• International Journal of Fuzzy Logic and Intelligent Systems
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• 제16권4호
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• pp.270-275
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• 2016

We present an approach for acceleration actor-critic algorithm for reinforcement learning with continuous action space. Actor-critic algorithm has already proved its robustness to the infinitely large action spaces in various high dimensional environments. Despite that success, the main problem of the actor-critic algorithm remains the same-speed of convergence to the optimal policy. In high dimensional state and action space, a searching for the correct action in each state takes enormously long time. Therefore, in this paper we suggest a search accelerating function that allows to leverage speed of algorithm convergence and reach optimal policy faster. In our method, we assume that actions may have their own distribution of preference, that independent on the state. Since in the beginning of learning agent act randomly in the environment, it would be more efficient if actions were taken according to the some heuristic function. We demonstrate that heuristically-accelerated actor-critic algorithm learns optimal policy faster, using Educational Process Mining dataset with records of students' course learning process and their grades.

• 한국통신학회논문지
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• 제13권4호
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• pp.352-359
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• 1988

학습제어 시스템은 제어대상 시스템의 파라미터를 모르는 경우에 파라미터 적응의 개념을 도입해서, 일종의 hybrid형 적응제어 시스템으로 간주하여 설계될 수 있다. 이러한 파라미터 적응형 학습제어 시스템은 이미 보고되었으나 연속시간 시스템에만 적용될 수 있었다. 본 논문에서는 메모리소자를 반드시 포함하여야 하는 학습시스템에 대하여, 위의 제어알고리즘을 이산화 함으로써 디지탈기술의 발전에 비추어 실제의 적용을 용이하도록 하였으며, 그 타당성을 시뮬레이션으 통하여 확인하였다.

• 공학교육연구
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• 제23권4호
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• pp.76-83
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• 2020

The purpose of this study is to conduct Project Based Learning (PBL) in collaboration with industry experts to operate practical subjects in an industry-university-linked teaching method. PBL is a teaching method in which students can learn through actively engaging in real-world and personally meaningful projects. For a long period of time, PBL methodologies have been found to be especially effective in engineering education. This case study deals with the operational results of a practice subject which has been conducted over three years from 2017 to 2019 in Korea University of Technology and Education. The course is for the 4^th grade students in the school of mechatronics engineering. The results of the surveyed learning outcomes (for example, Program Outcomes and Course Learning Outcomes) have been analyzed and reflected in the next years for the Continuous Quality Improvement. By working on practical projects linked to industry, students have been able to develop so-called 4C's capabilities which are Critical Thinking, Creativity, Communication and Collaboration.

• 한국체육학회지인문사회과학편
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• 제55권5호
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• pp.593-604
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• 2016

본 연구의 목적은 교양댄스수업참가자가 인식하는 교수행동, 지속적 참여의도, 그리고 수업몰입의 구조관계를 파악하고 수업몰입을 매개변수로 설정하여 효과를 검증하는 것이다. 이를 위하여 대학의 교양댄스수업에 참가하고 있는 대학생을 모집단으로 선정하고 비확률 표본표집법 중 유의표본표집법을 이용하여 서울, 경기지역의 대학에서 총 314명의 자료를 수집하였다. 자료처리는 빈도분석, 탐색적 요인분석, 확인적 요인분석, 신뢰도분석, 구조방정식모형 분석, 부트스트래핑(bootstrapping) 방법을 실시하였다. 이와 같은 방법 및 절차를 통하여 얻어진 결론은 다음과 같다. 첫째, 교양댄스수업참가자가 인식하는 교수행동은 수업몰입에 유의한 영향을 미쳤다. 둘째, 교양댄스수업참가자가 인식하는 교수행동은 지속적 참여의도에 유의한 영향을 미치지 않았다. 셋째, 교양댄스수업참가자가 인식하는 수업몰입은 지속적 참여의도에 유의한 영향을 미쳤다. 넷째, 교양댄스수업참가자가 인식하는 교수행동과 지속적 참여의도의 관계에서 수업몰입은 유의한 매개효과가 있는 것으로 나타났다.

