Earticle

The development of the Web has made it easy to access large amounts of data. However, it is one of the big issues how to effectively store and improve query performance about these huge data. This paper proposes an RDF query optimization with hybrid storage structures to solve the issue. Hot-cold detection relocates data properly to improve the RDF query performance. After collecting data access information for a period of time, we identify the data with hot-cold detection. It reduces the write performance degradation of the SSD by moving all hot data at a time to the SSD.

연구목적: 본 연구는 뉴스 빅데이터를 활용해 한파로 인한 사회적 피해와 위험요소를 체계적으로 분류 하는 것을 목표로 한다. 또한 한파 위험이 시대별로 어떻게 보도되는지 분석하고자 한다. 연구방법: 2010년부터 2025년까지 수집된 약 1억 건의 네이버 뉴스를 대상으로 BERT 기반 모델과 거대언어모델 (GPT-4o-mini)을 활용한 4단계 정제 프로세스를 설계하였다. 단순 날씨 예보와 비유적 표현을 제거하 여 최종 1,973건의 한파 관련 뉴스를 확정하고, 최종 선별된 뉴스에서 2,829개의 위험 문장을 추출하여 WHO 기준을 바탕으로 구축한 5개 대분류와 24개 세분류 체계에 따라 위험요소를 분류·분석하였다. 연구결과: 한파 위험요소 분류 결과 취약계층(42.0%), 취약시설(33.2%) 관련 보도가 가장 높은 비중을 차지하여, 뉴스에서는 한파가 사회적 약자에게 상대적으로 큰 영향을 미치는 재난으로 다루어지고 있다. 시대별로는 2010년대에 인프라 손상 등 물리적 피해에 집중되었으나, 2020년대에는 코로나19와 결합된 복합재난 및 취약계층 보호 이슈 가 부각되었다. 결론: 한파는 단순한 기상 현상을 넘어 취약계층과 취약시설에 피해가 집중되는 복합적 위험으로 인식되고 있으며, 감염병 등 복합재난 상황에서의 특수성 또한 부각되고 있는 것으로 확인되었다. 이에 한파 피해가 특정 계층에 집중되는 특성을 고려하여, 취약계층의 수요와 상황을 반영한 ‘수요자 중심’의 정책과 복합재난 관점에서의 한파 대응 체계 마련이 필요하다.

Purpose: This study aims to systematically classify social risks associated with cold waves using news big data and to analyze how media coverage of cold wave risks has changed over time. Method: A four-stage refinement process was designed using the BERT based model and a Large Language Model (GPT-4o-mini) on approximately 100 million Naver news articles collected from 2010 to 2025. After removing simple weather forecasts and figurative expressions, 1,973 news articles specifically related to cold waves were finalized. From these, 2,829 risk-related sentences were extracted and analyzed according to a classification system of five major and twenty-four subcategories established based on WHO standards. Result: The classification of cold wave risk factors revealed that the number of new articles related to vulnerable groups (42.0%) and vulnerable facilities (33.2%) accounted for the highest proportions, indicating that news media perceives cold waves as disasters with a disproportionately large impact on the socially disadvantaged. While reports in the 2010s focused on physical damage such as infrastructure failure, the 2020s saw a shift toward highlighting complex disasters associated with COVID-19 and issues regarding the protection of vulnerable populations. Conclusion: The study confirms that cold waves are recognized as complex risks where damage is concentrated on vulnerable groups and facilities, especially under special circumstances like pandemic-driven complex disasters. Considering that cold wave damage is concentrated on specific classes, it is necessary to establish “user-centered” policies that reflect the needs and situations of vulnerable groups, alongside a cold wave response system from a complex disaster perspective.

In this study, as part of the paradigm shift for manufacturing innovation, data from the multi-stage cold forging process was collected and based on this, a big data analysis technique was introduced to examine the possibility of quality prediction. In order for the analysis algorithm to be applied, the data collection infrastructure corresponding to the independent variable affecting the quality was built first. Similarly, an infrastructure for collecting data corresponding to the dependent variable was also built. In addition, a data set was created in the form of an independent variable-dependent variable, and the prediction accuracy of the quality prediction model according to the traditional statistical analysis and the tree-based regression model corresponding to the big data analysis technique was compared and analyzed. Lastly, the necessity of changing the manufacturing environment for the use of big data analysis in the manufacturing process was added.

