• Journal of Digital Convergence
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• v.14 no.4
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• pp.251-258
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• 2016

Big data processing is a broad term for processing data sets so large or complex that traditional data processing applications are inadequate. Widespread use of smart devices results in a huge impact on the way we process data. Many organizations are contemplating how to incorporate or integrate those devices into their enterprise data systems. We have proposed a way to process big data by way of integrating Raspberry Pi into a Hadoop cluster as a computational grid. We have then shown the efficiency through several experiments and the ease of scaling of the proposed system.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2021.06a
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• pp.283-285
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• 2021

본 논문에서는 수중에서 선박 하부에 붙은 오손생물(fouling organism)을 탐지하고 격자지도(grid map)로 나타내는 시스템을 제안한다. 제안하는 시스템은 소나(sound navigation and ranging: SONAR) 센서와 오손생물사이의 시간 데이터를 수집한 후, 라즈베리 파이 4(raspberry pi 4)에서 수집된 데이터를 이용해 격자지도에 맵핑(mapping)함으로써, 선저하부의 상태를 파악하는데 도움을 줄 수 있다. 본 논문에서는 제안된 지도 시스템을 이용하여 선박 하부에 붙은 오손생물의 분포를 확인할 수 있다.

• Journal of the Institute of Convergence Signal Processing
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• v.21 no.4
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• pp.154-161
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• 2020

This paper presented the basics of using a sorting system to reduce human effort and increase accuracy. The proposed system has consisted of a camera, motors, and a Raspberry Pi. This system can classify the apples as immature, mature, ripe condtion, and etc. In this experiment, 100 apples were randomly selected by purchasing various apples from a local market. The accuracy percentage was 95% and processing time was about 8 seconds per each apple. The proposed system could be useful to reduce labor.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.01a
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• pp.155-156
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• 2020

세상은 4차 산업 혁명 시대에 들어섰고 사회의 많은 부분들이 스마트화되었다. 이러한 기술들의 발전으로 집 안의 가전기기들을 태블릿 pc, 스마트폰 등을 통하여 장소와 시간에 구애받지 않고 관리할 수 있게 되었다. 하지만 모든 전자 제품들이 전력 사용량을 알 수 있는 것은 아니다. 그렇기에 대부분의 가정이 전력이 과소비되고 있는 것은 아닌지 외출 시 전열 기구의 전원이 제대로 꺼졌는지 등 이를 확인이 쉽지 않다. 본 과제물은 위의 문제를 해결하기 위해 아두이노로 라즈베리파이와 앱을 무선통신하여 '전력사용 확인이 가능한 전원제어 시스템'을 고안했다. 전력측정이 가능한 플러그를 사용하여 가전제품의 전력 사용량을 측정할 수 있으며 전원을 원격으로 제어할 수 있다. 또한, 터치스크린으로도 이것을 실시간으로 확인할 수 있으며, 애플리케이션과 같은 역할 수행이 가능하다. 이 기능으로 전력의 과소비 및 누전으로 인한 화재를 막고 전기세를 최대한으로 줄이면서 동시에 편리함을 증대 시킬 수 있다.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.4
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• pp.689-698
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• 2021

In this paper, lane recognition, steering control and speed control algorithms were developed using Bluetooth wireless communication and image processing techniques. Instead of recognizing road traffic signals based on image processing techniques, a methodology for recognizing the permissible road speed by receiving speed codes from electronic traffic signals using Bluetooth wireless communication was developed. In addition, a steering control algorithm based on PWM control that tracks the lanes using the Canny algorithm and Hough transform was developed. A vehicle prototype and a driving test track were developed to prove the accuracy of the developed algorithm. Raspberry Pi and Arduino were applied as main control devices for steering control and speed control, respectively. Also, Python and OpenCV were used as implementation languages. The effectiveness of the proposed methodology was confirmed by demonstrating effectiveness in the lane tracking and driving control evaluation experiments using a vehicle prototypes and a test track.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.4
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• pp.183-189
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• 2021

