PURPOSES : In this study, the existence of an optimal pattern among transition methods applied during changes in traffic signal timing was investigated. We aimed to develop this pattern into an artificial intelligence reinforcement-learning model to assess its effectiveness METHODS : By developing various traffic signal transition scenarios and considering 19 different traffic signal transition situations that can be applied to these scenarios, a simulation analysis was performed to identify patterns through statistical analysis. Subsequently, a reinforcement-learning model was developed to select an optimal transition time model suitable for various traffic conditions. This model was then tested by simulating a virtual experimental center environment and conducting performance comparison evaluations on a daily basis. RESULTS : The results indicated that when the change in the traffic signal cycle length was less than 50% in the negative direction, the subtraction method was efficient. In cases where the transition was less than 15% in the positive direction, the proposed center method for traffic signal transition was found to be advantageous. By applying the proposed optimal transition model selection, we observed that the transition time decreased by approximately 70%. CONCLUSIONS : The findings of this study provide guidance for the next level of traffic signal transitions. The importance of traffic signal transition will increase in future AI-based traffic signal control methods, requiring ongoing research in this field.

PURPOSES:This study suggests a specific methodology for the prediction of road surface temperature using vehicular ambient temperature sensors. In addition, four kind of models is developed based on machine learning algorithms.METHODS:Thermal Mapping System is employed to collect road surface and vehicular ambient temperature data on the defined survey route in 2015 and 2016 year, respectively. For modelling, all types of collected temperature data should be classified into response and predictor before applying a machine learning tool such as MATLAB. In this study, collected road surface temperature are considered as response while vehicular ambient temperatures defied as predictor. Through data learning using machine learning tool, models were developed and finally compared predicted and actual temperature based on average absolute error.RESULTS:According to comparison results, model enables to estimate actual road surface temperature variation pattern along the roads very well. Model III is slightly better than the rest of models in terms of estimation performance.CONCLUSIONS :When correlation between response and predictor is high, when plenty of historical data exists, and when a lot of predictors are available, estimation performance of would be much better.

본 연구는 성인들이 영어를 익히고 영어에 대한 자신감을 키우면서 회화 능력을 향상시킬 수 있는 학습 방법으로서 35개의 영어패턴을 활용한 70일간의 자기주도학습 과정으로 설계된 패턴영어학습법의 효과를 조사하고, 더 나은 패턴영어학습법을 설계하기 위한 요인들을 도출 할 목적으로 진행하였다. 본 연구를 진행하기 위해 비영어전공 성인 126명을 대상으로 학습자가 느끼는 영어수준과 영어의 필요성에 대한 정도, 선호하는 학습방법을 조사하였고, 이들 중 본 프로젝트에 참여한 10명을 대상으로 70일 동안 매일 평균 30분씩 패턴영어 학습과 일일과제 수행을 온라인으로 진행하였다. 학습결과로 학생들이 작성한 일일과제 결과와 사전설문지, 녹취자료를 종합적으 로 분석하여 다음과 같은 결론을 도출하였다. 패턴영어 학습법은 빠른 시간 내에 영어능력을 향상시키고 싶은 성인들에게 실용적인 방법 이며, 패턴영어는 뼈대가 되는 완성된 문장을 스스로 반복적으로 연습하여 익숙하게 만드는 과정이 우선적으로 중요하기 때문에 자기주도학습 프로그램으로 학습 과정을 설계하는 것도 용이한 것으로 나타났다.

This study aims to develop an English writing model using pattern-based reading materials and to apply it to the elementary classroom. The meaning of “patterns” was searched for in the language learning and teaching methods, and their roles were examined in terms of language acquisition and learning. The writing class was connected to the reading class so that learners could properly model and transfer their forms and meanings of the patterns recognized in the reading class to what they want to write in the writing class. The experiment was conducted on one class of grade 6 elementary school students in which the reading and writing class was integrated into the regular English class during one semester. Six pattern-based reading materials were selected with a range of genres including stories and poems. The effect of the pattern-based reading materials on the writing class was examined through writing test and a questionnaire about the affective domain before and after the experiment. The result showed that writing scores were increased significantly in all the leveled-group learners. As for the affective domain, interest, participation, confidence, and adventure each had a significantly increased score. The sense of adventure increased the most. This is considered attributable to the feedback which ignored grammatically trivial errors and focused on how to properly express the contents learners wanted to write.

미취학 아동의 창의적 사고, 다양한 경험 기반의 학습 활동, 그리고 인성 및 감성 중심의 교육에 대한 욕구를 충족시켜줄 수 있는 새로운 교육 방식으로써 체감형 교육 방식의 보급이 활발해지고 있다. 체감형 학습은 사용자의 움직임이나 감각을 통해 디지털 교육 콘텐츠를 조작하는 인간-컴퓨터 인터랙션을 활용한 교육 방식이다. 그러나 사람의 움직임이나 목소리 같은 음성/영상 인식의 정확도가 높지 않아 실제 교육 시스템 적용에는 한계를 보인다. 이러한 한계점의 극복을 위해 인간과 서비스 콘텐츠 사이에 매개체를 두고 이를 통해 사람의 움직임을 가속도, 각속도와 같은 숫자 값으로 변환/전송하여 인터랙션 하는 매개 인터페이스 개념이 제안된다. 본 연구에서는, 교육 시스템의 대상인 미취학 아동의 행동을 관찰하고 프로토콜 분석을 통하여 사용자 중심의 매개 디바이스 디자인 요구사항을 제안한다. 분석 결과, 미취학 아동들은 물체를 조작하는 데 서툴고, 무의식적으로 물체를 만지작거리거나 기대는 행동을 보였다. 또한 물건을 사용할 때 그에 종속되어 행동의 부자연스러움을 보였다. 따라서 체감형 교육을 위한 매개 디바이스는 사용자의 익숙하지 않은 조작을 보조할 수 있어야 하며, 디바이스 사용 중에도 자연스러운 행동을 유지할 수 있도록 디자인되어야 한다.

Surface electromyogram (sEMG), which is a bio-electrical signal originated from action potentials of nerves and muscle fibers activated by motor neurons, has been widely used for recognizing motion intention of robotic prosthesis for amputees because it enables a device to be operated intuitively by users without any artificial and additional work. In this paper, we propose a training-free unsupervised sEMG pattern recognition algorithm. It is useful for the gesture recognition for the amputees from whom we cannot achieve motion labels for the previous supervised pattern recognition algorithms. Using the proposed algorithm, we can classify the sEMG signals for gesture recognition and the calculated threshold probability value can be used as a sensitivity parameter for pattern registration. The proposed algorithm was verified by a case study of a patient with partial-hand amputation.