도로의 포장 상태의 노후화나 관리미흡으로 인하여 시민의 사유 재산 중 주요한 요소인 자동차 등의 손상이나 자동차 사고 로 이어질 수 있어 큰 사회적 비용이 발생할 뿐 아니라, 시민들의 불편과 불만을 초래할 수 있다. 최근 도로 포장의 경우 포트홀 발생 건수와 그에 따른 민원 및 소송 건수가 증가해 행정력 및 예산이 낭비되고 있으며, 서울시의 경우 포장도로 노후화 추이가 증가함에 따라 유 지 관리 비용 또한 증가하고 있다. SOC 시설물 안전성 강화에 대한 사회적 요구는 지속적으로 증가하고 있어 한정된 예산의 효율적 활용을 위한 첨단 유지관리기술 도입이 시급하다.

The government is implementing a policy to expand eco-friendly energy as a power source. However, the output of new and renewable energy is not constant. It is difficult to stably adjust the power supply to the power demand in the power system. Therefore, the government predicts day-ahead the amount of renewable energy generation to cope with the output volatility caused by the expansion of renewable energy. It is a system that pays a settlement amount if it transitions within a certain error rate the next day. In this paper, Machine Learning was used to study the prediction of power generation within the error rate.

Over the years, in the field of safety assessment of geological disposal system, system-level models have been widely employed, primarily due to considerations of computational efficiency and convenience. However, system-level models have their limitations when it comes to phenomenologically simulating the complex processes occurring within disposal systems, particularly when attempting to account for the coupled processes in the near-field. Therefore, this study investigates a machine learning-based methodology for incorporating phenomenological insights into system-level safety assessment models without compromising computational efficiency. The machine learning application targeted the calculation of waste degradation rates and the estimation of radionuclide flux around the deposition holes. To develop machine learning models for both degradation rates and radionuclide flux, key influencing factors or input parameters need to be identified. Subsequently, process models capable of computing degradation rates and radionuclide flux will be established. To facilitate the generation of machine learning data encompassing a wide range of input parameter combinations, Latin-hypercube sampling will be applied. Based on the predefined scenarios and input parameters, the machine learning models will generate time-series data for the degradation rates and radionuclide flux. The time-series data can subsequently be applied to the system-level safety assessment model as a time table format. The methodology presented in this study is expected to contribute to the enhancement of system-level safety assessment models when applied.

Conducting a TSPA (Total System Performance Assessment) of the entire spent nuclear fuel disposal system, which includes thousands of disposal holes and their geological surroundings over many thousands of years, is a challenging task. Typically, the TSPA relies on significant efforts involving numerous parts and finite elements, making it computationally demanding. To streamline this process and enhance efficiency, our study introduces a surrogate model built upon the widely recognized U-network machine learning framework. This surrogate model serves as a bridge, correcting the results from a detailed numerical model with a large number of small-sized elements into a simplified one with fewer and large-sized elements. This approach will significantly cut down on computation time while preserving accuracy comparable to those achieved through the detailed numerical model.

Nuclear Material Accountancy (NMA) system quantitatively evaluates whether nuclear material is diverted or not. Material balance is evaluated based on nuclear material measurements based on this system and these processes are based on statistical techniques. Therefore, it is possible to evaluate the performance based on modeling and simulation technique from the development stage. In the performance evaluation, several diversion scenarios are established, nuclear material diversion is attempted in a virtual simulation environment according to these scenarios, and the detection probability is evaluated. Therefore, one of the important things is to derive vulnerable diversion scenario in advance. However, in actual facilities, it is not easy to manually derive weak scenario because there are numerous factors that affect detection performance. In this study, reinforcement learning has been applied to automatically derive vulnerable diversion scenarios from virtual NMA system. Reinforcement learning trains agents to take optimal actions in a virtual environment, and based on this, it is possible to develop an agent that attempt to divert nuclear materials according to optimal weak scenario in the NMA system. A somewhat simple NMA system model has been considered to confirm the applicability of reinforcement learning in this study. The simple model performs 10 consecutive material balance evaluations per year and has the characteristic of increasing MUF uncertainty according to balance period. The expected vulnerable diversion scenario is a case where the amount of diverted nuclear material increases in proportion to the size of the MUF uncertainty, and total amount of diverted nuclear material was assumed to be 8 kg, which corresponds to one significant quantity of plutonium. Virtual NMA system model (environment) and a divertor (agent) attempting to divert nuclear material were modeled to apply reinforcement learning. The agent is designed to receive a negative reward if an action attempting to divert is detected by the NMA system. Reinforcement learning automatically trains the agent to receive the maximum reward, and through this, the weakest diversion scenario can be derived. As a result of the study, it was confirmed that the agent was trained to attempt to divert nuclear material in a direction with a low detection probability in this system model. Through these results, it is found that it was possible to sufficiently derive weak scenarios based on reinforcement learning. This technique considered in this study can suggest methods to derive and supplement weak diversion scenarios in NMA system in advance. However, in order to apply this technology smoothly, there are still issues to be solved, and further research will be needed in the future.

