Earticle

본 논문에서는 타겟의 산란계수를 통한 RCS 측정 시, 타겟 위치 변화에 따라 발생하는 FT(Fourier Transform) 윈도우 크기 패턴에 미치는 영향에 대해 분석하였다. 윈도우 크기 패턴에 미치는 영향의 객관성을 확 보하기 위해 측정 대상의 RCS 시뮬레이션 값과 실험 결과를 비교하여 오차율이 가장 작은 지점에서 윈도우 크기 값을 추출하였다. 실험 결과 측정 대상인 타겟의 거리가 증가함에 따라 윈도우의 오프셋 포인트는 점차 감소하였 으나, 윈도우의 폭은 증가하는 것을 확인하였다. 타겟 위치에 따라 변화하는 윈도우 크기 패턴을 시스템에 내재 되어있는 Lookup Table로부터 적용하고 획득 데이터를 저장하여, RCS 측정에 신뢰성을 확보하였다. 또한 연속적 으로 산란계수 기반의 실시간 RCS 데이터처리가 가능하도록 시스템을 구성하였다. 패턴 분석을 통해 RCS 측정 의 정확성과 소요 시간을 확보할 수 있다.

In this paper, the effect on the FT window size pattern that occurs according to the target position when measuring RCS through the scattering coefficient of the target is proposed. In order to get objectivity of the effect on the window size pattern, the RCS simulation value of the measurement target was compared with experimental results, and the window size value was extracted at the point where the error rate was lowest. As a result of the experiment, it was confirmed that the offset point of the window gradually decreased as the distance of the target to be measured increased, but the width of the window increased. Reliability was secured by applying the window size pattern that changes according to the target position from Lookup Table inherent in the system and saving the acquired data. In addition, the system was configured to enable continuous real-time RCS data processing. By analyzing the pattern, it can reduce time and the accuracy can be obtained.

최근 악성코드 발생률은 약 수만 건이 넘는 추세로, 전부 탐지/대응하는 것은 불가능에 가깝다고 알려졌다. 본 연구는 새로운 악성코드 대응방법으로 그래프 데이터베이스 기반 다중행위 패턴 탐지 기법을 제안한다. 기존 동적 분석 기법과는 다른 새로운 그래프 모델을 설계하고, 대표적인 악성코드 패턴(프로세스, PE, 레지스트리 등)의 그래프 연관 관계를 분석하는 방법을 적용했다. 패턴 검증 결과 기본 악성 패턴에 대한 행위 탐지와 기존 분석이 어려웠던 변종 공격 행위(5단계 이상)의 탐지를 확인했다. 또한, 성능 분석결과 5단계 이상의 복잡한 패턴에 대하여 관계형 데이터베이스 대비 약 9.84배 이상 성능이 향상되었음을 확인하였다.

Recently, the incidence rate of malicious codes is over tens of thousands of cases, and it is known that it is almost impossible to detect/respond all of them. This study proposes a method for detecting multiple behavior patterns based on a graph database as a new method for dealing with malicious codes. Traditional dynamic analysis techniques and has applied a method to design and analyze graphs of representative associations malware pattern(process, PE, registry, etc.), another new graph model. As a result of the pattern verification, it was confirmed that the behavior of the basic malicious pattern was detected and the variant attack behavior(at least 5 steps), which was difficult to analyze in the past. In addition, as a result of the performance analysis, it was confirmed that the performance was improved by about 9.84 times or more compared to the relational database for complex patterns of 5 or more steps.

Objectives: This study aimed to research the capability of securing financial soundness, such as profit-making and debt repayment, by analyzing cash flow trends in domestic general hospitals before and after the global financial crisis and investigating the current status of fund procurement and history of its management. Methods: In this study, the business performance was analyzed, taking into consideration the general characteristics of the hospital collected through vertical ratio analysis, horizontal trend analysis, and pattern analysis based on eight years of cash flow statements of 172 domestic general hospitals (2006~2013). Results: Most general hospitals in Korea showed the life cycle of the developing business type that comprises the positive (+) cash flow from sales activities, negative (-) cash flow from investment activities, and positive (+) cash flow from financing activities. Conclusions: Except for the time before and after 2008, when the global financial crisis took effect, and before and after 2012, when many large hospitals suffered from upheavals, the overall cash flows generally showed strengths. Moreover, sales capability, investment availability, and debt repayment were stable.

