• Proceedings of the Korean Society of Computer Information Conference
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• 2019.07a
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• pp.427-428
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• 2019

칠교놀이는 해외에서는 Tangram이라고 불리며 아주 예전부터 전해져 내려오는 세계적인 놀이이다. 친구와 여럿이서 놀이를 할 수 있을 뿐만아니라 혼자서도 즐길 수 있다. 칠교놀이는 특히 창의력 향상에 도움을 주는데 이번 논문에서는 혼자서 쉽게 칠교놀이를 즐길 수 있도록 비전시스템과 라즈베리파이를 이용해서 칠교를 카메라로 인식해 성공하면 보상으로 사탕을 지급하는 놀이를 개발해 보았다. 자판기에 동전을 넣으면, 게임을 시작해서 칠교놀이의 문제를 하나씩 맞출 때 마다 사탕 한 개가 지급되는 방식으로 4차산업혁명 시대에 걸맞는 재미있는 칠교놀이 게임을 만들어 보았다. 본 논문은 OPENCV라이브러리와 라즈베리파이 GPIO라이브러리를 사용하였다. 사용한 부품은 웹캠, 초음파 센서, 서보모터이다. 라즈베리파이를 서버로 설정하고, PC를 클라이언트로 설정하여 서로 데이터를 주고 받을 수 있게 하였다. 라즈베리파이에 OPENCV를 설치하지 않은 이유는 OPENCV가 꽤 높은 사양이 필요하다고 판단하여 비전영상처리는 PC(클라이언트)에서 진행하고, 게임의 진행상황(정답의 여부)을 라즈베리파이(서버)에 보내는 방식으로 정하였다. 반대로 라즈베리파이에서도 동전의 투입 유무를 판단하여 PC(클라이언트)에 게임 시작 신호를 보내는 방식으로 설정하였다. 언어는 라즈베리파이와 PC둘다 Pythond으로 구현하였다.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.35 no.5
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• pp.509-515
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• 2022

As the 4th industrial revolution based on ICT is progressing in the manufacturing field, interest in building smart factories that can be flexible and customized according to customer demand is increasing. To this end, it is necessary to maximize the efficiency of factory by performing an automated process in real time through a network communication between engineers and equipment to be able to link the established IT system. It is also necessary to collect and store real-time data from heterogeneous facilities and to analyze and visualize a vast amount of data to utilize necessary information. Therefore, in this study, four types of controllers such as PLC, Arduino, Raspberry Pi, and embedded system, which are generally used to build a smart factory that can connect technologies such as artificial intelligence (AI), Internet of Things (IoT), and big data, are configured. This study was conducted for the development of a program that can collect and store data in real time to visualize and manage information. For communication verification by controller, data communication was implemented and verified with the data log in the program, and 3D monitoring was implemented and verified to check the process status such as planned quantity for each controller, actual quantity, production progress, operation rate, and defect rate.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.481-483
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• 2023

본 논문에서는 다중 카메라환경에서의 안면인식 기반 영유아 활동 사진 자동 생성 시스템을 개발했다. 개발한 시스템은 어린이집에서 알림장 작성을 위한 촬영하는 동안 보육에 부주의하여 안전사고가 발생하는 것을 방지 할 수 있다. 시스템은 이동식 수집기와 분류 서버로 나뉘어 작동하게 된다. 이동식 수집기는 Raspberry Pi를 이용하였고 초당 1장 내외의 사진을 촬영하여 SAMBA를 사용 공유폴더에 저장한다. 분류 서버에서는 YOLOv5를 사용해 안면을 인식해 분류한다. OpenCV와 TensorFlow-Keras를 통해 분류된 사진에서의 표정을 파악하여 부모에게 전송할 웃는사진만을 분류하여 남겨둔다. 이외의 사진은 /dev/null로 이동하여 삭제된다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.661-664
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• 2023

이 프로젝트의 목표는 공유 공간에서의 고객 이용 시간 관리를 향상하기 위해 라즈베리 파이와 센서 기술을 활용한 자동 시간 체크 시스템의 개발입니다. 이 시스템은 로드셀, 진동 감지 센서, 그리고 LiDAR 센서, 이 세 가지 센서를 활용해 의자에서 사용자의 존재 여부를 감지하고, 사람과 물건을 구별하며, 사용자가 의자에서 일어나는 시점을 파악합니다. 특히, 고객이 의자에 앉게 되면 시스템이 자동으로 시간을 체크하여 실시간으로 이용 시간을 측정하게 됩니다. 이렇게 수집된 정보는 웹 기반의 사용자 인터페이스를 통해 제공되어, 이용 시간 관리가 보다 편리해집니다.

