Earticle

In this study, a service-learning instruction model of social contribution type that connects the Chinese learned in school with the local community organization is designed and developed. It is explored how this instructional model can be operated and, consequently, whether this teaching model has positive implications for students and community organizations, how constructively it is evaluated, and what is complementary and improved. Under the supervision of the instructor at the college's daycare center, students conducted a daily education service to teach Chinese language fairy tale and Chinese children's song. Based on this experience, students planned and participated in Chinese service learning activities based on a group of four times a week and at least two hours in each time. There were kindergartens, local children's centers, and the Korea-China Cultural Center as group activities. The students taught Chinese and Chinese culture, conducted Chinese language fairy tales, taught Chinese children’s songs, and provided services such as introducing and exhibiting museum exhibits at these institutions. As a result, a survey of students and institutions participating in the Chinese Service-learning Program showed positive evaluation of the program.

This paper deals with the complete design and real-time implementation of a novel mixed control based on the pruned model tree (M5P) and collected datasets of a fuzzy logic controller. This combination aims to benefit from both the decision tree rapidity and the fuzzy logic advantages. In harmonic mitigation systems with an active power filter, a strategy for identifying harmonic currents has a considerable influence on the quality and capacity of compensation. The proposed fuzzy-M5P model tree is assessed in the indirect current control identification algorithm along with an effective comparison using the artificial neural network approach. The two learning methods are described and contrasted in an organized manner to evaluate their respective advantages in both steady state and dynamic state operating conditions. In compliance with IEEE std 519-1992 harmonic limits, an experimental setup was realized using dSPACE 1103 hardware to verify the excellent behavior of the system and to confirm the effectiveness of the proposed M5P based control in terms of an almost unity power factor of 0.99, a low total harmonic distortion value of 3.07%, and satisfactory dynamic performances characterized by a fast response time of 100 ms.

Objectives This study aims to verify on the impact of the ARCS model on learning motivation and academic achievement in history English subjects for Chinese history major University students through teaching experiments. Furthermore, it aims to discuss the applicability and effectiveness of the teaching model, providing theoretical support for constructing an effective teaching model for English courses in university history majors. Methods This study employed a quantitative research approach to validate the effectiveness of the ARCS model in Chinese university history major English classroom teaching. The experimental class was instructed based on the ARCS motivational design model, while the control class followed conventional teaching methods. Using SPSS 25.0 statistical software, independent-sample t-tests and paired-sample t-tests were conducted to analyze the outcomes of the experimental intervention, comparing the changes in students' learning motivation and academic achievement before and after the experiment in the two classes. Results Before the application of the ARCS design model in the curriculum, there were no significant differences in learning motivation and academic achievement between the experimental group and the control group. After the application of the ARCS design model in the curriculum, significant differences in learning motivation and academic achievement were observed between the experimental group and the control group. Conclusions Using the ARCS motivational design model in teaching history English subjects to history major students in the classroom can increase students' levels of motivation. Using the ARCS motivational design model in teaching history English subjects to history majors in the classroom can improve to history major students' academic achievement.

목적: 본 연구는 딥러닝 기반 객체 탐지 모델 YOLOv8을 활용해 영상 속 흉기 위험 상황을 자동으로 탐지하고, 해당 상황에서 사람 객체 수와 행동 특성의 변화를 정량적으로 분석하는 데 목적이 있다. 특히 흉기와 사람이 동시에 탐지된 위험 프레임에서 사람의 반응 양상을 시각적으로 비교·분석하여 위협 상황에서 인간 행동 특성을 규명하고자 한다. 방법: 상업 영화 ‘범죄 도시’(2시간 1분 12초)를 분석대상으로 삼아 총 174,372 프레임으로 분할한 뒤, YOLOv8을 적용해 객체(class), 탐지 확률, 위치 좌표, 이동 방향, 속도 정보를 JSON 형식으로 저장하였다. 칼, 가위, 야구방망이와 사람이 동시에 탐지된 프레임을 ‘흉기 위험 상황’으로 정의하고, 이를 중심으로 사람 수와 속도 변화를 시각화하여 위험 상황과 일반 상황의 차이를 분석하였다. 결과: 174,372개 프레임 중 570개가 위험 프레임으로 식별되었으며, 칼과 사람은 238개, 가위는 118개, 야구방망이는 214개 프레임에서 동시에 탐지되었다. 위험 프레임에서는 사람 수가 전체 평균보다 감소하고 이동 속도는 크게 증가하는 경향을 보였다. 이는 흉기 등장 시 주변 인물의 회피 및 도주 반응이 즉각적으로 발생함을 정량적으로 확인한 결과이다. 결론: 본 연구는 YOLOv8을 활용해 흉기 위험 상황에서 사람 객체의 행동 변화를 분석하였다. 위험 상황에서 사람 수 감소와 속도 증가는 위협에 대한 즉각적인 대응임을 입증하며, 영상 기반 위험 감지 기술 발전에 기여할 수 있다.

