• Journal of the Society of Disaster Information
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• v.17 no.1
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• pp.25-38
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• 2021

Purpose: To reduce the damage caused by continuously occurring typhoons, we proposed a standardized grid so that it could be actively utilized in the prevention and preparation stage of typhoon response. We established grid-based convergence information on the typhoon risk area so that we showed the effectiveness of information used in disaster response. Method: To generate convergent information on typhoon hazard areas that can be useful in responding to typhoon situation, we used various types of data such as vector and raster to establish typhoon hazard area small grid-based information. A standardized grid model was applied for compatibility with already produced information and for compatibility of grid information generated by each local government. Result: By applying the grid system of National branch license plates, a grid of typhoon risk areas in Seoul was constructed that can be usefully used when responding to typhoon situations. The grid system of National branch license plates defines the grid size of a multi-dimensional hierarchical structure. And a grid of typhoon risk areas in Seoul was constructed using grids of 100m and 1,000m. Conclusion: Using real-time 5km resolution grid based weather information provided by Korea Meteorological Administration, in the future, it is possible to derive near-future typhoon hazard areas according to typhoon travel route prediction. In addition, the national branch number grid system can be expanded to global grid systems for global response to various disasters.

• 한국사회정책
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• v.23 no.2
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• pp.113-139
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• 2016

This article tries to compare exchange relations between welfare and dictatorship in the East Germany and the North Korea. Unlike capitalist welfare aiming at correcting market results socialist welfare has been proposed to satisfy people's basic needs, but it had operated as instrument of dictatorship. Relation between welfare and dictatorship could be distinguished as hard exchange and soft one in line with social construction of welfare. Welfare-dictatorship relation in East Germany had developed from its formation(1949-1970s), crisis(1980s) and dissolution(1989-1990). There had established hard exchange relation in which the legitimacy of dominance had debted to welfare as social rights. While crisis of the exchange relation had been modest in a form of insufficient supply of consumption goods, it was one of the elements of collapse of dictatorship, leading to the unification with West Germany. The journey of the exchange relation in North Korea can be characterized by its formation(1948-1980), crisis(1990s-2000s), and transformation(2010s). Unlike East Germany, welfare was socially constructed as gift form the ruler to the ruled, which made the combination of welfare and dictatorship loosely coupled. Although economic crisis was severe compared to East German one the rulers have succeeded maintaining dictatorial dominance by creating dual exchange relation. They separated core group and subordinated one supporting the former at the expense of the latter. They blocked out most of the people from soft exchange relation making bad use of muddling-through life style dependent on market activities. This strategy led to a 'dictatorship neutral welfare extinction'. Taking the high degree of institutionalization of newly establishing welfare-dictatorship relation into account, lives of most people are hardly expected to be improved by gift by their rulers even if North Korean economy will recover in the future.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.4
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• pp.125-132
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• 2021

As the layers of artificial neural network deepens, and the dimension of data used as an input increases, there is a problem of high arithmetic operation requiring a lot of arithmetic operation at a high speed in the learning and recognition of the neural network (NN). Thus, this study proposes a data dimensionality reduction method to reduce the dimension of the input data in the NN. The proposed Line-segment Feature Analysis (LFA) algorithm applies a gradient-based edge detection algorithm using median filters to analyze the line-segment features of the objects existing in an image. Concerning the extracted edge image, the eigenvalues corresponding to eight kinds of line-segment are calculated, using 3×3 or 5×5-sized detection filters consisting of the coefficient values, including [0, 1, 2, 4, 8, 16, 32, 64, and 128]. Two one-dimensional 256-sized data are produced, accumulating the same response values from the eigenvalue calculated with each detection filter, and the two data elements are added up. Two LFA256 data are merged to produce 512-sized LAF512 data. For the performance evaluation of the proposed LFA algorithm to reduce the data dimension for the recognition of handwritten numbers, as a result of a comparative experiment, using the PCA technique and AlexNet model, LFA256 and LFA512 showed a recognition performance respectively of 98.7% and 99%.