본 연구는 딥러닝을 위한 비선형 변환 접근법을 사용하여 Single-lap joint의 접착 영역을 조사하기 위한 진동 응답 기반 탐지 시스템 을 제시한다. 산업 혹은 공학 분야에서 분해가 쉽지 않은 구조 내에 보이지 않는 부분의 상태와 접착된 구조의 접착 부위 상태를 알기 어려운 문제가 있다. 이러한 문제를 해결하기 위해 본 연구는 비선형 변환을 이용하여 기준 시편의 진동 응답으로 다양한 시편의 접착 면적을 조사하는 탐지 방법을 제안한다. 이 연구에서는 CNN 기반 딥러닝으로 진동 특성을 파악하기 위해 비선형 변환을 적용한 주파 수 응답 함수를 사용했고 분류를 위해 가상의 스펙트로그램을 사용했다. 또한, 제시된 방법을 검증하기 위해 알루미늄, 탄소섬유복합 재 그리고 초고분자량 폴리에틸렌 시편에 대한 진동 실험, 분석적 해, 유한요소해석을 수행했다.

This work provided a review of three techniques—(1) spectrochemical, (2) electrochemical, and (3) spectroelectrochemical– for molten salt medias. A spectroelectrochemical system was designed by utilizing this information. Here, we designed a spectroelectrochemical cell (SEC) and calibrated temperature controllers, and performed initial tests to explore the system’s capability limit. There were several issues and a redesign of the cell was accomplished. The modification of the design allowed us to assemble, align the system with the light sources, and successfully transferred the setup inside a controlled environment. A preliminary run was executed to obtain transmission and absorption background of NaCl-CaCl2 salt at 600°C. It shows that the quartz cuvette has high transmittance effects across all wavelengths and there were lower transmittance effects at the lower wavelength in the molten salt media. Despite a successful initial run, the quartz vessel was mated to the inner cavity of the SEC body. Moreover, there was shearing in the patch cord which resulted in damage to the fiber optic cable, deterioration of the SEC, corrosion in the connection of the cell body, and fiber optic damage. The next generation of the SEC should attach a high temperature fiber optic patch cords without introducing internal mechanical stress to the patch cord body. In addition, MACOR should be used as the cell body materials to prevent corrosion of the surface and avoid the mating issue and a use of an adapter from a manufacturer that combines the free beam to a fiber optic cable should be incorporated in the future design.

Purpose: Since the COVID-19 pandemic, virtual simulation practice has been increasingly activated as an alternative to clinical practice in nursing colleges. This study aimed to provide basic data by confirming changes in self-efficacy and nursing knowledge in the virtual simulations of nursing students, and identifying virtual presence, virtual patient learning system evaluation (VPLSE), and practical satisfaction. Methods: This was a single-group pre-post quasi-experimental study. The subjects were 28 third-grade nursing students. Results: Self-efficacy and nursing knowledge increased significantly (p<.001). Virtual presence had a significant positive correlation with VPLSE) (p=.002) and practice satisfaction (p=.011). There was also a significant positive correlation between virtual simulation learning evaluation and practice satisfaction (p<.001). Conclusion: Based on these results, virtual simulation practice can be used with clinical practice as an educational method to improve nursing students' self-efficacy and nursing knowledge in nursing education. Virtual presence was confirmed as a significant variable to improve practice satisfaction and VPLSE. It is necessary to develop a virtual simulation program that can improve virtual presence through collaboration with virtual reality technology experts.

