Recently, the status of North Korea’s denuclearization has become an international issue, and there are also indications of potential nuclear proliferation among neighboring countries. So, the need for establishment of nuclear activity verification technology and strategy is growing. In terms of ensuring verification completeness, sample collection-based analysis is essential. The concepts of Chain of Custody (CoC) and Continuity of Knowledge (CoK) can be defined in the process of sample extraction as follows: CoC is interpreted as the ‘system for managing the flow of information subjected by the examinee’, and CoK is interpreted as the ‘Continuity of information collection through CoC subjected by the inspector’. In the case of sample collection process in unreported areas for nuclear activity verification, there are additional risks such as worker exposure/kidnapping or sample theft/tampering. Therefore, the introduction of additional devices might be required to maintain CoC and CoK in the unreported area. In this study, an Environmental Geometrical Data Transfer (EGDT) was developed to ensure the safety of workers and the CoC/CoK of the samples during the collection process. This device was designed for achieving both mobility and rechargeability. It is categorized into two modes based on its intended users: sample mode and worker mode. Through the sensors, which is positioned in the rear part of device, such as radiation, gyroscope, light, temperature, humidity and proximity sensors, it can be easily achievable various environmental information in real-time. Additionally, GPS information can also be received, allowing for responsiveness to various hazardous scenarios. Moreover, the OLED display positioned on the front gives us for checking device information such as the current status of the device such as the battery level, the connectivity of wifi, and etc. Finally, an alarm function was integrated to enable rapid awareness during emergency situations. These functions can be updated and modified through Arduino-based firmware, and both the device and the information collected through it can be remotely controlled via custom software. Based on the presented design conditions, a prototype was developed and field assessments were conducted, yielding results within an acceptable margin of error for various scenarios. Through the application of the EGDT developed in this study to the sample collection process for nuclear activity verification purposes, it is expected to achieve a stable maintenance of CoC/CoK through more accurate information transmission and reception.

Physical protection education was legislated by the Ministry of Education, Science and Technology (MEST) in November 2010. KINAC (Korea Institute of Nuclear Nonproliferation and Control) was designated as the exclusive institution for physical protection education and training by MEST in October 2011, and it has since functioned as the sole institution responsible for this critical aspect of nuclear security education in the country. Over the past decade, KINAC has undertaken a variety of training initiatives aimed at enhancing the capabilities of nuclear operators’ physical protection personnel. Furthermore, it has consistently pursued annual curriculum revisions based on insights gleaned from surveys and workshops. In conventional curriculum assessments, general surveys often rely on Likert scale or short-answer questions as primary indicators, mainly due to their ease of data processing. Descriptive questions, while capable of capturing diverse opinions, have historically been relegated to a secondary role owing to the inherent challenges associated with data analysis. While physical protection education has made concerted efforts to solicit diverse opinions through descriptive questions, difficulties in organizing and leveraging this valuable data have resulted in it primarily serving as reference material. This study introduces a novel approach by employing ChatGPT, a chatbot, to conduct a comprehensive analysis of the descriptive questions from the physical protection education survey administered in the first half of 2023. The primary objective is to formulate a robust plan for curriculum enhancement based on a wide spectrum of opinions. Following the completion of physical protection training by 2,014 individuals in the first half of 2023, a survey was distributed, yielding an impressive response rate of 95.7% with 1,927 respondents. Chatbots were harnessed to extract major keywords and perform frequency analyses on approximately 360 responses to descriptive questions in the survey. The analysis revealed that certain keywords emerged with notable frequency, in the following order: “drone” (mentioned 51 times), “access management” (mentioned 28 times), “inspection and search” (mentioned 27 times), and “cybersecurity” (mentioned 20 times). Further analysis of these major keywords and related content revealed a consensus among trainees that there is a pressing need to incorporate topics addressing drone threats and responses, as well as strategies to fortify access management into the curriculum. This study underscores the potential to harness standardized data analysis techniques to synthesize and integrate trainees’ subjective opinions, thereby providing a solid foundation for the refinement of the curriculum.

