• The Journal of Korea Robotics Society
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• v.14 no.2
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• pp.122-130
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• 2019

Collecting a rich but meaningful training data plays a key role in machine learning and deep learning researches for a self-driving vehicle. This paper introduces a detailed overview of existing open-source simulators which could be used for training self-driving vehicles. After reviewing the simulators, we propose a new effective approach to make a synthetic autonomous vehicle simulation platform suitable for learning and training artificial intelligence algorithms. Specially, we develop a synthetic simulator with various realistic situations and weather conditions which make the autonomous shuttle to learn more realistic situations and handle some unexpected events. The virtual environment is the mimics of the activity of a genuine shuttle vehicle on a physical world. Instead of doing the whole experiment of training in the real physical world, scenarios in 3D virtual worlds are made to calculate the parameters and training the model. From the simulator, the user can obtain data for the various situation and utilize it for the training purpose. Flexible options are available to choose sensors, monitor the output and implement any autonomous driving algorithm. Finally, we verify the effectiveness of the developed simulator by implementing an end-to-end CNN algorithm for training a self-driving shuttle.

• Journal of Korean Elementary Science Education
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• v.36 no.3
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• pp.286-301
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• 2017

Nowadays, the public as well as science educators pays much attention to the fourth industrial revolution and wonders what will happen to the societies in the future. Thus, this study aimed at predicting the education environment which will be brought from the fourth industrial revolution, and suggesting the solutions or tasks to be investigated in science education. Through the literature review, this study categorized the major changes of future society into a wild fluctuation of job market, the shift from possession-based economy to sharing economy, post-urbanized and distributed system, and the crisis of dehumanization. According to the four major changes, this study predicted the future environment that will occur to the educational system. First, the students should the competences necessary for the future and the school curriculum will be changed in terms of width and depth. Second, sharing economy may bring about the open platform similar to MOOC (Massive Open Online Course) or TED. Third, the manifestation of artificial intelligence in education will enable the individual and paced learning, and thanks to the change, the concept of distributed cognition will be more focused in education research. Fourth, the collaborative learning and character education should be more stressed to resist the dehumanization. This study suggests relevant tasks and issues that should be tackled for the successful change in primary and secondary schools.

• Journal of Research and Publication Ethics
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• v.4 no.2
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• pp.1-5
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• 2023

Purpose: There are already hundreds of millions of users of the Metaverse platform, and within a few years, it is expected to develop into a stage for new economic activities with huge industrial ripple effects due to the size of users. The purpose of this study is to derive considerations for the development of metaverse ethical guidelines. Research design, data, and methodology: The concept of the metaverse was examined through various opinions of industry and experts on the metaverse, and literature related to metaverse ethics was analyzed in the Korean journal database. Results: Six issues were identified through the existing research. (1) Establishing a unified definition of metaverse (2) Necessity of establishing ethical principles considering the operator (3) Personal information protection and privacy (4) Expression in a virtual environment (5) Copyright and intellectual property rights of creations (6) Virtual economy and fairness of trade. Conclusions: Metaverse ethics will be developed and implemented in a form and method different from the real world, but basically, continuous discussions on ethical rationality are needed in the process. In addition, since the ethical judgment in the metaverse environment accompanies cultural differences and epochal changes, it is necessary to focus on metaverse ethics cases.

• Proceedings of The KACE
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• 2017.08a
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• pp.51-54
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• 2017

본 연구의 목적은 오픈 러닝과 소셜 러닝 플랫폼의 핵심요소 비교분석 결과를 바탕으로 학습자와 학습 환경 개선을 위해 고려해야 할 핵심 요소와 방향에 대한 시사점을 고찰하는 데 있다. MOOC로 대변되는 오픈 러닝 사례들의 핵심요소를 분석하여 각각의 공통점과 차이점은 무엇인지 알아보고자 했다. 소셜 러닝 플랫폼을 활용한 사례들을 살펴봄으로써 소셜 러닝 플랫폼의 정의와 특이사항을 살펴보았다. 오픈 러닝과 소셜 러닝 플랫폼의 특이사항은 서로 어떤 상호작용을 가질 수 있을지 비교분석 내용을 바탕으로 오픈 러닝에서 발견 된 장단점과 소셜 러닝 플랫폼의 장단점으로 대치하여 서로 보완해줄 수 있는 진화한 러닝 플랫폼을 구상해보는 것은 유의미 할 것이다.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.4
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• pp.262-272
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• 2020

A large number of studies on wave breaking have been carried out, and many experimental data have been documented. Moreover, on the basis of various experimental data set, many empirical or semi-empirical formulas based primarily on regression analysis have been proposed to quantitatively estimate wave breaking for engineering applications. However, wave breaking has an inherent variability, which imply that a linear statistical approach such as linear regression analysis might be inadequate. This study presents an alternative nonlinear method using an neural network, one of the machine learning methods, to estimate breaking wave height and breaking depth. The neural network is modeled using Tensorflow, a machine learning open source platform distributed by Google. The neural network is trained by randomly selecting the collected experimental data, and the trained neural network is evaluated using data not used for learning process. The results for wave breaking height and depth predicted by fully trained neural network are more accurate than those obtained by existing empirical formulas. These results show that neural network is an useful tool for the prediction of wave breaking.

