In this study, the biogeochemistry management (BGC-MAN) model was applied to North and South Korea pine and oak forest stands to evaluate the Net Primary Productivity (NPP), an indicator of forest ecosystem productivity. For meteorological information, historical records and East Asian climate scenario data of Shared Socioeconomic Pathways (SSPs) were used. For vegetation information, pine (Pinus densiflora) and oak (Quercus spp.) forest stands were selected at the Gwangneung and Seolmacheon in South Korea and Sariwon, Sohung, Haeju, Jongju, and Wonsan, which are known to have tree nurseries in North Korea. Among the biophysical information, we used the elevation model for topographic data such as longitude, altitude, and slope direction, and the global soil database for soil data. For management factors, we considered the destruction of forests in North and South Korea due to the Korean War in 1950 and the subsequent reforestation process. The overall mean value of simulated NPP from 1991 to 2100 was 5.17 Mg C ha-1, with a range of 3.30-8.19 Mg C ha-1. In addition, increased variability in climate scenarios resulted in variations in forest productivity, with a notable decline in the growth of pine forests. The applicability of the BGC-MAN model to the Korean Peninsula was examined at a time when the ecosystem process-based models were becoming increasingly important due to climate change. In this study, the data on the effects of climate change disturbances on forest ecosystems that was analyzed was limited; therefore, future modeling methods should be improved to simulate more precise ecosystem changes across the Korean Peninsula through processbased models.

폴리에틸렌 글라이콜 다이아크릴레이트 (polyethylene glycol diacrylate, PEGDA) 하이드로젤을 정삼투 (forward osmosis, FO) 분리막의 지지체로 사용하여 고성능의 FO 분리막을 제조하였다. 친수성의 PEGDA를 자외선 조사를 통한 중합 과 그에 따른 상분리를 이용하여 다공성으로 구조화하였고, 매우 높은 친수성을 가진 하이드로젤 지지체를 얻을 수 있었다. 제조된 친수성 PEGDA 지지체 위에 높은 수투과도와 염 선택도를 확보하기 위해서 일반적인 계면중합 방식이 아닌 톨루엔 을 유기 용매로 사용한 계면중합 방식(TIP)으로 선택층을 도입하였다. 제조된 PEGDA 지지체 기반 분리막은 1.0 M NaCl 유 도 용액과 증류수 유입수를 통한 FO 성능 측정에서 상용 HTI 분리막들에 비해서 매우 높은 수투과도와 낮은 염 선택도를 나 타내었다. 본 연구를 통해, 기존의 소수성 지지체를 추가적으로 개질하는 방식이 아닌 새로운 물질과 제조방식을 사용한 FO 지지체의 가능성을 제시하고자 한다.

PURPOSES : Evaluation of the effectiveness of changing the form of yellow carpet installation as a way to reduce child pedestrian traffic accidents. METHODS : Through expert opinion, two improvement plans for yellow carpet installation (oblique type, extended type) were derived. The improvement paln was built in virtual reality, and a virtual driving experiment was performed using a driving simulator and eye-trakcing device. The improvement effects of the two alternatives were evaluated by analyzing eye-tracking data and driving behavior. RESULTS : In the case of the oblique type, it was analyzed that it was effective in improving the total gaze time and first gaze position compared to the normal type. In the case of the extended type, it was analyzed that the workload during operation can be reduced. However, neither of them had a significant effect on driving behavior. CONCLUSIONS : Although the change in the yellow carpet installation type did not affect the driver's driving behavior, it had advantages in terms of visual behavior and workload while driving, so it can be considered as an alternative among measures to improve traffic accidents involving children and pedestrians.

