본 연구는 사회적 구조의 변화로 인한 유기견 증가로 인해 유기견 입양률 향상의 필요 성을 인지하여 입양률을 향상시키고자 유기견 미용이 입양률 향상에 미치는 영향을 분석 하고 미용을 접목시킨 애플리케이션 개선 방안을 제시하는 것을 목적으로 한다. 본 연구 팀은 임실 N 펫스타에 참가한 참가자들 356명에게 ‘유기견 애플리케이션 개선을 위한 설 문조사’를 실시하였고 미용사와 애플리케이션의 협업을 위한 현실적 견해를 얻기 위해 현 장 종사자 3인에게 인터뷰를 진행하였다. 설문조사 및 선행연구 결과 유기견의 미용 상태 는 입양자들에게 긍정적인 반응을 이끌어낼 수 있으며 입양률 증가에 긍정적인 영향을 줄 수 있는 것으로 확인되었다. 또한 현장 종사자 3인에게 전문가 인터뷰를 진행한 결과 미용사들과 애플리케이션 간의 협업의 가능성을 보았으며 현실적인 견해를 얻을 수 있었 다. 본 연구는 미용과 입양률의 상관관계를 밝혀내었으나 미용사와 애플리케이션 간 연계 를 위해서는 해결해야 할 과제들이 남아있기에 이 문제들을 해결하기 위한 후속연구의 필요성이 있다. 또한 유기견 품종 및 성격과 같은 세세한 요소들은 통제되지 않았기에 향 후에는 정교한 분석을 실시한 연구의 필요성이 있다.

This study assessed the feasibility of deploying mobile safety-sign robots to replace human flaggers in highway work zones and determined the optimal Dynamic Message Display (DMD) configurations. The study consisted of two phases. The first phase involved a pilot test on a test road in Yeoju, where the work zone conditions were replicated by following the highway work zone traffic management guidelines. Eight drivers participated in a pilot test. All four driving behavior indicators demonstrated improvements in driving safety under the robotbased scenario compared with the conventional human flagger scenario. The second phase adopted a Virtual Reality (VR)-integrated Driving Simulator (DS) to analyze the driver behavior across various DMD types. Six robot-based scenarios were designed by combining three DMD message types with two display sizes along with one baseline scenario based on existing guidelines for comparison. Twenty drivers participated in this experiment. A rank-based comparative analysis incorporating five evaluation indicators was performed to derive the optimal DMD display type. Scenario 3 (vertical ‘60’ display) and Scenario 6 (horizontal ‘감속60’ display, ‘Reduce speed to 60’ in English) were identified as the optimal DMD display types. These findings establish a foundation for the development of traffic management standards for safety sign robots in highway work zones.

Background: Hallux valgus (HV) is a common forefoot deformity that can lead to pain, altered gait, and musculoskeletal dysfunctions. Accurate severity assessment is essential for clinical decision-making, yet radiographic methods, though accurate—are costly and less accessible. Objects: This study aimed to develop and clinically validate an end-to-end artificial intelligence (AI)-based mobile application for HV severity classification from smartphone-captured dorsal foot photographs. Methods: The study comprised two phases. In Phase 1 (App & Model Development), we developed a mobile application integrating foot Red-Green-Blue (RGB) image capture, HV severity classification, and immediate reporting. Paired (weight-bearing anteroposterior foot) radiographs and smartphone dorsal foot photographs were collected from 180 adults with HV. Radiographic HV angle and intermetatarsal angle were measured to categorize severity (mild, moderate, severe) as ground truth. A MobileNetV2 convolutional neural network (CNN) was trained on dorsal foot images to predict severity. In Phase 2 (External Validation & Usability Assessment), 30 independent participants underwent both radiographic and app-based severity assessments. Diagnostic times were recorded for both assessments. Participants then completed a 10-item Likert-scale usability questionnaire, with internal consistency assessed using Cronbach’s α. Results: The CNN successfully classified HV severity based on radiographic ground truth and showed consistent performance on an external dataset. App-based assessment was on average approximately 12 minutes faster than radiographic evaluation (p < 0.001). Usability evaluation indicated positive user experience (overall mean = 3.84/5, Cronbach’s α = 0.706). Conclusion: This study presents fully operational mobile AI application that enables rapid, accurate, and user-friendly classification of HV severity directly from smartphone photographs. By combining machine learning with an accessible mobile platform, it can support point-ofcare screening, patient self-monitoring, and community-based care where radiographic evaluation is impractical.

