The pressure sensor had been widely used to effectively monitor the flow status of the water distribution system for ensuring the reliable water supply to urban residents for providing the prompt response to potential issues such as burst and leakage. This study aims to present a method for evaluating the performance of pressure sensors in an existing water distribution system using transient data from a field pipeline system. The water distribution system in Y District, D Metropolitan City, was selected for this research. The pressure data was collected using low-accuracy pressure sensors, capturing two types of data: daily data with 1Hz and high-frequency recording data (200 Hz) according to specific transient events. The analysis of these data was grounded in the information theory, introducing entropy as a measure of the information content within the signal. This method makes it possible to evaluate the performance of pressure sensors, including identifying the most sensitive point from daily data and determining the possible errors in data collected from designated pressure sensors.

PURPOSES : The reliability of traffic volume estimates based on location intelligence data (LID) is evaluated using various statistical techniques. There are several methods for determining statistical significance or relationships between different database sets. We propose a method that best represents the statistical difference between actual LID-based traffic volume estimates and the VDS values (i.e., true values) for the same road segment. METHODS : A total of 2,496 datasets aggregated for 1-h LID and VDS data were subjected to various statistical analyses to evaluate the consistency of the two datasets. The VDS data were defined as the true values for comparison. Four different statistical techniques (procrutes, 2-sample t-test, paired-sample t-test, and model performance rating scale) were applied. RESULTS : In cases where there is a specific pattern (e.g., traffic volume distribution considering peak and off-peak times), distribution tests such as Procrustes or Kolmogorov-Smirnov are useful because not only the prediction accuracy but also the similarity of the data distribution shape is important. CONCLUSIONS : The findings of this study provide important insight into the reliability of LID-based traffic volume estimation. To evaluate the reliability between the two groups, a paired-sample t-test was considered more appropriate than the performance evaluation measure of the machine-learning model. However, it is important to set the acceptance criteria necessary to statistically determine whether the difference between the two groups in the paired-sample t-test varies according to the given problem.

PURPOSES : For autonomous vehicles, abnormal situations, such as sudden changes in driving speed and sudden stops, may occur when they leave the operational design domain. This may adversely affect the overall traffic flow by affecting not only autonomous vehicles but also the driving environment of manual vehicles. Therefore, to minimize the traffic problems and adverse effects that may occur in mixed traffic situations involving manual and autonomous vehicles, an autonomous vehicle driving support system based on traffic operation optimization is required. The main purpose of this study was to build a big-data-classification system by specifying data classification to support the self-driving of Lv.4 autonomous vehicles and matching it with spatio-temporal data. METHODS : The research methodology is explained through a review of related literature, and a traffic management index and big-dataclassification system were built. After collecting and mapping the ITS history traffic information data of an actual Living Lab city, the data were classified using the traffic management indexing method. An AI-based model was used to automatically classify traffic management indices for real-time driving support of Lv.4 autonomous vehicles. RESULTS : By evaluating the AI-based model performance using the test data from the Living Lab city, it was confirmed that the data indexing accuracy was more than 98% for the KNN, Random Forest, LightGBM, and CatBoost algorithms, but not for Logistics Regression. The data were severely unbalanced, and it was necessary to classify very low probability nonconformities; therefore, precision is also important. All four algorithms showed similarly good performances in terms of accuracy. CONCLUSIONS : This paper presents a method for efficient data classification by developing a traffic management index to easily fuse and analyze traffic data collected from various institutions and big data collected from autonomous vehicles. Additionally, EdgeRSU is presented to support the driving of Lv.4 autonomous vehicles in mixed autonomous and manual vehicles traffic situations. Finally, a database was established by classifying data automatically indexed through AI-based models to quickly collect and use data in real-time in large quantities.

Abstract Handling imbalanced datasets in binary classification, especially in employment big data, is challenging. Traditional methods like oversampling and undersampling have limitations. This paper integrates TabNet and Generative Adversarial Networks (GANs) to address class imbalance. The generator creates synthetic samples for the minority class, and the discriminator, using TabNet, ensures authenticity. Evaluations on benchmark datasets show significant improvements in accuracy, precision, recall, and F1-score for the minority class, outperforming traditional methods. This integration offers a robust solution for imbalanced datasets in employment big data, leading to fairer and more effective predictive models.