• Journal of Practical Engineering Education
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• v.15 no.1
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• pp.31-38
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• 2023

In this paper, a basic machine learning model learning and utilization education curriculum for non-majors is proposed, and an education method using Orange machine learning model learning and analysis tools is proposed. Orange is an open-source machine learning and data visualization tool that can create machine learning models by learning data using visual widgets without complex programming. Orange is a platform that is widely used by non-major undergraduates to expert groups. In this paper, a basic machine learning model learning and utilization education curriculum and weekly practice contents for one semester are proposed. In addition, in order to demonstrate the reality of practice contents for machine learning model learning and utilization, we used the Orange tool to learn machine learning models from categorical data samples and numerical data samples, and utilized the models. Thus, use cases for predicting the outcome of the population were proposed. Finally, the educational satisfaction of this curriculum is surveyed and analyzed for non-majors.

• Archives of Plastic Surgery
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• v.44 no.3
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• pp.223-227
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• 2017

Background Carpal tunnel release is one of the most common surgical procedures performed by hand surgeons. The authors created a surgical simulation of open carpal tunnel release utilizing a mobile and rehearsal platform app. This study was performed in order to validate the simulator as an effective training platform for carpal tunnel release. Methods The simulator was evaluated using a number of metrics: construct validity (the ability to identify variability in skill levels), face validity (the perceived ability of the simulator to teach the intended material), content validity (that the simulator was an accurate representation of the intended operation), and acceptability validity (willingness of the desired user group to adopt this method of training). Novices and experts were recruited. Each group was tested, and all participants were assigned an objective score, which served as construct validation. A Likert-scale questionnaire was administered to gauge face, content, and acceptability validity. Results Twenty novices and 10 experts were recruited for this study. The objective performance scores from the expert group were significantly higher than those of the novice group, with surgeons scoring a median of 74% and medical students scoring a median of 45%. The questionnaire responses indicated face, content, and acceptability validation. Conclusions This mobile-based surgical simulation platform provides step-by-step instruction for a variety of surgical procedures. The findings of this study help to demonstrate its utility as a learning tool, as we confirmed construct, face, content, and acceptability validity for carpal tunnel release. This easy-to-use educational tool may help bring surgical education to a new- and highly mobile-level.

• Journal of Multimedia Information System
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• v.4 no.4
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• pp.195-204
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• 2017

Mobile robots are used for years as a valuable research and educational tool in form of available open-platform designs and Do-It-Yourself kits. Rapid development and costs reduction of Unmanned Air Vehicles (UAV) and ground based mobile robots in recent years allowed researchers to utilize them as an affordable research platform. Despite of recent developments in the area of ground and airborne robotics, only few examples of Unmanned Surface Vehicle (USV) platforms targeted for research purposes can be found. Aim of this paper is to present the development of open-design USV drone with integrated multi-level control hardware architecture. Proposed catamaran - type water surface drone enables direct control over wireless radio link, separate development of algorithms for optimal propulsion control, navigation and communication with the ground-based control station. Whole design is highly modular, where each component can be replaced or modified according to desired task, payload or environmental conditions. Developed USV is planned to be utilized as a part of the system for detection and identification of marine and lake waste. Cameras mounted to the USV would record sea or lake surfaces, and recorded video sequences and images would be processed by state-of-the-art computer vision and machine learning algorithms in order to identify and classify marine and lake waste.

• Journal of the Korean Society for information Management
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• v.37 no.3
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• pp.221-252
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• 2020

According to the participants, the current K-MOOC (Korean Massive Open Online Course) has a few problems, such as too few courses, low content quality, and useless learner management system compared to MOOCs abroad. These problems caused diminished learner motivation. Consequently, the K-MOOC service has recorded a low course completion rate despite high expenses spent to develop the contents and thus requires remedies to fix the issues. This study drew research subjects from a pool of college and graduate students representing the primary users of the K-MOOC. This study limited the research scope to the four categories: motivation, learning experience, recognition, and performance of the Biggs' 3P Learning System Model. Based on the literature review, ten variables were selected and explored how the subjects perceived four categories using the survey questionnaire. This study also examined the relationship between ten variables and generated suggestions for the instructors, course managers, and platform developers to make the K-MOOC better.

• Journal of Internet of Things and Convergence
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• v.9 no.6
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• pp.37-44
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• 2023

As a primary technology of Industry 4.0, human-robot collaboration (HRC) requires additional measures to ensure worker safety. Previous studies on avoiding collisions between collaborative robots and workers mainly detect collisions based on sensors and cameras attached to the robot. This method requires complex algorithms to continuously track robots, people, and objects and has the disadvantage of not being able to respond quickly to changes in the work environment. The present study was conducted to implement a web-based platform that manages collaborative robots by recognizing the emotions of workers - specifically their perception of danger - in the collaborative process. To this end, we developed a web-based application that collects and stores emotion-related brain waves via a wearable device; a deep-learning model that extracts and classifies the characteristics of neutral, positive, and negative emotions; and an Internet-of-things (IoT) interface program that controls motor operation according to classified emotions. We conducted a comparative analysis of our system's performance using a public open dataset and a dataset collected through actual measurement, achieving validation accuracies of 96.8% and 70.7%, respectively.