• 대한전기학회논문지:시스템및제어부문D
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• 제52권9호
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• pp.533-539
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• 2003

Reinforcement learning (RL) has been widely used as a learning mechanism of an artificial life system. However, RL usually suffers from slow convergence to the optimum state-action sequence or a sequence of stimulus-response (SR) behaviors, and may not correctly work in non-Markov processes. In this paper, first, to cope with slow-convergence problem, if some state-action pairs are considered as disturbance for optimum sequence, then they no to be eliminated in long-term memory (LTM), where such disturbances are found by a shortest path-finding algorithm. This process is shown to let the system get an enhanced learning speed. Second, to partly solve a non-Markov problem, if a stimulus is frequently met in a searching-process, then the stimulus will be classified as a sequential percept for a non-Markov hidden state. And thus, a correct behavior for a non-Markov hidden state can be learned as in a Markov environment. To show the validity of our proposed learning technologies, several simulation result j will be illustrated.

• International journal of advanced smart convergence
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• 제9권4호
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• pp.115-119
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• 2020

Recently, CCTV installations are rapidly increasing in the public and private sectors to prevent various crimes. In accordance with the increasing number of CCTVs, video-based abnormal behavior detection in control systems is one of the key technologies for safety. This is because it is difficult for the surveillance personnel who control multiple CCTVs to manually monitor all abnormal behaviors in the video. In order to solve this problem, research to recognize abnormal behavior using deep learning is being actively conducted. In this paper, we propose a model for detecting abnormal behavior based on the deep learning model that is currently widely used. Based on the abnormal behavior video data provided by AI Hub, we performed a comparative experiment to detect anomalous behavior through violence learning and fainting in videos using 2D CNN-LSTM, 3D CNN, and I3D models. We hope that the experimental results of this abnormal behavior learning model will be helpful in developing intelligent CCTV.

• Computers and Concrete
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• 제32권5호
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• pp.513-525
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• 2023

Recently, research on predicting the behavior of reinforced concrete (RC) columns using machine learning methods has been actively conducted. However, most studies have focused on predicting the ultimate strength of RC columns using a regression algorithm. Therefore, this study develops a successive machine learning process for predicting multiple nonlinear behaviors of rectangular RC columns. This process consists of three stages: single machine learning, bagging ensemble, and stacking ensemble. In the case of strength prediction, sufficient prediction accuracy is confirmed even in the first stage. In the case of displacement, although sufficient accuracy is not achieved in the first and second stages, the stacking ensemble model in the third stage performs better than the machine learning models in the first and second stages. In addition, the performance of the final prediction models is verified by comparing the backbone curves and hysteresis loops obtained from predicted outputs with actual experimental data.

• 대한가정학회지
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• 제42권8호
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• pp.15-32
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• 2004

The purpose of this study is to develop an environmental education program for environmentally friendly consumer behaviors by analyzing factors influencing the attitude-behavior relationship, and examining consumer information needs about environmental problems. Environmental information demanded by consumers could be classified into five main areas; 'use and disposal of environmentally friendly resources', 'purchase of environmentally friendly goods', 'environmental problems and consumer sovereignty', 'environmental laws and regulations' and 'environmental values and consumer's civil consciousness'. Based on the study results, an environmental education program for consumers was developed which consisted of two main parts, basis and practice. The former aimed to strengthen consumer consciousness about environmental problems and the latter, to make regular environmentally friendly consumer behaviors. The two parts were correlated. Thus strengthening environment-related consumer consciousness by learning the part of basis could promote of environmentally friendly consumer behaviors.

• 한국컴퓨터정보학회논문지
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• 제23권2호
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• pp.63-70
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• 2018

Safety management agent manages the risk behavior of the worker with the naked eye, but there is a real difficulty for one the agent to manage all the workers. In this paper, IoT device is attached to a harness safety belt that a worker wears to solve this problem, and behavior data is upload to the cloud in real time. We analyze the upload data through the deep learning and analyze the risk behavior of the worker. When the analysis result is judged to be dangerous behavior, we designed and implemented a system that informs the manager through monitoring application. In order to confirm that the risk behavior analysis through the deep learning is normally performed, the data values of 4 behaviors (walking, running, standing and sitting) were collected from IMU sensor for 60 minutes and learned through Tensorflow, Inception model. In order to verify the accuracy of the proposed system, we conducted inference experiments five times for each of the four behaviors, and confirmed the accuracy of the inference result to be 96.0%.

