Speed management in Korea currently emphasizes the setting of speed limits and controlling vehicle speeds to align with these standards. However, monitoring safe and stable speeds tailored to specific road sections is essential for enhancing pedestrian safety in urban areas. In this study, a crash frequency model was developed to define the speed stability range and identify the critical threshold at which the crash frequency changes rapidly. This threshold serves as a reference point for assessing the speed stability levels. Individual vehicle trajectory data collected from 20 road segments in Daejeon-si were used to calculate the speed-related safety evaluation indicators that served as input variables for the safety model. The speed stability range calculation incorporates speed-related indicators and road facility data from Daejeon-si, allowing the model to consider the surrounding infrastructure. The findings revealed that intersections and crosswalks are positively correlated with cumulative crash occurrences. Crash frequency predictions showed higher crash likelihoods at average driving speeds below 30 km/h, indicating that congested conditions at intersections or at peak times necessitate increased safety management. Measures for maintaining safe and appropriate vehicle speeds within identified safe ranges are critical. The speed stability range calculation methodology provides a foundation for establishing traffic safety management strategies that focus on speed control in urban areas. These results can guide the development of targeted safety interventions that prioritize pedestrian protection and optimize safe driving speeds across various road segments.

This study focused on Issey Miyake’s 14 womenswear collections from spring 2018 to fall 2024. Reviews and reports on these collections and the designer were collected from the American Vogue and the Women’s Wear Daily websites using the keyword “Issey Miyake.” Drawing upon the dress criticism model, this study analyzed the form of Miyake’s fashion designs identifying styles, descriptive features, and aesthetic values through the collection photographs. Implicit evaluations were derived through external interpretations and sociocultural analysis based on reviews and reports of Miyake’s collections and designs. The results were as follows: Miyake’s fashion design is avant-garde in style. Descriptively, Miyake introduced variations in silhouette through the three-dimensionality of pleats, exploring a new sculptural order. The aesthetic values of Miyake’s works extend beyond the fashion show realm, embodying natural beauty wearable in daily life, artistic beauty through the three-dimensionality of pleats, and a pursuit of new aesthetic values challenging existing systems and traditional norms. Externally interpreted, Issey Miyake is recognized for his diverse, practical, and innovative designs, and as the first Japanese designer to present shows in Paris. Sociocultural interpretations characterize of Issey Miyake’s works as a blend of tradition and modernity, an expansion of form through pleats and three-dimensional sculptural qualities, the result of ongoing research and development, possessing timeless qualities transcending eras, and a pursuit of practicality and artistic excellence.

As digital transformation accelerates, platform business has become a core business model in modern society. Platform business has a network effect where the winner takes all. For this reason, it is crucial for a company's pricing policy to attract as many customers as possible in the early stages of business. Telecommunication service companies are experiencing stagnant growth due to the saturation of the smartphone market and intensifying competition in rates, but the burden of maintaining communication networks is increasing due to the rapid increase in traffic caused by domestic and foreign CSPs. This study aims to understand the dynamic characteristics of the telecommunications market by focusing on pricing policy. To this end, we analyzed how ISPs, CSPs, and consumers react to changes in pricing policy based on the prisoner's dilemma theory. The analysis of the dynamic characteristics of the market was conducted through simulation using the Agent-Based Model.

In order to design a seismic safety of a cabinet affected by an earthquake, vibration analysis was performed using a model cabinet. In order to analyze the vibration characteristics of the cabinet under earthquake conditions, 3D finite element analysis was performed using ANSYS Workbench and SolidWorks. The modal analysis of the cabinet showed nine natural modes and natural frequencies, and showed the deformation and vibration of the cabinet panel for each mode. The frequencies of the 1st and 2nd modes, which are low modes, were 10% of the natural frequency value of the 9th mode, so it was easy to predict the possibility of resonance occurrence. The response spectrum due to the earthquake showed that the displacement, acceleration, and stress distribution of the cabinet had different behaviors in the x, y, and z directions, and especially showed very large values in the z direction. Although the vibration characteristics of the structure were evaluated using the modal characteristics and response spectrum for the cabinet, research on the application of a tuned mass damper is necessary for the dynamic characteristics process of the cabinet due to an earthquake and resonance reduction.

원전 내 전기기기의 내진성능 평가는 안전성 확보에 매우 중요하다. 이 연구에서는 원전에 설치되는 전기기기의 동특성 및 현장조사 결과를 참고하여 모형 캐비닛과 앵커기초를 설계 및 제작하였다. 제작된 모형 캐비닛을 대상으로 진동대실험을 수행하였다. 실험 결과를 바탕으로 유한요소모델을 작성하고 지진응답해석을 수행하였다. 입력지진동이 커짐에 따른 실험 및 해석 결과를 비교하여 모형 캐비닛의 지진거동특성을 분석하였다. 두 결과에 대한 모형 캐비닛의 지진거동은 다르며 내진성능에 큰 차이가 발생할 수 있다. 따라서 캐비닛과 콘크리트 기초 사이의 상호작용을 고려할 수 없는 경우 캐비닛의 지진거동 특성은 실험적으로 평가하는 것이 적절할 것으로 판단하였다.