PURPOSES : This study is to develop an comprehensive validation methodology for autonomous mobility-on-demand system with level 4 automated driving system. METHODS : The proposed method includes the quantitative techniques for validating both automated driving system and center system using each optimal indicators. In addition, a novel method for validating the whole system applying multi-criteria decision methodology is suggested. RESULTS : The relative weights for the vehicle system was higher than the center systems. Moreover, the relative weights of failure rate for validating the vehicle system was the highest, in addition to, a relative weight for accuracy of dynamic routing algorithm within center system was the highest. CONCLUSIONS : The proposed methodology will be applicable to validate the autonomous mobility on demand system quantitatively considering the relative weights for each systems.

PURPOSES : This study aims to perform a quantitative analysis of Forward Collision Warning and crash frequency using heavy vehicle driving data collected in expressway driving environments, and to classify the driving environments where Forward Collision Warnings of heavy vehicles occur for accident-prone areas and analyze their occurrence characteristics. METHODS : A bivariate Gaussian mixture model based on inter-vehicle distance gap and speed-acceleration parameters is used to classify the environment in which Forward Collision Warning occurs for heavy vehicles driving on expressways. For this analysis, Probe Vehicle Data of 80 large trucks collected by C-ITS devices of Korea Expressway Corporation from May to June 2022. Combined with accident information from the past five years, a detailed analysis of the classified driving environments is conducted. RESULTS : The results of the clustering analysis categorizes Forward Collision Warning environments into three groups: Group I (highdensity, high-speed), Group II (high-density, low-speed), and Group III (low-density, high-speed). It reveals a positive correlation between Forward Collision Warning frequency and accident rates at these points, with Group I prevailing. Road characteristics at sites with different accident incidences showed that on-ramps and toll gates had high occurrences of both accidents and warnings. Furthermore, acceleration deviation at high-accident sites was significant across all groups, with variable speed deviations noted for each warning group. CONCLUSIONS : The Forward Collision Warning of heavy vehicles on expressways is classified into three types depending on the driving environment, and the results of these environmental classifications can be used as a basis for building a road environment that reduces the risk of crashes for heavy vehicles.

Particulate matter is known to have adverse effects on health, making it crucial to accurately gauge its concentration levels. While the recent advent of low-cost air sensors has enabled real-time measurement of particulate matter, discrepancies in concentrations can arise depending on the sensor used, the measuring environment, and the manufacturer. In light of this, we aimed to propose a method to calibrate measurements between low-cost air sensor devices. In our study, we introduced decision tree techniques, commonly used in machine learning for classification and regression problems, to categorize particulate matter concentration intervals. For each interval, both univariate and multivariate multiple linear regression analyses were conducted to derive calibration equations. The concentrations of PM10 and PM2.5 measured indoors and outdoors with two types of LCS equipment and the GRIMM 11-A device were compared and analyzed, confirming the necessity for distinguishing between indoor and outdoor spaces and categorizing concentration intervals. Furthermore, the decision tree calibration method showed greater accuracy than traditional methods. On the other hand, during univariate regression analysis, the proportion exceeding a PM2.5/PM10 ratio of 1 was significantly high. However, using multivariate regression analysis, the exceedance rate decreased to 79.1% for IAQ-C7 and 89.3% for PMM-130, demonstrating that calibration through multivariate regression analysis considering both PM10 and PM2.5 is more effective. The results of this study are expected to contribute to the accurate calibration of particulate matter measurements and have showcased the potential for scientifically and rationally calibrating data using machine learning.

PURPOSES : This study aims to identify the thresholds at which various factors affecting traffic crashes lead to actual traffic crashes METHODS : To verify the thresholds, we created scenarios and ran simulations with a combination of factors that affect traffic crashes. Lateral offset and minimum TTC were used to evaluate whether an incident occurred. RESULTS : In the first scenario, the most significant factor affecting traffic crashes is curvature, and it was found that the smaller the curvature(200 meters or less), the greater the deviation from the lane. And in the second scenario, especially the passenger car scenario, no accidents occurred when the curvature was greater than 90 meters and the speed was 40 km/h or less. The smaller the curvature and the higher the speed, the more accidents occurred. Similarly, in the bus scenario, no accidents occurred when the curvature was 120 meters or more and the speed was 30 km/h or less. Also, accidents tended to occur when the curvature was smaller and the speed was higher. CONCLUSIONS : Through this study, we derived the thresholds of factors that influence traffic crashes. The results are expected to help design and operate roads in the future and contribute to reducing traffic crashes.