• 한국과학교육학회지
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• 제21권4호
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• pp.668-676
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• 2001

이질적으로 구성된 소집단 협동학습에서의 내적 과정을 이해하기 위해서 이 연구에서는 과학 수업에서의 협동학습 과정에서 나타나는 성취 수준별 상호작용 양상을 조사하였다. 성취 수준별 언어적 행동의 빈도를 비교하여 언어적 상호작용에의 참여 정도 및 방식을 조사하였다. 그리고 상호작용에 대한 학생들의 인식을 조사하여 성취 수준에 따른 상호작용 양상도 분석하였다. 조사 결과, 상위와 중위 학생 사이에는 언어적 행동의 빈도에 차이가 없었으며, 소집단의 언어적 상호작용은 상위와 중위 학생이 서로 협력하여 공동으로 의미를 구성해나가는 방식으로 일어났다. 상위와 중위 학생 사이의 활발한 상호작용은 학생들의 인식 조사를 통해서도 확인되었다. 즉, 과학수업에서의 협동학습 과정에서는 중위 학생도 적극적이고 활발하게 소집단 활동에 참여하고 있었다.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2022년도 춘계학술대회
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• pp.498-500
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• 2022

CCTV는 위험 상황을 파악하고 신속히 대응함으로써, 인명과 자산을 안전하게 보호한다. 하지만, 점점 많아지는 CCTV 영상을 지속적으로 모니터링하기는 어렵다. 이런 이유로 CCTV 영상을 지속적으로 모니터링하면서 이상행동이 발생했을 때 알려주는 장치가 필요하다. 최근 영상데이터 분석에 인공지능 모델을 활용한 많은 연구가 이루어지고 있다. 본 연구는 CCTV 영상에서 관측할 수 있는 다양한 이상 행동을 분류하기 위해 영상데이터 사이의 공간적, 시간적 특성 정보를 동시에 학습한다. 학습에 이용되는 인공지능 모델로 End-to-End 방식의 3D-Convolution Neural Network(CNN)와 ResNet을 결합한 다중 분류 딥러닝 모델을 제안한다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 26-27 Oct. 1990
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• pp.710-715
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• 1990

This paper presents a path control method for mobile robot using neural network and a systematic method for the kinematic and dynamic modelling of a mobile robot. The robot finds its path deviation by taking the signals of an optical array sensor and determined its moving behaviors using neural net control method. A robot can be taught behaviors by changing the given patterns, in this work, Back Propagation rule is used as a learning method.

• Educational Technology International
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• 제17권1호
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• pp.117-150
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• 2016

The purpose of this study was to develop design guidelines on the learning analytics which can help to promote students' self-regulated learning (SRL) strategies in MOOCs learning environments. First of all, to develop the first draft of design guidelines, relevant literature review and case analysis on current MOOCs platforms such as edX, K-MOOC, Coursera, Khan Academy and FutureLearn were conducted. Then, to validate the design guidelines, expert reviews (validation questionnaires and in-depth interviews) and learner evaluation (in-depth interviews) were conducted. Through the recursive validation, the design guidelines were finalized. Overall, the final version of design guidelines on learning analytics to facilitate SRL strategies was suggested. The final design guidelines consist of 15 items in 10 categories related to the information analyzed based on individual student's learning behaviors and activities on MOOCs environments. Moreover, the results of interview also revealed that the social comparisons, learning progress reports, and personalization might contribute to the improvements of their SRL competences. This study has an implication that MOOCs could offer a higher success or completion rate to students with low SRL skills by taking advantage of the information on learning analytics

• Educational Technology International
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• 제16권2호
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• pp.201-229
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• 2015

Culture is a crucial concept that forms the thinking and behaviors of a group of people, and it influences interactions in learning. Thus, it is also essential to consider cultural sensitivity in online learning technologies and instructional design as education is a set of learning actions based on values and perceptions. MOOCs, the latest online learning platform, are global online learning platforms that provide global learners with free and various learning resources including courses from different world-class institutions. Despite globalization having brought learners closer to sharing similar learning resources, the actual experiences with the resource are expected to vary according to cultures, mainly because learning behavior is a set of outcomes based on cultural differences. Taking this into consideration, this study aims to examine MOOCs from a cultural perspective in order to facilitate global learners, especially Korean learners, to utilize MOOCs with user-friendly services and contents. To achieve this objective, the study first identified and developed an evaluation criteria to examine the cultural sensitivity of MOOCs and conducted case studies on courses from major MOOC providers including edX and Coursera. From the findings, design recommendations of contents and courses on MOOCs were suggested to provide Korean learners with optimal learning experiences.