PURPOSES : Pavement surface friction depends significantly on pavement surface texture characteristics. The mean texture depth (MTD), which is an index representing pavement surface texture characteristics, is typically used to predict pavement surface friction. However, the MTD may not be sufficient to represent the texture characteristics to predict friction. To enhance the prediction of pavement surface friction, one must select additional variables that can explain complex pavement surface textures. METHODS : In this study, pavement surface texture characteristics that affect pavement surface friction were analyzed based on the friction mechanism. The wavelength, pavement surface texture shape, and pavement texture depth were hypothesized to significantly affect the surface friction of pavement. To verify this, the effects of the three abovementioned pavement surface texture characteristics on pavement surface friction must be investigated. However, because the surface texture of actual pavements is irregular, examining the individual effects of these characteristics is difficult. To achieve this goal, the selected pavement surface texture characteristics were formed quantitatively, and the irregularities of the actual pavement surface texture were improved by artificially forming the pavement surface texture using threedimensionally printed specimens. To reflect the pavement surface texture characteristics in the specimen, the MTD was set as the pavement surface texture depth, and the exposed aggregate number (EAN) was set as a variable. Additionally, the aggregate shape was controlled to reflect the characteristics of the pavement surface texture of the specimen. Subsequently, a shape index was proposed and implemented in a statistical analysis to investigate its effect on pavement friction. The pavement surface friction was measured via the British pendulum test, which enables measurement to be performed in narrow areas, considering the limited size of the three-dimensionally printed specimens. On wet pavement surfaces, the pavement surface friction reduced significantly because of the water film, which intensified the effect of the pavement surface texture. Therefore, the pavement surface friction was measured under wet conditions. Accordingly, a BPN (wet) prediction model was proposed by statistically analyzing the relationship among the MTD, EAN, aggregate shape, and BPN (wet). RESULTS : Pavement surface friction is affected by adhesion and hysteresis, with hysteresis being the predominant factor under wet conditions. Because hysteresis is caused by the deformation of rubber, pavement surface friction can be secured through the formation of a pavement surface texture that causes rubber deformation. Hysteresis occurs through the function of macro-textures among pavement surface textures, and the effects of macro-texture factors such as the EAN, MTD, and aggregate shape on the BPN (wet) are as follows: 1) The MTD ranges set in this study are 0.8, 1.0, and 1.2, and under the experimental conditions, the BPN (wet) increases linearly with the MTD. 2) An optimum EAN is indicated when the BPN (wet) is the maximum, and the BPN decreases after its maximum value is attained. This may be because when the EAN increases excessively, the space for the rubber to penetrate decreases, thereby reducing the hysteresis. 3) The shape of the aggregate is closely related to the EAN; meanwhile, the maximum value of the pavement surface friction and the optimum EAN change depending on the aggregate shape. This is believed to be due to changes in the rubber penetration volume based on the aggregate shape. Based on the results above, a statistical prediction model for the BPN (wet) is proposed using the MTD, EAN, and shape index as variables. CONCLUSIONS : The EAN, MTD, and aggregate shape are crucial factors in predicting skid resistance. Notably, the EAN and aggregate shape, which are not incorporated into existing pavement surface friction prediction models, affect the pavement surface friction. However, the texture of the specimen created via three-dimensional printing differs significantly from the actual pavement surface texture. Therefore, the pavement surface friction prediction model proposed in this study should be supplemented with comparisons with actual pavement surface data in the future.

본 연구는 제5차 국가산림자원조사(2006-2010)에서 조사된 편백을 대상으로 흉고직경에 따른 수고 생장곡선식과 초기 수고생장 모델을 개발하여 편백의 초기 생장특성을 고려한 합리적인 산림경영계획 수립에 필요한 기초자료를 제공할 목적으로 실시하였다. 연구자료는 제5차 국가산림자원조사 자료 중 편백 표준목 353본에 대한 수고, 흉고직경, 연륜생장 자료를 이용하였다. 흉고직경에 따른 수고 생장곡선식은 Petterson 식, Log 식, Michailow 식을 이용하여 개발하였으며, 연령에 따른 초기 수고생장 모델은 Chapman-Richards 식, Gompertz 식, Schumacher 식을 이용하여 개발하였다. 본 연구 결과, 모델 검정을 통하여 흉고직경에 따른 수고 생장곡선식은 Petterson 식이 가장 적합한 것으로 나타났으며, 초기 수고생장 모델은 Gompertz 식이 가장 적합한 것으로 나타났다. 본 연구에서 개발한 초기 수고생장 모델을 그래프로 나타낸 결과 편백은 13년생일 때 연간 수고생장량이 0.54m로 가장 많은 것으로 나타났다. 본 연구 결과는 편백의 생장 특성 관련 연구에 활용할 수 있을 뿐 아니라 초기의 편백 조림지에 대하여 합리적인 산림경영계획 수립에 유용한 기초자료가 될 것으로 기대된다.