• Korean Journal of Soil Science and Fertilizer
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• v.43 no.6
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• pp.923-929
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• 2010

This study was conducted to investigate the relations between soil chemical properties and Korean raspberry (Rubus coreanus Miq.) growth. Soils were collected from 54 sampling sites in 1 to 6 years old Korean raspberry cultivation fields (RCFs) at Gochang-gun, Jeollabuk-Do, Korea. In RCF with two to six years old plants, soil pH and exchangeable $Ca^{2+}$ and $Mg^{2+}$ contents were higher at the leaf-emergence time, but EC, available phosphorus content, CEC, and total nitrogen content were higher at the harvesting time; especially at the harvesting time, the content of available phosphorus in the RCF with 3~6 years old plants were at least three times higher than in the RCF with 2 years old plants. Water-soluble anions ($Cl^-$, ${NO_3}^-$, ${PO_4}^{2-}$, and ${SO_4}^{2-}$) in the RCF were also measured. The contents of ${NO_3}^-$ and ${SO_4}^{2-}$ were always higher in the RCF with older plants than in the RCF with younger plants, which were not affected by sampling time and sampling locations (rhizosphere and non-rhizosphere sites). However, soils collected from non-rhizosphere at the leaf-emergence time contained higher ${PO_4}^{2-}$ content, but $Cl^-$ content was higher in the soils collected from rhizosphere at the harvesting time. In general, soils in the RCFs contained excess amounts of inorganic nutrients such as available phosphorus and exchangeable $K^+$. Thus, optimal levels of soil properties for Korean raspberry cultivation should be reconsidered.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.681-682
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• 2018

There are many damages due to the unstable climate. Among them, agriculture will be the most affected by the climate. Agriculture has limited crops that can be grown along with climate and seasons. We will develop smart farms that integrate information technology(ICT) into agricultural technology and improve the productivity of existing agricultural technology. It uses Raspberry Pi and Arduino to control the hardware and software, and uses various sensors to recognize the environment necessary for crop cultivation and maintain optimal environment. In addition, it is possible to manipulate these Smart Farm as mobile or personal PC to implement a flexible Smart Farm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.651-653
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• 2015

Common Embedded boards like the Arduino, Raspberry Pi, BeagleBone Black, leverages smart home systems, machine tools and various products in our day to day life. Managing and dealing frequent large scale incidents involving recent reagents and hazardous materials can be dangerous and difficult to detect in advance like in an event of an accidents or fires. In this paper I have done research by utilizing an Embedded (BeagleBone Black) boards sensors and RFID management system to detect a hazardous situation like fire in real time and avoiding it by sending out an alert message to the admin user to minimizing the risk. This system provides immediate information to the administrator of any hazardous situation and prevents any accidents from occurring.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.400-403
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• 2021

As IoT technology becomes popular, more and more data is being generated, and the diversity of data is also increasing. In particular, in smart factory or Home IoT systems, data processing is very important because various data is collected and processed in real time through sensors. In this paper, we present a method for collecting, analyzing, and monitoring various data generated by sensors in IoT environment through Raspberry Pi. We also validate its usefulness by demonstrating that the above processed data can be operated in conjunction with smart mirror and mobile application.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.425-426
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• 2021

Recently, there have been frequent incidents of talent accidents caused by youth driving without a license. Driving without a license is becoming a hotbed of curiosity and challenge for some young people, and there is a limit to managing smart keys at home to prevent this. Therefore, in this paper, using the facial recognition algorithm, the face of the driver sitting in the driver's seat is compared with the information stored in advance, and the system is designed to control the engine by determining that it is a registered driver. If the registered driver authentication is successful, the matching accuracy and Unlock message are output to the LCD connected to the Raspberry Pi.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.640-642
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• 2021

In order to collect a lot of data clearly and efficiently, it is essential to know the locations of the current facilities and analyze the movement data. The current location collection technology can collect data using a GPS (Global Positioning System) sensor, but in the case of GPS, it has strong straightness and low diffraction and reflectivity, making it difficult to position indoors. It is impossible to measure the distance between the server and the client because the signal sensitivity cannot be received. This paper implements an indoor positioning system using beacons and scanners in Raspberry Pi 3 B+. It controls Advertise Mode and Connection Mode at the same time using the scanner algorithm.