• Journal of the Institute of Convergence Signal Processing
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• v.20 no.3
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• pp.145-150
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• 2019

We develop the system realization of whale sound reconstruction by inverse MFCC algorithm with the weighted L2-norm minimization techniques. The output products from this research will contribute to the whale tourism and multimedia content industry by combining whale sound contents with the prototype of 3D printing. First of all, we develop the softwares for generating whale sounds and install them into Raspberry Pi hardware and fasten them inside a 3D printed whale. The languages used in the development of this system are the C++ for whale-sounding classification, MATLAB and Python for whale-sounding playback algorithm, and Rhino 6 for 3D printing.

• 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.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.62-62
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• 2018

무선영상취득시스템(WIA 시스템, Wireless Image Acquisition System)은 라즈베리 파이에 전용 카메라와 WiFi 모듈을 장착하여, 하천의 영상을 실시간으로 촬영하여 무선으로 서버로 전송하는 시스템이다. 이 시스템이 갖는 가장 큰 이점은 시스템을 구성하는 비용이 매우 저렴하다는 점이다. 라즈베리 본체와 카메라 모듈, WiFi 모듈 모두 매우 저렴하고, 또 사용하는 전력이 작아서 상용 전원이 아닌 태양광 발전이나 배터리 등을 이용할 수 있다. 따라서 비용과 장소에 구애받지 않고 손쉽게 어디든지 설치하여 하천의 상시 감시나 계측에 활용할 수 있다. 또한, 상용 전원을 이용하지 않아도 되기 때문에, 산간벽지나 오지 등의 소하천 관리에도 적합하다. 본 연구에서는 이 WIA 시스템을 경상남도 김해시의 대청천에 적용하여 홍수 시 하천의 수표면을 촬영하고, 촬영된 동영상을 분석하여 수위와 유속을 동시에 계측하여 유량을 산정하였다. 라즈베리 파이에 $640{\times}480$ 화소의 카메라를 장착하여 10분 간격으로 10초간의 동영상을 촬영하고, 이를 WiFi 모듈을 이용하여 무선으로 서버로 전송한다. 전송된 동영상을 분석하기 전에 설치 지점의 3차원 좌표 변환 자료와 횡단면 좌표를 입력하여 대상 지점의 측정 매개변수를 설정한다. 즉, 이들 자료에서 영상 내의 표정점과 측정선을 설정해 둔다. 그 다음, 전송된 동영상을 시공간 영상으로 만들어 수위를 분석한다. 비슷한 방법으로 동영상에서 유속을 분석하고, 분석된 수위와 유속, 그리고 미리 설정된 횡단면 좌표를 이용하여 유량을 산정해 낸다. 설치된 WIA 시스템을 실제로 운용하여, 2017년 9월 11일의 06:10~19:00의 호우 사상 전체를 분석하였다. 10분 간격으로 촬영된 10초간 동영상 중에서 적절한 분석이 가능한 영상 77개에서 수위와 유속을 분석한 결과, 최대 수위는 0.746 m(간이수위표 기준), 최대 유속은 0.962 m/s, 최대 유량은 $12.977m^3/s$에 이르렀다. 지점 특성상 다른 유속계를 이용한 검증은 사실상 불가능하였다. 또, 하폭이 넓어서 일출 전과 일몰 후의 촬영 자료는 분석이 어려운 점이 있다. 이러한 기술적 문제들을 보완하면, WIA 시스템을 이용한 소하천의 수위와 유속 측정 시스템은 경제성이고 효율이 높은 관측시스템으로 유망할 것으로 기대된다.

• 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.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.1
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• pp.213-218
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• 2019

In this paper, we study autonomous navigation system for path guide system by using RFID that is enable to navigate and load in hotel. In case of a mobile robot used in a general autonomous navigation guidance system, a large amount of sensors are added to the system in order to improve the accuracy, resulting in cost problems. Therefore, to reduce the number of sensors, and to increase the accuracy and recognition rate, an autonomous driving guidance system was implemented using one of the inexpensive small micro controller units (MCU) such as Raspberry Pi3.

• Journal of Internet of Things and Convergence
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• v.3 no.1
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• pp.1-8
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• 2017

Information and time are very important for a modem life, and researches about the personal secretary system that provides specific information for each individual are being studied. In this research, we developed the personal secretary system called Genie which provides the user with desired informations such as weather, news, and traffic information with time. It is expected that the Genie system will provide information on news, weather, traffic information including bus arrival times, and memos with time so that users can find their own leisure time and live a comfortable life.

• Smart Media Journal
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• v.9 no.3
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• pp.41-45
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• 2020

Recently, artificial intelligence (AI) and the Internet of things (IoT), which are represented by machine learning and deep learning among IT technologies related to the Fourth Industrial Revolution, are applied to our real life in various fields through various researches. In this paper, IoT and AI using object recognition technology are applied to classify clothing. For this purpose, the image dataset was taken using webcam and raspberry pi, and GoogLeNet, a convolutional neural network artificial intelligence network, was applied to transfer the photographed image data. The clothing image dataset was classified into two categories (shirtwaist, trousers): 900 clean images, 900 loss images, and total 1800 images. The classification measurement results showed that the accuracy of the clean clothing image was about 97.78%. In conclusion, the study confirmed the applicability of other objects using artificial intelligence networks on the Internet of Things based platform through the measurement results and the supplementation of more image data in the future.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.9
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• pp.1674-1680
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• 2017

In this paper, an embedded system for face authentication, which exploits high-dimensional local binary pattern (LBP) descriptor and joint Bayesian algorithm, is proposed. We also present a feasible embedded system for the proposed algorithm implemented with a Raspberry Pi 3 model B. Computer simulation for performance evaluation of the presented face authentication algorithm is carried out using a face database of 500 persons. The face data of a person consist of 2 images, one for training and the other for test. As performance measures, we exploit score distribution and face authentication time with respect to the dimensions of principal component analysis (PCA). As a result, it is confirmed that an embedded system having a good face authentication performance can be implemented with a relatively low cost under an optimized embedded environment.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.3
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• pp.105-114
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• 2019

As the Industrial Revolution progressed, Big Data technologies were used to develop a system that instantly identified the consequences of older vehicles using mobile devices. First, data from the vehicle was collected using the OBD2 sensor, and the data collected was stored in the raspberry pie, giving it the same situation that the raspberry pie was driving. In the event that vehicle data is generated, the data is collected in real time, stored in multiple nodes, and visualized and printed based on the processed, refined, processed and processed data. We can use Big Data in this process and quickly process vehicle data to identify it effectively through mobile devices.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.5
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• pp.835-844
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• 2021

In this paper, a power facility communication control device was designed to autonomously determine and separate the fault section through communication between power facilities in the smart grid distribution system. In the power facility communication control device, the control module was designed as a DSP to measure three-phase voltage and current, and the communication module was designed as an embedded-based Raspberry Pi to determine the fault section and realize the fault section separation through communication between power facilities. Communication between DSP and Raspberry Pi was designed by SPI communication, and communication between Raspberry Pi was designed based on Wi-Fi. Finally, a performance evaluation system based on three power facility communication control devices was built, and simulation verification was conducted for various fault events that may occur on the distribution line. As a result of the test evaluation, it was possible to confirm the effectiveness of the design methodology of the communication control device by showing the required response of the communication control device to all test cases.