Since the National Emergency Management Agency’s seismic fragility function, developed in 2009, classified domestic buildings by structural type, numerous studies have used this classification. In 2021, the updated seismic fragility function adopted a slightly more complex classification logic, limited to concrete structures. Data for structural-type classification were derived from information in the building register, including primary use, floor area, permit date, and number of stories. To verify and improve the accuracy of the classification logic, a sample of approximately 1,800 from about 13,000 concrete buildings in a specific region was selected. Structural types classified by the logic were compared with those identified through road views provided by the Architecture HUB. The results confirmed that the existing classification logic requires revision to incorporate additional variables, including the sub-use of the building and the area-by-use on the first floor. The revised logic significantly improved classification accuracy by including those variables.

In digital games, typography serves not only as a vehicle for conveying information but also as a crucial visual element that shapes the game’s identity and emotional atmosphere. However, prior research has predominantly focused on graphics, backgrounds, and character design, with systematic analyses of typographic expression remaining limited. This study concentrates on the emotional functions of typography in games by analyzing 25 PC games across five representative genres: role-playing (RPG), shooting (FPS/TPS), strategy (RTS/TBS), MOBA (AOS), and horror. The titles of these games were assessed using a seven-point scale based on typographic variables—weight, form, spacing, slant, baseline, and visual effects—and subsequently translated into emotional dimensions: robustness, stability, dynamism, traditionality, and fantasy. Based on this framework, genre-specific emotional typologies were identified. The results indicate that RPGs emphasize grandeur and mythic symbolism; FPS/TPS games highlight robustness and dynamism; strategy games exhibit order and stability; MOBAs convey competitive dynamism; and horror games strongly employ fantasy and anxiety. By classifying genre-specific emotional types of typography, this study expands the scope of game graphic design research to include textual expression. Practically, it provides design guidelines that help align typographic choices with genre-specific emotional characteristics. Nonetheless, the study is limited to PC games and a single-researcher evaluation, suggesting the need for future research to incorporate diverse platforms and user-based assessments.

항로표지는 해상에서 항행안전을 보장하기 위한 핵심 수단으로서, 국제적 표준화가 필수적인 분야이다. 국제항로표지기구 (International Organization for Marine Aids to Navigation, IALA)는 1980년대 초 해상부표식(Maritime Buoyage System, MBS)을 제정·채택하여 이 를 중심으로 전 세계 항로표지 표준화를 주도해왔으며, 해상부표식은 현재 국제해사기구(IMO)와의 협력을 통해 사실상 국제 규정으로 기 능하고 있다. 이에 따라 세계 각국은 IALA 기준을 자국 법령에 반영하여 항로표지의 통일성과 해상안전을 확보해 왔다. 우리나라도 항로 표지법과 해양수산부령 고시를 통해 IALA 기준을 수용하고 있으나, 분류체계의 명확성과 국제표준과의 용어 통일성 측면에서 보완과 정 비가 필요한 상황이다. 본 논문은 IALA의 역사적 배경과 문서 체계를 살펴보고, 해상부표식의 원칙과 국제법적 지위를 고찰하였다. 나아 가 우리나라의 항로표지법, 시행령, 시행규칙 및 해양수산부 고시상 항로표지 분류체계를 해상부표식과 비교·분석함으로써 국내 법령의 국제표준 반영 현황과 문제점을 도출하였다. 이를 바탕으로 항로표지법령에 IALA 표준 준수에 대한 명확한 법적 근거를 마련하고, 항로 표지 분류체계를 직관적으로 이해할 수 있도록 법령을 재편하는 방안을 제시하였다. 이를 통해 국제표준에 부합하는 체계를 구축하여 효 과적인 항로표지 운영 기반이 마련될 수 있을 것으로 기대된다.