Spent nuclear fuel continues to be generated domestically and abroad, and various studies are actively being conducted for interim dry storage and disposal of spent nuclear fuel. The characteristics vary depending on the type of spent nuclear fuel and the initial specifications, and based on these characteristics, it is essential to estimate the burnup and enrichment of spent nuclear fuel as a nondestructive assay. In particular, it is important to estimate the characteristics of spent nuclear fuel with non-destructive tests because destructive tests cannot be performed on all encapsulated spent nuclear fuel in case of intrusion traces in safeguards. Data is made by measuring spent nuclear fuel directly to evaluate burnup of spent nuclear fuel, but computer simulation research is also important to understand its characteristics because past burnup history is not accurately written, and destructive testing is difficult. In Sweden, the dependency of the burnup history in source strength and mass of light-water reactor-type spent nuclear fuel was evaluated, and this part was also applied to MAGNOX in consideration of the possibility of being used to verify DPRK’s denuclearization. SCALE 6.2 TRITON modeling was performed based on public information on DPRK’s 5 MWe Yongbyon reactor, and the source strength of Nb-95, Zr-95, Ru-106, Cs-134, Cs-137, Ce-141, Ce- 144, Eu-154 nuclides were evaluated. Since the burnup of MAGNOX is lower than that of lightwater reactors, major nuclides in decay heat were not considered. The cooling period was evaluated based on 0, 5, 10, and 20 years. In case the discharge timing was different, the total period of discharge and reloading was the same, and the end-cycle burnup was the same, calculations showed that the source strength emitted from major nuclides was evaluated within 2-3% except for Ru-106 and Ce-144 nuclides. Even the burnup step of nuclear fuel is the same, and the reloaded length after discharge is different, i.e., the cooling period between is different at 5, 10, and 20, the source strength of Nb-95, Zr-95, Ce-144, and Cs-137 was evaluated as an error of 1%. Except for Ru-106 and Ce-144, nuclides are highly dependent on burnup. Compared to the case of light-water reactors, the possibility of a decrease in error needs to be considered later because the specific power is low. As a result, radionuclides in released fuel depend on the effects of burnup, discharged and reloaded period, and a cooling period after release, and research is needed to correct the cooling period within the future burnup history. In addition, in this study, it is necessary to select a scenario -based burnup because the standard burnup due to the statistical treatment of discharged fuels was not considered as conducted in previous studies.

The importance of Structural Health Monitoring (SHM) in the industry is increasing due to various loads, such as earthquakes and wind, having a significant impact on the performance of structures and equipment. Estimating responses is crucial for the effective health management of these assets. However, using numerous sensors in facilities and equipment for response estimation causes economic challenges. Additionally, it could require a response from locations where sensors cannot be attached. Digital twin technology has garnered significant attention in the industry to address these challenges. This paper constructs a digital twin system utilizing the Long Short-Term Memory (LSTM) model to estimate responses in a pipe system under simultaneous seismic load and arbitrary loads. The performance of the data-driven digital twin system was verified through a comparative analysis of experimental data, demonstrating that the constructed digital twin system successfully estimated the responses.

This study analyzes consumer fashion purchase patterns from a big data perspective. Transaction data from 1 million transactions at two Korean fashion brands were collected. To analyze the data, R, Python, the SPADE algorithm, and network analysis were used. Various consumer purchase patterns, including overall purchase patterns, seasonal purchase patterns, and age-specific purchase patterns, were analyzed. Overall pattern analysis found that a continuous purchase pattern was formed around the brands’ popular items such as t-shirts and blouses. Network analysis also showed that t-shirts and blouses were highly centralized items. This suggests that there are items that make consumers loyal to a brand rather than the cachet of the brand name itself. These results help us better understand the process of brand equity construction. Additionally, buying patterns varied by season, and more items were purchased in a single shopping trip during the spring season compared to other seasons. Consumer age also affected purchase patterns; findings showed an increase in purchasing the same item repeatedly as age increased. This likely reflects the difference in purchasing power according to age, and it suggests that the decision-making process for purchasing products simplifies as age increases. These findings offer insight for fashion companies’ establishment of item-specific marketing strategies.

