This study investigated the characteristics of personal PM2.5 exposure among 109 participants residing in Seoul over a two-month period, from February 2024 to April 2024. The participants were categorized into four sub-populations, and personal exposure to PM2.5 was assessed using portable monitors, GPS, and time-activity diaries. To understand the time-activity patterns, the daily occupancy rate for different microenvironments was calculated. Additionally, daily PM2.5 exposure contribution and integrated exposure were quantified. A time series analysis was conducted to identify differences in time-activity patterns and PM2.5 exposure among the sub-populations. ANOVA analysis indicated statistically significant differences in PM2.5 concentrations across populations and microenvironments (p<0.05). However, post-hoc analysis revealed specific microenvironments within certain sub-populations where PM2.5 concentration differences were not significant (p>0.05). All sub-populations spent more than 90% of their time indoors, and the results for exposure contribution and integrated exposure indicated that the home, which had the highest occupancy rate, was the most significant contributor to PM2.5 exposure. This study is expected to serve as foundational data for future indoor air quality management and the development of personalized strategies for reducing PM2.5 exposure.

Researching and estimating the ecological characteristics of target fish species is crucial for fisheries resource management. The results of these estimates significantly influence stock assessments and management reference points such as size limit and closed seasons. Recently, ecological characteristics have been changing due to overfishing, climate change, and marine pollution, making continuous estimation and monitoring essential. This study analyzed the ecological changes in small yellow croaker (Larimichthys polyactis) resources in Korea over 24 years (2000-2023) using biological data (growth and gonad traits). By estimating the annual length-weight relationship and length at maturity (L50 and L95), we interpreted the numerical trends of early maturation due to resource depletion. The parameter b of the length-weight relationship, indicating the nutritional status of the resources, showed a slight increase over the years, suggesting relatively good nutritional status (b > 3.0) during most periods. Trend analysis between length at maturity and biomass indicated that as biomass decreased, maturity length also decreased.

In this study, we explored the potential of the Maillard reaction-based time-temperature indicators (TTI) as a tool for predicting and visualizing moisture variations during high-temperature drying. Using activation energy analysis, we found that the Maillard reaction-based TTI could not only visualize but also predict changes in moisture contents during high-temperature drying of 60-80oC. The color changes of the Maillard reaction solutions were distinct enough to be discerned with the naked eye, transitioning from colorless to black via the shift of yellow, light brown, brown, and dark brown. The dynamic characteristics for the color change in the Maillard reaction solutions and the moisture changes in the drying of thin-layer apples could be expressed with high suitability using a logistic model. This suggests that the Maillard reaction-based TTI can potentially be a practical and reliable tool for predicting the moisture changes for the high-temperature drying of thin-layer apples, offering a promising avenue for future research and applications.

Vertical takeoff and landing (VTOL) is a core feature of unmanned aerial vehicles (UAVs), which are commonly referred to as drones. In emerging smart logistics, drones are expected to play an increasingly important role as mobile platforms. Therefore, research on last-mile delivery using drones is on the rise. There is a growing trend toward providing drone delivery services, particularly among retailers that handle small and lightweight items. However, there is still a lack of research on a structural definition of the VTOL drone flight model for multi-point delivery service. This paper describes a VTOL drone flight route structure for a multi-drone delivery service using rotary-wing type VTOL drones. First, we briefly explore the factors to be considered when providing drone delivery services. Second, a VTOL drone flight route model is introduced using the idea of the nested graph. Based on the proposed model, we describe various time-related attributes for delivery services using drones and present corresponding calculation methods. Additionally, as an application of the drone route model and the time attributes, we comprehensively describe a simple example of the multi-drone delivery for first-come-first-served (FCFS) services.

본 연구에서는 노인의 건강 증진 및 건강 유지를 위해 노인 맞춤형 운동 애플리케이션 개발을 목표로, 스마트폰을 활용한 실시간 동작 추적 기술과 영상과 사진을 바탕으로 한 AI 학습을 통 해 단계별 동작 인식과 판단이 가능한 운동 동작 모델을 구현하였다. 노인 맞춤형 운동 애플리 케이션은 실시간 피드백을 지원하고, 노인의 운동 능력과 신체 가동 범위에 적합하게 단계적 운동이 가능하도록 구현되어야 할 것이다. 이를 위해 본 논문에서는 골포스트 스퀴즈(Goal Post Squeeze) 운동 동작을 대상으로 하여 이를 일련의 단위 동작으로 설계하고, MoveNet 포 즈 추정 기법을 기반으로 동작 인식 모델을 개발하였다. 구현한 운동 동작 모델에 대한 작동 실험 결과 단계별 데이터 인식과 판단, 정동작과 오동작 판단, 수평유지를 판단하고 이를 바탕 으로 사용자에게 실시간 피드백을 제공할 수 있음을 확인하였다.