태풍은 지구 시스템 내의 해양-대기-육상 상호작용을 일으키는 매우 중요한 현상으로 특히 태풍의 특성 인자 중 하나인 풍속은 중심 기압, 이동 경로, 해수면 온도 등의 매개변수에 의해 복잡하게 변화하여 실제 관측 자료를 기반으 로 이해하는 것이 중요하다. 2015 개정 교육과정 기반 중등학교 교과서에서 태풍 풍속은 본문 내용 및 삽화의 형태로 제시되고 있어 풍속에 대한 심층적 이해가 가능한 탐구활동이 무엇보다 필요한 실정이다. 본 연구에서는 교수-학습 과 정에서 간단한 조작만으로도 태풍의 풍속을 이해할 수 있도록 그래픽 사용자 인터페이스(GUI)를 기반으로 한 데이터 시각화 프로그램을 개발하였다. 2023년 발생한 태풍 마와르, 구촐, 볼라벤의 천리안 위성 2 A호 RGB (Red-Green-Blue) 영상 자료를 입력 자료로 활용하였다. 태풍 주변의 구름 이동 좌표를 입력하여 태풍의 풍속을 산출하고 태풍 중심 기 압, 폭풍 반경, 최대 풍속 등의 매개 변수를 입력하여 태풍 풍속 분포를 시각화 할 수 있도록 설계하였다. 본 연구에서 개발된 GUI 기반 프로그램은 천리안 위성 2 A호로 관측 가능한 태풍에 대해 오류 없이 적용 가능하며 교과서의 시공 간적 한계를 벗어난 실제 관측 자료 기반의 과학탐구활동이 가능하다. 학생과 교사는 별도의 유료 프로그램 및 전문적 인 코딩 지식이 없어도 실제 관측 자료를 수집, 처리, 분석, 시각화하는 과정을 경험할 수 있으며, 이를 통해 미래 정보 화 사회에서의 필수 역량인 디지털 소양을 함양시킬 수 있을 것으로 기대된다.

The objective of this study is to analyze the indoor air quality of multi-use facilities using an IoT-based monitoring and control system. Thise study aims to identify effective management strategies and propose policy improvements. This research focused on 50 multi-use facilities, including daycare centers, medical centers, and libraries. Data on PM10, PM2.5, CO2, temperature, and humidity were collected 24 hours a day from June 2019 to April 2020. The analysis included variations in indoor air quality by season, hour, and day of the week (including both weekdays and weekends). Additionally, ways to utilize IoT monitoring systems using big data were propsed. The reliability analysis of the IoT monitoring network showed an accuracy of 81.0% for PM10 and 76.1% for PM2.5. Indoor air quality varied significantly by season, with higher particulate matter levels in winter and spring, and slightly higher levels on weekends compared to weekdays. There was a positive correlation found between outdoor and indoor pollutant levels. Indoor air quality management in multi-use facilities requires season-specific strategies, particularly during the winter and spring. Furhtermore, enhanced management is necessary during weekends due to higher pollutant levels.

본 연구는 관광 관련 서비스 분야에 필요한 특수 목적 중국어(CSP) 교육과정 개 발을 위한 첫 단계로 AI 데이터 기반으로 구축된 구어체 병렬 코퍼스에서 CSP 어휘 리스트를 선정하여 용어색인과 어휘다발(n-gram)등을 분석하였다. 어휘리스트 어휘 규모는 토큰 수 총 304, 228개와 타입 수 17, 286개로 나타났으며, 어휘 누적 증가율 을 분석하면 2-Gram과 3-Gram의 어휘다발이 가장 많았고, 실무 현장에서 가장 많 이 활용되고 있음을 알 수 있었다. 본 연구에서 구축된 특수 목적 관광 중국어 어휘 리스트는 실제 교육 자료로 제공하여 관광 중국어 학습자와 교수자에게 실용적으로 사용될 수 있을 것이라 기대한다.

In recent automated manufacturing systems, compressed air-based pneumatic cylinders have been widely used for basic perpetration including picking up and moving a target object. They are relatively categorized as small machines, but many linear or rotary cylinders play an important role in discrete manufacturing systems. Therefore, sudden operation stop or interruption due to a fault occurrence in pneumatic cylinders leads to a decrease in repair costs and production and even threatens the safety of workers. In this regard, this study proposed a fault detection technique by developing a time-variant deep learning model from multivariate sensor data analysis for estimating a current health state as four levels. In addition, it aims to establish a real-time fault detection system that allows workers to immediately identify and manage the cylinder’s status in either an actual shop floor or a remote management situation. To validate and verify the performance of the proposed system, we collected multivariate sensor signals from a rotary cylinder and it was successful in detecting the health state of the pneumatic cylinder with four severity levels. Furthermore, the optimal sensor location and signal type were analyzed through statistical inferences.

PURPOSES : This study aimed to predict the number of future COVID-19 confirmed cases more accurately using public and transportation big data and suggested priorities for introducing major policies by region. METHODS : Prediction analysis was performed using a long short-term memory (LSTM) model with excellent prediction accuracy for time-series data. Random forest (RF) classification analysis was used to derive regional priorities and major influencing factors. RESULTS : Based on the daily number of COVID-19 confirmed cases from January 26 to December 12, 2020, as well as the daily number of confirmed cases in Gyeonggi Province, which was expected to occur on December 24 and 25, depending on social distancing, the accuracy of the LSTM artificial neural network was approximately 95.8%. In addition, as a result of deriving the major influencing factors of COVID-19 through random forest classification analysis, according to the number of people, social distancing stages, and masks worn, Bucheon, Yongin, and Pyeongtaek were identified as regions expected to be at high risk in the future. CONCLUSIONS : The results of this study can help predict pandemics such as COVID-19.

