In this study, we propose a novel approach to analyze big data related to patents in the field of smart factories, utilizing the Latent Dirichlet Allocation (LDA) topic modeling method and the generative artificial intelligence technology, ChatGPT. Our method includes extracting valuable insights from a large data-set of associated patents using LDA to identify latent topics and their corresponding patent documents. Additionally, we validate the suitability of the topics generated using generative AI technology and review the results with domain experts. We also employ the powerful big data analysis tool, KNIME, to preprocess and visualize the patent data, facilitating a better understanding of the global patent landscape and enabling a comparative analysis with the domestic patent environment. In order to explore quantitative and qualitative comparative advantages at this juncture, we have selected six indicators for conducting a quantitative analysis. Consequently, our approach allows us to explore the distinctive characteristics and investment directions of individual countries in the context of research and development and commercialization, based on a global-scale patent analysis in the field of smart factories. We anticipate that our findings, based on the analysis of global patent data in the field of smart factories, will serve as vital guidance for determining individual countries' directions in research and development investment. Furthermore, we propose a novel utilization of GhatGPT as a tool for validating the suitability of selected topics for policy makers who must choose topics across various scientific and technological domains.

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.

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.

Recently, a study of prognosis and health management (PHM) was conducted to diagnose failure and predict the life of air craft engine parts using sensor data. PHM is a framework that provides individualized solutions for managing system health. This study predicted the remaining useful life (RUL) of aeroengine using degradation data collected by sensors provided by the IEEE 2008 PHM Conference Challenge. There are 218 engine sensor data that has initial wear and production deviations. It was difficult to determine the characteristics of the engine parts since the system and domain-specific information was not provided. Each engine has a different cycle, making it difficult to use time series models. Therefore, this analysis was performed using machine learning algorithms rather than statistical time series models. The machine learning algorithms used were a random forest, gradient boost tree analysis and XG boost. A sliding window was applied to develop RUL predictions. We compared model performance before and after applying the sliding window, and proposed a data preprocessing method to develop RUL predictions. The model was evaluated by R-square scores and root mean squares error (RMSE). It was shown that the XG boost model of the random split method using the sliding window preprocessing approach has the best predictive performance.

국내에는 독자적으로 연구가 수행되어 개인적으로 보관 중인 지질 연구 자료가 다량 존재하는데, 이 자료에 대한 접근성이 떨어지기 때문에 다른 연구자들과의 공유가 용이하지 않다. 이런 자료에 대한 메타데이터를 체계적으로 구축하고 총괄적으로 관리하여 이 자료를 필요로 하는 연구자들이 효과적으로 연구를 수행할 수 있는 기회를 제공하는 것이 이 연구의 목적이다. 국내에서 연구된 약 1000여개의 지질 시료(900여개의 암석과 화석 시료, 100여개의 박편 시 료)를 수집하였고, 각 시료의 고화질 사진, 분류, 시료명, 보유기관, 산지, 좌표, 특징 등에 대한 메타데이터를 구축하였다. 암석과 화석 시료 100개에 대해 추가적으로 3D 모델링을 수행하였다. 이 연구를 통해 유실되거나 방치되는 중요한 지질 자료에 대한 연구자들의 접근성이 높아지고 자료의 공유가 가능해진다. 따라서 연구자들은 반복적인 연구 자료 수 집 작업으로 인한 시간과 비용의 낭비를 줄일 수 있고, 효율적인 연구를 수행하여 경쟁력을 갖춘 연구 결과를 획득할 수 있다. 또한 이미 확보된 시료에 대한 무분별한 반복 채집으로 인해 중요한, 그리고 피해에 취약한 자료가 훼손되는 것을 방지할 수 있다. 향후 전국의 대학과 연구기관에서 보관중인 다양한 암석과 박편 시료에 대한 메타데이터를 추가로 구축하면 자료의 식별 및 진전된 연구가 가능하고, 더불어 전문적인 광물학 및 암석학의 기초 지식에 대한 비교와 분석을 기대할 수 있다.

