When decommissioning a nuclear power plant, it is expected that clearance or radioactive waste (e.g., soil, concrete, metal, etc.) below the low-level will be generated in a short period on a large scale. Among the various types of waste, most of the contaminated soil is known to be classified as clearance or the (very) low-level radioactive waste. Accordingly, an accurate measurement and classification of contaminated soil in real-time during the decommissioning process can efficiently reduce the amount of soil waste and the possibility of contamination diffusion. However, in order to apply a system that measures and classifies contaminated soil in real-time according to the level of contamination to the decommissioning site, a demonstration is required to evaluate whether the system is applicable to the site. In this study, to establish requirements for determining the applicability of the system to the decommissioning site, preceding cases from countries with abundant decommissioning experience were investigated. For example, MACTEC of the U.S. demonstrated the developed system at the Saxton nuclear power plant in the U.S. and confirmed that the amount of soil that can be analyzed per hour in the system is affected by radionuclides, minimum detectable activity (MDA), and applicable volume. In the future, therefore, we will utilize the result of this study to develop the requirements of demonstrating the system for measurement and classification of contaminated soil in real-time.

The Fourth Industrial Revolution and sensor technology have led to increased utilization of sensor data. In our modern society, data complexity is rising, and the extraction of valuable information has become crucial with the rapid changes in information technology (IT). Recurrent neural networks (RNN) and long short-term memory (LSTM) models have shown remarkable performance in natural language processing (NLP) and time series prediction. Consequently, there is a strong expectation that models excelling in NLP will also excel in time series prediction. However, current research on Transformer models for time series prediction remains limited. Traditional RNN and LSTM models have demonstrated superior performance compared to Transformers in big data analysis. Nevertheless, with continuous advancements in Transformer models, such as GPT-2 (Generative Pre-trained Transformer 2) and ProphetNet, they have gained attention in the field of time series prediction. This study aims to evaluate the classification performance and interval prediction of remaining useful life (RUL) using an advanced Transformer model. The performance of each model will be utilized to establish a health index (HI) for cutting blades, enabling real-time monitoring of machine health. The results are expected to provide valuable insights for machine monitoring, evaluation, and management, confirming the effectiveness of advanced Transformer models in time series analysis when applied in industrial settings.

As the capacity of renewable power generation facilities rapidly increases, the variability of electric power system and gas turbine power generation is also increasing. Therefore, problems may occur that require urgent repair while the gas turbine rotor is stopped. When the gas turbine rotor turning is stopped and then restarted, if the turning period is not appropriate, severe vibration may occur due to rotor bending. As a result of the experiment, it was confirmed that normal operation is possible when the gap data measured at the start of rotor turning after maintenance work is similar to the existing value. And the vibration value at the start of rotor turning was lower as the rotor temperature was lower or the stop period was shorter.

In recent years, advances in the digital and live streaming economy have led to exponential growth in the number of self-employed streamers who have become an integral part of the self-driven digital labor force. However, previous research on the impact of streamers' work time arrangements on their virtual gifts remains scarce. To fill this gap in the literature, using large-scale data from Kuaishou live streaming platform, we demonstrate that several features of streamers' work time have an important impact on their virtual gifts. Specifically, our results suggest that work time duration and timing improve streamers' virtual gifts; meanwhile, work time tempo has an adverse effect on streamers' earnings. Taken together, our results provide novel and actionable insights for millions of self-employed streamers, agencies, platforms, and policymakers.

본 연구는 실시간 온라인으로 진행되는 학부 교양 초급 한국 어 수업에서 강의식 수업과 플립 러닝식 수업을 실시하고 각 수업 방 식에 대한 학습자 인식 및 수업 효율성을 살펴보는 데 목적이 있다. 이를 위해 ZOOM에서 8주간 각 수업 방식을 적용한 한국어 수업을 진행하고 사후 설문 조사를 실시하였다. 그 결과 학습자들은 강의식 수업보다는 플립 러닝식 수업에 긍정적인 반응을 보였으며, 강의식 수 업에서는 ‘어휘 및 문법’ 영역, 플립 러닝식 수업에서는 ‘듣기, 읽기, 쓰 기, 말하기’ 영역의 수업 효율성이 높게 나타났다. 이에 실시간 온라인 으로 진행되는 초급 한국어 수업의 효율을 높이기 위해서는 수업 환 경과 언어 영역의 특성을 고려한 혼합된 수업 방식이 필요할 것이다.

The cutting process, which is a key processing technology in various industrial fields is achieving continuous growth, and the demand for high-quality cutting surfaces is continuously demanded. Plasma cutting continues to be studied for its excellent workability and productivity, but problems with cutting surface quality such as dross formation occur, so research to secure excellent cutting surface quality through appropriate control of process variables is essential. In this study, we propose a method for predicting surface roughness using real-time current and cutting speed data obtained while performing plasma cutting on A106 B steel pipe. Surface roughness was predicted based on the RBF algorithm applicable to prediction and control models. It was shown that the surface roughness of the plasma cutting surface can be predicted with the arc current waveform and process speed data. This study can be used as a basic study to control the surface roughness of the cut surface in real time.