• 한국운동역학회지
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• 제17권1호
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• pp.81-90
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• 2007

The effort identifying positioning information of the moving object in real time has been a issue not only in sport biomechanics but also other academic areas. In order to solve this issue, this study tried to track the movement of a pitched ball that might provide an easier prediction because of a clear focus and simple movement of the object. Machine learning has been leading the research of extracting information from continuous images such as object tracking. Though the rule-based methods in artificial intelligence prevailed for decades, it has evolved into the methods of statistical approach that finds the maximum a posterior location in the image. The development of machine learning, accompanied by the development of recording technology and computational power of computer, made it possible to extract the trajectory of pitched baseball from recorded images. We present a method of baseball tracking, based on object tracking methods in machine learning. We introduce three state-of-the-art researches regarding the object tracking and show how we can combine these researches to yield a novel engine that finds trajectory from continuous pitching images. The first research is about mean shift method which finds the mode of a supposed continuous distribution from a set of data. The second research is about the research that explains how we can find the mode and object region effectively when we are given the previous image's location of object and the region. The third is about the research of representing data into features that we can deal with. From those features, we can establish a distribution to generate a set of data for mean shift. In this paper, we combine three works to track baseball's location in the continuous image frames. From the information of locations from two sets of images, we can reconstruct the real 3-D trajectory of pitched ball. We show how this works in real pitching images.

• Asia pacific journal of information systems
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• 제34권1호
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• pp.209-225
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• 2024

This research aims to use machine learning technology in human resource management to predict employees' work-life balance. The study utilized a dataset from IBM Watson Analytics in the IBM Community for the machine learning analysis. Multinomial dependent variables concerning workers' work-life balance were examined, categorized into continuous and categorical types using the Generalized Linear Model. The complexity of assessing variable roles and their varied impact based on the type of model used was highlighted. The study's outcomes are academically and practically relevant, showcasing how machine learning can offer further understanding of psychological variables like work-life balance through analyzing employee profiles.

• 대한음성학회:학술대회논문집
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• 대한음성학회 2005년도 추계 학술대회 발표논문집
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• pp.103-106
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• 2005

In this paper, we propose a language modeling approach to improve the performance of a large vocabulary continuous speech recognition system. The proposed approach is based on the active learning framework that helps to select a text corpus from a plenty amount of text data required for language modeling. The perplexity is used as a measure for the corpus selection in the active learning. From the recognition experiments on the task of continuous Korean speech, the speech recognition system employing the language model by the proposed language modeling approach reduces the word error rate by about 6.6 % with less computational complexity than that using a language model constructed with randomly selected texts.

• 농업과학연구
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• 제49권4호
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• pp.785-793
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• 2022

In this study, tomato maturity was estimated based on deep learning for a harvesting robot. Tomato images were obtained using a RGB camera installed on a monitoring robot, which was developed previously, and the samples were cropped to 128 × 128 size images to generate a dataset for training the classification model. The classification model was constructed based on convolutional neural networks, and the mean-variance loss was used to learn implicitly the distribution of the data features by class. In the test stage, the tomato maturity was estimated as a continuous index, which has a range of 0 to 1, by calculating the expected class value. The results show that the F1-score of the classification was approximately 0.94, and the performance was similar to that of a deep learning-based classification task in the agriculture field. In addition, it was possible to estimate the distribution in each maturity stage. From the results, it was found that our approach can not only classify the discrete maturation stages of the tomatoes but also can estimate the continuous maturity.

• 지식경영연구
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• 제20권4호
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• pp.149-168
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• 2019

In the era of the Fourth Industrial Revolution, digital transformation is emerging in the education and learning fields. As the use of the mobile Internet and mobile devices has become a daily life, mobile learning that supports a variety of learning in a mobile environment is drawing attention. Mobile learning applications (apps) are expected to expand their use by providing a convenient learning environment anytime, anywhere. This study investigates the use of mobile learning apps in English education, which is one of the most popular learning areas, and empirically examines the factors that influence the continuous use of mobile learning apps. In particular, it analyzes the differences between Chinese and Korean users. The results of this study provide theoretical and practical implications to promote the development of mobile apps suitable for mobile learning environments and the sustainable user growth in mobile learning.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국내학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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• pp.870-876
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• 1993

This paper presents a binary clustering network (BCN) and a heuristic algorithm to detect pitch for recognition of keywords in continuous speech. In order to classify nonlinear patterns, BCN separates patterns into binary clusters hierarchically and links same patterns at root level by using the supervised learning and the unsupervised learning. BCN has many desirable properties such as flexibility of dynamic structure, high classification accuracy, short learning time, and short recall time. Pitch Detection algorithm is a heuristic model that can solve the difficulties such as scaling invariance, time warping, time-shift invariance, and redundance. This recognition algorithm has shown recognition rates as high as 95% for speaker-dependent as well as multispeaker-dependent tests.