4차 산업 혁명과 데이터 환경의 급격한 변화는 기존 관계형 데이터베이스(RDB)는 기술적 한계를 드러내 고 있다. IDC/금융/보험 등 전 분야에서 비정형 데이터에 대한 새로운 분석방안으로 그래프 데이터베이스(GDB) 기술에 관심이 높아지고 있다. 그래프 데이터베이스는 상호 연동된 데이터를 표현하고 광범위한 네트워크에서 연관 관계 분석에 효율적인 기술이다. 본 연구는 기존 RDB를 GDB 모델로 확장하고, 새로운 이상징후 탐지를 위해 기계학습 알고리즘(패턴인식, 클러스터링, 경로거리, 핵심추출)을 적용하였다. 성능분석 결과 이상 행위 성능 (약 180배 이상)이 크게 향상되었고, RDB로 분석 불가능한 5단계 이후 이상징후 패턴을 추출할 수 있음을 확인 하였다.

The 4th industrial revolution and the rapid change in the data environment revealed technical limitations in the existing relational database(RDB). As a new analysis method for unstructured data in all fields such as IDC/finance/insurance, interest in graph database(GDB) technology is increasing. The graph database is an efficient technique for expressing interlocked data and analyzing associations in a wide range of networks. This study extended the existing RDB to the GDB model and applied machine learning algorithms (pattern recognition, clustering, path distance, core extraction) to detect new abnormal signs. As a result of the performance analysis, it was confirmed that the performance of abnormal behavior(about 180 times or more) was greatly improved, and that it was possible to extract an abnormal symptom pattern after 5 steps that could not be analyzed by RDB.

최근 전국 각 지역 AMI(Advanced Metering Infrastructure) 원격검침 시스템의 보급사업이 활성화되고 있으 며, 전력수요 관리를 위한 양방향 통신 및 보안 요금제 기능 등 다양한 계량 기능을 제공하고 있다. 현재 AMI 시스템은 새로운 내부 IoT 장비 및 네트워크 규모의 증가로 인해 기존 RDB(Relational Database) 기반 장애 분석이 어렵다. 본 연구는 기존 RDB 데이터를 활용하는 새로운 GDB(Graph Database)기반 장애 분석 방법을 제안한다. 내부 임계치 와 상태 값 등 누적된 데이터를 통해 새로운 장애 패턴의 상관관계를 분석한다. GDB 기반 시뮬레이션 결과 RDB에서 분석이 어려웠던 새로운 장애 패턴을 예측할 수 있음을 확인하였다.

Recently, the spreading business of AMI (Advanced Metering Infrastructure) remote metering systems in various regions of the country has been activated, and it provides various metering functions such as two-way communication and security plan functions for power demand management. Current AMI system is difficult to analyze based on the existing RDB(Relational Database) due to the increase in the size of new internal IoT devices and networks. This study proposes a new GDB(Graph Database) based failure analysis method that utilizes existing RDB data. It analyzes the correlation of new failure patterns through accumulated data such as internal thresholds and status values. As a result of GDB-based simulation, it was confirmed that RDB can predict to a new obstacle pattern that was difficult to analyze.

최근 국제적인 테러 위협이 불특정 다수를 대상으로 발생하고 있으며, 이러한 위협에서 시민을 보호하기 위한 다양한 대책이 논의 중이다. 저렴해진 센서 기술을 활용한 사전 감시 시스템에 대한 요구가 높아지고 있으나, 보행 궤적의 고유 특성 검출 및 상세 분석 연구가 미비한 실정이다. 본 연구에서는 상용화된 보행 동선 솔루션을 활용하여, 삼성역 개찰구에서 코엑스와 직접 연결되는 연결 통로 (3-6번 출구 근처) 일대의 보행 동선 궤적 조사를 수행하 였다. 조사된 궤적 자료를 바탕으로, 궤적 자료의 정규화 기법, Clustering 방법을 중심으로 보 행 궤적을 유형화하고 배회 동선을 추출하는 분석 방법론을 제시하였다. 분석 결과, 동일 군 집내에서 유사성이 크게 떨어지는 보행 궤적의 검출 가능성을 검증하였다.

Recently terrorism targets unspecified masses and causes massive destruction, which is so-called Super Terrorism. Many countries have tried hard to protect their citizens with various preparation and safety net. With inexpensive and advanced technologies of sensors, the surveillance systems have been paid attention, but few studies associated with the classification of the pedestrians’ trajectories and the difference among themselves have attempted. Therefore, we collected individual trajectories at Samseoung Station using an analytical solution (system) of pedestrian trajectory by LiDAR sensor. Based on the collected trajectory data, a comprehensive framework of classifying the types of pedestrians’ trajectories has been developed with data normalization and “trajectory association rule-based algorithm.” As a result, trajectories with low similarity within the very same cluster is possibly detected.