This paper designed and analyzed the communication protocol for the data communication between the IOT device and the middleware server of the IOT based Cold Chain Monitoring System. In this regard, we have referenced the CoAP Protocol Model of IETF CoRE WG, and analyzed and designed the process status flow, data flow and frame format of the IOT device (IOTD) and the middleware server. In order to test the effectiveness of the proposed Cold Chain Communication Protocol, we have produced the Zigbee, Bluetooth and wifi module mounted gateway and the temperature, humidity sensor. After sending the temperature data from the IOTD to the middleware server, we analyzed the received data from the middleware server. As the result, we were able to interpretate the data sent from the IOTD, and identify that in using these data the cold chain data such as the temperature, humidity, and battery remains data can be collected in real time.

Wear leveling techniques have been studied to prolong the lifetime of NAND flash memory. Most of studies have used Program/Erase(P/E) cycles as wear index for wear leveling. Unfortunately, P/E cycles could not predict the real lifetime of NAND flash blocks. Therefore, these algorithms have the limited performance from prolonging the lifetime when applied to the SSD. In order to apply the real lifetime, wear leveling algorithms, which use raw Bit Error Rate(rBER) as wear index, have been studied in recent years. In this paper, we propose CrEWL(Cold data identification using raw Bit error rate in Wear Leveling), which uses rBER as wear index to apply to the real lifetime. The proposed wear leveling reduces an overhead of garbage collections by using HBSQ(Hot Block Sequence Queue) which identifies hot data. In order to reduce overhead of wear leveling, CrEWL does not perform wear leveling until rBER of the some blocks reaches a threshold value. We evaluate CrEWL in comparison with the previous studies under the traces having the different Hot/Cold rate, and the experimental results show that our wear leveling technique can reduce the overhead up to 41% and prolong the lifetime up to 72% compared with previous wear leveling techniques.

YAFFS2는 빠른 마운트와 안정성 등의 이유로 휴대용 기기의 파일 시스템으로 오랫동안 사용되었다. YAFFS2의 복사되는 유효 페이지를 최소화 하고 가비지 콜렉션 효율성을 최대화하기 위해 탐욕 기반 정책을 가진다. 하지만 탐욕 기반 가비지 콜렉션으로 인해 오랫동안 값이 변하지 않는 콜드 데이터를 가진 더티 블록은 가비지 콜렉션 대상 블록으로 선정되지 못하는 문제가 있다. 따라서 본 논문에서는 YAFFS2에서 콜드 데이터를 고려한 가비지 콜렉션 기법을 제안하고 성능 평가를 통해 제안 기법의 우수성을 증명한다.

This study purposes to derive the predictive model for the cold tolerance of Brassica napus, using the data collected in the Tree Breeding Lab of Gyeongsang National University during July and August of 2016. Three Brassica napus samples were treated at each of low temperatures from 4℃ to -12℃ by decrement of 4℃, step by step, and electrolyte leakage levels were measured at each stage. Electrolyte leakages were observed tangibly from ?4℃. We tried to fit the six nonlinear regression models to the electrolyte leakage data of Brassica napus: 3-parameter logistic model, baseline logistic model, 4-parameter logistic model, (4-1)-parameter logistic model, 3-parameter Gompertz model, and (3-1)-parameter Gompertz model. The baseline levels of the electrolyte leakage estimated by these models were 4.81%, 4.07%, 4.19%, 4.07%, 4.55%, and 0%, respectively. The estimated median lethal temperature, LT50, were ?5.87℃, -6.31℃, -6.05℃, -6.35℃, -4.98℃, and -5.15℃, respectively. We compared and discussed the measures of goodness of fit to select the appropriate nonlinear regression model.