밸브의 내부 누설 현상은 밸브의 내부 부품의 손상에 의해 발생하며 배관 시스템의 사고와 운전정지를 일으키는 주요 요인이 다. 본 연구는 버터플라이형 밸브의 내부 누설에 따라 배관계에서 발생하는 음향방출 신호를 이용하여 배관 가동 중 실시간 누설 진단의 가능성을 검토하였다. 이를 위해 밸브의 작동 모드별로 측정한 시간영역의 AE 원시신호를 취득하였으며 이로부터 구축한 데이터셋은 데 이터 기반의 인공지능 알고리즘에 적용하여 밸브의 내부 누설 유무를 진단하는 모델을 생성하였다. 누설 유무진단을 분류의 문제로 정의 하여 SVM 기반의 머신러닝과 CNN 기반의 딥러닝 분류 알고리즘을 적용하였다. 데이터의 특징 추출에 기반한 SVM 분류 모델의 경우, 이 진분류 모델에서 구축된 모델에 따라 83~90%의 정확도를 나타냈으며, 다중 클래스인 경우 분류 정확도가 66%로 감소하였다. 반면, CNN 기반의 다중 클래스 분류 모델의 경우 99.85%의 분류 정확도를 얻을 수 있었다. 결론적으로 밸브 내부 누설 진단을 위한 SVM 분류모델은 다중 클래스의 정확도 향상을 위해 적절한 특징 추출이 필요하며, CNN 기반의 분류모델은 프로세서의 성능 저하만 없다면 누설진단과 밸브 개도 분류에 효율적인 접근방법임을 확인하였다.

Through the process of chemical vapor deposition, Tungsten Hexafluoride (WF6) is widely used by the semiconductor industry to form tungsten films. Tungsten Hexafluoride (WF6) is produced through manufacturing processes such as pulverization, wet smelting, calcination and reduction of tungsten ores. The manufacturing process of Tungsten Hexafluoride (WF6) is required thorough quality control to improve productivity. In this paper, a real-time detection system for oxidation defects that occur in the manufacturing process of Tungsten Hexafluoride (WF6) is proposed. The proposed system is implemented by applying YOLOv5 based on Convolutional Neural Network (CNN); it is expected to enable more stable management than existing management, which relies on skilled workers. The implementation method of the proposed system and the results of performance comparison are presented to prove the feasibility of the method for improving the efficiency of the WF6 manufacturing process in this paper. The proposed system applying YOLOv5s, which is the most suitable material in the actual production environment, demonstrates high accuracy (mAP@0.5 99.4 %) and real-time detection speed (FPS 46).

A mid-story isolation system was proposed for seismic response reduction of high-rise buildings and presented good control performance. Control performance of a mid-story isolation system was enhanced by introducing semi-active control devices into isolation systems. Seismic response reduction capacity of a semi-active mid-story isolation system mainly depends on effect of control algorithm. AI(Artificial Intelligence)-based control algorithm was developed for control of a semi-active mid-story isolation system in this study. For this research, an practical structure of Shiodome Sumitomo building in Japan which has a mid-story isolation system was used as an example structure. An MR (magnetorheological) damper was used to make a semi-active mid-story isolation system in example model. In numerical simulation, seismic response prediction model was generated by one of supervised learning model, i.e. an RNN (Recurrent Neural Network). Deep Q-network (DQN) out of reinforcement learning algorithms was employed to develop control algorithm The numerical simulation results presented that the DQN algorithm can effectively control a semi-active mid-story isolation system resulting in successful reduction of seismic responses.

구조물에 장기적으로 발생하는 노후화를 정량적으로 파악하기 위해 상시진동 데이터를 활용한 일반화된 모니터링 시스템에 관한 연구가 세계적으로 활발히 수행중이다. 본 연구에서는 구조물에서 장기적으로 취득되는 동특성을 앙상블 학습에 활용하여 구조물의 이상을 감지하기 위한 보급형 엣지 컴퓨팅 시스템을 구축하였다. 시스템의 하드웨어는 라즈베리파이와 보급형 가속도계, 기울기센서, GPS RTK 모듈, 로라 모듈로 구성됐다. 실험실 규모의 구조물 모형 진동실험을 통해 동특성을 활용한 앙상블 학습의 구조물 이상 감지를 검증하였으며, 실험을 기반으로 한 실시간 동특성 추출 분산처리 알고리즘을 라즈베리파이에 탑재하였다. 구축된 시스템을 하우징하고 포항시 행정복지센터에 설치하여 데이터를 취득함으로써 개발된 시스템의 현장 적용성을 검증하였다.