The disaster and safety accidents in any construction site occur inevitably. Since the on-site disaster and accident occurrence became one of major concerning factors in construction site, architects & building executers have been flooded with safety management advices. The purpose of this paper is to survey and analyze the effect of the application of Safety Patrol Management System(SPMS) upon the reduction of disaster & accident occurrences in construction site. In order to estimate and reduce the disaster and accident ratio in construction site, various surveyed data has been analysed. It will establish the suitable design standards and suggest the basic database for estimating disaster and accident ratio.

온라인게임 서비스와 다른 콘텐츠 비즈니스와의 차이점은 제품의 라이프 사이클이다. 특히 MMORPG는 온라인게임 중에서도 특히 라이프 사이클이 길다. 이러한 라이프 사이클 유지하는 가장 핵심적인 요소는 패치 서비스다. 패치 서비 스는 온라인게임의 세계를 지속적으로 확장하면서 고객의 플레이 동기를 유발시킨다. 본 논문은 블리자드의 MMORPG <월드오브워크래프트>의 5년간(2005~2010) 패치 내용을 분석한 것이다. 분석을 바탕으로 하여 문헌연구와 전문가 인터 뷰를 통해 패치 서비스의 4가지 유형을 도출하고자 하였다.

An important difference between online game service and other contents business is product life cycle. Of all other online games, a MMORPG especially has longer life cycle. A patch service is the most significant factor which helps a game product life cycle keeps up longer. The patch service enlarges steadily a world of online games and creates game player’s motivation. This archive analyzes patch contents on <The World of Warcraft> of Blizzard for the past five years. (2005-2010) And being based on this analysis, we will deduce four pattern of patch service with research and interview.

In this paper, the Self-Organizing Feature Maps (SOFM) neural network is applied to analyse the multi dimensional process data, and to diagnose the inter-relationship of the process variables in a real municipal sewage treatment plant. The data set had been collected from a sewer system in the Gwangyang city, Korea. The data had been measured in the period of 1st January, 2004 and 31th December, 2006. The data set contains daily averaged values for each of the twenty three variables. Through the component planes visualization, it is evident that the effluent is related to rainfall, flow rate, temperature, MLSS, SRT, RAS and DO. Especially, rainfall, flow rate and temperature are the most important driving force to increase in effluent levels in the Gwangyang municipal sewage treatment plant. It is concluded that the SOFM technique provides an effective analyzing and diagnosing tool to understand the system behavior and to extract knowledge contained in multi-dimensional data of a large-scale sewage treatment plant.

The global population is increasing rapidly because of increasing urbanization and such increasing urbanization directs the up-growing need of urban safety and preventions. This urbanization is also responsible for two things that is increased job opportunities and increased the crime rates. In this era technology has gone far more forward in a positive way. By making use of these technologies such as machine learning, artificial intelligence and big data we presented an approach through which crime pattern analysis is done. We have used apache spark (scala-programming) and machine learning algorithm for predictive crime pattern analysis. The data that we have used is a real-world data set based on Chicago city of United State of America. Our main goal of work is to define a predictive crime analysis which shows top crime patterns related to the top community areas of Chicago city.

The coat and muzzled colors in mammals are determined by the relative distribution of pheomelanin and eumelanin. In melanocytes, the expression of these two pigments is controlled by the melanocortin 1 receptor (MC1R) as well as by agouti locus alleles. For example, high expression of the AC (Adenylate cyclase 1) family genes, which interact with MC1R may affect the expression of CREB. leading to the over-expression of melanogenesis-associated genes such as PRKACB and CAMK2α. Further, increased expression of genes involved in tyrosine metabolism, such as TYR, is likely to affect the synthesis of eumelanin in the dark-muzzle cows. Our previous result, that the found intriguing distinction between the dark and light muzzle with respect to the expression of genes involved in the MAPK vs. Wnt signaling pathways. The aim of the present study was to identify melanogenesis-associated genes that are differentially expressed in the yellow and brindle coat of Korean native cattles. The first results, The expressed evaluation of MC1R protein were mostly highly between KBC to KDC, and there level at ST of KDC was higher than we expected, and decreased expressed in large sweat gland of epidermis zone. The RT-PCR analysis found that MC1R expressed at the KBC showed the same results like it was localization analyzed. Especially, levels of MC1R mRNA were elevated in HC compared to another group, while that the expression mostly higher in KDC. Next secondly results, was showed that the associated- genes of Want and MARK pathway were differential expression between KBC and KHC. Especially, in the KBC of malanogenesis major genes expression was highly, but the Want and minor genes were highly expression from KHC. In addition, analysis result of DNA methylation in Bos taurus genomes to survey the coat color–related epigenetic markers (differential methylated CpGs, DM CpGs) by RRBS was the appears that KBC has the biological functions of the DM CpG-associated genes. Regarding the increasing interest in the genetic diversity of cattle stocks, genes we identified for differential expression of melanogenesis and DM CpG-associated genes in the KBC vs. KHC may serve as novel markers for genetic diversity among cattle based on the coat color phenotype.