The market basket data in the form of a binary user-item matrix or a binary item-user matrix can be modelled as a binary classification problem. The binary logistic regression approach tackles the binary classification problem, where principal components are predictor variables. If users or items are sparse in the training data, the binary classification problem can be considered as a cold-start problem. The binary logistic regression approach may not function appropriately if the principal components are inefficient for the cold-start problem. Assuming that the market basket data can also be considered as a special regression problem whose response is either 0 or 1, we propose three supervised learning approaches: random forest regression, random forest classification, and elastic net to tackle the cold-start problem, comparing the performance in a variety of experimental settings. The experimental results show that the proposed supervised learning approaches outperform the conventional approaches.

When do states intervene in humanitarian crises? Although humanitarian interventions are indicative of normative changes in the way states expand the scope of humanity worth protecting from crises, to date, humanitarian interventions have been implemented selectively. The implications of this selectivity regarding the conditions under which the norm of humanitarian intervention constrains policymakers to take action in crises remain uncharted. I argue that as the norm entrepreneurs competitively diffuse norms dictating their own policy responses toward humanitarian crises among domestic constituencies,such normative pressures impose political constraints on policymakers,who adjust their policy positions along the distribution of contending norms to minimize the political cost of repudiating the domestic constituency’s normative preferences. As the distribution of contending norms varies with the effort of entrepreneurs across humanitarian crises,resultant political constraints lead policymakers to articulate differential foreign policy vis-a-vis a perpetrator in crisis. Among the many findings,I discover that as the norm of humanitarian intervention resonates more than other norms, policymakers are more likely to adopt and substitute more forceful foreign policies during crises.

IT 기술의 발전으로 인해 데이터 처리 시스템은 많은 양의 데이터를 처리 및 가공해야 한다. 하지만, 기존에 사용되던 온디스크(On-Disk) 시스템으로는 급증하는 데이터를 빠르게 처리하는 데 한계점을 가졌다. 이로 인해 많은 분야에서 하드디스크에 데이터를 저장하는 것이 아닌 속도가 빠른 메모리에 데이터를 저장 및 관리하는 인메모리(In-Memory) 시스템이 도입되고 있다. 하지만, 메모리에 데이터를 관리하는 것은 메모리의 특성 중 하나인 휘발성으로 인해 데이터 손실이라는 위험을 갖기 때문에 항상 결함 허용 기법이 뒤따라야 한다. 결함 허용 기법은 인메모리 시스템의 처리 속도를 낮추는 성능 저하 원인이 된다. 따라서 본 논문에서는 인메모리 시스템의 데이터 사용 특성을 고려하여 핫콜드 데이터로 분류하고, 데이터 영속성 보장을 위한 복합 백업 기법을 제안한다. 제안하는 기법은 영속성을 높이고, 성능 저하 원인을 보완한다.

As the IT technology advances, data processing system is required to handle and process large amounts of data. However, the existing On-Disk system has limit to process data which increase rapidly. For that reason, the In-Memory system is being used which saves and manages data on the fast memory not saving data into hard disk. Although it has fast processing capability, it is necessary to use the fault tolerance techniques in the In-Memory system because it has a risk of data loss due to volatility which is one of the memory characteristics. These fault tolerance techniques lead to performance degradation of In-Memory system. In this paper, we classify the data into Hot and Cold data in consideration of the data usage characteristics in the In-Memory system and propose compound backup technique to ensure data persistence. The proposed technique increases the persistence and improves performance degradation.

본 연구에서는 운동부 지도자들이 학생 선수들의 결과 데이터를 핫 존(Hot Zone)과 콜드 존(Cold Zone)으로 구분하여 지도에 참고할 수 있는 어플리케이션을 개발하였다. 본 연구에서 개발된 종목은 농구이다. 운동부 지도자들은 학생 선수들의 득점 확률이 높은 곳을 분석할 수 있고, 이를 이용하여 훈련 성과를 더욱 높일 수 있다. 그리고 학생 선수들도 축적된 데이터를 통하여 자신의 장단점을 파악하여 동기 부여와 경기력 향상을 기대할 수 있으며, 이에 따른 후속 연구를 할 수 있을 것으로 기대한다.