In recent years, active research has been devoted toward developing a monitoring system using ambient vibration data in order to quantitatively determine the deterioration occurring in a structure over a long period of time. This study developed a low-cost edge computing system that detects the abnormalities in structures by utilizing the dynamic characteristics acquired from the structure over the long term for ensemble learning. The system hardware consists of the Raspberry Pi, an accelerometer, an inclinometer, a GPS RTK module, and a LoRa communication module. The structural abnormality detection afforded by the ensemble learning using dynamic characteristics is verified using a laboratory-scale structure model vibration experiment. A real-time distributed processing algorithm with dynamic feature extraction based on the experiment is installed on the Raspberry Pi. Based on the stable operation of installed systems at the Community Service Center, Pohang-si, Korea, the validity of the developed system was verified on-site.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.1
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• pp.125-132
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• 2022

In this paper, we presented a cart which can automatically transport loads to the distribution center of the appointed indoor place, based on Raspberry pi 4. It can recognize the obstacles by using the ultrasonic sensors so that it prevents the collision and takes a detour. Further, we entered the direction control code in the RFID. It has installed at important points such as the intersections of the destinations, so that if the RFID reader of the cart senses the RFID, the cart would stop or change the direction. After the transportation, if the load cell(weight sensor) recognizes that the baggage is unloaded, the cart returns to the initial point and would be retrieved. Therefore, we embodied the transportation cart which reduces the use of manpower and solves the problems conveniently across the transportation strategies.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.5
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• pp.181-186
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• 2017

IEC 61850 is a standard for power utility automation. Using IEC 61850 that uses ethernet may consume more costs for the automation than its value in small distribution substations. Thus, less expense and installation cost are required for the automation of small distribution substations. This study used inexpensive and easy-to-install IEEE 802.15.4 and implemented a bridge between IEC 61850 and IEEE 802.15.4, using Raspberry Pi to connect the existing IEC 61850. Using IEEE 1588, IEC 61850 traffic performances were evaluated, such as SV, GOOSE and MMS. Analyzing IEC 61850 requirements and performance evaluation results, the scope of application of IEEE 802.15.4 was decided.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.5
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• pp.859-864
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• 2017

IoT is a new emerging technology to lead the $4^{th}$ industry renovation and has been widely used in industry and home to increase the quality of human being. In this paper, IoT based face detection and recognition system for a smart elevator is developed. Haar cascade classifier is used in a face detection system and a proposed PCA algorithm written in Python in the face recognition system is implemented to reduce the execution time and calculates the eigenfaces. SVM or Euclidean metric is used to recognize the faces detected in the face detection system. The proposed system runs on RaspberryPi 3. 200 sample images in ORL face database are used for training and 200 samples for testing. The simulation results show that the recognition rate is over 93% for PP+EU and over 96% for PP+SVM. The execution times of the proposed PCA and the conventional PCA are 0.11sec and 1.1sec respectively, so the proposed PCA is much faster than the conventional one. The proposed system can be suitable for an elevator monitoring system, real time home security system, etc.

• Journal of IKEEE
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• v.23 no.3
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• pp.777-784
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• 2019

A pre-fire awareness and automatic notification system are required because it is possible to minimize the damage if the fire situation is precisely detected after a fire occurs in a place where people are unusual or in a mountainous area. In this study, we developed a RaspberryPi-based fire recognition system using Faster-recurrent convolutional neural network (F-RCNN) and single shot multibox detector (SSD) and demonstrated a fire alarm system that works with power line communication. Image recognition was performed with a pie camera of RaspberryPi, and the detected fire image was transmitted to a monitoring PC through an inductive power line communication network. The frame rate per second (fps) for each learning model was 0.05 fps for Faster-RCNN and 1.4 fps for SSD. SSD was 28 times faster than F-RCNN.