This study quantitatively evaluated the real-world performance of an IoTbased, context-aware mobile air purification system. Additionally, this system is proposed as a practical alternative to conventional stationary purifiers, overcoming their spatial limitations. To analyze concentration variations, removal efficiency, and air cleaning ratio (ACR) for PM2.5, PM10, and HCHO, three scenarios were tested: S1 (natural ventilation), S2 (stationary purifier), and S3 (IoT-based mobile air purification system). The mobile system (S3) achieved a 1.6-fold higher removal efficiency for PM2.5 compared with the stationary purifier (S2) and reduced the ACR to below 0.4 within 30 minutes after high-concentration events. In contrast, stationary purifiers required approximately 333 minutes to reach background levels (17.11 μg/m3), revealing about a 10-fold difference in cleaning speed. Monte Carlo simulations confirmed the consistent superiority of S3 for both particulate and gaseous pollutants, with HCHO concentrations 36.7% lower (90th percentile) than under S2. According to the health risk assessment, the asthma hospitalization rate decreased by over 40%, the HQ for PM2.5 decreased from 1.1 (S1) to 0.64 (S3), and the ECR for HCHO was 0.62 times that of S2. These findings highlight that spatial responsiveness and mobility, along with filter capacity, are key determinants of air purification performance. In conclusion, the mobile air purifier effectively overcomes the structural constraints of stationary devices and establishes a new paradigm for realtime, adaptive indoor air quality management that helps safeguard occupant health.

This study proposes a novel diagnostic methodology combining mobile measurement using selected ion flow tube mass spectrometry (SIFT-MS) and explainable artificial intelligence (XAI) to effectively monitor and diagnose localized highozone (O3) events in industrial complexes. The methodology was applied to a highconcentration ozone episode (maximum 94.0 ppb) observed in the Hwaseong Bio Valley, an industrial complex. A nonlinear regression model based on the Random Forest algorithm was developed to quantify the contribution of precursor species. Specifically, to precisely diagnose the individual contributions of volatile organic compounds (VOCs), which are critical determinants of ozone formation, a modeling approach centered on VOCs was employed by excluding inorganic precursors (NOx). Contrary to traditional ozone formation potential (OFP) analysis, which prioritized high-reactivity alkenes such as propene, the AI model identified cyclohexane and butanone (MEK) as the key drivers positively correlated with ozone concentration fluctuations. This discrepancy is attributed to the “abundance effect,” where atmospheric partial pressures of organic solvents, extensively emitted from pharmaceutical and bio-industrial processes, overwhelm the differences in chemical reactivity of individual species. The findings suggest that AI techniques can interpret the nonlinearity of complex photochemical reactions based on observational data, serving as a complementary site-specific diagnostic tool to existing property-based assessments (e.g., MIR). Consequently, future air quality policies should shift from uniform regulations to a more targeted approach, utilizing the proposed methodology to establish precise emission tracking and management systems.

This study proposes a mobile-based lightweight deep learning model (Lite-MCC) capable of reconstructing three-dimensional (3D) spatial structures from a single RGB image. Conventional 3D reconstruction models require multi-view inputs or point cloud data and depend on large-scale computational resources, which limits their real-time applicability in practical environments. To address this limitation, the proposed Lite-MCC model simplifies the existing Multiview Compressive Coding (MCC) architecture, enabling accurate 3D reconstruction using only a single image. The model adopts a parallel structure consisting of a Vision Transformer (ViT-Tiny) and a Geometry Encoder to extract visual and spatial features simultaneously, while a Transformer Decoder generates the corresponding 3D point cloud. Furthermore, depth map–based input transformation and ONNX-based optimization are employed to achieve efficient real-time inference on edge devices. Experimental results on the CO3D dataset demonstrate that Lite-MCC reduces computational cost by 87% and memory usage by 65%, while maintaining a Chamfer Distance of 0.045, comparable to the original MCC model. These results indicate that the proposed method provides a promising direction for lightweight AI models enabling low-cost, real-time 3D recording and visualization.