Tomato is one of the major widely cultivated crops around the world. The leaf area is directly related to the total amount of photosynthesis, which affects the yield and quality of the fruit. Traditional methods of measuring the leaf area are time-consuming and can cause damage to the leaves. To address these problems, various studies are being conducted for measuring the leaf area. In this study, we introduced a model to estimate the leaf area using images of tomatoes. Using images captured by a camera, we measured the leaf length and width and used linear regression analysis to derive the leaf area estimation formula. Furthermore, we used a Neural Network (NN) for additional analysis to compare the accuracy of the models. Initially, to verify the reliability of the image data, we conducted a correlation analysis between the actual measurement data and the image data, which showed a high positive correlation. The leaf area estimation model presented 23 estimation formulas. We used regression analysis to estimate the coefficients of each model and also used employed an artificial neural network analysis to derive high R-squared (R2) values and low Root Mean Square Error (RMSE) values. Among the estimation formulas, the ninth model showed the highest reliability with an R-squared value of 0.863. We conducted a verification experiment to confirm the accuracy of the selected model, and the R-squared value was 0.925. This study confirmed the reliability of data measured from images and the reliability of the leaf area estimation model using image data. These methods are expected to be an important tool in agriculture, using imaging equipment for measuring and monitoring the crop growth.

Recently, due to the expansion of the logistics industry, demand for logistics automation equipment is increasing. The modern logistics industry is a high-tech industry that combines various technologies. In general, as various technologies are grafted, the complexity of the system increases, and the occurrence rate of defects and failures also increases. As such, it is time for a predictive maintenance model specialized for logistics automation equipment. In this paper, in order to secure the operational safety and reliability of the parcel loading system, a predictive maintenance platform was implemented based on the Naive Bayes-LSTM(Long Short Term Memory) model. The predictive maintenance platform presented in this paper works by collecting data and receiving data based on a RabbitMQ, loading data in an InMemory method using a Redis, and managing snapshot DB in real time. Also, in this paper, as a verification of the Naive Bayes-LSTM predictive maintenance platform, the function of measuring the time for data collection/storage/processing and determining outliers/normal values was confirmed. The predictive maintenance platform can contribute to securing reliability and safety by identifying potential failures and defects that may occur in the operation of the parcel loading system in the future.

이 연구는 위성사진을 활용하여 건설지점의 기대풍속을 예측하기 위한 인공신경망 방법론을 제안한다. 제안된 방법은 기존 의 엔지니어의 판단을 대체하여, Auto-Encoder를 사용해 지형적 특성을 정량화하고, 이를 바탕으로 대상지점과 유사한 지역의 관측소 풍속 데이터를 선형 조합해 기대 풍속을 예측한다. 또한, 머신러닝과 인공신경망을 활용한 종단간 풍속 예측 모델을 제안하고, 성능을 비교 분석하였다. 그 결과 관측소의 풍속 데이터의 선형 조합보다는 종단간 모델을 구성하는 방법이 더 높은 정확도를 보였으며, 특히 Graph Neural Network (GNN)이 Multi-Layer Perceptron (MLP)에 비해 상당히 우수한 예측 성능을 나타내었다.

This paper proposes an algorithm for the Unrelated Parallel Machine Scheduling Problem(UPMSP) without setup times, aiming to minimize total tardiness. As an NP-hard problem, the UPMSP is hard to get an optimal solution. Consequently, practical scenarios are solved by relying on operator's experiences or simple heuristic approaches. The proposed algorithm has adapted two methods: a policy network method, based on Transformer to compute the correlation between individual jobs and machines, and another method to train the network with a reinforcement learning algorithm based on the REINFORCE with Baseline algorithm. The proposed algorithm was evaluated on randomly generated problems and the results were compared with those obtained using CPLEX, as well as three scheduling algorithms. This paper confirms that the proposed algorithm outperforms the comparison algorithms, as evidenced by the test results.