증산은 적정 관수 관리에 중요한 역할을 하므로 수분 스트레스에 취약한 토마토와 같은 작물의 관개 수요에 대한 지식이 필요하다. 관수량을 결정하는 한 가지 방법은 증산량을 측정하는 것인데, 이는 환경이나 생육 수준의 영향을 받는다. 본 연구는 분단위 데이터를 통해 수학적 모델과 딥러닝 모델을 활용하여 토마토의 증발량을 추정하 고 적합한 모델을 찾는 것을 목표로 한다. 라이시미터 데이터는 1분 간격으로 배지무게 변화를 측정함으로써 증산 량을 직접 측정했다. 피어슨 상관관계는 관찰된 환경 변수가 작물 증산과 유의미한 상관관계가 있음을 보여주었다. 온실온도와 태양복사는 증산량과 양의 상관관계를 보인 반면, 상대습도는 음의 상관관계를 보였다. 다중 선형 회귀 (MLR), 다항 회귀 모델, 인공 신경망(ANN), Long short-term memory(LSTM), Gated Recurrent Unit(GRU) 모델을 구 축하고 정확도를 비교했다. 모든 모델은 테스트 데이터 세트에서 0.770－0.948 범위의 R2 값과 0.495mm/min－ 1.038mm/min의 RMSE로 증산을 잠재적으로 추정하였다. 딥러닝 모델은 수학적 모델보다 성능이 뛰어났다. GRU 는 0.948의 R2 및 0.495mm/min의 RMSE로 테스트 데이터에서 최고의 성능을 보여주었다. LSTM과 ANN은 R2 값이 각각 0.946과 0.944, RMSE가 각각 0.504m/min과 0.511로 그 뒤를 이었다. GRU 모델은 단기 예측에서 우수한 성능 을 보였고 LSTM은 장기 예측에서 우수한 성능을 보였지만 대규모 데이터 셋을 사용한 추가 검증이 필요하다. FAO56 Penman-Monteith(PM) 방정식과 비교하여 PM은 MLR 및 다항식 모델 2차 및 3차보다 RMSE가 0.598mm/min으로 낮지만 분단위 증산의 변동성을 포착하는 데 있어 모든 모델 중에서 가장 성능이 낮다. 따라서 본 연구 결과는 온실 내 토마토 증산을 단기적으로 추정하기 위해 GRU 및 LSTM 모델을 권장한다.

본 연구는 대도시에서 미세먼지 없는 학교 부지를 찾는 Model Eliciting Activity (이하 MEA) 활동을 통해 고 등학교 학생들의 문제 해결 특성을 조사하기 위한 것이다. 5차시로 개발된 MEA 활동에 79명의 고등학교 2학년 학생 들이 참여 하였으며, MEA 활동지를 주요 데이터로 수집하였다. 학생들이 작성한 활동지의 개방형 질문에 대한 답을 기반으로 학생들의 문제 해결 모델을 귀납적 및 질적 방법으로 분석하였다. 먼저 학생들이 다른 데이터보다 어떤 데이 터를 우선적으로 사용했는지 순서를 분석한 후 주어진 데이터 세트를 어떻게 상호 연결하여 순서를 결정하는지 분석하 였다. 분석결과 학생들은 미세먼지 배출량이 많은 곳을 기피하기 위해 미세먼지 배출농도, 산업단지 분포 등 미세먼지 와 직접적으로 관련된 데이터를 먼저 활용하는 경향이 있음을 알 수 있었다. 흥미롭게도 MEA 활동에서 고등학생의 문 제 해결 특성은 매우 다양하여 76명의 학생이 총 61가지 유형의 문제 해결 모델을 제작한 것으로 나타났다. 문제를 해 결하기 위해 동일한 순서의 데이터를 사용하는 학생의 최대 수는 6명으로 학생들의 문제 해결 방법은 매우 다양함을 보여준다. 그러나 공통적으로 미세먼지 농도가 높은 곳을 제외하는 방법으로 미세먼지 배출과 직접적으로 관련된 데이 터를 먼저 선택하는 특성을 보였다.