미세구조 특성의 불확실성은 재료 특성에 많은 영향을 준다. 시멘트 기반 재료의 공극 분포 특성은 재료의 역학적 특성에 큰 영향을 미치며, 재료에 랜덤하게 분포되어 있는 많은 공극은 재료의 물성 예측을 어렵게 한다. 공극의 특성 분석과 재료 응답 간의 상관관계 규명에 대한 기존 연구는 통계적 관계 분석에 국한되어 있으며, 그 상관관계가 아직 명확히 규명되어 있지 않다. 본 연구에서는 합성곱 신경망(CNN, convolutional neural network)을 활용한 이미지 기반 데이터 접근법을 통해 시멘트 기반 재료의 역학적 응답을 예측하 고, 공극분포와 재료 응답의 상관관계를 분석하였다. 머신러닝을 위한 데이터는 고해상도 마이크로-CT 이미지와 시멘트 기반 재료의 물성(인장강도)로 구성하였다. 재료의 메시 구조 특성을 분석하였으며, 재료의 응답은 상장균열모델(phase-field fracture model)에 기 반을 둔 2D 직접 인장(direct tension) 유한요소해석 시뮬레이션을 활용하여 평가하였다. 입력 이미지 영역의 기여도를 분석하여 시편 에서 재료 응답 예측에 가장 큰 영향을 미치는 영역을 CNN을 통하여 식별하였다. CNN 과정 중 활성 영역과 공극분포를 비교 분석하 여 공극분포특성과 재료 응답의 상관관계를 분석하여 제시하였다.

PURPOSES : To prevent an increasing number of drowsiness-related accidents, considering driver fatigue is necessary, which is the main cause of drowsiness accidents. The purpose of this study is to propose a methodology for selecting drowsiness hotspots using continuous driving time, a variable that quantifies driver fatigue. METHODS : An analysis was conducted by dividing driver fatigue, which changes according to time and space, into temporal and spatiotemporal scenarios. The analysis technique derived four evaluation indicators (precision, recall, accuracy, and F1 score) using a random forest classification model that is effective for processing large amounts of data. RESULTS : Both the temporal and spatiotemporal scenarios performed better in models that reflected the characteristics of road sections with changes in time and space. Comparing the two scenarios, it was found that the spatiotemporal scenario showed a difference in precision of approximately 10% compared with the temporal scenarios. In addition, [Model 2-2] of the spatiotemporal scenario showed the best predictive power by assessing the model’s accuracy via a comparison of (1-recall) and precision. This shows better performance in predicting drowsy accidents by considering changes in time and space together rather than constructing only temporal changes. CONCLUSIONS : To classify hotspots of drowsiness, spatiotemporal factors must be considered. However, it is possible to develop a methodology with better performance if data on individuals driving vehicles can be collected.

In this study, the safety aspects were studied by comparing the charge control characteristics of the two vehicles when a failure occurs between the OBC including the charging port or the charging door module (CDM) during slow charging using the In Cable Control Box (ICCB) for a long time.When the AC terminal was momentarily disconnected during charging, the Model-3 vehicle was charged normally if the AC circuit was disconnected up to three times, and the charging control was stopped when the number of disconnects reached four times. However, in the Ioniq-5 vehicle, charging control was normally performed when the disconnected AC circuit was normally connected regardless of the number of disconnection.

The prediction of algal bloom is an important field of study in algal bloom management, and chlorophyll-a concentration(Chl-a) is commonly used to represent the status of algal bloom. In, recent years advanced machine learning algorithms are increasingly used for the prediction of algal bloom. In this study, XGBoost(XGB), an ensemble machine learning algorithm, was used to develop a model to predict Chl-a in a reservoir. The daily observation of water quality data and climate data was used for the training and testing of the model. In the first step of the study, the input variables were clustered into two groups(low and high value groups) based on the observed value of water temperature(TEMP), total organic carbon concentration(TOC), total nitrogen concentration(TN) and total phosphorus concentration(TP). For each of the four water quality items, two XGB models were developed using only the data in each clustered group(Model 1). The results were compared to the prediction of an XGB model developed by using the entire data before clustering(Model 2). The model performance was evaluated using three indices including root mean squared error-observation standard deviation ratio(RSR). The model performance was improved using Model 1 for TEMP, TN, TP as the RSR of each model was 0.503, 0.477 and 0.493, respectively, while the RSR of Model 2 was 0.521. On the other hand, Model 2 shows better performance than Model 1 for TOC, where the RSR was 0.532. Explainable artificial intelligence(XAI) is an ongoing field of research in machine learning study. Shapley value analysis, a novel XAI algorithm, was also used for the quantitative interpretation of the XGB model performance developed in this study.