This study aimed to explore nursing students' experience of learning cardiopulmonary resuscitation (CPR) in a web-based virtual simulation (vSim) through analysis of the reflection journals. Method: From June to July 2020, data were collected from 48 fourth-year nursing students who performed the simulation by reviewing prompt feedback on their CPR performance. The contents of the reflection journals were analyzed using NVivo qualitative data analysis software. Results: Nursing students experienced unfamiliarity with the English-based virtual environment as well as psychological pressure and anxiety about emergencies. Incorrect interventions were identified in the following order of frequency: violation of defibrillator guidelines, missing fundamental nursing care, error in applying an electrocardiogram monitor, inadequate initial response to cardiac arrest, insufficient chest compression, and inadequate ventilation. Lastly, the participants learned the importance of embodied knowledge, for knowing and acting accurately and reacting immediately, and their attitudes as nurses, such as responsibility, calmness, and attentiveness. Learning strategies included memory retention through repetition, real-time feedback analysis, pre-learning, and imagining action sequences in advance. The level of achievement, time required, CPR quality, and confidence improved with behavior-modification strategies developed through self-reflection. Conclusion: Educational interventions that are based on understanding accurate algorithms can strengthen selfawareness of mistakes to improve efficient imparting of CPR education.

In this study, quantitative analysis is attempted on data collected from Chilgapsan Observatory Star Park in Cheongyang-gun, Chungcheongnam-do. The aim of this experimental study in which quantitative analysis of the Astronomical Science Museum in Korea is conducted is to investigate its current situation and secure basic data. As of July 31, 2023, it has had 283,931 cumulative visitors in total. It had the largest number of visitors when it opened (2009 year), after which their number reduced steadily until the pandemic (COVID-19, 2020–2022). Recently, however, the number of visitors has increased. Generally, the number of visitors is highest in August (20.8$\%$) and least in April (4.1$\%$). The visit rate is higher on weekends (Saturday and Sunday) than on weekdays (Monday–Friday), and groups comprise only about 5.3$\%$ of the total number of visitors. Moreover, it can be confirmed that the number of visitors increases sharply during events. Finally, it was confirmed that the visit rate was unaffected by weather conditions. Considering these results, we propose the following strategies: 1) Establish a special program that reflects “the weekend effect.” 2) Prepare a plan to attract group visitors during the weekdays using “the event effect.” 3) Arrange alternative programs (e.g., experiential activities) that can be conducted indoors regardless of weather conditions. We think that our findings will help establish a roadmap for the direction the Astronomical Science Museum should take and aid in preparing a strategic foundation to preemptively respond to unexpected situations (e.g., pandemics).