• The Journal of the Korea Contents Association
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• v.21 no.8
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• pp.356-366
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• 2021

The high-tech industry, a leader in the national economy, has a larger investment cost compared to general buildings, a shorter construction period, and requires continuous investment. Therefore, accurate construction cost prediction and quick decision-making are important factors for efficient cost and process management. Overseas, the construction information classification system has been standardized since 1980 and has been continuously developed, improving construction productivity by systematically collecting and utilizing project life cycle information. At domestic construction sites, attempts have been made to standardize the classification system of construction information, but it is difficult to achieve continuous standardization and systematization due to the absence of a standardization body and differences in cost and process management methods for each construction company. Particular, in the case of the high-tech industry, the standardization and systematization level of the construction information classification system for high-tech facility construction is very low due to problems such as large scale, numerous types of work, complex construction and security. Therefore, the purpose of this study is to construct a construction information classification system suitable for high-tech facility construction through collection, classification, and analysis of related project data constructed in Korea. Based on the WBS (Work Breakdown Structure) and CBS (Cost Breakdown Structure) classified and analyzed through this study, a code system through hierarchical classification was proposed, and the cost model of buildings by linking WBS and CBS was three-dimensionalized and the utilized method was presented. Through this, an information classification system based on inter-relationships can be developed beyond the one-way tree structure, which is a general construction information classification system, and effects such as shortening of construction period and cost reduction will be maximized.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.1
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• pp.51-58
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• 2023

In this paper, we propose an organ segmentation technique for the automatic extraction of medical diagnostic indicators from X-ray images. In order to calculate diagnostic indicators of heart disease and spinal disease such as VHS(vertebral heart scale) and Cobb angle, it is necessary to accurately segment the thoracic spine, carina, and heart in a chest X-ray image. A deep neural network model in which the high-resolution representation of the image for each layer and the structure converted into a low-resolution feature map are connected in parallel was adopted. This structure enables the relative position information in the image to be effectively reflected in the segmentation process. It is shown that learning performance can be improved by combining the OCR module, in which pixel information and object information are mutually interacted in a multi-step process, and the channel attention module, which allows each channel of the network to be reflected as different weight values. In addition, a method of augmenting learning data is presented in order to provide robust performance against changes in the position, shape, and size of the subject in the X-ray image. The effectiveness of the proposed theory was evaluated through an experiment using 145 human chest X-ray images and 118 animal X-ray images.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.327-351
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• 2023

This paper aims to derive success factors that successfully lead an artificial intelligence (AI) project and prioritize importance. To this end, we first reviewed prior related studies to select success factors and finally derived 17 factors through expert interviews. Then, we developed a hierarchical model based on the TOE framework. With a hierarchical model, a survey was conducted on experts from AI-using companies and experts from supplier companies that support AI advice and technologies, platforms, and applications and analyzed using AHP methods. As a result of the analysis, organizational and technical factors are more important than environmental factors, but organizational factors are a little more critical. Among the organizational factors, strategic/clear business needs, AI implementation/utilization capabilities, and collaboration/communication between departments were the most important. Among the technical factors, sufficient amount and quality of data for AI learning were derived as the most important factors, followed by IT infrastructure/compatibility. Regarding environmental factors, customer preparation and support for the direct use of AI were essential. Looking at the importance of each 17 individual factors, data availability and quality (0.2245) were the most important, followed by strategy/clear business needs (0.1076) and customer readiness/support (0.0763). These results can guide successful implementation and development for companies considering or implementing AI adoption, service providers supporting AI adoption, and government policymakers seeking to foster the AI industry. In addition, they are expected to contribute to researchers who aim to study AI success models.