본 연구의 목적은 2023년 4월 충청남도 홍성군 대형산불피해지를 대상으로 산불로 인한 온실가스 배출량을 산정하여 국가 온실가스 인벤토리 고도화에 기여하고자 한다. 산불로 인한 온실가스 배출량은 2006년 IPCC 가이드라인에 따라 산정하였으며, 산정 인자인 연소면적은 Sentinel-2A 위성영상 기반의 differenced Normalized Burn Ratio (dNBR)을 활용하여 제작한 산불피해등급도를 이용하였고 지표층 및 수관층의 연료량 및 연소효율은 현장자료를 바탕으로 추정하였다. dNBR을 활용하여 제작한 산불피해등급도를 기반으로 산정한 온실가스 배출량은 약 19,336.9톤으로, 국립산림과학원 자료를 이용한 결과보다 약 4.0% 증가한 것으로 나타났다. 본 연구는 현장자료를 반영하여 산불로 인한 온실가스 배출량을 보다 정밀하게 산정한 데 의의가 있다. 향후에는 국내 생태계 특성을 반영한 각 요소별 고유 지표의 도입이 요구된다.

Flagpole supports were stone structures used in temples, symbolizing the temple or hanging a Dang on the Danggan to announce event, and played a crucial role in supporting both the Dang and the Danggan. The flagpole supports are not merely stone structures but hold significance in their relationship with the temple itself. Currently, around 86 of these flagpole supports remain in Korea. Despite a considerable number of flagpole supports still remaining, research papers and materials on the subject have been scarce, except for recent survey reports, precise measurements, and Um Ki-pyo's “A Study on the Dangcan and the Dangcan-Jiju in Korea.” While art historical research is abundant, architectural studies are still needed, and this study aims to fill that gap. First of all, the status of Flagpole Supports was identified based on the field survey, a precision survey report and other data were used, and an analysis study was conducted. This study, therefore, focuses on (1) the current status of the altered flagpole supports, and (2) the classification of the types of jointing, categorizing the methods of flagpole support jointing into five types based on the presence or absence of a base. Therefore, this study aims to identify the current status of flagpole supports and, through analysis, classify the jointing methods to provide foundational data for future research.

This study presents a truck classification method using panoramic side-view images to meet the Ministry of Land, Infrastructure and Transport’s 12-category standard (types 4–12). The system captures a vehicle’s full side profile via a panoramic imaging device, ensuring complete wheel visibility. A YOLOv12-based deep learning model detects wheels, and image processing extracts their center coordinates. Pixel distances between adjacent wheels are calculated and normalized to determine axle spacing patterns, which, together with wheel count, are applied to a rule-based classifier. Tests on 1,200 real-world panoramic truck images (1,000 for training, 200 for testing) achieved a mean average precision of 96.1% for wheel detection and 90.5% overall classification accuracy. The method offers explainable classification through measurable structural features, supporting applications in smart tolling, road usage billing, overloading enforcement, and autonomous vehicle perception.

This paper reviews ordinal decision tree algorithms for ordinal classification, exploring theoretical foundations, key algorithms (MDT, QMDT), specialized splitting criteria (Ordinal Gini, Weighted Information Gain), and ensemble methods. It discusses applications in healthcare and social sciences, highlighting interpretability and flexibility while acknowledging overfitting and instability. As implications for future research, this study points out advantages such as interpretability and flexibility, and limitations such as overfitting and instability.

Passive acoustic monitoring (PAM) has emerged as an effective tool for studying underwater soundscapes and monitoring marine organisms. In this study, the biological sounds of three fish species that mainly inhabit or occur in the Korean coastal oceans, brown croaker (Miichthys miiuy), Pacific cod (Gadus macrocephalus), and small yellow croaker (Larimichthys polyactis) were recorded using the PAM method. The possibility of automatic classification was evaluated using a deep learning-based convolutional neural network (CNN) model based on the measured data. The biological fish sounds were recorded using hydrophones in the sea cage environments. The three fish species data were converted into spectrogram images and used as input for training and evaluating the CNN model. Gaussian noise was added to the test data to simulate low signal-to-noise ratio (SNR) environments. The model achieved high classification performance, with F1-score of about 96% on raw data and about 77% accuracy under signal-to-noise ratio conditions. These results suggest that CNN-based models be adequate for fish sound classification, even in acoustically complex underwater environments. Applying CNN models to classify and detect fish sounds can improve the automation and efficiency of PAM-based acoustic analysis, thereby improving the monitoring of fish populations, resource assessment, and ecological management in the future.

