• Atmosphere
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• v.28 no.3
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• pp.317-324
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• 2018

In this study, we review the ground-based whole-sky camera (WSC), which is developed to continuously capture day and night cloud images using the illumination data from a precision Lightmeter with a high temporal resolution. The WSC is combined with a precision Lightmeter developed in IYA (International Year of Astronomy) for analysis of an artificial light pollution at night and a DSLR camera equipped with a fish-eye lens widely applied in observational astronomy. The WSC is designed to adjust the shutter speed and ISO of the equipped camera according to illumination data in order to stably capture cloud images. And Raspberry Pi is applied to control automatically the related process of taking cloud and sky images every minute under various conditions depending on illumination data from Lightmeter for 24 hours. In addition, it is utilized to post-process and store the cloud images and to upload the data to web page in real time. Finally, we check the technical possibility of the method to observe the cloud distribution (cover, type, height) quantitatively and objectively by the optical system, through analysis of the captured cloud images from the developed device.

• Journal of IKEEE
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• v.23 no.4
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• pp.1423-1429
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• 2019

Recently, a system that supports efficient interaction, a human machine interface (HMI), has become a hot topic. In this paper, we propose a new real time low-cost hand gesture control system as one of vehicle interaction methods. In order to reduce computation time, depth information was acquired using a time-of-flight (TOF) camera because it requires a large amount of computation when detecting hand regions using an RGB camera. In addition, fourier descriptor were used to reduce the learning model. Since the Fourier descriptor uses only a small number of points in the whole image, it is possible to miniaturize the learning model. In order to evaluate the performance of the proposed technique, we compared the speeds of desktop and raspberry pi2. Experimental results show that performance difference between small embedded and desktop is not significant. In the gesture recognition experiment, the recognition rate of 95.16% is confirmed.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.1
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• pp.1-8
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• 2020

If a dangerous situation arises in a place where out of reach from the human, UAVs can be used to determine the size and location of the situation to reduce the further damage. With this in mind, this paper sets the minimum value of the roll, pitch, and yaw using beta flight to detect the UAV's smooth hovering, integration, and derivative (PID) values to ensure that the UAV stays horizontal, minimizing errors for safe hovering, and the camera uses Open CV to install the Raspberry Pi program and then HSV (color, saturation, Brightness) using the color palette, the filter is black and white except for the red color, which is the closest to the fire we want, so that the UAV detects the image in the air in real time. Finally, it was confirmed that hovering was possible at a height of 0.5 to 5m, and red color recognition was possible at a distance of 5cm and at a distance of 5m.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.6
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• pp.197-202
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• 2016

In this paper, we propose optimal smart home security monitoring system. Proposeed optimal smart home security system using the three types of ultrasonic sensors were tested to obtain reliable data. and Using Raspberry Pi3, the smart home security system was implemented. In addition, It was verified through experiments optimal efficiency with a small amount compared to the conventional sensor of the home security system by the two ultrasonic sensors located in the optimal position. It was able to use two ultrasonic sensors to determine whether the intruder's highly efficient and reliable intrusion, and connect the servo motor at the bottom of the camera so you can shoot adjusted to the attacker's location to shoot the intruder's image. In addition, by using a Web server and stored the recorded image and two ultrasonic sensor data and provide a Web page for a user to monitor at all remote locations.

• 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 the Institute of Convergence Signal Processing
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• v.19 no.4
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• pp.174-179
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• 2018

Currently, various security technologies such as fingerprint recognition and face recognition are being developed. However, although many technologies have been developed, the field of incorporating technologies is quite limited. In particular, it is easy to adapt modern security technologies into existing digital systems, but it is difficult to introduce new digital technologies in systems using analog systems. However, if the system can be widely used, it is worth replacing the analog system with the digital system. Therefore, the selected topic is the electronic attendance system. In this paper, a camera is installed to a door to perform a Haar-like feature training for face detecting and real-time face recognition with a Eigenface in principal component analysis(PCA) based face recognition using raspberry pi. The collected data was transmitted to the smartphone using wireless communication, and the application for the viewer who can receive and manage the information on the smartphone was completed.

• 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 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.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.1
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• pp.139-146
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• 2022

Mountain accidents such as forest fires and missing people are increasing as hikers increase due to indoor activities restrictions caused by the prolonged COVID-19 incident. If a dangerous situation occurs at outside where rescue workers cannot reach, the search time for person can be reduced using a quadcopter. Considering this, in this paper, Multiwii is used to smoothly hover the quadcopter by setting optimized PID values of the x-axis, y-axis, and z-axis (Yaw) according to the change in the inclination of the gas. In addition, after installing Open CV on Raspberry Pie, the camera uses HSV color space to filter the color such as the description of the person, and uses a thermal imaging camera to receive thermal sensing images in real time in environments where color extraction is difficult. As a result, it was confirmed that hovering was possible at a height of 2 to 8 m, blue extraction was possible at a height of 5 m, and heat detection was possible at a distance of less than 10 cm.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.1
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• pp.93-100
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• 2020

In this paper, we implemented a smart mirror that helps users to measure the results before the procedure by reflecting various hairstyles on the head. When the camera is used to capture a face in real time and recognize the face, the stored hair image is uploaded to provide a virtual image. In addition, the high production cost, which is a problem of the existing smart mirrors, was reduced by using Raspberry Pi, Open CV, and half mirror film, and various functions were implemented through touch control. It is also designed to provide information such as weather, calendar and time.