In this study, we developed an application that athletic leaders can refer to the map data, separated by a hot zone results of student athletes (Hot Zone) and cold zones (Cold Zone). The sport developed in this study is basketball. Athletic club's coachs can analyze a high probability of a student scoring players can use them to further improve training results. And student athletes also can through the accumulated data to identify their strengths and weaknesses expect motivation and performance enhancement, and accordingly expected to further study.

This study is to estimate cold weather concrete periods by analyzing data of Korea Meteorological Administration in terms of recently revised cold weather concrete regulation of Korea Concrete Institute.

Objectives We analyzed visiting patterns to medical institutions and cost per visit according to the common cold patients aged 0-19 years. We analyzed Korean medical treatment for common cold. Methods Using the Pediatric Patient Sample data of the Health Insurance Review and Assessment Service (HIRA-PPS), we analyzed the data on health insurance claims of approximately 1 million people from 2017 to 2019. The data included the number of patients who visited the hospital due to common cold for the first and second time, the ratio of second visits by type of medical institution, and the status of prescriptions in Korean medical institutions. Results The number of patients visiting healthcare providers for common cold was higher in Western medical institutions than in Korean medical institutions. However, the number of second visits was higher in Korean medical institutions. Acupuncture is the most commonly used medical treatment in Korean medical institutions for common cold. Herbal medicine for common cold was usually prescribed for 2-3 days for children and adolescents. Conclusions Although the average medical cost of Korean medical institutions was higher than that of Western medical institutions, the rate of second visits to Korean medical institutions was higher because of the demand for Korean medical treatment

본 연구는 기상자료를 토대로 우리나라 각 지역별 한중 콘크리트 적용기간을 KCI 및 AIJ 규정에 의한 방법으로 각각 산정하고 초기동해 위험일을 각 지역 및 온도 단계별로 제안하고자 하였다. 한중 콘크리트 적용기간의 경우 최근 5년간의 기상자료를 토대로 조사한 결과와 종전의 연구결과를 비교하였다. 연구결과에 따르면 국내 91개 지역의 한중 콘크리트 적용기간은 평균 98일로 조사되었고, 고위도 지역일수록, 산악 및 내륙지역일수록 적용기간은 증가되는 것으로 나타났으며, 종전 연구 대비 본 연구의 적용기간이 1~2일 정도 감소되는 것으로 나타났다. 지구온난화, 도시화 등의 영향으로 판단된다. 초기동해 위험일의 경우 $-5^{\circ}C$, $-2^{\circ}C$ 및 $0^{\circ}C$ 단계별로 시작일 및 종료일을 제시하였으며, 이를 토대로 $0^{\circ}C$ 이하 출현일 수, 출현일 및 평균 추위도를 제시하였다. 본 연구 범위에서 제시된 초기동해 위험일은 KCI에 의한 한중콘크리트 적용기간보다 길게 나타난 지역이 다수 존재하여 한중콘크리트 적용기간 이외에도 초기동해 위험이 있음을 확인할 수 있었다.

In this paper, the periods of cold weather concrete and early frost damage depending on each region in South Korea were studied using the climate data from Korea meteorological administration. The specifications of Korea Concrete Institute(KCI) and Architectural Institute of Japan(AIJ) were applied to provide the periods of cold weather concrete. The periods of early frost damage risk(EFD) were calculated by Hasegawa's suggestion depending on 91 cities in Korea. Climate data for 5 years (2008~2012) were used to obtain both of the periods. Existing data from 1971 to 2000 were also used to compare differences in the periods between past and present study. The periods of cold weather concrete by KCI were calculated about 98 days on average. As the latitude goes up and close to mountain areas, the periods tend to be increased. The periods by present study was shown to be reduced compared to that of previous study by 1~2days. The period of EFD was provided with the level of daily lowest temperature from $-5^{\circ}C$, $-2^{\circ}C$ and $0^{\circ}C$. The beginning day of the period of EFD was earlier than the period of cold weather concrete and the finishing day of the period of EFD was later than the period of cold weather concrete.

