• Journal of Digital Convergence
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• v.12 no.2
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• pp.531-537
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• 2014

In this paper, we introduce a model of the teaching and learning method for multidisciplinary convergence capstone design at Kongju National University's Engineering Department. At Kongju national University, various capstone design works are designed and proceeded by multidisciplinary students at the summer session. The multidisciplinary approach described in this paper includes the involvement of five department's student who have not collaborated in capstone design experience. This study focuses on multidisciplinary capstone design education by using the micro controller board called Arduino Uno that consists of an assortment of sensors and actuators. The result of self-satisfaction survey was shown the meaningful teaching process for the engineering department students who could have more creative and industrial experiences. As a result, we are able to get the result of the possible directions for future technology education in the area of convergence multidisciplinary capstone design.

• Fashion & Textile Research Journal
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• v.24 no.3
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• pp.333-345
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• 2022

This study developed and evaluated the motion control of 3D printed fingers applied to smart gloves. Four motions were programmed by assembling the module using the Arduino program: cylindrical grasping, spherical grasping, tip-to-tip pinch gripping, and three-jaw pinch gripping. Cap and re-entrant (RE) strip types were designed to model the finger. Two types of modeling were printed using filaments of thermoplastic elastomer (TPE) and thermoplastic polyurethane (TPU). The prepared samples were evaluated using three types of pens for cylidrical grasping, three types of balls for spherical grasping, and two types of cards for tip-to-tip pinch gripping and three-jaw pinch gripping. The motion control of fingers was connected using five servo motors to the number of each control board. Cylindrical and spherical grasping were moved by controlling the fingers at 180° and 150°, respectively. Pinch gripping was controlled using a tip-to-tip pinch motion controlled by the thumb at 30° and index-middle at 0° besides a three-jaw pinch motion controlled by the thumb-index finger-middle at 30°, 0°, and 0°, respectively. As a result of the functional evaluation, the TPE of 3D-printed fingers was more flexible than those of TPU. RE strip type of 3D-printed fingers was more suitable for the motion control of fingers than the 3D-printed finger.

• Journal of the Korea Society of Computer and Information
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• v.27 no.2
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• pp.25-32
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• 2022

In this paper, we propose a pet monitoring system based on deep learning using an activity recognition device. The system consists of a pet's activity recognition device, a pet owner's smart device, and a server. Accelerometer and gyroscope data were collected from an Arduino-based activity recognition device, and the number of steps was calculated. The collected data is pre-processed and the amount of activity is measured by recognizing the activity in five types (sitting, standing, lying, walking, running) through a deep learning model that hybridizes CNN and LSTM. Finally, monitoring of changes in the activity, such as daily and weekly briefing charts, is provided on the pet owner's smart device. As a result of the performance evaluation, it was confirmed that specific activity recognition and activity measurement of pets were possible. Abnormal behavior detection of pets and expansion of health care services can be expected through data accumulation in the future.

• Journal of the Korea Society of Computer and Information
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• v.28 no.11
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• pp.235-243
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• 2023

In this paper, we developed a problem-solving based safety education program using physical computing for middle school students and applied it to verify the impact on self-efficacy and interest. The safety education program developed in this study includes four stages of the creative problem-solving model: problem identification, planning, implementation, and evaluation, and learning activities using Arduino, a physical computing tool. After implementing the education program with 77 third-year middle school students, both self-efficacy and interest of middle school students increased significantly. Based on the research results, the effectiveness of the safety education program that used physical computing and problem-solving steps was confirmed, and practical implications were presented to promote the activation of physical computing education in the school field.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.6
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• pp.719-729
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• 2017

Recently, the field of agriculture has been gaining a new leap with the integration of ICT technology in agriculture. In particular, smart farms, which incorporate the Internet of Things (IoT) technology in agriculture, are in the spotlight. Smart farm technology collects and analyzes information such as temperature and humidity of the environment where crops are cultivated in real time using sensors to automatically control the devices necessary for harvesting crops in the control device, Environment. Although smart farm technology is paying attention as if it can solve everything, most of the research focuses only on increasing crop yields. This paper focuses on the development of a system architecture that can harvest high quality crops at the optimum stage rather than increase crop yields. In this paper, we have developed an architecture using apple trees as a sample and used the color information and weight information to predict the harvest time of apple trees. The simple board that collects color information and weight information and transmits it to the server side uses Arduino and adopts model-driven development (MDD) as development methodology. We have developed an architecture to provide services to PC users in the form of Web and to provide Smart Phone users with services in the form of hybrid apps. We also developed an architecture that uses beacon technology to provide orchestration information to users in real time.

• Journal of the Korean Society of Industry Convergence
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• v.24 no.6_2
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• pp.729-735
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• 2021

Recently Artificial Intelligence(AI) has been developed and used in various fields. Especially AI recognition technology can perceive and distinguish images so it should plays a significant role in quality inspection process. For stability of autonomous driving technology, semiconductors inside automobiles must be protected from external electromagnetic wave(EM wave). As a shield film, a thin polymeric material with hole shaped micro-patterns created by a laser processing could be used for the protection. The shielding efficiency of the film can be increased by the hole structure with appropriate pitch and size. However, since the sensitivity of micro-machining for some parameters, the shape of every single hole can not be same, even it is possible to make defective patterns during process. And it is absolutely time consuming way to inspect all patterns by just using optical microscope. In this paper, we introduce a AI inspection system which is based on web site AI tool. And we evaluate the usefulness of AI model by calculate Area Under ROC curve(Receiver Operating Characteristics). The AI system can classify the micro-patterns into normal or abnormal ones displaying the text of the result on real-time images and save them as image files respectively. Furthermore, pressing the running button, the Hardware of robot arm with two Arduino motors move the film on the optical microscopy stage in order for raster scanning. So this AI system can inspect the entire micro-patterns of a film automatically. If our system could collect much more identified data, it is believed that this system should be a more precise and accurate process for the efficiency of the AI inspection. Also this one could be applied to image-based inspection process of other products.