With increasing interest, there have been studies on LiDAR(Light Detection And Ranging)-based DEM(Digital Elevation Model) to acquire three dimensional topographic information. For producing LiDAR DEM with better accuracy, Filtering process is crucial, where only surface reflected LiDAR points are left to construct DEM while non-surface reflected LiDAR points need to be removed from the raw LiDAR data. In particular, the changes of input values for filtering algorithm-constructing parameters are supposed to produce different products. Therefore, this study is aimed to contribute to better understanding the effects of the changes of the levels of GroundFilter Algrothm’s Mean parameter(GFmn) embedded in FUSION software on the accuracy of the LiDAR DEM products, using LiDAR data collected for Hwacheon, Yangju, Gyeongsan and Jangheung watershed experimental area. The effect of GFmn level changes on the products’ accuracy is estimated by measuring and comparing the residuals between the elevations at the same locations of a field and different GFmn level-produced LiDAR DEM sample points. In order to test whether there are any differences among the five GFmn levels; 1, 3, 5, 7 and 9, One-way ANOVA is conducted. In result of One-way ANOVA test, it is found that the change in GFmn level significantly affects the accuracy (F-value: 4.915, p<0.01). After finding significance of the GFmn level effect, Tukey HSD test is also conducted as a Post hoc test for grouping levels by the significant differences. In result, GFmn levels are divided into two subsets ( ‘7, 5, 9, 3’ vs. ‘1’). From the observation of the residuals of each individual level, it is possible to say that LiDAR DEM is generated most accurately when GFmn is given as 7. Through this study, the most desirable parameter value can be suggested to produce filtered LiDAR DEM data which can provide the most accurate elevation information.

3D-based BIM(Building Information Modeling) technologies can be utilized effectively as a means of systematic management of facility information for safety assurance and effective maintenance of waterworks facilities. In this study, BIM models of water treatment facilities that can be used as basic data for BIM-based maintenance of waterworks facilities were developed. Information exchange and generality of the developed BIM models were evaluated by conducting interoperability analysis of IFC(Industry Foundation Classes) conversion models. In addition, the application of COBie(Construction Operations Building information exchange) was recommended as an effective countermeasure to deal with technical limitation regarding exchange and utilization of facilities-related information through current IFC models. The results of this study can contribute to the development of BIM-based maintenance system for waterworks facilities.

본 논문에서는 주성분 회귀법과 부분최소자승 회귀법을 비교하여 보여준다. 이 비교의 목적은 선형형태를 보유한 근적외선 분광 데이터의 분석에 사용할 수 있는 적합한 예측 방법을 찾기 위해서이다. 두 가지 데이터 마이닝 방법 론인 주성분 회귀법과 부분최소자승 회귀법이 비교되어 질 것이다. 본 논문에서는 부분최소자승 회귀법은 주성분 회귀법과 비교했을 때 약간 나은 예측능력을 가진 결과를 보여준다. 주성분 회귀법에서 50개의 주성분이 모델을 생 성하기 위해서 사용지만 부분최소자승 회귀법에서는 12개의 잠재요소가 사용되었다. 평균제곱오차가 예측능력을 측 정하는 도구로 사용되었다. 본 논문의 근적외선 분광데이터 분석에 따르면 부분최소자승회귀법이 선형경향을 가진 데이터의 예측에 가장 적합한 모델로 판명되었다.

In order to achieve the optimized pest control, correct estimation of pest densities is a prerequisite to monitor pest damage and to provide efficient pest management plans. Parameters regarding diffusion (e.g., diffusion constant) and population size (e.g., growth rate) were estimated by using diffusion equation. The time series dispersal data of Whiteflies collected in greenhouse were used for modeling. Cross-correlation analysis was conducted to reveal the range and direction of pest population invasion. Sampling theory was further investigated regarding estimation of densities, and population dynamics of Whiteflies were discussed in two dimensions.