제약산업은 고령화 가속에 따른 월빙(Well-Being), 웰에이징(Well-Aging)에 대한 수요증가와 만성질환 및 신종질병 증가 등으로 지속 성장하는 산업이다. 본 연구에서는 의약품 콜드체인 온도 유지에 대한 사회적 관심이 증대함에 따라 온도 데이터 분석을 통 해 온도 이탈을 살펴보고 그 원인을 파악하였다. 국내 의약품 3PL을 전담하고 있는 A사의 B제약사 독감 백신 자료를 바탕으로 데이터 분석을 진행하였으며, 시간적 범위는 2021년부터 2024년, 공간적 범위는 충청도 천안지역을 한정하였다. 온도 규정범위를 이탈하는 온도를 살펴보고 그 원인을 현장인터뷰를 통해 파악하였다. 규정상 의약품은 1시간 이상 온도 범위를 이탈했을 경우에 만 제품을 폐기하지만, 본 연구에서는 온도가 규정 범위를 잠깐 이탈하였어도 이상치로 판단하여 분석하였다. 극저온과 극고온 등 온도가 이탈한 원인으로는 보냉재 컨디셔닝 미실시로 인한 극저온 온도 측정, 배송 과정에서 차량의 냉기가 포장재로 유입 또는 외부 열기가 포장재로 유입, 택배 서비스의 포장재 및 온도 측정 장비 파손으로 들 수 있다. 이러한 원인은 현장 작업자의 문제인 식 및 개선을 통해 의약품 콜드체인의 안전성을 강화할 수 있을 것으로 기대된다.

The pharmaceutical industry is a continuously growing industry due to the increasing demand for well-being and well-aging as the population ages, as well as the increase in chronic and new diseases. In this study, we looked at temperature deviations and identified their causes through temperature data analysis as social interest in maintaining the temperature of the cold chain for pharmaceuticals increases. The data analysis was conducted based on the data of flu vaccine from pharmaceutical company B, which is in charge of the 3PL of domestic pharmaceuticals, and the time range was limited to 2021 to 2024, and the spatial range was limited to the Cheon-an area. We looked at the temperatures that deviated from the temperature regulation range and identified the causes through on-site interviews. According to regulations, pharmaceutical products are only discarded if they have been out of the temperature range for more than an hour, but in this study, even if the temperature has been out of the range for a short time, it is judged as an outlier and analyzed. The causes of temperature deviations, such as extreme low and high temperatures, include the measurement of extreme low temperatures due to the failure to condition the insulation, the entry of cold air from the vehicle into the packaging during the delivery process, or the entry of external heat into the packaging, and the damage to the packaging and temperature measuring equipment of the delivery service. It is expected that these causes can strengthen the safety of the pharmaceutical cold chain through the problem recognition and improvement of on-site workers.

이 연구는 여름철 동해안 냉수대의 시공간적 이동에 따른 변화 분석에 관한 것이다. 2013년 여름에 위성 및 현장자료(바람, 기온, 수온)를 이용하여 다양한 환경 요인들에 기반을 둔 자료 분석을 시도하였다. 냉수대의 생성과 소멸의 영상을 분석하기 위해 AVISO 지형류 및 하루 동안의 천리안 해양관측위성(Geostationary Ocean Color Imager; GOCI) 엽력소 농도 자료를 사용하였다. 또한, 냉수대의 이동을 추적하기 위해 Advanced Very High Resolution Radiometer-Sea Surface Temperature (AVHRR-SST) 자료를 사용하였다. 초여름에 냉수대가 발현하여 늦여름에 소멸한다는 것과 이 기간 동안 냉수대의 생성과 소멸이 지속적으로 반복되었다는 것을 확인할 수 있었다. 추가적으로 냉수대의 후속 영향으로 인해 늦여름에 엽력소 농도가 증가되었다는 것을 확인할 수 있었다.

We investigated the changes with temporal and spatial movements of cold water events in summer season around the East Sea of Korea. Several data analyses were performed based on the various environmental factors using satellite and in-situ (winds, air/sea surface temperatures) data in the summer season during 2013. For analyzing the influence of cold water life cycle we employed AVISO geostrophic current and daily Geostationary Ocean Color Imager (GOCI) chlorophyll concentration (chl) data. Also, we used daily Advanced Very High Resolution Radiometer-Sea Surface Temperature (AVHRR-SST) data to trace the movements of cold water events. We found out the cold water events occurred in the early summer season and disappeared in the late summer season, and the cold water life cycle is repeated in this period. Additionally, we could show that the chl were increased in late summer season due to the inertial influence of cold water zone.

