• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.1
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• pp.143-149
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• 2018

This paper presents an implementation of a streaming system which can forward live video stream to multiple users from a Phantom4, which is a drone made by DJI. We constructed the streaming server on Raspberry Pi 3 board for high mobility. Also We implemented the system so that the video stream can be played on any devices if the HTML5 standard web browser is utilized. We compiled C codes of FFmpeg open sources and installed in the Raspberry Pi3 as the streaming server and developed a Java application to execute as the integrated server that controls the other softwares on the streaming server. Also we developed an Android application which receives the live video stream from the drone and sends the streaming server continuously. The implemented system in this paper can successfully stream the live video on 24 frames per second at the resolution of 148x112 in considering the low hardware throughput of the streaming server.

• Journal of IKEEE
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• v.24 no.2
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• pp.392-402
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• 2020

This paper proposes a solar tracking system without photo-sensors. The system can be classified into four modules: Solar Tracking, MPPT, ESS, and Real-Time Monitoring. Nine solar panels, as a basic unit, are adopted with grid structures of different heights to reduce wind influence and to enable solar tracking without photo-sensors. The low-cost MCU implements MPPT method which generates PWM switching signal for boost converter. The unit of ESS consists of three-series and four-parallel lithium-ion batteries in order to enable monitoring for abnormalities in temperature and electrical characteristics of battery. Four MCUs used in the system consists of two AVR Atmega128, and two Raspberry PI, and they exchanges operation informations. Experimental results of the proposed system show the solar tracking performance, the possibility of on-site and remote monitoring and the convenience of maintenance based on IoT technology.

• Structural Monitoring and Maintenance
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• v.2 no.4
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• pp.319-338
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• 2015

The electro-mechanical impedance (EMI) technique makes use of surface-bonded lead zirconate titanate (PZT) patches as impedance transducers measuring impedance variations monitored on host structural components. The present experimental work further evaluate an alternative to the conventional EMI technique which performs measurements of the variations in the output voltage of PZT transducers rather than computing electromechanical impedance (or admittance) itself. This paper further evaluates a variant of the EMI approach presented in a previous work of the present authors, suitable, for low-cost concrete structures monitoring applications making use of a credit card-sized Raspberry Pi single board computer as core hardware unit. This monitoring approach is also deployed by introducing a new damage identification index based on the ratio between the area of the 2-D error ellipse of specific probability of EMI-based measurements containment over that of the 2-D error circle of equivalent probability. Experimental results of damages occurring in concrete cubic and beam specimens are investigated under increasing loading conditions. Results illustrate that the proposed technique is an efficient approach for identification and early detection of damage in concrete structures.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.4
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• pp.72-79
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• 2015

A number of plating companies have been exposed to the risk of fire due to unexpected temperature increasing of water in a plating bath. Since the companies are not able to forecast the unexpected temperature increasing of water and most of raw materials in the plating process have low ignition temperature, it is easy to be exposed to the risk of fire. Thus, the companies have to notice the changes immediately to prevent the risk of fire from plating process. Due to this reason, an agile and systematic temperature monitoring system is required for the plating companies. Unfortunately, in case of small size companies, it is hard to purchase a systematic solution and be offered consulting from one of the risk management consulting companies due to an expensive cost. In addition, most of the companies have insufficient research and development (R&D) experts to autonomously develop the risk management solution. In this article, we developed a real time remote temperature monitoring system which is easy to operate with a lower cost. The system is constructed by using Raspberry Pi single board computer and Android application to release an economic issue for the small sized plating manufacturing companies. The derived system is able to monitor the temperature continuously with tracking the temperature in the batch in a short time and transmit a push-alarm to a target-device located in a remoted area when the temperature exceeds a certain hazardous-temperature level. Therefore, the target small plating company achieves a risk management system with a small cost.

• Journal of the Korea Convergence Society
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• v.10 no.1
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• pp.45-51
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• 2019

The use of the Internet of things plays a major role in the Fourth Industrial Revolution, and a series of tasks of accumulating, converging, analyzing and reusing various data and services becomes very important. Because the pace and scope if the paradigm shift in Fourth Industrial Revolution is so rapid and unpredictable, the development and utilization of a system to fulfill this role for IOT are urgently required. In this paper, we introduce the Web-based IOT management system, which connects the IOT with OKMindmap, which is a domestic open source software and service, and the Node-RED service. This system combines the advantages of OKMindmap with the advantages of Node-RED, which is capable of visual component based programming, so that it can easily and flexibly connect the IOT based on Web browsers, and various data and services can be integrated and linked. We developed a camera module, a temperature and humidity sensor module, and the motor control module in Raspberry PI basically, and tested the operation successfully. We plan to extend the IOT component gradually by using Arduino and System On Chip.

• 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.16 no.4
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• pp.699-708
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• 2021

In this paper, a steering control and speed control algorithm was presented for autonomous driving based on two vision sensors and road speed sign board. A car speed control algorithm was developed to recognize the speed sign by using TensorFlow, a deep learning program provided by Google to the road speed sign image provided from vision sensor B, and then let the car follows the recognized speed. At the same time, a steering angle control algorithm that detects lanes by analyzing road images transmitted from vision sensor A in real time, calculates steering angles, controls the front axle through PWM control, and allows the vehicle to track the lane. To verify the effectiveness of the proposed algorithm's steering and speed control algorithms, a car's prototype based on the Python language, Raspberry Pi and OpenCV was made. In addition, accuracy could be confirmed by verifying various scenarios related to steering and speed control on the test produced track.

• Advanced Industrial SCIence
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• v.1 no.1
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• pp.23-31
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• 2022

Cloud services, which started in the early 2000s as a method of using idle servers, are more active with the advent of the 4th industrial revolution, and are being used in many fields as an optimal platform that can be used for business by collecting and analyzing data. On the other hand, the Internet of Things is an environment in which all surrounding objects can freely connect to the Internet network anytime and anywhere to transmit sensed data. In the Internet of Things, data is transmitted in real time, so BaaS, that is, a cloud service for data only has been added. In this paper, among BaaS services for the Internet of Things, a back-end service method that manages data based on Parse Server is explained, and a service that helps patients in rehabilitation is presented using this. For this, a Raspberry Pi is used as a hardware environment, and it is connected to the Internet, collects patient movement information in real time, and manages it through the Parse Server.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.99-105
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• 2023

In this paper, an intelligent CCTV was designed and experimentally developed by using an IOT device, Raspberry Pi, and artificial intelligence technology. Object Detection technology was used to detect the number of people on the CCTV screen, and Action Detection technology provided by OpenPose was used to detect emergency situations. The proposed system has a structure of CCTV, server and client. CCTV uses Raspberry Pi and USB camera, server uses Linux, and client uses iPhone. Communication between each subsystem was implemented using the MQTT protocol. The system developed as a prototype could transmit images at 2.7 frames per second and detect emergencies from images at 0.2 frames per second.

• 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.