해수의 탁도는 수중의 부유 물질이나 생물에 의해 혼탁해지는 정도를 정량적으로 나타낸 변수로 연안 환경을 이해하는 데 중요한 해양 변수이다. 한반도의 서해안은 얕은 수심, 조류, 하천 유래 부유 퇴적물의 영향으로 광학적으로 강한 시공간 변동성을 가지고 있어서 인공위성 자료를 활용한 탁도 산출은 해양학적으로 다양한 활용 가능성을 가 진다. 본 연구에서는 경기만을 연구 해역으로 설정하고, 해수의 탁도 산출 알고리즘 개발을 위하여 2018년부터 2023년 7월까지 해양환경공단의 해양수질자동측정망 기반 현장 관측 탁도 자료와 Sentinel-2 인공위성의 MSI (Multi-Spectral Instrument) Level-2 자료를 사용하여 위성-현장 관측치 사이의 일치점 데이터베이스를 생산하였다. 이전의 다양한 탁도 산출식을 조사하여 정확도를 상호 비교하였고 경기만 해역에서 최적 파장대를 조사하고 분석하였다. 그 결과 녹색 밴드 (560 nm)를 기반으로 한 탁도 산출식이 0.08 NTU의 상대적으로 작은 평균 제곱근 오차를 보였다. 인공위성 광학 자료 를 기반으로 산출된 탁도는 해수의 광학적 특성과 연안 환경의 변동성을 이해하고 다양한 해상 활동에 도움을 줄 수 있을 것으로 기대된다.

국내 소비자들의 식품 영양성분에 대한 관심이 계속적 으로 증가하고 있지만 영양성분과 관련된 식품의 소비자 선호도 분석 연구는 부족한 실정이다. 본 연구는 대국민 정보 서비스인 식품영양성분 데이터베이스 플랫폼에 수집 된 빅데이터의 로그분석을 수행하여 소비자들이 영양학적 측면에서 관심을 가지는 식품에 대한 선호도 결과를 제시 하였다. 수집 기간은 2020년 1월부터 2022년 12월까지의 3개년으로 설정하여 총 2,243,168건의 식품명 검색어가 수 집되었으며, 식품명을 병합하여 품목대표 식품명으로 가 공하였다. 분석도구는 R프로그램을 이용하였으며, 영양정 보를 확인하고자 하는 식품명의 검색 빈도를 전체 기간 및 계절별로 분석하였다. 전체 기간 동안 빈도수 분석 결 과, 한국인이 일반적으로 자주 섭취하는 쌀밥, 닭고기, 달 걀의 빈도수가 가장 높았다. 계절성에 따른 선호도 분석 결과, 봄과 여름에는 대체적으로 국물이 없고 뜨겁지 않 은 음식의 빈도수가 높았으며, 가을과 겨울에는 국물이 있 고 따뜻한 음식의 빈도수가 높았다. 또한, 외식업체에서 계절식품으로 판매하는 냉면, 콩국수 등과 같은 식품의 빈 도수도 계절성을 가지는 것으로 확인되었다. 이러한 결과 는 소비자들이 일반적으로 자주 섭취하는 식품의 영양정 보에 관심을 가지는 패턴을 확인할 수 있었으며, 소비 트 렌드와 간접적인 연관성을 가진다는 점에서 외식업계에서 계절별 마케팅 전략 수립 시 기초 자료로 활용될 수 있을 것으로 기대된다.