The application of the information and communication technologies (ICTs) towards sound solid waste management (SWM) is a challenging research area. This study is aimed at identifying evolving technological trends, competitor’s distribution and technological convergence pattern between ICTs and SWM technology. A total of 1041 patents’ applications that were submitted to Korean Intellectual Property Office (KIPO) from 1996 to 2016 were investigated as a dataset. Convergence patterns were obtained by applying the association rules of the International Patent Classification (IPC) codes. The results show some related technological fields where convergence technologies are mostly adopted and can contribute to establishing the development strategies of the ICTs - SWM technology in Korea.

Spatial point-patterns analyses were conducted to provide insight into the ecological process behind competition and mortality in two lodgepole pine (Pinus contorta Dougl. ex Loud. var. latifolia Engelm.) stands, one in the Lower Foothills, and the other in the Upper Foothills natural subregions in the boreal forest of Alberta, Canada. Spatial statistical tests were applied to live and dead trees and included Clark-Evans nearest neighbor statistic (R), nearest neighbor distribution function (G(r)), and a variant of Ripley’s K function (L(r)). In both lodgepole pine plots, the results indicated that there was significant regularity in the spatial point-pattern of the surviving trees which indicates that competition has been a key driver of mortality and forest dynamics in these plots. Dead trees generally showed a clumping pattern in higher density patches. There were also significant bivariate relationships between live and dead trees, but the relationships differed by natural subregion. In the Lower Foothills plot there was significant attraction between live and dead tees which suggests mainly one-sided competition for light. In contrast, in the Upper Foothills plot, there was significant repulsion between live and dead trees which suggests two-sided competition for soil nutrients and soil moisture.

The purpose of this study was to comparatively analyze normal gait on the plains by gender for old people reference data for the normal gait pattern for the old people. Participants were selected according to the Korean standard body type provided by the Ministry of Health and Wel-fare and used a three-dimensional motion analysis system. Cortex, Or-thotrak, and Excel were used as the software for analyzing the extract-ed data, and IBM SPSS Statistics ver. 24.0 was used for statistical anal-ysis. When data standardization was performed using the dimension-less numbers conversion, the step length and stride length of the lower extremities, which had differences between genders before dimension-less numbers conversion, showed no difference after dimensionless numbers conversion. Cadence, step time, and single support time of the left lower extremity, which had no difference between genders before dimensionless numbers conversion, were found to have significant dif-ferences after dimensionless numbers conversion. In addition, as a re-sult of analyzing the coefficient of variation value to find out the degree of change in data due to dimensionless numbers conversion, there were increase and decrease in the coefficient of variation value rang-ing from -8.11% to 6.67% before and after dimensionless numbers con-version, which means dimensionless numbers conversion can affect the statistical test.

The game of Go represents a major challenge for artificial as well as human intelligences due to its profound complexity. Although computer programs capable of playing Go have existed for decades, the period from 2015 to 2025 has marked a turning point, with these systems achieving—and even exceeding—professional human performance. A notable shift in gameplay strategy emerged around 2016, driven by the adoption of unconventional yet effective moves demonstrated by AI programs, challenging previous conventions of preferred moves. Nowadays, the process of learning Go has evolved from traditional knowledge transfer, based on centuries of established conventions regarding strategy, positional judgement and move value to modern trends developed by AI playing style, lacking the explanatory heuristics inherent in both human style of playing and oral tradition. Facing this new challenge, we rely on our curiosity to understand our opponents’ moves beyond technological and language barriers as we continue to explore the mysterious depths of this timeless game.

The proliferation of social network services affects individual’ lifestyle. Users get a lot of information through social network services. Accordingly, the social network services are useful tools, as means of promotion and marketing. In this research, we analyze the information exchange network using social network analysis in a specific-purpose trade show. That is, social features of interested parties (organizer, exhibitors and visitors) can be analyzed in information exchange network of the trade show. The aim of this research is that how the social network services influence the behavior of interested parties. We expected that the analysis can utilize the social features of interested parties to design better trade shows for promotion and be a role as an exploratory analysis for discovering a useful communication channel.