Flow prediction was carried out through observational survey and three dimensional multi-layered numerical diagnostic model experiment to clarify the time and spatial structure of tidal current and residual flow dominant in the sea exchange and material circulation of the waters around Geumo Islands in the southern waters of Korea. The horizontal variation of tidal current is so large that it causes asymmetric tidal mixing due to horizontal eddies and the topographical effect creating convergence and dispersion of flow direction and velocity. Due to strong tidal currents flowing northwest-southeast, counterclockwise and clockwise eddies are formed on the left and right sides of the south of Sori Island. These topographical eddies are created by horizontal turbulence and bottom friction causing nonlinear effects. Baroclinic density flows are less than 5 cm/s at coastal area in summer and the entire sea area in winter. The wind driven currents assuming summer and winter seasonal winds are also less than 5 cm/s and the current flow rate is high in winter. Density current in summer and wind driven current in winter have a relatively greater effect on the net residual flows (tidal residual current + density current + density driven current) around Geumo Islands Sea area.

In this study, Target strength for multi-frequency (38 kHz, 70 kHz, 120 kHz and 200 kHz) of chub mackerel (Scomber japonicus) was estimated using by the KRM model. The body shape of the Chub mackerel was described by an X-ray system and the body length of 20 individuals ranged from 16 cm to 28 cm. The swimbladder tilt angle ranged between – 8 and – 14°, the maximum TS value according to the swimming angle of chub mackerel was – 33.0 dB at – 11°. The averaged TScm according to fork length was – 66.02 dB at 38 kHz, – 66.50 dB at 70 kHz, – 66.00 dB at 120 kHz and – 67.35 dB at 200kHz, respectively.

Algal bloom is an ongoing issue in the management of freshwater systems for drinking water supply, and the chlorophyll-a concentration is commonly used to represent the status of algal bloom. Thus, the prediction of chlorophyll-a concentration is essential for the proper management of water quality. However, the chlorophyll-a concentration is affected by various water quality and environmental factors, so the prediction of its concentration is not an easy task. In recent years, many advanced machine learning algorithms have increasingly been used for the development of surrogate models to prediction the chlorophyll-a concentration in freshwater systems such as rivers or reservoirs. This study used a light gradient boosting machine(LightGBM), a gradient boosting decision tree algorithm, to develop an ensemble machine learning model to predict chlorophyll-a concentration. The field water quality data observed at Daecheong Lake, obtained from the real-time water information system in Korea, were used for the development of the model. The data include temperature, pH, electric conductivity, dissolved oxygen, total organic carbon, total nitrogen, total phosphorus, and chlorophyll-a. First, a LightGBM model was developed to predict the chlorophyll-a concentration by using the other seven items as independent input variables. Second, the time-lagged values of all the input variables were added as input variables to understand the effect of time lag of input variables on model performance. The time lag (i) ranges from 1 to 50 days. The model performance was evaluated using three indices, root mean squared error-observation standard deviation ration (RSR), Nash-Sutcliffe coefficient of efficiency (NSE) and mean absolute error (MAE). The model showed the best performance by adding a dataset with a one-day time lag (i=1) where RSR, NSE, and MAE were 0.359, 0.871 and 1.510, respectively. The improvement of model performance was observed when a dataset with a time lag up of about 15 days (i=15) was added.

This paper proposes a mathematical model that can calculate the luminescence characteristics driven by alternating current (AC) power using the current-voltage-luminance (I-V-L) properties of organic light emitting devices (OLED) driven by direct current power. Fluorescent OLEDs are manufactured to verify the model, and I-V-L characteristics driven by DC and AC are measured. The current efficiency of DC driven OLED can be divided into three sections. Region 1 is a section where the recombination efficiency increases as the carrier reaches the emission layer in proportion to the increase of the DC voltage. Region 2 is a section in which the maximum luminous efficiency is stably maintained. Region 3 is a section where the luminous efficiency decreases due to excess carriers. Therefore, the fitting equation is derived by dividing the current density and luminance of the DC driven OLED into three regions, and the current density and luminance of the AC driven OLED are calculated from the fitting equation. As a result, the measured and calculated values of the AC driving I-V-L characteristics show deviations of 4.7% for current density, 2.9% for luminance, and 1.9% for luminous efficiency.