This study was conducted to develop a model for predicting the growth of kimchi cabbage using image data and environmental data. Kimchi cabbages of the ‘Cheongmyeong Gaual’ variety were planted three times on July 11th, July 19th, and July 27th at a test field located at Pyeongchang-gun, Gangwon-do (37°37′ N 128°32′ E, 510 elevation), and data on growth, images, and environmental conditions were collected until September 12th. To select key factors for the kimchi cabbage growth prediction model, a correlation analysis was conducted using the collected growth data and meteorological data. The correlation coefficient between fresh weight and growth degree days (GDD) and between fresh weight and integrated solar radiation showed a high correlation coefficient of 0.88. Additionally, fresh weight had significant correlations with height and leaf area of kimchi cabbages, with correlation coefficients of 0.78 and 0.79, respectively. Canopy coverage was selected from the image data and GDD was selected from the environmental data based on references from previous researches. A prediction model for kimchi cabbage of biomass, leaf count, and leaf area was developed by combining GDD, canopy coverage and growth data. Single-factor models, including quadratic, sigmoid, and logistic models, were created and the sigmoid prediction model showed the best explanatory power according to the evaluation results. Developing a multi-factor growth prediction model by combining GDD and canopy coverage resulted in improved determination coefficients of 0.9, 0.95, and 0.89 for biomass, leaf count, and leaf area, respectively, compared to single-factor prediction models. To validate the developed model, validation was conducted and the determination coefficient between measured and predicted fresh weight was 0.91, with an RMSE of 134.2 g, indicating high prediction accuracy. In the past, kimchi cabbage growth prediction was often based on meteorological or image data, which resulted in low predictive accuracy due to the inability to reflect on-site conditions or the heading up of kimchi cabbage. Combining these two prediction methods is expected to enhance the accuracy of crop yield predictions by compensating for the weaknesses of each observation method.

장대교량은 낮은 고유진동수와 감쇠비를 가지는 초유연구조물로 진동사용성 문제에 취약하다. 하지만 현재 국내 설계지침에서는 풍속이나 진폭에 대한 임계값을 기반으로 유해진동 발생 여부를 평가하고 있다. 본 연구에서는 장대교량에서 발생하는 유해진동을 보다 정교하게 식별하기 위하여 딥러닝 기반 신호분할 모델을 활용한 데이터 포인트 단위의 와류진동 식별 방법론을 제안한다. 특별 히 포락선을 가지는 사인파를 활용하여 와류진동에 해당하는 데이터를 합성함으로써 모델 구축에 필수적인 와류진동 데이터 획득 및 라벨링 과정을 대체하였다. 이후 푸리에 싱크로스퀴즈드 변환를 적용하여 시간-주파수 특징을 추출하여 신경망의 인풋 데이터로 사 용하였다. 합성데이터만을 이용하여 양방향 장단기 기억신경망(Bidirectional Long-Short-Term-Memory) 모델을 훈련하였고 이를 라 벨 정보를 포함한 실제 사장교의 계측데이터를 이용하여 학습한 모델과 비교하여 모델의 실시간 와류진동 식별 성능을 검증하였다.

Considering that the number of middle-aged single-person households is increasing, this study investigates dietary behaviors, nutrient intake, and mental health according to household type. Data were procured from the 2015-2019 Korea National Health and Nutrition Examination Survey (KNHANES). Totally, 5,466 participants aged 50-64 years were classified into 2 groups: a household with one member was defined as a single-person household, and households with two or more members were described as multi-person households. Single-person households comprised 10.63% of the total, with a higher average age, and lower income and economic levels than multi-person households. Compared to multiperson households, single-person households had a higher frequency of skipping breakfast, eating alone, and dining out, the moderately and severely food insecure group was more than 5 times, and nutrient intake and dietary quality were poorer. In the fully adjusted model, the odds ratios (ORs) of depressive symptoms were 2.35 times (95% CI: 1.39-3.96), and suicide ideation was 1.95 times (95% CI: 1.35-2.82) in single-person compared to multi-person households. Our results lead us to conclude that poor dietary intake in middle-aged single-person households affects the mental health, and the above factors should be considered when framing the dietary policy.