Models are useful tools for understanding and improving biological control of arthropod pests by means of natural enemies. Thus, models can be applied to simulate various scenarios in order to identify optimal control strategies. Although simulations can never replace real experiments, they can often serve as guidelines for choosing relevant field experiments and thereby save a lot of laborious and costly field work. Whereas the processes underlying population dynamics (e.g. dispersal, functional response, mutual interference) can be studied under laboratory conditions, large-scaled experiments in the field or in greenhouses are unsuited for this purpose. Instead such experiments may provide information about the patterns (e.g. spatial distributions of prey and predators) generated by the underlying processes. A major purpose of modeling is to link the patterns to the processes that generate these patterns. Petri-dish and single plant experiments have clearly demonstrated the capacity of predacious mite Phytoseiulus persimilis to feed effectively on the two-spotted spider mite Tetranychus urticae. This quickly leads to reductions in the abundance of prey, followed by a decline in predator abundance and eventual extinction. However, when larger systems, consisting of many hundred plants, are infested with the two mite species, extinction of one or both species seems less likely at the system level, although it may still occur at the individual plant level. The qualitative difference between small and large systems with respect to persistence and extinction risks is attributed to the fact that mites move among plants, but to prove that dispersal per se plays a role for the overall dynamics is hard to demonstrate experimentally. To circumvent this problem, I developed a stochastic simulation model of a greenhouse system that explicitly incorporates within and between plant dynamics. The model is used for analyzing a series of experiments with biological control of spider mites in multi-plant systems. In these experiments, the number of plants as well as their connectivity and the numbers of introduced mites were varied in order to examine whether these factors affect e.g. the predator-prey ratio or the time to extinction of one or both species. In my presentation I will also demonstrate an interactive version of the model (called DynaMite). It allows the user to interfere in the system during a simulation so as to mimic the options a grower has in order to prevent losses and to maximize his profit. Such options include spraying with acaricides, releasing predators, and replanting in substitute of damaged plants. By choosing different control strategies, the user may gradually improve his skills according to the principle of learning by experience. The model can be freely downloaded from http://www1.bio.ku.dk/ansatte/beskrivelse/?id=43077

We projected the temperature changes in the mid-21st century with Representative Concentration Pathway (RCP) 4.5 and RCP8.5 using the temperature data simulated by four regional climate models (RCMs: WRF, CCLM, MM5, RegCM4) in Korea. The simulation area and spatial resolution of RCMs were the CORDEXEA (COordinated Regional Climate Downscaling Experiment-East Asia) area and 25 km, respectively. We defined the temperature change as the difference (ratio) between the average annual temperature (IAV: Interannual Variation) over the projected 25 years (2026-2050) and that over the present 25 years (1981-2005). The fact that the average annual temperature bias of the four RCMs is within ±2.5°C suggests that the RCM simulation level is reasonable in Korea. Across all RCMs, scenarios, and geographic locations, we observed increased temperatures (IAV) in the mid-21st century. In RCP4.5 and RCP8.5, 1.27°C and 1.57°C will be increased by 2050, respectively. The ensemble suggests that the temperature increase is higher in winter (RCP4.5: 1.36°C, RCP8.5: 1.75°C) than summer (RCP4.5: 1.25°C, RCP8.5: 1.49°C). Central Korea exhibited a higher temperature increase than southern Korea. A slightly larger IAV is expected in the southeastern region than in the Midwest of Korea. IAV is also expected to increase significantly in RCP4.5 (summer) than in RCP8.5 (winter).