• 전기의세계
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• v.27 no.5
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• pp.33-54
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• 1978

(차례) 1.산학협동심포지업 (1)우리나라에서의 연구개발과 산학협동 (2)산학협동과 산업계의 역할 (3)산학협동의 현황과 진로 2.학술회의A (1)전력게통의 계층구조와 협조원리에 관한 연구 (2)2중층괴상회전자 유도전동기의 이론해석 (3)초고주파가열장치에 사용하는 철공진변압기의 해석적 설계 (4)한국전기기시험연구소 대전력단락 시험설비설계 (5)직류전동기제어를 위한 Thyristor Chopper정류회로에 관한 연구 (6)선로의 개폐정보를 포함하는 전력계통의 상태추정 (7)단일신경세포에 대한 ITEM 신호 특성 3.학술회의B (1)MMM-1 Computer System의 설계 및 제작 (2)Adaptive Delta Modulation System의 성능비교 연구 (3)6GHZ FMD마이크로파 무선전송장치의 개발 (4)적선도에 의한 회로망함수의 결정 (5)동맥혈압의 해석과 그의 전기적 유사모델 (6)피부감각의 정보전달 특성에 관하여 (7)선형직접회로의 공정설계 및 그 특성 조성 (8)DH L.D의 전기적포화현상에 관한 이론적 해석 (9)Potocoupler를 이용한 Isolator 4.학술회의C (1)Al-Al$_{2}$O$_{3}$ -Al박막구조의 전기적 특성 (2)이종금속에 샌드위치된 고분자물질의 단락전조 (3)유전체가 일부체워진 직 6면체의 캐비티의 다중모오드 해석 (4)반도체 가스 검지소자의 제조 및 그의 전기적 특성 (5)실리콘 산화공정에 대한 실험적 고찰 (6)진공증착법에 의한 InSb 박막제도에서 열처리효과 (7)(Ba$_{1}$-xBix) Tio$_{3}$ PTC thermistor의 첨가량의 최적건안 (8)금속박막증착시 두께조절 5.특별강연회 (1)일본에 있어서의 절력계통공학연구 (2)Linear Motor의 최근개발동향량도 높았다. valine과 leucine 및 aspartic acid, glycine과 glutamic acid, leucine과 aspartic acid 간에는 고도의 정상관, glycine과 serine, valine과 phenylalanine, threonine과 proline, phenylalanine과 arginine, methionine과 glutamic acid, histidine과 lysine 간에는 유의 정상관, 그리고 isoleucine과 lysine 간에는 유의한 부상관이 있었다. 4. lysine 함량은 단백질 함량과 정산곤, isoleucine 함량은 단빅질 함량과 부상관을 보였으며, alanine, valine, leucine 함량은 지방함량과 각각 유의한 정산관을 보였다. 5. 대두 단백질은 7.5% acrylamide gel 전기영동에 의해 품종에 따라 12～16개의 구성분으로 분리되었으며, 이들중 주구성분들은 상대이동도가 0.06(a), 0.14(b). 0.24(d) 이었고, 구성분 b의 함량이 품종간에 가장 변이가 컸으며, 구성분 b는 그밖의 주요 구성분들의 함량과 부의 상관이 있었고, 구성분 a는 단백질 함량과 정상관이 있었다. 6. 종실단백질 구성분들의 조합 특성 면에서 공시 86품종은 11개 유형군으로 분류되었으며, 우리나라와 일본품종은 미국품종에 비해 단백질구성분 조성이 훨씬 다양하였다. 7. 이동도가 매우 빠른 단백질 구성분 o(Rm 0.77) p(Rm 0.81)를 모두 갖고 있는 품종은 3품종, 모두 갖고 있지 않은 품종은 1품종이었고, 나머지 82품종은 o나 p중 한 구성분을 갖고 있었으며 그 분포율은 30 : 65 이었는데 미국계 품종은 우리나라 품종에 비해 구성분 o를 간고 있는 비율이 현저히 적었다. 8. 대두 종실은 개화후 22일까지 완만히, 그 이후 20～30일간 급속히

• Convergence Security Journal
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• v.23 no.2
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• pp.47-54
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• 2023

