• 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.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.4
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• pp.295-300
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• 2016

Internet of things which helps communication between human and thing and between things by connecting networks on them is developing. Develops of Internet of things, network technique, and smart machine result interest on home network system. In this paper, we suggested a system with the home network to the dormitory using pressure sensors, infrared sensor, ultrasonic sensor, switch, arduino, raspberrypi, android application. Smart dormitory system based on the internet of things provide information whether public things in dormitory like laundry machine, computer, treadmill is being used now, and package is arrived through android application.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.25-27
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• 2022

Recently, with the development of the 4th industry, environmental issues such as air pollution have become serious, and in particular, a lot of air pollutants are generated in industrial sites. There are various types of air pollutants, and among them, carbon monoxide is essential for fires occurring in industrial sites, so it should be possible to monitor in real time. In addition, there is a need for a remote monitoring system that can measure various environmental factors other than air pollutants in real time. In this paper, we propose a monitoring system using wireless communication to remotely measure the industrial environment. The proposed monitoring system collects data to the Arduino of the transmitter by using a carbon monoxide sensor, a combustible gas sensor, a temperature and humidity sensor, and a flame sensor, and then transmits it to the receiver using ZigBee. The transmitted data is stored in the database of the receiver Raspberry Pi, and the stored data can be monitored in real time through the monitoring system.

• 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 Convergence for Information Technology
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• v.11 no.9
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• pp.28-34
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• 2021

In the case of object detection using deep learning, both accuracy and real-time are required. However, it is difficult to use a deep learning model that processes a large amount of data in a limited resource environment. To solve this problem, this paper proposes an object detection model for small embedded devices. Unlike the general detection model, the model size was minimized by using a structure in which the pre-trained feature extractor was removed. The structure of the model was designed by repeatedly stacking lightweight convolution blocks. In addition, the number of region proposals is greatly reduced to reduce detection overhead. The proposed model was trained and evaluated using the public dataset PASCAL VOC. For quantitative evaluation of the model, detection performance was measured with average precision used in the detection field. And the detection speed was measured in a Raspberry Pi similar to an actual embedded device. Through the experiment, we achieved improved accuracy and faster reasoning speed compared to the existing detection method.

• 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.

• 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 Platform Technology
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• v.8 no.4
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• pp.59-69
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• 2020

Recently, the seriousness of ultrafine dust and fine dust has emerged as a national disaster, but small and medium-sized cities in provincial areas lack fine dust monitoring stations compared to their area, making it difficult to manage fine dust. Although the computing resources for collecting and processing fine dust data are not large, it is necessary to utilize cloud and private and public data to share data. In this paper, we proposed a small edge computing system that can measure fine dust, ultrafine dust and temperature and humidity and process it to provide real-time control of fine dust and service to the public. Collecting fine dust data and using public and private data to service fine dust ratings is efficient to handle with edge computing using raspberry pie because the amount of data is not large and the processing load is not large. For the experiment, the experiment system was constructed using three sensors, raspberry pie and Thinkspeak, and the fine dust measurement was conducted in northern part of kyongbuk region. The results of the experiment confirmed the measured fine dust measurement results over time based on the GIS data of the private sector.