자율주행차 상용화 시대를 가속화하기 위해 실제 도로에서 다양한 실증 프로젝트를 수행중이다. 그러나, 자율주행차와 비자율주행차 가 혼재된 혼합교통류 환경에서 발생할 수 있는 다양한 문제의 원인을 파악하고 선제적인 안전대책을 강구하는 노력은 미비한 실정이 다. 특히, 기존 비자율주행차 측면의 주행안전성을 고려하여 설계된 도로 시설 특성으로 인해 자율주행차의 주행안전성이 저하될 수 있다. 또한 기존 비자율주행차의 주행안전성을 저해함과 동시에 자율주행차의 주행안전성도 저해하는 도로 시설 특성이 존재할 가능 성이 있다. 본 연구에서는 상암 자율주행차 시범운행지구에서 수집된 automated vehicle data (AVD)를 활용하여 자율주행차와 비자율주 행차의 주행안전성을 평가하고 도로 시설 특성 측면의 영향요인을 도출하였다. 주행모드별 주행안전성 평가를 위해 autonomous emergency braking system (AEBS) 위험 이벤트 기반의 driving risk index (DRI)를 개발하였다. 구간별 DRI가 발생하지 않은 구간을 very good으로 정의하고 발생한 구간을 25 percentile로 구분하여 good, moderate, poor, very poor 등급으로 정의하여 총 5개의 등급으로 구분 하였다. 또한, 현장조사을 수행함으로써 구간별 포함되어 있는 도로 시설 특성을 수집하였다. 주행모드별 주행안전성에 영향을 미치는 도로 시설 특성을 도출하기 위해 이항로지스틱 회귀분석을 수행하였다. 종속 변수의 경우 DRI 기반 안전등급 중 poor 이상 등급을 1, 그 외의 등급을 0으로 정의하였으며, 독립변수의 경우 현장조사를 통해 수집된 교차로 유형, 차로 수, 차로 폭, 추가차로 유무, 차량 진행방향, 불법주정차 유무, 버스정류장 유무, 자전거 차로 유무에 대해 명목형 변수로 설정하였다. 도출된 주행모드별 주행안전성 영 향 요인을 검토하고 향후 자율주행차 시대에 대비하여 선제적으로 개선이 요구되는 도로 시설 특성을 도출하고 도로 운영성 및 효율 성, 안전성 측면의 개선 방향을 제시하였다.

본 연구는 독일어권의 사물인터넷을 이용한 데이터 거래와 블록체인 기술로 인한 사회혁신을 조망하고자 한다. 먼저, 독일어권 국가에서의 빅 데이터와 블록체인 기술의 활용을 조사하기 위해 문헌 연구 및 선행 연 구 검토가 수행되었다. 또한, 데이터레이드(Datarade)와 같은 독일의 데 이터 회사 및 정부의 데이터 경제 관련 프로젝트(GAIA-X)에 대한 사례 연구가 진행되었다. 이를 통해 독일에서의 데이터 및 블록체인 활용 현 황을 파악하고, 각 산업 분야에서의 적용 사례를 식별하였다. 금융 산업 에서는 블록체인 기술을 활용하여 계좌 번호 및 구매 세부 정보를 안전 하게 저장하고 있으며, 부동산 산업에서는 임대 계약, 임대료 결제 확인 등을 블록체인을 통해 효율적으로 관리하고 있다. 특히 교육 부문에서 블록체인 기술의 활용에 대한 현지 사례 및 연구 결과를 종합하여 분석 하였다. 블록체인의 보안이라는 장점을 살려 학습자의 학습 성과나 평가, 성적 증명, 학습낙오자나 성적부진자의 학습활동 추적, 부정행위 방지, 스마트 계약을 통한 과제 관리, 평생학습증 및 학습이력부 제공 등의 방 식으로 이미 독일은 교육계에 혁신을 이루어나가고 있다. 교육 부문에서 의 이러한 조사 방법을 통해 독일에서의 기술 혁신 및 사회적 변화에 대 한 종합적인 이해를 제공하고자 한다. 이러한 결과들은 독일정부 주도의 데이터거래와 블록체인 분야의 기술혁신의 효과를 입증하기에 한국정부 의 산업혁신에도 활용할 수 있는 중요한 통찰을 제공할 것이다.

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).

This study selected two labor-intensive processes in harsh environments among domestic food production processes. It analyzed their improvement effectiveness using 3-dimensional (3D) simulation. The selected processes were the “frozen storage source transfer and dismantling process” (Case 1) and the “heavily loaded box transfer process” (Case 2). The layout, process sequence, man-hours, and output of each process were measured during a visit to a real food manufacturing factory. Based on the data measured, the 3D simulation model was visually analyzed to evaluate the operational processes. The number of workers, work rate, and throughput were also used as comparison and verification indicators before and after the improvement. The throughput of Case 1 and Case 2 increased by 44.8% and 69.7%, respectively, compared to the previous one, while the utilization rate showed high values despite the decrease, confirming that the actual selected process alone is a high-fatigue and high-risk process for workers. As a result of this study, it was determined that 3D simulation can provide a visual comparison to assess whether the actual process improvement has been accurately designed and implemented. Additionally, it was confirmed that preliminary verification of the process improvement is achievable.