PURPOSES : This study aimed to identify factors affecting the duration of traffic incidents in tunnel sections, as accidents in tunnels tend to cause more congestion than those on main roads. Survival analysis and a Cox proportional hazards model were used to analyze the determinants of incident clearance times. METHODS : Tunnel traffic accidents were categorized into tunnel access sections versus inner tunnel sections according to the point of occurrence. The factors affecting duration were compared between main road and tunnel locations. The Cox model was applied to quantify the effects of various factors on incident duration time by location. RESULTS : Key factors influencing mainline incident duration included collision type, driver behavior and gender, number of vehicles involved, number of accidents, and post-collision vehicle status. In tunnels, the primary factors identified were collision type, driver behavior, single vs multi-vehicle involvement, and vehicles stopping in the tunnel after collisions. Incidents lasted longest when vehicles stopped at tunnel entrances and exits. In addition, we hypothesize that incident duration in tunnels is longer than in main roads due to the reduced space for vehicle handling. CONCLUSIONS : These results can inform the development of future incident management strategies and congestion mitigation for tunnels and underpasses. The Cox model provided new insights into the determinants of incident duration times in constrained tunnel environments compared to open main roads.

일반적으로 적합직교분해(proper orthogonal decomposition, POD) 기반의 침습적(intrusive) 차수축소모델(reduced order model, ROM)을 활용하면 구조 시스템의 전체 자유도를 크게 줄이고 외연적 시간 적분법에서 해의 안정성을 만족하는 임계 시간 간격을 증가 시킬 수 있다. 따라서 본 연구에서는 POD-ROM을 활용하여 Voronoi-cell 격자 요소로 이산화된 구조 시스템의 축소와 이에 따른 외연 적 시간 적분법의 임계 시간 간격 및 해석 정확도 변화를 살펴보았다. 또한 지진하중과 같은 불규칙한 하중 이력을 받는 구조물 응답 해석에 POD-ROM을 적용하였다. 해석 결과 ROM을 통해 해의 정확도를 충분히 확보하면서 연산 시간을 크게 단축할 수 있음을 확인 하였다. 또한 POD-ROM과 VCLM의 연계 방안의 적절성을 확인하였다. 향후 해당 연구는 고정밀 대용량 동적 구조해석의 실용성을 높이고, 설계 변수에 따른 구조물 동적 거동의 실시간 예측을 위한 기반 연구로 활용될 수 있다.

Spent fuels (SFs) are stored in a storage pool after discharge from nuclear power plants. They can be transferred to for the further processes such as dry storage sites, processing plants, or disposal sites. One of important measures of SF is the burnup. Since the radioactivity of SF is strongly dependent on its burnup, the burnup of SF should be well estimated for the safe management, storage, and final disposal. Published papers about the methodology for the burnup estimation from the known activities of important radioactive sources are somewhat rare. In this study, we analyzed the dependency of the burnup on the important radiation source activities using ORIGEN-ARP, and suggested simple correlations that relate the burnup and the important source activities directly. A burnup estimation equation is suggested for PWR fuels relating burnup with total neutron source intensity (TNSI), initial enrichment, and cooling time. And three burnup estimation equations for major gamma sources, 137Cs, 134Cs, and 154Eu are also suggested.

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.

On pig farms, the highest mortality rate is observed among nursing piglets. To reduce this mortality rate, farmers need to carefully observe the piglets to prevent accidents such as being crushed and to maintain a proper body temperature. However, observing a large number of pigs individually can be challenging for farmers. Therefore, our aim was to detect the behavior of piglets and sows in real-time using deep learning models, such as YOLOv4-CSP and YOLOv7-E6E, that allow for real-time object detection. YOLOv4-CSP reduces computational cost by partitioning feature maps and utilizing Cross-stage Hierarchy to remove redundant gradient calculation. YOLOv7-E6E analyzes and controls gradient paths such that the weights of each layer learn diverse features. We detected standing, sitting, and lying behaviors in sows and lactating and starving behaviors in piglets, which indicate nursing behavior and movement to colder areas away from the group. We optimized the model parameters for the best object detection and improved reliability by acquiring data through experts. We conducted object detection for the five different behaviors. The YOLOv4-CSP model achieved an accuracy of 0.63 and mAP of 0.662, whereas the YOLOv7-E6E model showed an accuracy of 0.65 and mAP of 0.637. Therefore, based on mAP, which includes both class and localization performance, YOLOv4-CSP showed the superior performance. Such research is anticipated to be effectively utilized for the behavioral analysis of fattening pigs and in preventing piglet crushing in the future.