This paper presents a real-time, false-pick filter based on deep learning to reduce false alarms of an onsite Earthquake Early Warning (EEW) system. Most onsite EEW systems use P-wave to predict S-wave. Therefore, it is essential to properly distinguish P-waves from noises or other seismic phases to avoid false alarms. To reduce false-picks causing false alarms, this study made the EEWNet Part 1 'False-Pick Filter' model based on Convolutional Neural Network (CNN). Specifically, it modified the Pick_FP (Lomax et al.) to generate input data such as the amplitude, velocity, and displacement of three components from 2 seconds ahead and 2 seconds after the P-wave arrival following one-second time steps. This model extracts log-mel power spectrum features from this input data, then classifies P-waves and others using these features. The dataset consisted of 3,189,583 samples: 81,394 samples from event data (727 events in the Korean Peninsula, 103 teleseismic events, and 1,734 events in Taiwan) and 3,108,189 samples from continuous data (recorded by seismic stations in South Korea for 27 months from 2018 to 2020). This model was trained with 1,826,357 samples through balancing, then tested on continuous data samples of the year 2019, filtering more than 99% of strong false-picks that could trigger false alarms. This model was developed as a module for USGS Earthworm and is written in C language to operate with minimal computing resources.

Second language (L2) peer response literature is defined in part by discourse research, yet there is scant research on text-specific comments, or comments that make explicit text references, thus resisting generic qualities. The purpose of this case study was to examine such peer response activities in an English writing course at a South Korean university. The data comprises two essay assignments with peer response conducted between two drafts – as accomplished during class time on the class learning management system (LMS) – as well as the subsequent revisions in second drafts. This paper expands on previous coding schemes accounting for area, nature and type commentary to account for a specificity dimension, and also links these categories to revision practices. While students entertained diverse commenting and revising options, popular practices included generic evaluating or revising local or surface-level concerns. This paper offers implications for modelling response activities as well as for how to better define specific and complex idea construction exhibited during response.

본 연구는 학점은행제 체육학전공 학습자의 사회적지지가 전공만족 및 학습지속의향에 미치는 영향을 규명하여 학점은행제의 체육학 전공자들의 중도탈락을 방지하고 효과적인 운영 방안을 모색하고자 하는데 연구의 목적이 있다. 연구방법은 학점은행제 체육학전공에 학습자를 대상으로 서울의 학점은행제 3곳을 선정하여 총 118부(90.8%)를 유효 표본으로 사용하였다. 조사도구의 타당도와 신뢰도 검증을 위해 확인적 요인분석과 집중타당도, 판별타당도, 평균분산추출(AVE), 개념신뢰도, Cronbach’s α 계수를 검증하였다. 자료처리방법은 IBM SPSS statistics 21과 IBM AMOS 21을 이용하여 빈도분석, 확인적 요인분석, 집중타당도, 판별타당도, Cronbach’s α 계수 산출을 통한 신뢰도분석(reliability analysis), 상관관계분석 (correlation analysis), 구조방정식모형(SEM) 검증을 실시하였다. 연구결과는 다음과 같다. 첫째, 학점은행제 체육학전공 학습자의 교수의 사회적지지와 전공만족 및 학습지속의향의 관계를 분석하기 연구모형의 적합도 검증한 결과 기준을 충족하였다. 둘째, 가설 1의 검증 결과, 학점은행제 체육학전공 학습자의 교수 의 사회적지지는 전공만족에 유의한 영향을 미치는 것으로 나타났다. 가설 2의 검증 결과, 학점은행제 체육 학전공 학습자의 교수의 사회적지지는 학습지속의향에 영향을 미치는 것으로 나타났다. 가설 3의 검증 결과, 전공만족은 학습지속의향에 유의한 영향을 미치는 것으로 나타났다.

본 논문은 자발적 지리정보인 오픈스트리트맵(OpenStreetMap, OSM)을 활용하여 중학교 자유학기제의 지도 수업과 학생 활동을 분석한 연구이다. 본 연구를 통해 지도 수정과 편집이 자유로운 오픈스트리트맵을 활용하여 학생들이 직접 지도 편집을 수행하고 중학교 자유학기제 수업에 적용할 수 있는 학생 활동 및 수행 평가 내용을 제안하였다. 이를 위해 총 8차시의 주제선택 활동을 계획하였고, 지형지물의 위치, 굴곡, 형태, 크기, 면적에 대해 학생들은 점, 선, 면의 지도 기호를 사용하여 지도 입력과 수정. 편집 활동을 진행하였다. 학생 활동 평가를 위해 학생들의 지도 정보 입력 및 편집 활동 결과 내용을 분석하고 이를 다섯 단계의 지도 습득 수준으로 분류하여 각 활동 수준의 특성을 살펴보았다. 이러한 논의를 바탕으로 본 연구는 인터넷 지도와 모바일 기기의 지도 활용 환경을 감안하여 학생 주도의 디지털 지도 학습과 지도 교육 관련 연구의 필요성을 제기하고자 한다.