The entire industry is increasing the use of big data analysis using artificial intelligence technology due to the Fourth Industrial Revolution. The value of big data is increasing, and the same is true of the production technology. However, small and medium -sized manufacturers with small size are difficult to use for work due to lack of data management ability, and it is difficult to enter smart factories. Therefore, to help small and medium -sized manufacturing companies use big data, we will predict the gross production time through machine learning. In previous studies, machine learning was conducted as a time and quantity factor for production, and the excellence of the ExtraTree Algorithm was confirmed by predicting gross product time. In this study, the worker's proficiency factors were added to the time and quantity factors necessary for production, and the prediction rate of LightGBM Algorithm knowing was the highest. The results of the study will help to enhance the company's competitiveness and enhance the competitiveness of the company by identifying the possibility of data utilization of the MES system and supporting systematic production schedule management.

원전 구조물의 실시간 모니터링 기술이 요구되고 있지만, 현재 운영 중인 지진 감시계통으로는 동특성 추출 등 시스템 식별이 제한 된다. 전역적인 거동 데이터 및 동특성 추출을 위해서는 다수의 센서를 최적 배치하여야 한다. 최적 센서배치 연구는 많이 진행되어 왔 지만 주로 토목, 기계 구조물이 대상이었으며 원전 구조물 대상으로 수행된 연구는 없었다. 원전 구조물은 미미한 신호대잡음비에도 강건한 신호를 획득하여야 하며, 모드 기여도가 저차 모드에 집중되어 있어 모드별 잡음 영향을 고려해야 하는 등 구조물 특성을 고려 해야 한다. 이에 본 연구에서는 잡음에 대한 강건도와 모드별 영향을 평가할 수 있는 최적 센서배치 방법론을 제시하였다. 활용한 지표 로서 auto MAC(Modal Assurance Criterion), cross MAC, 노드별 모드형상 분포를 분석하였으며, 잡음에 대한 강건도 평가의 적합성을 수치해석으로 검증하였다.

This paper is proposing a novel machine scheduling model for the unrelated parallel machine scheduling problem without setup times to minimize the total completion time, also known as “makespan”. This problem is a NP-complete problem, and to date, most approaches for real-life situations are based on the operator’s experience or simple heuristics. The new model based on the Memetic Algorithm, which was proposed by P. Moscato in 1989, is a hybrid algorithm that includes genetic algorithm and local search optimization. The new model is tested on randomly generated datasets, and is compared to optimal solution, and four scheduling models; three rule-based heuristic algorithms, and a genetic algorithm based scheduling model from literature; the test results show that the new model performed better than scheduling models from literature.

Radioactive products generated by long-term operation at NPP can become deposited on the surfaces of the system and equipment, leading to radiation exposure for workers during the decommissioning process. Chemical decontamination is one of the methods to reduce radiation exposure of workers, and there are HP CORD UV, CITROX, CAN-DECON. In the chemical decontamination process, organic acids are generally used, and representative organic acids include oxalic acid and citric acid. There are various methods for removing residual organic acid in decontamination liquid waste, such as using an oxidizing agent and an ion exchange methods. However, there is a problem in that oxidizing agent is used excessively or secondary wastes are generated in excess during organic waste treatment. However, when organic acid is decomposed using a UV lamp, the amount of secondary waste is reduced because it tis decomposed into CO2 and H2O. In this study, organic acid decomposition was evaluated as the contact time of the UV lamp. The experimental equipment consists of a UV reactor, a mixing tank, a circulation pump. The experimental conditions involved preparing 60 L of organic liquid waste containing oxalic acid, hydrogen peroxide and iron chloride. Test A was conducted using one UV reactor, and Test B was performed by connecting two UV reactors in series. As a result of the experiment, a decomposition rate of over 95% was shown after one hour for oxalic acid, and it was confirmed that the initial decomposition rate was faster as the contact time increases. Therefore, in order to increase the initial decomposition rate, it is necessary to increase the contact time of the UV lamp by connecting the UV reactors in series.