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.

During the decommissioning of a nuclear power plant, the structures must be dismantled to a disposal size. Thermal cutting methods are used to reduce metal structures to a disposal size. When metal is cut using thermal cutting methods, aerosols of 1 μm or less are generated. To protect workers from aerosols in the work environment during cutting, it is necessary to understand the characteristics of the aerosols generated during the cutting process. In this study, changes in aerosol characteristics in the working environment were observed during metal thermal cutting. The cutting was done using the plasma arc cutting method. To simulate the aerosols generated during metal cutting in the decommissioning of a nuclear power plant, a non-radioactive stainless steel plate with a thickness of 20 mm was cut. The cutting condition was set to plasma current: 80 A cutting speed: 100 mm/min. The aerosols generated during cutting were measured using a highresolution aerosol measurement device called HR-ELPI+ (Dekati®). The HR-ELPI+ is an instrument that can measure the range of aerodynamic diameter from 0.006 μm to 10 μm divided into 500 channels. Using the HR-ELPI+, the number concentration of aerosols generated during the cutting process was measured in real-time. We measured the aerosols generated during cutting at regular intervals from the beginning of cutting. The analyzed aerosol concentration increased almost 10 times, from 5.22×106 [1/cm3] at the start of cutting to 6.03×107 [1/cm3] at the end. To investigate the characteristics of the distribution, we calculated the Count Median Aerodynamic Diameter (CMAD), which showed that the overall diameter of the aerosol increased from 0.0848 μm at the start of cutting to 0.1247 μm at the end of the cutting. The calculation results were compared with the concentration by diameter over time. During the cutting process, particles with a diameter of 0.06 μm or smaller were continuously measured. In comparison, particles with a diameter of 0.2 μm or larger were found to increase in concentration after a certain time following the start of cutting. In addition, when the aerosol was measured after the cutting process had ended, particles with a diameter of 0.06 μm or less, which were measured during cutting, were hardly detected. These results show that the nucleation-sized aerosols are generated during the cutting process, which can explain the measurement of small particles at the beginning of cutting. In addition, it can be speculated that the generated aerosols undergo a process of growth by contact with the atmosphere. This study presents the results of real-time aerosol analysis during the plasma arc cutting of stainless steel. This study shows the generation of nucleation-sized particles at the beginning of the cutting process and the subsequent increase in the aerosol particle size over time at the worksite. The analysis results can characterize the size of aerosol particles that workers may inhale during the dismantling of nuclear power plants.

Spent nuclear fuel (SNF) characterization is important in terms of nuclear safety and safeguards. Regardless of whether SNF is waste or energy resource, the International Atomic Energy Agency (IAEA) Specific Safety Guide-15 states that the storage requirements of SNF comply with IAEA General Safety Requirement Part 5 (GSR Part 5) for predisposal management of radioactive waste. GSR Part 5 requires a classifying and characterizing of radioactive waste at various steps of predisposal management. Accordingly, SNF fuel should be stored/handled as accurately characterized in the storage stage before permanent disposal. Appropriate characterization methods must exist to meet the above requirements. The characterization of SNF is basically performed through destructive analysis/non-destructive analysis in addition to the calculation based on the reactor operation history. Burnup, Initial enrichment, and Cooling time (BIC) are the primary identification targets for SNF fuel characterization, and the analysis mainly uses the correlation identified between the BIC set and the other SNF characteristics (e.g., Burnup - neutron emission rate) for characterizing. So further identification of the correlation among SNF characteristics will be the basis for proposing a new analysis method. Therefore, we aimed to simulate a SNF assembly with varying burnup, initial enrichment, and cooling time, then correlate other SNF properties with BIC sets, and identify correlations available for SNF characterization. In this study, the ‘CE 16×16’ type assembly was simulated using the SCALEORIGAMI code by changing the BIC set, and decay heat, radiation emission characteristics, and nuclide inventory of the assembly were calculated. After that, it was analyzed how these characteristics change according to the change in the BIC set. This study is expected to be the basic data for proposing new method for characterizing the SNF assembly of PWR.

It is essential to determine a proper earthquake time history as a seismic load in a seismic design for a critical structure. In the code, a seismic load should satisfy a design response spectrum and include the characteristic of a target fault. The characteristic of a fault can be represented by a definition of a type of possible earthquake time history shape that occurred in a target fault. In this paper, the pseudo-basis function is proposed to be used to construct a specific type of earthquake, including the characteristic of a target fault. The pseudo-basis function is derived from analyzing the earthquake time history of specific fault harmonic wavelet transform. To show the feasibility of this method, the proposed method was applied to the faults causing the Gyeong-Ju ML5.8 and Pohang ML5.3 earthquakes.