본 연구에서는 위성관측 해양표면수온과 클로로필 영상 및 현장관측 자료를 이용하여 동해남부 냉수대 발생 주변해역의 단기변동을 조사하였다. 특히 본 연구에서는 2007년 8월 하계 계절풍 기간 동안의 동한난류 주변의 해양표면수온과 클로로필의 공간변동에 주목하였다. 본 연구기간에 조사한 동한난류 해역의 해양표면수온과 클로로필의 공간분포는 (1) 연안 냉수대해역, (2) 수온전선의 냉수대해역, (3) 동한난류해역, (4) 외해 냉수해역으로 구분할 수 있었다.

The objective of this paper is to explore the short-term variability of sea surface temperature (SST) and chlorophyll a (Chl-a) derived from satellite in the upwelling region of the southeast coast of Korea in summer. We particularly emphasize the spatial variability of SST and Chl-a in the East Korean Warm Current (EKWC) during summer monsoon. Spatial distribution of SST and Chl-a in the EKWC are described using SeaWiFS and AVHRR images in August, 2007. Spatial distribution of SST and Chl-a around EKWC can be classified into four categories in the profile of SST and Chl-a images: (1) coastal cold water region, (2) cold water region of thermal front, (3) warm water region, (4) cold water of offshore region.

본 연구는 탈냉전기 역대 한국정부의 대북정책을 전략적 상호주의(TFT 전략)와 포 괄적 상호주의(GRIT 전략)로 대별하고, 이러한 정부의 전략이 남북 사이의 협력과 갈 등의 굴곡에 어떤 영향을 주었는가를 경험적으로 분석하려는 시도이다. 특히 남북간 협 력과 갈등의 경험적 측정을 위해 본 연구는 이벤트 데이터 분석기법을 한반도 상황에 적용하고 있다. 본 연구는 우선 탈냉전기 한국정부의 정책변화를 최고정책결정자인 대 통령의 집권기로 구별하고 각 정부의 대북정책을 TFT 전략과 GRIT 전략으로 분류하 였다. 이러한 각 정부의 대북정책은 각각 남북간의 협력과 갈등에 어떤 영향을 미치고 있는가를 경험적으로 분석하였다. 탈냉전기 한국정부의 대북정책 중 GRIT 전략은 북 한의 협력을 이끌어 내는 데 성공적이었다고 평가되었다. 그러나 북한의 대남갈등의 완 화에는 경험적 영향을 미치지 못하고 있다. 한편 미국과 북한과의 협력－갈등관계를 분 석했을 경우 미국의 정책결정자의 변화에 따라 북한의 미국에 대한 협력－갈등은 모두 유의미하게 변화되고 있어, 한국과 미국의 대북정책 공조가 북한의 갈등행위 완화에 기 여할 것으로 평가되고 있다.

The study tries to analyse empirically South Korean governments’ policies toward North Korea in the post Cold War era. It applies event data analysis to the situation of the Korean peninsula in order to examine cooperation(positive) and conflict (negative) between two Koreas in each South Korean government. In the study South Korean governments’ North Korea policies are classified into two categories: tit-for-tat (TFT) strategy and graduated reciprocation in tension -reduction (GRIT) strategy. The GRIT strategy as a South Korea’s policy toward Pyongyang has been evaluated as successful in inducing North Korea’s cooperation. However, it has not been so in reducing the North’s conflict against the South. In the case of the analysis of positive-negative relations between the U.S.A. and North Korea, there is meaningful change in the cooperation-conflict relations between both countries according to the change of American policy makers. When the U.S. showed cooperation, the North was cooperative, and vice versa. This gives us the fact that coordination and cooperation between South Korea and the United States in dealing North Korea contribute to reduction of North Korea’s conflict behavior.