Purpose: This study aims to automatically detect weapon-related threat situations in video footage using the deep learning-based object detection model YOLOv8, and to quantitatively analyze changes in the number and behavioral characteristics of human objects in such situations. In particular, it seeks to experimentally identify human behavioral responses by visually comparing and analyzing frames where weapons and humans are detected simultaneously. Method: The study analyzed a commercial film, The Outlaws(2 hours, 1 minute, and 12 seconds in duration), dividing it into 174,372 frames. YOLOv8 was applied to extract and structure information for each frame, including object class, detection confidence, bounding box coordinates, movement direction, and speed in JSON format. Frames where weapons(knife, scissors, baseball bat) and humans appeared simultaneously were defined as “weapon-related threat situations.” The changes in the number and speed of human objects were visualized using Matplotlib and Seaborn to compare normal and dangerous situations. Results: Among the 174,372 frames, 570 were identified as threat frames. Knives appeared with humans in 238 frames, scissors in 118, and baseball bats in 214. In these threat frames, the number of people decreased significantly compared to the overall average, while movement speed sharply increased. This indicates an immediate flight or avoidance response from individuals upon the appearance of weapons, quantitatively verifying behavioral changes under threat. Conclusion: This study used YOLOv8 to analyze human behavioral changes during weapon-related threat situations. The observed decrease in the number of people and increase in movement speed in these frames demonstrate a rapid human response to danger. The results offer empirical support for advancing video-based threat detection technologies.

본 연구의 목적은 경험학습 이론 중심 교수-학습 과정 기본 모형에 터한 유아 인권교육 프로그램을 개발하는 데 있다. 이를 위하여 첫째, 프로그램 개발을 위한 이론적 준거 추출 연구를 진행하였다. 둘째, 프로그램의 실천적 준거를 추출하기 위하여 유아교육기관의 교사의 인식과 요구 분석 연구, 현행 유치원교육과정과 「유치원 교육활동 지도자료」에 나타난 유아 인권교육 관련 내용과 활동 분석 및 유아 인권교육 관련 교육활동 운영 실체 분석 연구를 진행하였다. 셋째, 이론과 실천적 준거 추출 연구를 기초로 만 4세 유아와 만 5세 유아를 대상으로 각 16개의 인권교육 프로그램 활동 시안을 개발하였다. 넷째, 개발된 프로그램 시안들에 대한 적합성과 현장 적용성 및 효율성 분석 결과에 따른 수정․보완을 통해 최종의 경험학습 이론 중심 교수-학습 과정 기본 모형에 터한 유아 인권교육 프로그램을 개발하였다.

The purpose of this study was to develop a human rights education program for young children based on the theory of experimental learning. For the purpose of this study, studies of six types were conducted. The first type of study was to extract theoretical criteria. The second types of study was to extract practical criteria. The third type of study was to develop a draft of a human rights education program for young children based on the theory of experimental learning. The fourth type of study was to demonstrate the draft of a human rights education program for young children based on the theory of experimental learning. According to the above studies, the Final Human Rights Education Program Based on the Basic Process Model of Teaching-Learning of Experimental Learning Theory for Early Childhood Education was developed.

본 연구는 음악이 전달하는 의미가 한국어 어휘 학습에 미치는 영향을 살펴보았다. 이를 위해 음악의 의미를 활용한 한국어 어휘 학습 모델을 설계하고 실험을 통해 그 효과성을 알아보았다. 음악이 언어와 같이 의미도 전달할 수 있다는 여러 연구를 근거로 실제 음악의 의미가 한국어 어휘 학습에 효과가 있는지 음악 집단과 암기 집단으로 나누어 이들의 한국어 어휘 이해 능력, 단어 회상 능력, 그리고 두 학습법에 대한 인식을 비교 분석하였다. 정량적 분석 결과 음악 집단(M=19.52, SD=7.1)이 암기 집단(M=14.2, SD=6.8)보다 높은 어휘 이해력을 보였고 단어 회상 능력에서도 음악 집단(M=31.24, SD=10.39)이 암기 집단(M=21.76, SD=12.22)보다 높은 결과를 확인하였다. 또한, 음악(M=4.68, SD=0.63)을 활용한 학습 방법이 암기(M=1.8, SD=0.82)에 의한 방법보다 더 선호되는 것으로 나타났다. 음악의 비자발적 기억 소환 능력과 인간의 뇌가 음악과 언어를 처리할 때 같은 인지적 메커니즘을 사용한다는 신경학적 유사성은 음악이 언어 학습을 촉진할 수 있는 강력한 도구임을 시사한다. 이와 함께 본 연구는 음악이 전달하는 의미가 단어의 의미를 더욱 강화하여 어휘 학습을 향상시킬 수 있다는 것을 확인하였다.