최근 기후변화 및 유역개발로 인하여 메콩강 유역의 수문환경이 급격히 변화하고 있으며, 메콩강을 공유하는 국가의 수재해 예방 및 지속가능한 수자원개발을 위해서는 메콩강 주요지점에서의 유량 정보의 분석 및 예측이 요구된다. 본 연구에서는 물리적 기반의 수문모형인 SWAT과 데이터기반 딥러닝 알고리즘인 LSTM을 이용하여 메콩강 하류 Kratie 지점의 유출모의를 수행하고, 유출모의 정확도 및 두 가지 방법론의 장 ․ 단점을 비교 ․ 분석한다. SWAT 모형의 구축을 위해 범용 입력자료(지형: HydroSHED, 토지이용: GLCF-MODIS, 토양: FAO-Soil map, 강우: APHRODITE 등)을 이용하였으며 warming-up 및 매개변수 보정 후 2003~2007년 일유량 모의를 수행하였다. LSTM을 이용한 유출모의의 경우, 딥러닝 오픈소스 라이브러리인 TensorFlow를 활용하여 Kratie 지점기준 메콩강 상류 10개 수위관측소의 두 기간(2000~2002, 2008~2014) 일수위 정보만을 이용하여 심층신경망을 학습하고, SWAT 모형과 마찬가지로 2003~2007년을 대상으로 Kratie 지점에 대한 일수위 모의 후 수위-유량관계곡선식을 이용하여 유출량으로 환산하였다. 두 모형의 모의성능 비교 ․ 검토를 위하여 모의기간에 대해 NSE (Nash-Sutcliffe Efficiency)을 산정한 결과, SWAT은 0.9, LSTM은 보다 높은 0.99의 정확도를 나타내는 것으로 분석되었다. 메콩강과 같은 대유역의 특정 지점에 대한 수문시계열 자료의 모의를 위해서는 다양한 입력자료를 요구하는 물리적 수문모형 대신 선행시계열자료의 변동성을 기억 ․ 학습하여 이를 예측에 반영하는 LSTM 기법 등 데이터기반의 심층신경망 모형의 적용이 가능할 것으로 판단된다.

Rotation-powered pulsars are excellent laboratories for studying particle acceleration as well as fundamental physics of strong gravity, strong magnetic fields and relativity. Particle acceleration and high-energy emission from the polar caps is expected to occur in connection with electron-positron pair cascades. I will review acceleration and gamma-ray emission from the pulsar polar cap and associated slot gap. Predictions of these models can be tested with the data set on pulsars collected by the Large Area Telescope on the Fermi Gamma-Ray Telescope over the last four years, using both detailed light curve fitting, population synthesis and phase-resolved spectroscopy.

지난 20여년간 화물수송 분야에는 큰 변화가 있었다. 수송 공급 측면에서는 보다 다양하고 기술적으로 앞선 수송수단들이 등장했으며, 수송 수요 측면에서는 로지스틱스 개념의 도입으로 화주들의 보다 높은 수송 서비스가 요구 되었다. 수송수단의 수송 분담에 있어서도 특히 철도에서 공로로의 두드러진 화물이동 현상이 나타났다. 이러한 변화는 수송 현안 해결에 대한 관심을 높이고 화물수송수요 예측기법의 이론적, 개념적인 발달을 가져왔다. 그 중 두드러진 발달은 화주의 행태를 반영하는 행태모형의 개발과 새로운 자료수집 방법 및 자료형태이다. 전통적으로 화물수송 및 교통 연구에 널리 사용된 행태모형은 확률표본을 사용하여 왔으나, 80년대 부터 비확률 표본 사용에 관심이 높아졌다. 그 대표적인 것으로 기반근거 데이터를 들 수 있다. 이 데이터는 제한된 정보를 제공한다는 자료자체의 한계를 지니고 있으나, 자료수집이 용이하고 비용이 저렴하다는 장점을 가지고 있다. 화물수송 분야에서 선택기반 데이터를 이용한 연구는 현재까지 두 편이 발표 되어 있다. 따라서 볼 연구는 선택기반 데이터를 이용한 개별선택모형의 잠재력을 검증하는 것을 그 목적으로 하고, 네 종류의 제조품 그룹을 대상으로 기반근거 데이터를 수집하여 로짓모형을 추정하였으며, 추정결과를 이전 연구들의 결과와 비교하여 그 타당성을 검토 하였다. 추정된 결과는 통계적으로 유의하며 직관적으로 타당한 것으로 나타난다. 또한 그 결과는 문헌의 결과와도 일치하였다. 수송계획에 있어서 자료수집비용 절감의 필요성을 생각할 때 이것은 중요한 의미를 지닌다.