PURPOSES : The number of snowfall and the amount of snowfall are gradually increasing, and due to the characteristics of Seoul, which has a lot of traffic, it is difficult to respond quickly with a snow removal method that relies on snow removal vehicles. Therefore, it is necessary to develop an IoT based eco-friendly snow removal system that can respond to unexpected heavy snow in winter. In this study, the low temperature operation and snow removal performance of the IoT road condition snow removal detector and the snow removal system using CNT and PCM are evaluated in the climatic environment chamber. METHODS : To make artificial snow, it consists of an climatic environment chamber that can simulate a low temperature environment and equipment that can supply compressed air and cold water. Depending on the usage characteristics of the climatic environment chamber, use an air-water type snow maker. In order to make artificial snow, wet temperature, which takes into account the actual air temperature and the amount of moisture in the air, acts as the most important variable and is suitable for making snow, below –1.5 ℃. The lower the water temperature, the easier it is to freeze, so the water source was continuously supplied at 0 ℃ to 4 ℃. One of the two different pipes is connected to the water tank to supply water, and the other pipe is connected to the compressor to supply high-pressure air. Water is dispersed by compressed air in the form of many small droplets. The sprayed microscopic water particles freeze quickly in the low temperature environmental climatic chamber air and naturally fall to the floor, forming snow. Based on the KS C IEC 60068-2-1 cold resistance test standard, an integrated environmental test procedure was prepared to apply to IoT-based snow removal systems and performance evaluation was performed accordingly. The IoT based eco-friendly snow removal system is needed to in winter, so visual check and inspect the operation at the climatic chamber before setting up it to the actual site. In addition, grid type equipment was manufactured for consistent and reliable snow removal performance evaluation under controlled environmental conditions. RESULTS : The IoT-based eco-friendly snow removal system normally carried out the task of acquiring data and images without damaging the appearance or freezing in a low temperature environment. It showed clear snow removal performance in areas where PCM and CNT heating technology were applied to the concrete slab. This experiment shows that normal snow removal tasks can be carried out in low temperature environments in winter. CONCLUSIONS : The integrated environmental test procedures and grid type evaluation equipment are applied to low temperature operation and snow removal performance evaluation of snow removal systems. In the climatic environment chamber, where low temperature environments can be simulated, artificial snow is created regardless of the season to derive quantitative experimental results on snow removal performance. PCM and CNT heating technology showed high snow removal performance. The system is expected to be applied to road site situations to preemptively respond to unexpected heavy snow in winter.

본고에서는 유형론 연구에 기반하여 가장 전형적인 ‘능격-절대격’ 언어에서 능격동 사가 어떻게 실현되는지 살펴보았고 한·중 능격동사의 판단기준, 유형, 특징을 알아 보았다. 아울러 능격동사와 비슷한 개념인 절대격동사, 비능격동사, 비대격동사들의 정의와 하위분류들을 살펴보고 능격동사와의 차이점을 제시하였다. 그리고 한·중 능 격구문의 실현양상을 제시하고 그 통사적 특징을 분석하였으며 능격구문과 관련된 중간구문, 사동구문, 피동구문의 실현양상, 판단기준, 발전과정을 살펴보았다.

본 연구의 목적은 급변화하는 사회환경속에서 사회복지 조직의 적시성 있는 대처를 위해 필요한 새 로운 경영가치 체계의 필요성을 고민하면서 최근 이슈가 되고 있는 ESG 가치를 통해 사회복지 경영의 새로운 변화를 돕고자 실증적인 연구를 수행하는 것이다. 이를 위해 인천시에 있는 지역사회복지관 종사자들이 생각하는 ESG 개념, 관련 운영 사업에 대한 조사, ESG에 대한 인식도, 조직에 적용하기 위한 필수조건, 사회복지 조직이 ESG를 적용해야 하는 이 유 등과 관련된 내용을 조사, 분석하였다. 또한 설문조사 결과를 바탕으로 FGI를 추가 진행하고 최종 합의하는 방식으로 연구를 수행하였다. 구체적으로, 인천시 지역사회복지관이 적용할 수 있는 ESG에 대한 개념과 지표 그리고 전략을 선정 하였으며, 현재 진행하고 있는 ESG 관련 사업과 내부 전략 등을 조사 분석하였다. 연구결과를 통해 ESG에 대한 필요성과 기관 적용 모델을 가치적 실천 모델(A), 사업적 적용 모델 (B), 전략적 실천모델(C)로 제안하였다. 이 연구를 통해 ESG를 기반으로 사회복지 조직 운영에 필요한 7가지 정책적 대안을 제시하였다.