This study examines the effect of meanings conveyed by music on Korean vocabulary learning by designing a learning model. Based on previous research suggesting that music can convey meanings similarly to language, the study divided participants into a music group and a memorization group to compare their Korean vocabulary comprehension, word recall abilities, and perceptions of the two learning methods. Quantitative analysis revealed that the music group (M=19.52, SD=7.1) outperformed the memorization group (M=14.2, SD=6.8) in vocabulary comprehension and also showed superior word recall abilities (music group: M=31.24, SD=10.39; memorization group: M=21.76, SD=12.22). Additionally, the music-based learning method (M=4.68, SD=0.63) was preferred over the memorization method (M=1.8, SD=0.82). The findings suggest that music, with its involuntary memory recall ability and shared cognitive mechanisms with language processing in the brain, is an effective tool for language learning. This study confirms that music can enhance vocabulary learning by strengthening the semantic context of words and is expected to serve as a valuable foundational resource for future vocabulary learning methods utilizing music.

본 연구는 돼지 간 거리(PD), 돈사 내 상대 습도(RRH), 돈사 내 이산화탄소(RCO2) 세 가지 변수를 사용하여, 네 개의 데이터 세트를 구성하고,이를 다중 선형 회귀(MLR), 서포트 벡터 회귀(SVR) 및 랜덤 포레스트 회귀(RFR) 세 가지 모델 기계학습(ML)에 적용하여, 돈사 내 온도(RT)를예측하고자 한다. 2022년 10월 5일부터 11월 19일까지 실험을 진행하였다. Hik-vision 2D카메라를 사용하여, 돈사 내 영상을 기록하였다. 이후ArcMap 프로그램을 사용하여, 돈사 내 영상에서 추출한 이미지 안 돼지의 PD를 계산하였다. 축산환경관리시스템(LEMS) 센서를 사용하여, RT,RRH 및 RCO2를 측정하였다. 연구 결과 각 변수 간 상관분석 시 RT와 PD 간의 강한 양의 상관관계가 나타났다(r > 0.75). 네 가지 데이터세트 중 데이터 세트 3을 사용한 ML 모델이 높은 정확도가 나타났으며, 세 가지 회귀 모델 중에서 RFR 모델이 가장 우수한 성능을 보였다

This study aims to predict the room temperature (RT) in pig barn using three machine learning (ML) models: multiple linear regression(MLR), support vector regression (SVR), and random forest regression (RFR) models. These models were applied across four datasets,with the input parameters including the distance between each pig (PD), room relative humidity (RRH), and room carbon dioxide (RCO2)levels in the pig barn. The experiment was conducted from October 5th to November 19th, 2022. A Hik-vision 2D camera was usedto record pig videos, and the ArcMap program was utilized to calculate PD using images extracted from these videos. Moreover, aLivestock Environment Management System (LEMS) sensor was used to measure RT, RRH, and RCO2. The results of the study revealeda strong positive correlation between RT and PD (r > 0.75). Among the four datasets constructed through different input combinations,ML models using dataset 3 observed high accuracy, and among the three regression models, the RFR model exhibited the best performance.

The unit-water content of concrete is one of the important factors in determining the quality of concrete and is directly related to the durability of the construction structure, and the current method of measuring the unit-water content of concrete is applied by the Air Meta Act and the Electrostatic Capacity Act. However, there are complex and time-consuming problems with measurement methods. Therefore, high frequency moisture sensor was used for quick and high measurement, and unit-water content of mortar was evaluated through machine running and deep running based on measurement big data. The multi-input deep learning model is as accurate as 24.25% higher than the OLS linear regression model, which shows that deep learning can more effectively identify the nonlinear relationship between high-frequency moisture sensor data and unit quantity than linear regression.

In this study, an artificial neural network model was constructed to convert CAE analysis data into similar experimental data. In the analysis and experiment, the injection molding data for 50 conditions were acquired through the design of experiment and random selection method. The injection molding conditions and the weight, height, and diameter of the product derived from CAE results were used as the input parameters for learning of the convert model. Also the product qualities of experimental results were used as the output parameters for learning of the convert model. The accuracy of the convert model showed RMSE values of 0.06g, 0.03mm, and 0.03mm in weight, height, and diameter, respectively. As the next step, additional randomly selected conditions were created and CAE analysis was performed. Then, the additional CAE analysis data were converted to similar experimental data through the conversion model. An artificial neural network model was constructed to predict the quality of injection molded product by using converted similar experimental data and injection molding experiment data. The injection molding conditions were used as input parameters for learning of the predicted model and weight, height, and diameter of the product were used as output parameters for learning. As a result of evaluating the performance of the prediction model, the predicted weight, height, and diameter showed RMSE values of 0.11g, 0.03mm, and 0.05mm and in terms of quality criteria of the target product, all of them showed accurate results satisfying the criteria range.