PURPOSES : Traffic volume, an important basic data in the field of road traffic, is collected from traffic survey equipment installed at certain locations, which sometimes results in missing traffic volume data and abnormal detection. Therefore, this study presents various missing correction techniques using traffic characteristic analysis to obtain accurate traffic volume statistics. METHODS : The fundamental premise behind the development of a traffic volume correction and prediction model is to set the corrected data as the reference value, and the traffic volume correction and prediction process for the outliers and missing values in the raw data were performed based on the set values. RESULTS : The simulation results confirmed that the algorithm combining seasonal composition, quantile AD, and aggregation techniques showed a detection performance of more than 91% compared with actual values. CONCLUSIONS : Raw data collected due to difficulties faced by traffic survey equipment will result in missing traffic volume data and abnormal detection. If these abnormal data are used without appropriate corrections, it is difficult to accurately predict traffic demand. Therefore, it is necessary to improve the accuracy of demand prediction through characteristic analysis and the correction of missing data or outliers in the traffic data.

PURPOSES : The purpose of this study was to evaluate the common performance of asphalt pavements, determine the timing of preventive maintenance, and determine the optimal timing of application of the preventive maintenance methods by analyzing PMS data. METHODS : Using PMS data on asphalt pavement performance on highways, we derived the major damage factors and evaluated them according to the public period and traffic level. Among the factors evaluated, we determined those that could be improved by preventive maintenance, calculated the amount of change annually, and derived the timing of the application of the preventive maintenance method through correlation analysis. RESULTS : Among highway PMS data factors, crack variation was found to affect preventive maintenance, which increased rapidly after five years of performance. Traffic analysis showed that changes increased rapidly in the fifth, sixth, and seventh years when AADT exceeded 20,000, exceeded 10,000, and was under 10,000, respectively. Analysis of the amount of crack variation according to the pavement type showed that crack variation increased rapidly in the overlay section compared to the general AP section. CONCLUSIONS : Crack variation is the performance factor that was expected to be effective in preventive maintenance, and the PMS data showed that the initial application time of the preventive maintenance method varied by one year, depending on the traffic volume.

우리나라는 삼면이 바다로 이루어져 있고, 이에 따라 많은 해양 시설로 인한 위험유해물질이 배출되고 있으나, 배출관리 및 규제 시스템이 미비한 상황이다. 따라서, 위험유해물질(HNS) 관리를 위하여 효율적으로 데이터를 수집할 수 있는 시스템이 필요하 다. 본 연구에서는 HNS 데이터를 효율적으로 관리 및 저장하기 위한 데이터 표준화 시스템을 설계하고 이의 표준화 방안을 제시하고 자 한다.

인공위성은 최첨단 기술로써 시공간적 관측제약이 적어 해양 사고에 효과적 대응과 해양 변동 특성 분석 등으로 각국의 국가 기관들이 위성 정보를 활용하고 있다. 하지만 고해상도 위성 관측 기반 해수면 온도 자료(Operational Sea Surface Temperature and Sea Ice Analysis, OSTIA)는 위성의 기기적, 또는 지리적 오류와 구름으로 인해 낮게 관측되거나 공백으로 처리되며 이를 복원하기까지 수 시간이 소요된다. 본 연구는 최신 딥러닝 기반 알고리즘인 LaMa 기법을 활용하여 결측된 OSTIA 자료를 복원하고, 그 성능을 기존에 이용되어 온 세 가지 영상처리 기법들의 성능과 비교하여 평가하였다. 결정계수(R²)와 평균절대오차(MAE) 값을 이용하여 각 기법의 위성 영상 복원 성 능을 평가한 결과, LaMa 알고리즘을 적용하였을 때의 R²과 MAE 값이 각각 0.9 이상, 0.5℃ 이하로, 기존에 사용되어 온 쌍 선형보간법, 쌍 삼차보간법, DeepFill v1 기법을 적용한 것보다 더 우수한 성능을 보였다. 향후에는 현업 위성 자료 제공 시스템에 LaMa 기법을 적용하여 그 가능성을 평가해 보고자 한다.