본 연구는 범죄를 저지른 청소년 중 소년보호처분에 따라 소년원에 입소하였다가 일정 기간 이후 퇴소한 ‘소년원 퇴원생’에게 관심을 가졌다. 이들이 퇴소한 후 다시 범죄를 저지르지 않고 안정적으로 지역사회에 정착하고 자립할 수 있도록 소년원 안 팎에서 각종 노력을 실시하고 있지만, 소년원 퇴원생의 약 20%가 재범을 통해 다시금 소년원에 들어오는 상황에 놓여있다. 그러므로 소년원 퇴원생이 지역사회에 정착하고 자립하는 방안에 대해 교정복지적인 관점에서 관심을 가지고 개입을 시도할 필요가 있다. 이를 위해 본 연구는 소년 범죄와 소년원에 관한 현황과 함께 현재 실시되고 있는 자립지원 프로그램에 대해 소개하였다. 또한 자립지원을 위한 교정복지적 개입 방안 을 제시하였고 그 내용은 다음과 같다. 첫째, 소년원 퇴원생을 위한 자립생활관의 분위기를 퇴원생 친화적으로 개선하고 해당 기관 직원들의 전문성 개발이 필요하다. 둘 째, 소년원 퇴원생을 위한 기숙형 훈련학교의 교육과정을 다양화하고 맞춤형 교육과 정이 개설되어야 한다. 셋째로 다양한 기관들의 다각적 연계를 통해 지역사회 네트워 크 활용이 요구된다. 넷째, 민간 자원봉사자를 활용하여 소년원 퇴원생에게 사회적 지 지체계 마련이 필요하다. 마지막으로 소년원 퇴원생을 위한 법적 제도가 구체화되어 야 한다.

문화원형은 전통문화와 다른 의미로 해석된다. 한 사회의 주 요한 행동양식이나 상징체계를 문화라고 한다면 문화원형은 민 족 또는 지역의 특징을 잘 담고 다른 것과 구별되는 원래모습 에 해당되는 문화라 할 수 있고 전통문화는 과거부터 현재에 이르는 사회의 관습이나 행동양식 등을 일컫는다. 문화원형을 한국문화콘텐츠진흥원에서는 ‘한국적인 정체성, 나아가 고유성 을 가진 문화에 대한 원천자료를 지칭하는 것으로 문화원형은 콘텐츠 제작을 위한 원천소스 또는 원천자료‘라고 하였다. 문화 원형이라는 말이 본격적으로 사용된 것은 20여년 된다. 2002년 문화관광부와 한국콘텐츠진흥원이 ’문화원형 디지털콘텐츠화 사업‘을 진행하면서 문화원형에 대한 관심이 높아지게 되었고 문화원형이라는 말이 보편화되었다. 그러나 문화원형 발굴에 대한 결과물에 치중하다보니 다양한 산업에서 원천콘텐츠로 활 용되지 못하고 있다. 1990년대 중반부터 대중문화를 중심으로 우리나라의 문화가 외국에서 인기를 얻는 한류는 우리나라의 한류(韓流)는 아시아를 넘어 국가를 초월하여 전 세계로 퍼져 나가고 있다. 1990년대에 드라마와 영화를 시작으로 게임과 K-pop 등 다양한 형태의 문화를 전 세계인이 교감하고 있다. 무엇보다 한류는 소셜미디어, OTT 플랫폼들과 결합하여 동시 다발적으로 확장되고 있다. 따라서 본 연구는 발굴사업을 통해 발굴된 문화원형과 그 밖에 무궁무진한 우리나라의 문화원형(Culture Archetype)에 대하여 더욱 다양해지고 있는 유통 채 널을 활용하는 등 캐릭터 콘텐츠에 대한 OSMU(One Source Multi Use)를 확보할 수 있는 접근방법론과 활용 가능성을 고 찰해 보고자 하였다. 우선 본 연구에서는 캐릭터 콘텐츠의 IP관리 체계와 OSMU 의 전개 유형을 알아보고 문화원형을 기반으로 하는 OSMU 사 례 등을 분석하여 문화원형을 기반으로 하는 캐릭터 콘텐츠의 OSMU에 대하여 전략적 관점에서 진흥방안을 제안하고자 한 다.