목적 : 지역사회 거주 노인을 대상으로 치매 예방을 위해 개발된 인지훈련용 모바일 앱 ‘기억하리’를 활용한 프로그램의 효과를 확인하고자 하였다. 연구방법 : 지역사회 거주 노인 100명을 대상으로 실험군과 대조군에 50명씩 무작위로 할당하였다. 실험군은 기억하리 앱 프로그램을 주 5회, 회기당 20분, 총 6개월(135회) 수행하였고, 대조군은 중재 없이 사전⋅사후 평가만 하였다. 사전⋅사후 평가는 인지선별검사(Cognitive Impairment Screening test; CIST)를 하였고, 프로그램 종료 후에는 실험군만 만족도 검사를 수행하였다. 결과 : 실험군은 CIST 기억력과 집행기능, 총점에서 유의미한 향상을 보였고 정상 수준의 인지기능을 유지하고 있었다. 대조군은 CIST 기억력에서 유의미한 감소가 나타났고, 대조군 중 3명은 인지 저하의 결과를 보였다. 두 군의 CIST 점수 변화량 비교는 실험군이 대조군과 비교하여 기억력, 집행기능, 총점에서 유의미한 향상을 보였다. 실험군의 만족도 조사 결과 모든 문항에서 긍정적 반응을 보였다. 결론 : ‘기억하리’ 앱을 활용한 인지학습 훈련은 오프라인 치매 예방프로그램의 제한점을 보완하는 효과적인 중재 프로그램이며, 건강한 노년기를 위한 의미 있는 중재가 될 것이다.

This study aims to systematically analyze and categorize the types of loyalty programs utilized in the domestic mobile game market. As of June 2025, loyalty programs actually implemented in the top 50 grossing mobile games in Korea were examined. Building upon Barry Berman’s four-stage loyalty program model (instant discounts, frequency rewards, point-based programs, and personalized rewards), this research proposes an expanded classification system of seven types by adding three categories that reflect the characteristics of mobile games: subscription/season-based (Type 5), stepwise purchase (Type 6), and gamification-based (Type 7). This study is significant in that it offers a systematic classification framework in a field where academic analyses of loyalty programs in the mobile game industry remain scarce, and provides foundational data for future research and practical application of loyalty programs in the gaming sector.

이동로봇은 인공지능, 센서 기술 등과 융합함으로서 다양한 산업 및 서비스 분야에서 광범위하게 사용되고 있으며, 조선 및 해 양 분야에서도 이동로봇을 활용한 물품 운반, 현장 모니터링, 위험한 업무 등에 대한 연구가 수행됨으로서 생산성 향상 및 안정성 강화를 향상시키고자 하고 있다. 본 연구에서는 선박기관실처럼 내연기관, 선반, 드릴머신, 공구대, 용접실습대 등 다양한 기기 및 장비의 간격이 좁고 구조가 복잡한 환경의 기관실습실 내에서 이동로봇의 자율주행을 구현함으로서 선박기관실에 적용 가능여부를 확인하고자 하였다. ROS2기반의 이동로봇으로 SLAM 라이브러리 중 하나인 Cartographer를 사용하여 지도를 작성한 후 여러 위치에서 자율주행 시험과 지도에 없는 장애물을 놓은 경우 자율주행 시험결과 복잡한 환경에서도 높은 자율주행 성능을 확인하였다. 선박기관실은 실험한 장소와 여러환 경의 차이는 있으나 구조의 변화가 거의 없어 자율주행이 가능할 것으로 사료된다.

This study was conducted to quantitatively verify whether payment motives and usage patterns differ according to the motivations of users of mobile character-collecting games. For this purpose, three user types were derived based on game motivation profiles tailored to mobile character-collecting games: Cluster 1 (Communicators), Cluster 2 (Character Collectors), and Cluster 3 (Competitors). It was confirmed that each cluster was mutually exclusive and exhibited different payment motives and usage patterns. These findings emphasize the value of a motivation-based segmentation strategy in understanding the behavior of mobile game users. Furthermore, based on the results of this study, game developers and publishers can more sophisticatedly tailor game content, monetization strategies, and marketing campaigns based on users' motivational profiles, rather than relying on simple demographic variables. Future research needs to expand the diversity of the sample by including a wider age range and considering a balanced gender distribution. Additionally, to gain a more in-depth understanding of the temporal changes in user motivation, future studies should explore potential causal relationships through a longitudinal research design.

In this paper, a water rescue mission system was developed for water safety management areas by utilizing unmanned mobility( drone systems) and AI-based visual recognition technology to enable automatic detection and localization of drowning persons, allowing timely response within the golden time. First, we detected suspected human subjects in daytime and nighttime videos, then estimated human skeleton-based poses to extract human features and patterns using LSTM models. After detecting the drowning person, we proposed an algorithm to obtain accurate GPS location information of the drowning person for rescue activities. In our experimental results, the accuracy of the Drown detection rate is 80.1% as F1-Score, and the average error of position estimation is about 0.29 meters.