The casting manufacturing process of aluminum automotive wheels often involves processing various wheel models during stages such as flow forming, machining, packaging, and delivery. Traditionally, separate equipment or production lines were required for each model, which led to higher facility investment costs and increased labor costs for classification. However, the implementation of machine learning-based model classification technology has made it possible to automatically and accurately distinguish between different wheel models, resulting in significant cost savings and enhanced production efficiency. Additionally, this approach helps prevent product mix-ups during the final inspection process and allows for the quick and precise identification of wheel models during packaging and delivery, reducing shipping errors and improving customer satisfaction. Despite these benefits, the high cost of machine learning equipment presents a challenge for small and medium-sized enterprises(SMEs) to adopt such technologies. Therefore, this paper analyzes the characteristics of existing machine learning architectures applicable to the automotive wheel manufacturing process and proposes a custom CNN(Convolutional Neural Network) that can be used efficiently and cost-effectively.

This study aimed to categorize grammar items in English textbooks used across Korean elementary, middle, and high schools by specific grade levels. To achieve this, we developed an automated grammar item analyzer using natural language processing techniques, which analyzed 52,964 sentences from the textbooks. We selected 173 grammar items from the 2022 revised national curriculum and classified them according to the methodology used to determine CEFR levels in the English Grammar Profile. The classification results are as follows: 18 items for Grade 3 of elementary school, 6 for Grade 4, 11 for Grade 5, 8 for Grade 6, 29 for the first year of middle school, 43 for the second year, 22 for the third year, 29 for the first year of high school, and 7 for the second year. Based on these findings, this study discusses pedagogical implications, focusing on practical applications such as refining assessment tools, more precisely defining curricular objectives, and developing grade-specific instructional materials.

This study aims to identify latent classes among shared e-scooter users based on their characteristics and analyze the differences in personal and usage characteristics across these classes. Specifically, the study has the following key objectives: (1) to select variables related to the personal and usage characteristics of shared e-scooter users; (2) to collect data on the personal and usage characteristics of shared e-scooter users; (3) to derive the latent classes of shared e-scooter users; and (4) to test the differences in personal and usage characteristics across the identified latent classes. Variables related to the personal and usage characteristics of shared e-scooter users were selected based on a literature review. Through a survey, data on the personal and usage characteristics of shared e-scooter users were collected. A latent class analysis (LCA) was performed to derive the latent classes of shared e-scooter users. Finally, a chi-square analysis was conducted to test the differences in personal and usage characteristics across the latent classes of shared e-scooter users. The results of this study are as follows. The personal characteristics of shared e-scooter users were identified as age and sex, whereas the usage characteristics were identified as usage frequency, time periods of e-scooter usage, return/rental zones, return/rental places, and types of roads used. Data on sex, age, usage frequency, periods of e-scooter usage, and return/rental locations were collected from 278 shared e-scooter users. Based on information criterion, statistical validation, and the entropy index, four latent classes of shared e-scooter users were identified: “male users with a commuting purpose in business zones,” “male users with a homeward commuting purpose in residential zones,” “female users with a leisure purpose in park/green zones,” and “users in their 20s with a commuting purpose in residential zones.” The results of a chisquare analysis revealed statistically significant differences (p < 0.05) in the personal and usage characteristics across the latent classes. Shared e-scooter user types were classified through Latent Class Analysis (LCA), and differences in personal and usage characteristics were identified across the classes. The preferred usage environments and conditions for each class of shared e-scooter users are determined. Variables related to the return/rental zone and periods of e-scooter usage showed the most significant differences among the classes. These findings can contribute to the development of customized user policies and the improvement of services based on the characteristics of shared e-scooter users.