal of Korea Water Resources Association
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• v.53 no.2
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• pp.107-119
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• 2020

This study analyzed past drought characteristics based on the observed rainfall data and performed a long-term outlook for future extreme droughts using Representative Concentration Pathways 8.5 (RCP 8.5) climate change scenarios. Standardized Precipitation Index (SPI) used duration of 1, 3, 6, 9 and 12 months, a meteorological drought index, was applied for quantitative drought analysis. A single long-term time series was constructed by combining daily rainfall observation data and RCP scenario. The constructed data was used as SPI input factors for each different duration. For the analysis of meteorological drought observed relatively long-term since 1954 in Korea, 12 rainfall stations were selected and applied 10 general circulation models (GCM) at the same point. In order to analyze drought characteristics according to climate change, trend analysis and clustering were performed. For non-stationary frequency analysis using sampling technique, we adopted the technique DEMC that combines Bayesian-based differential evolution ("DE") and Markov chain Monte Carlo ("MCMC"). A non-stationary drought frequency analysis was used to derive Severity-Duration-Frequency (SDF) curves for the 12 locations. A quantitative outlook for future droughts was carried out by deriving SDF curves with long-term hydrologic data assuming non-stationarity, and by quantitatively identifying potential drought risks. As a result of performing cluster analysis to identify the spatial characteristics, it was analyzed that there is a high risk of drought in the future in Jeonju, Gwangju, Yeosun, Mokpo, and Chupyeongryeong except Jeju corresponding to Zone 1-2, 2, and 3-2. They could be efficiently utilized in future drought management policies.

• The Journal of Society for e-Business Studies
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• v.17 no.4
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• pp.69-93
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• 2012

Various influence-related relationships in Social Network Services (SNS) among users, posts, and user-and-post, can be expressed using links. The current research evaluates the influence of specific users or posts by analyzing the link structure of relevant social network graphs to identify influential users. We applied the concept of mutual interactions proposed for ranking semantic web resources, rather than the voting notion of Page Rank or HITS, to blogosphere, one of the early SNS. Through many experiments with network models, where the performance and validity of each alternative approach can be analyzed, we showed the applicability and strengths of our approach. The weight tuning processes for the links of these network models enabled us to control the experiment errors form the link weight differences and compare the implementation easiness of alternatives. An additional example of how to enter the content scores of commercial or spam posts into the graph-based method is suggested on a small network model as well. This research, as a starting point of the study on identifying influential users in SNS, is distinctive from the previous researches in the following points. First, various influence-related properties that are deemed important but are disregarded, such as scraping, commenting, subscribing to RSS feeds, and trusting friends, can be considered simultaneously. Second, the framework reflects the general phenomenon where objects interacting with more influential objects increase their influence. Third, regarding the extent to which a bloggers causes other bloggers to act after him or her as the most important factor of influence, we treated sequential referencing relationships with a viewpoint from that of PageRank or HITS (Hypertext Induced Topic Selection).

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.4
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• pp.24-38
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• 2019

Due to the dramatic spatial changes caused by industrialization, environmental problems such as air pollution and urban heat island phenomenon, etc. are occurring in cities. In this case, the wind corridor, which is a passage through which fresh and cool air generated in forests outside cities move to the downtown, can be used as a spatial planning method for improving urban environmental problems. Cold air is determined by the characteristics of the flow depending on the topography and land use of cities, and based on this, the medium- and long-term plan should be established. Therefore, this study analyzed the flow of cold air at night through the KLAM_21 model in Suwon-si, Gyeonggi-do, to prepare the basic data required to apply the wind corridors. As a result, it turned out that cold air of Suwon-si was mainly generated from Gwanggyo Mountain that is a large mountain area in the north, and flowed into the urbanization promotion area, and about three hours after sunset, cold air flowed into the downtown. By district, the depth, wind speed, and direction of the cold air layer were formed differently according to the characteristics of the topography and land use. In the areas where large forests were adjacent, the flow of cold air was active. There are three main wind corridors where cold air flows to the downtown of Suwon-si, all of which are formed around rivers. Especially, if the connection between rivers and the surrounding green areas is high, the effect of wind corridors is found to be significant. In order to utilize the wind corridors of Suwon-si, based on the results of this study, it is necessary to make climate maps through actual survey and complex analysis of cold air flow and establish mid-to-long-term plans for the conservation and expansion of major wind corridors.