본 논문에서는 2자유도 매니퓰레이터(manipulator)가 탑재된 지상형 이동 로봇을 활용한 균열 지도 작성 기법을 소개한다. 로봇의 앞·측면에 각각 스테레오 비전 센서를 설치하였으며, 앞면에 설치된 센서의 포인트 클라우드 정보를 이용하여 로봇의 위치를 인식하 고 지도를 작성하며, 측면에 설치된 센서의 영상 정보를 바탕으로 벽면 내 균열을 검출한다. 이때, 두 센서의 좌표계를 좌표 변환식을 통해 통일하여 정합 및 검출된 균열 정보를 생성된 지도에 실시간으로 표기하고, 손상의 정보가 기록 및 관리될 수 있도록 하였다. 2자 유도 움직임이 가능한 매니퓰레이터 장치를 이동로봇에 탑재하고 사각지역의 제한 없이 로봇의 진행 방향을 벗어난 균열을 촬영할 수 있도록 하였다. 촬영된 영상 내 딥러닝 기법을 적용하여 균열을 검출하고, 해당 균열이 촬영된 영상 내 일부만 존재한다고 판단하 는 경우 매니퓰레이터를 동작하여 남은 균열의 위치를 추정 및 추가 촬영, 스티칭할 수 있도록 하였다. 본 시스템의 성능 확인을 위하 여 실내 환경에서 실험을 진행하였으며, IoU기반 검출율 0.6 이상의 정확도로 실시간 균열 정보를 구축된 지도 위에 작성하는 것을 확 인하였다.

This study explored how teachers could provide support to enhance students’ out-ofclass mobile-assisted language learning (MALL) engagement. We interviewed five Korean English teachers who used Class Card, a focal technology of this study, for their students’ self-directed vocabulary learning. Additionally, students of the interviewed teachers completed a survey on their perceptions of teacher support and MALL engagement. This study has three major findings. First, the teachers adopted either a proactive or a passive approach to promoting students’ out-of-class MALL engagement, which was influenced by their beliefs about whether teachers or students should be responsible for learning beyond the classroom. Second, all teachers provided orientation and behavioral support to enhance out-of-class MALL engagement, although the consistency and intensity in providing this support varied between proactive and passive teachers. Finally, students who perceived higher levels of teacher support reported greater out-of-class MALL engagement. We discuss the importance of classroom-based teacher support to enhance MALL engagement beyond the classroom as pedagogical implications.

에듀테크 시대에 접어들면서 디지털 기술을 활용한 학습 방식이 점점 확대되고 있으며, 특히 모바일 기반 애플리케이션 을 활용한 학습이 적극적으로 도입되고 있다. 이러한 학습 방식은 학습자의 참여도를 높이고, 흥미를 유발하며, 학습 효율성 향상에 긍정적인 영향을 미치고 것으로 보고되고 있다. 본 연구는 자기공명영상학 학습에서 모바일 기반 애플리 케이션 사용에 대한 학습자들의 인식, 학습 효과, 학습 만족도를 알아보고자 하였다. 대구시 소재 S 대학교 자기공명영 상학을 수강한 2, 3학년 학생 70명을 대상으로 2024년 11월 24일부터 29일까지 수업 후 모바일 애플리케이션을 활용한 퀴즈 활동을 시행하였다. 연구 결과, 애플리케이션 활용에 대한 학습자들의 인식 평균 점수는 4.58±0.66, 학습 효과는 4.61±0.62, 학습 만족도는 4.58±0.65로 나타났다. 또한, 애플리케이션 활용 전후 비교 분석에서 인식 (활용 전 3.62±0.97, 활용 후 4.58±0.66), 학습 효과(활용 전 3.60±0.92, 활용 후 4.61±0.62), 학습 만족도(활용 전 3.64±0.93, 활용 후 4.58±0.65) 모두 통계적으로 유의한 차이가 있었다(p<0.05). 이러한 결과는 자기공명영상학 교육에서 모바일 애플리케이션 기반 학습이 학습자의 참여도, 이해도, 만족도를 높이는 데 효과적임을 시사한다. 따라서 자기공명영상학뿐만 아니라 다양한 전공 분야에서도 애플리케이션 기반 학습이 유용한 교육 도구로 활용될 수 있으며, 향후 교육 및 임상 실습 현장에서 적용 가능한 기초자료로 활용될 수 있을 것 기대된다.