어니스트 헤밍웨이(Ernest Hemingway)는 미국 모더니즘 작가로서 자전적인 소설을 많이 집필하였다. 본 연구에서는 그의 유수한 작품들 중 초기 단편집『우리들의 시대에』(In Our Time)에 그려진 이분법적 사유와 종교성을 고찰한 다. 이 작품은 15장의 단편들로 구성되었으며, 각 장의 짧은 일기형식의 서문 은 1920년대 혼란의 세계를 반영하고 있다. 헤밍웨이는 의사인 아버지를 따라 다니며 의학적 간접 경험과 더불어 낚시, 사냥, 투우와 같은 다양한 경험과 종 교적 분위기의 가정환경에서 자랐다. 이러한 그의 경험이 『우리들의 시대에』 에 근간을 이루는데 이 작품은 그의 어린시절의 순진함, 어른들에 대한 실망과 미래세계에 대한 불안, 그리고 정신적 성장으로 구성되어 있다. 미래세계에 대 한 불안에서는 남자는 여자로 인하여 불편할 수 있다는 남·녀의 이분법적 사유 로 인해 미래에 대한 불안감을 갖게 되며, 불안한 사회에서 기도하는 삶이 자 신을 안전하게 지켜주는 유일한 방법임을 시사한다.

In the case of a die-casting process, defects that are difficult to confirm by visual inspection, such as shrinkage bubbles, may occur due to an error in maintaining a vacuum state. Since these casting defects are discovered during post-processing operations such as heat treatment or finishing work, they cannot be taken in advance at the casting time, which can cause a large number of defects. In this study, we propose an approach that can predict the occurrence of casting defects by defect type using machine learning technology based on casting parameter data collected from equipment in the die casting process in real time. Die-casting parameter data can basically be collected through the casting equipment controller. In order to perform classification analysis for predicting defects by defect type, labeling of casting parameters must be performed. In this study, first, the defective data set is separated by performing the primary clustering based on the total defect rate obtained during the post-processing. Second, the secondary cluster analysis is performed using the defect rate by type for the separated defect data set, and the labeling task is performed by defect type using the cluster analysis result. Finally, a classification learning model is created by collecting the entire labeled data set, and a real-time monitoring system for defect prediction using LabView and Python was implemented. When a defect is predicted, notification is performed so that the operator can cope with it, such as displaying on the monitoring screen and alarm notification.

본 논문은 미술가 정규(鄭圭, 1923-1971)의 미술비평에 나타난 시대의식을 통해 그가 구 상한 전후 새로운 한국 현대미술을 분석하여 규명하는 데 목적을 둔다. 창작 활동 이외에도 비평, 교육, 행정 등 미술의 다방면에서 활약한 그의 예술관 중심에는 전전(戰前)에 대한 종합적 반성을 토대로 전후(戰後)라는 새 시대에 적합한 한국 현대미술의 모색이 있었다. 전후 한국미술계는 거 대한 흐름을 형성한 한국 앵포르멜을 중심으로 실존주의의 불안과 부조리를 내적으로 탐구했다. 반면 정규는 실존주의의 휴머니즘에 주목하여 극복의 징후로 이해한 뒤 동시대 한국의 현실에 반응 한 미술을 모색하고자 했다. 이러한 지점은 그가 회화를 중심으로 재편되어가던 한국미술계에서 낮은 장르로 취급되던 판화, 도예 등 독자적인 활동을 펼치게 추동했다. 본 연구는 초기 한국 현대 미술이 형성되어 가는 과정을 다각도로 이해하기 위하여 전후 시기 새로운 차원의 미술을 시도한 정규를 주목하면서 시작됐다. 이에 전후 시기 주요 논객으로 활동했던 그의 저술을 분석하여 그가 궁극적으로 정립하고자 했던 한국 현대미술을 체계화하고자 했다. 이를 통해 한국 현대미술의 형성 기인 전후 시기 미술에 관해 보다 입체적으로 해석할 가능성을 제안할 수 있을 것으로 기대한다.

This study is a plan on the way to strengthen the survival ability by increasing the use time of air respirator by applying emergency breathing method in the situation where firefighter has to wait for RIT (Rapid Intervention Teams) because it is impossible to escape by himself or emergency escape during isolation during field activities. The research procedure first drew problems by conducting a survey on fire fighters under the Seoul Fire and Disaster Headquarters, and conducted an experiment to compare normal breathing and emergency breathing with 20 members of the Seoul 119 Special Rescue Team, and suggested emergency breathing method and training process modeling that can be applied to each field situation based on the data obtained. The experiment was conducted over 9 weeks, and it was divided into three categories: field activity situation, movement (emergency escape assumption) situation, and place (assume waiting for RIT). In the field activity situation experiment, it was confirmed that the application of skip breathing method was appropriate and the use time of air respirator increased about 1.6 ~ 1.9 times. In the moving situation or the in-situ situation, wheel breathing method was appropriate and the use time of air respirator increased about 1.6 ~ 2.4 times. However, when conducting intense activities in the field activity situation, it is recommended to use it limitedly because it is difficult to apply the emergency breathing method. If emergency breathing is not clearly learned in the body, it is difficult to use in the field, so I think systematic and continuous training is necessary. This study suggests that the application of emergency breathing method is effective enough to strengthen the survival ability of firefighters in the field.