• Journal of Korean Society of Forest Science
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• v.102 no.3
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• pp.390-397
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• 2013

The study, utilizing a data envelopment analysis (DEA) which is one of the nonparametric estimation methods, aims to evaluate the management efficiency of chestnut tree cultivators in such provinces in Chungchungnam-do as Cheong-yang, Gong-ju, Bu-yeo and so on. The analysis data of this study is based on inputs and outputs of 20 forestry households surveyed in the 2012 survey titled 'A Study on Current Level and Condition of Chestnut Cultivation and Management', which was conducted from March 2012 to October 2012. The elements of inputs are composed of management cost, harvesting cost, material cost, non-operation expenses and cultivation area, while the element of output is a gross margin only. Then the study analyzes a technical efficiency, a puretechnical efficiency and a scale efficiency using CCR and BCC model among DEA methods. Based on that, it also provides improvement methods for forestry households that turned out to be inefficient. In order to verify the result of DEA analysis, the study additionally compares a result of this efficiency study with that of chestnuts management standard diagnostic table. According to the result, the average value of technical efficiency analyzed was 0.667, proving to be inefficient in general. Given that the average value of pure-technical efficiency was 0.944 and that of scale efficiency was 0.703, it can be inferred that inefficiency exists in the field of scale, not in the field of cultivation techniques. As for forestry households with the efficiency score of 1, it is shown that there were 6 households that recorded 1 in the technical efficiency field and 13 households that recorded 1 in the pure technical efficiency. Meanwhile, there were 6 households that recorded 1 in all of the three aspects. In the comparison with the scores from chestnuts management standard diagnostic table, there were 5 households made a high score of over 80, among which are 3 households with score 1 in the technical efficiency. Also, the results of this study and the chestnuts management standard diagnostic table are proved to have the same result, both of them showing the same households that recorded the highest score and the lowest score. This means the management efficiency evaluation using DEA can be applied to the fieldwork along with the chestnuts management standard diagnostic table.

• Journal of the Korean Society of Food Science and Nutrition
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• v.45 no.7
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• pp.1071-1076
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• 2016

The feasibility of incorporating aronia powder (AP) as a value-added food ingredient into convenient food products was investigated using cookie as a model system. AP was incorporated into cookies at amounts of 0, 1, 2, 3, and 4% (w/w) based on total weight of wheat flour. The pH level of cookie dough decreased significantly with increasing levels of AP (P<0.05), whereas moisture content and density were not directly affected by levels of AP incorporation. The spread ratio increased significantly upon addition of AP; however, it was not significantly affected by level of AP incorporation. The loss rate of cookies decreased significantly with increasing levels of AP (P<0.05). In terms of color, lightness and yellowness decreased while redness increased significantly (P<0.05) with increasing levels of AP. Use of AP significantly decreased hardness of cookies (P<0.05), but no significant differences were found between control and 1% sample, 1% and 2% sample, 2% and 3% sample, and 3% and 4% sample (P>0.05). 2,2-Diphenyl-1-picrylhydrazyl (DPPH) and 2,2'-azino-bis-3-ethylbenzthiazoline-6-sulphonic acid (ABTS) radical scavenging activities were significantly elevated by AP addition, and they increased significantly as AP concentration increased in the formulation (P<0.05). Correlation analysis indicated that acceptances of color and chewiness were negatively correlated well with the level of AP incorporation, density and moisture content of cookie dough, and cookie redness, whereas they were positively correlated with pH of cookie dough, loss rate, and hardness (P<0.01 or P<0.05). Finally, consumer acceptance test indicated that the highest levels of AP incorporation (4%) had an adverse effect on general consumer preferences. In contrast, cookies with moderate levels of AP (2%) are recommended based on overall scores to take advantage of the antioxidant properties of AP without sacrificing consumer acceptability.

• 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.