• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.1
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• pp.22-27
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• 2020

Recently, as the number of households raising pets increases due to the increase of single households, there is a need for a system for monitoring the status or behavior of pets. There are regional limitations in the monitoring of pets using domestic CCTVs, which requires a large number of CCTVs or restricts the behavior of pets. In this paper, we propose a mobile system for detecting and tracking cats using deep learning to solve the regional limitations of pet monitoring. We use YOLO (You Look Only Once), an object detection neural network model, to learn the characteristics of pets and apply them to Raspberry Pi to track objects detected in an image. We have designed a mobile monitoring system that connects Raspberry Pi and a laptop via wireless LAN and can check the movement and condition of cats in real time.

• Smart Media Journal
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• v.10 no.1
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• pp.55-62
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• 2021

We applied machine learning of semi-supervised learning, transfer learning, and federated learning as examples of AI use cases that can be applied to the three major industries(Automobile industry, Energy industry, and AI/Healthcare industry) of Gwangju Metro-city, and established an ML strategy for AI services for the major industries. Based on the ML strategy of AI service, practical approaches are suggested, the semi-supervised learning approach is used for automobile image recognition technology, and the transfer learning approach is used for diabetic retinopathy detection in the healthcare field. Finally, the case of the federated learning approach is to be used to predict electricity demand. These approaches were tested based on hardware such as single board computer Raspberry Pi, Jaetson Nano, and Intel i-7, and the validity of practical approaches was verified.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.146-152
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• 2022

Information collected from temperature, humidity, inclination, and pressure sensors using Raspberry Pi or Arduino is used in automatic constant temperature and constant humidity systems. In addition, by using it in the agricultural and livestock industry to remotely control the system with only a smartphone, workers in the agricultural and livestock industry can use it conveniently. In general, temperature and humidity are expressed in a line graph, etc., and the change is monitored in real time. The technology to visually express the temperature has recently been used intuitively by using an infrared device to test the fever of Corona 19. In this paper, the information collected from the Raspberry Pi and the DHT11 sensor is used to predict the temperature change in space through intuitive visualization and to make a immediate response. To this end, an algorithm was created to effectively visualize temperature and humidity, and data representation is possible even if some sensors are defective.

• 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.

• Journal of Advanced Navigation Technology
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• v.26 no.2
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• pp.119-124
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• 2022

The international gnss service (IGS) provides real-time service (RTS) orbit and clock correction applicable to the broadcast ephemeris of GNSS satellites. However, since the RTS correction cannot be received if the Internet connection is lost, the RTS correction should be predicted and used when a signal interruption occurs in order to perform stable precise point positioning (PPP). In this paper, PPP was performed by predicting orbit and clock correction using a long short-term memory (LSTM) algorithm in real-time during the signal loss. The prediction performance was analyzed by implementing the LSTM algorithm in RPI (raspberry pi), the processing speed of which is not high. Compared to the polynomial prediction model, LSTM showed excellent performance in long-term prediction.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.1
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• pp.91-98
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• 2022

In this study, Pukyong National University's floating population survey and analysis were conducted using Raspberry Pie, an open source hardware, and object detection algorithms based on deep learning technology. After collecting images using Raspberry Pie, the person detection of the collected images using YOLO3's IMAGEAI and YOLOv5 models was performed, and Haar-like features and HOG models were used for accuracy comparison analysis. As a result of the analysis, the smallest floating population was observed due to the school anniversary. In general, the floating population at the entrance was larger than the floating population at the exit, and both the entrance and exit were found to be greatly affected by the school's anniversary and events.

• Journal of Software Assessment and Valuation
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• v.16 no.1
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• pp.13-19
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• 2020

It can be seen that the infringement of copyright cases is increasing as the society becomes more complex and advanced. During the software copyright dispute, there may be a dispute over whether the software is duplicated and made into upgraded software. In this paper, we intend to propose an analysis method for determining whether to upgrade software. For the software upgrade analysis, a software similarity analysis technique was used. The analysis program covers servers, management programs, and Raspberry PC programs. The first analysis confirms the correspondence between program creation information and content. In addition, it analyzes the similarity of functions and screen composition between the submitted program and the program installed in the field. The second comparative analysis compares and analyzes similarities by operating two programs in the same environment. As a result of comparative analysis, it was confirmed that the operation and configuration screens of the two programs were identical. Thus, minor differences were found in a few files, but it was confirmed that the two programs were mostly made using the same or almost similar source code. Therefore, this program can be judged as an upgrade program.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.279-286
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• 2022

The purpose of this study is to present a case of the development and application of a one-time special lecture program that requires the use of computers in frontline elementary and secondary schools. For this purpose, the researcher developed an Arduino-based special lecture program that works as a teaching tool to help with the functions of a student PC with a Raspberry Pi. This special lecture program was applied at three elementary and middle schools near K-University, and then the program was evaluated. The results of this study are as follows. First, the researcher developed a teaching aid for PC functions to be used in special lectures. Second, teaching and learning materials for visiting special lecture education programs using Arduino were developed. Third, in the special lecture, a teaching-learning method was used to guide a small number of students individually. Fourth, the special lecture program resulted in high satisfaction. The results of this study can be a useful reference for teachers who plan one-time special lecture programs requiring computers or for those who want to apply physical computing-related devices in an educational field.

• 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.