The global popularity of K-content(Korean Wave) has led to a continuous increase in copyright infringement cases involving domestic works, not only within the country but also overseas. In response to this trend, there is active research on technologies for detecting illegal distribution sites of domestic copyrighted materials, with recent studies utilizing the characteristics of domestic illegal distribution sites that often include a significant number of advertising banners. However, the application of detection techniques similar to those used domestically is limited for overseas illegal distribution sites. These sites may not include advertising banners or may have significantly fewer ads compared to domestic sites, making the application of detection technologies used domestically challenging. In this study, we propose a detection technique based on the similarity comparison of links and text trees, leveraging the characteristic of including illegal sharing posts and images of copyrighted materials in a similar hierarchical structure. Additionally, to accurately compare the similarity of large-scale trees composed of a massive number of links, we utilize Graph Neural Network (GNN). The experiments conducted in this study demonstrated a high accuracy rate of over 95% in classifying regular sites and sites involved in the illegal distribution of copyrighted materials. Applying this algorithm to automate the detection of illegal distribution sites is expected to enable swift responses to copyright infringements.

• Information Systems Review
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• v.24 no.1
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• pp.21-37
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• 2022

Drug repositioning, one of the methods of developing new drugs, is a useful way to discover new indications by allowing drugs that have already been approved for use in people to be used for other purposes. Recently, with the development of machine learning technology, the case of analyzing vast amounts of biological information and using it to develop new drugs is increasing. The use of machine learning technology to drug repositioning will help quickly find effective treatments. Currently, the world is having a difficult time due to a new disease caused by coronavirus (COVID-19), a severe acute respiratory syndrome. Drug repositioning that repurposes drugsthat have already been clinically approved could be an alternative to therapeutics to treat COVID-19 patients. This study intends to examine research trends in the field of drug repositioning using machine learning techniques. In Pub Med, a total of 4,821 papers were collected with the keyword 'Drug Repositioning'using the web scraping technique. After data preprocessing, frequency analysis, LDA-based topic modeling, random forest classification analysis, and prediction performance evaluation were performed on 4,419 papers. Associated words were analyzed based on the Word2vec model, and after reducing the PCA dimension, K-Means clustered to generate labels, and then the structured organization of the literature was visualized using the t-SNE algorithm. Hierarchical clustering was applied to the LDA results and visualized as a heat map. This study identified the research topics related to drug repositioning, and presented a method to derive and visualize meaningful topics from a large amount of literature using a machine learning algorithm. It is expected that it will help to be used as basic data for establishing research or development strategies in the field of drug repositioning in the future.

• The Transactions of the Korea Information Processing Society
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• v.13 no.2
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• pp.48-59
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• 2024

As functions that support virtualization on their own in hardware are developed, user applications having various workloads are operating efficiently in the virtualization system. SR-IOV is a virtualization support function that takes direct access to PCI devices, thus giving a high I/O performance by minimizing the need for hypervisor or operating system interventions. With SR-IOV, network I/O acceleration can be realized in virtualization systems that have relatively long I/O paths compared to bare-metal systems and frequent context switches between the user area and kernel area. To take performance advantages of SR-IOV, network resource management policies that can derive optimal network performance when SR-IOV is applied to an instance such as a virtual machine(VM) or container are being actively studied.This paper evaluates and analyzes the network performance of SR-IOV implementing I/O acceleration is compared with Virtio in terms of 1) network delay, 2) network throughput, 3) network fairness, 4) performance interference, and 5) multi-network. The contributions of this paper are as follows. First, the network I/O process of Virtio and SR-IOV was clearly explained in the virtualization system, and second, the evaluation results of the network performance of Virtio and SR-IOV were analyzed based on various performance metrics. Third, the system overhead and the possibility of optimization for the SR-IOV network in a virtualization system with high VM density were experimentally confirmed. The experimental results and analysis of the paper are expected to be referenced in the network resource management policy for virtualization systems that operate network-intensive services such as smart factories, connected cars, deep learning inference models, and crowdsourcing.