• Journal of Auto-vehicle Safety Association
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• v.12 no.4
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• pp.23-30
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• 2020

Recently, self-driving research has been actively studied in various institutions. Accurate recognition is important because information about surrounding objects is needed for safe autonomous driving. This study mainly deals with the signal processing of LiDAR among sensors for object recognition. LiDAR is a sensor that is widely used for high recognition accuracy. First, we clustered and tracked objects by predicting relative position and speed of objects. The characteristic points of all objects were extracted using point cloud data of each objects through proposed algorithm. The Classification between vehicle and pedestrians is estimated using number of characteristic points and distances among characteristic points. The algorithm for classifying cars and pedestrians was implemented and verified using test vehicle equipped with LiDAR sensors. The accuracy of proposed object classification algorithm was about 97%. The classification accuracy was improved by about 13.5% compared with deep learning based algorithm.

• Journal of Integrative Natural Science
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• v.15 no.4
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• pp.163-169
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• 2022

Currently, many universities are implementing software-oriented universities and artificial intelligence-oriented universities to foster software-oriented manpower. We are educating students to design and produce computational thinking and coding directly with their major knowledge. However, computer education is not easy for non-majors, and there are many difficulties in coding. The results of responses from 104 students from the College of Health Sciences and College of Social Management who took the liberal arts computer at University H were analyzed using SPSS 26.0 version. In the liberal arts computer class for non-majors, a PJBL-based class plan was proposed. The effectiveness of PJBL-based classes was confirmed through a questionnaire for the improvement of artificial intelligence liberal arts courses. As a result, PJBL-based education showed statistically significant results in terms of satisfaction, effectiveness, and self-efficiency of classes regardless of major.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.1-4
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• 2022

The growth in global population and industrialization has led to an increasing demand for electricity. Accordingly, the electricity providers need to increase the electricity generation. Due to the economical and environmental concerns associated with the generation of electricity from fossil fuels. Alternative power recourses that can potentially mitigate the economical and environmental are of interest. Renewable energy resources are promising recourses that can participate in producing power. Among renewable power resources, solar energy is an abundant resource and is currently a field of research interest. Photovoltaic solar power is a promising renewable energy resource. The power output of PV systems is mainly affected by the solar irradiation and ambient temperature. this paper investigates the utilization of machine learning unsupervised neural network techniques that potentially improves the reliability of PV solar power systems during integration into the electrical grid.

• Journal of East-West Nursing Research
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• v.16 no.2
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• pp.147-155
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• 2010

Purpose: The purpose of this study is to identify the simulation experiences of nursing baccalaureate students in their pediatric rotation. Methods: Fifty-three students responded to open-ended questions after three sessions of pediatric simulation experiences. The practice reflection notes of the students were also analyzed to identify educational outcomes coming from nursing simulation experiences. Results: The open-ended questions and practice reflection notes showed that simulated pediatric practice provided a virtual experience within safe environment. It also increased learning motivation, clinical decision making, and overall self-confidence of the nursing students. Conclusion: The findings of this study suggest that simulation can enhance the quality of nursing education through positive clinical experiences.

• East Asian mathematical journal
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• v.39 no.2
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• pp.183-198
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• 2023

Classroom observation instruments are often used to evaluate teachers' instructional practices and provide feedback to inform interventions or research studies, or professional development efforts. While designed as research tools, many classroom observation instruments can provide important information to support teachers' learning and instructional change by providing a focus for formative assessment or self-evaluation of practice. In this paper, we review two classroom observation tools and the protocols for their use with an implementation example for one of the tools. These tools are more foreign to the field compared to others but have features that might serve as affordances in relation to the purposes of a specific investigation.

• Annual Conference of KIPS
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• 2021.05a
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• pp.491-493
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• 2021

최근 코로나 19 상황으로 인해 많은 사람들이 모이는 병원 방문을 꺼리거나, 치료비에 부담을 느끼는 근골격계 재활 환자들이 많다. 이러한 환자들을 위해 이 프로젝트에서는 재활 치료 빈도가 높은 어깨와 손목 등 여섯 가지 근골격 부위의 자가 재활 치료를 돕는 기계 학습 기반 웹 페이지을 구현한다. 이 웹 페이지는 각 부위에 대한 재활 치료 자세를 구글 티처블 머신으로 학습 시킨 데이터를 기반으로 환자가 올바른 자세로 운동하는지를 판별해 준다. 이 때, 사용자의 재활 치료 자세는 웹 카메라로부터 캡쳐한다.

• Annual Conference of KIPS
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• 2021.11a
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• pp.745-748
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• 2021

가상 환경의 시뮬레이션을 이용해 지능형 로봇에 강화 학습 기법을 적용하는 접근법은 실제 세계의 로봇들의 학습에 유용하다. 우리는 이러한 방법을 적용해서 장애물을 회피하고, 로봇이 특정 목표물을 인식하면 목표물로 자율적으로 이동하는 알고리즘을 개발하였다. 제안된 방법의 유용성 검증은 구현과 실험으로 확인하였다.

• Annual Conference of KIPS
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• 2021.11a
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• pp.1047-1050
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• 2021

코로나19의 영향으로 발열 측정의 중요성은 매우 높아졌다. 현재 이용되고 있는 발열 측정 기기는 사람의 능동적 측정이 요구된다. 본 연구에서 개발된 SDTMBOT은 강화학습기반의 자율 주행과, 딥러닝 기반의 발열 측정 기능을 통하여 특정 장소에 국한되지 않고 넓은 공간에서 자율적이고 지속적인 발열 측정이 가능하다. 이는 기존 사용되고 있는 측정방식과 다른 새로운 방식이며 다가올 With 코로나 시대의 방역에 대한 새로운 시각을 제시한다.

• Phonetics and Speech Sciences
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• v.15 no.2
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• pp.13-20
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• 2023

This study explores the potential of automated speech recognition (ASR) in assessing English learners' pronunciation. We employed ASR technology, acknowledged for its impartiality and consistent results, to analyze speech audio files, including synthesized speech, both native-like English and Korean-accented English, and speech recordings from a native English speaker. Through this analysis, we establish baseline values for the word error rate (WER). These were then compared with those obtained for human raters in perception experiments that assessed the speech productions of 30 first-year college students before and after taking a pronunciation course. Our sub-group analyses revealed positive training effects for Whisper, an ASR tool, and human raters, and identified distinct human rater strategies in different assessment aspects, such as proficiency, intelligibility, accuracy, and comprehensibility, that were not observed in ASR. Despite such challenges as recognizing accented speech traits, our findings suggest that digital tools such as ASR can streamline the pronunciation assessment process. With ongoing advancements in ASR technology, its potential as not only an assessment aid but also a self-directed learning tool for pronunciation feedback merits further exploration.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.12
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• pp.120-129
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• 1995

This study introduces a tool wear monitoring technology in face milling operation comprised of an unsupervised neural network. The monitoring system employs two types of sensor signal such as cutting force and acceleration in sensory detection state. The RMS value and band frequency energy of the sensor signals are calculated for te input patterns of neural network. ART2 neural network, which is capable of self organizing without supervised learning, is used for clustering of tool wear states. The experimental results show that tool wear can be effectively detected under various cutting conditions without prior knowledge of cutting processes.