• Journal of IKEEE
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• v.23 no.4
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• pp.1343-1352
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• 2019

To manage the Internet of Things(IoT) Network, which consists of a large number of various devices, a secure and automatic method of strengthening the IoT network is being proposed. Blockchain has a 'smart contract' element of autonomous execution method, which is emerging as a way to not only exchange data quickly without mediators but also securely and automatically manage processes between IoT devices. In this paper, we implement a prototype of the entire IoT device management system based on the EOSIO with DPoS(Distributed Proof of Stake)-based blockchain structure, proposed as a prior study, including the user application DApp(Decentralized Application) and the actual IoT devices (Raspberry Pi-based device, and smart lamp) that interact with the blockchain platform. We analyze the benefits of the system and measure the time overhead to show the feasibility of the system.

• International journal of advanced smart convergence
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• v.9 no.1
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• pp.63-69
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• 2020

The rapid development in modern industrialization pollutant the water and atmospheric air across the globe that have a major impact on the human and livings health. In worldwide, every country government increasing the importance to improve the outdoor air pollution monitoring and control to provide quality of life and prevent the citizens and livings life from hazard disease. We proposed the environmental dust level detection method for outdoor facilities using sensor fusion technology to measure precise micro-dust level and monitor in realtime. In this proposed approach use the camera sensor and commercial dust level sensor data to predict the micro-dust level with data fusion method. The camera sensor based dust level detection uses the optical flow based machine learning method to detect the dust level and then fused with commercial dust level sensor data to predict the precise micro-dust level of the outdoor facilities and send the dust level informations to the outdoor air pollution monitoring system. The proposed method implemented on raspberry pi based open-source hardware with Internet-of-Things (IoT) framework and evaluated the performance of the system in realtime. The experimental results confirm that the proposed micro-dust level detection is precise and reliable in sensing the air dust and pollution, which helps to indicate the change in the air pollution more precisely than the commercial sensor based method in some extent.

• The Journal of Korean Association of Computer Education
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• v.17 no.6
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• pp.9-20
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• 2014

With the change of global awareness of Computing education and introspection about Computer education focused on ICT literacy, efforts are being made to reflect computational thinking in the new curriculum. But if computational thinking would be possible at school, it require tremendous cost to prepare computers for school. In this study, we investigate potentiality and constraints of the one board computer to teach computational thinking in school. We study fundamental performance, application of physical computing and programming education, maintenance of the computers, power consumption of the one board computers which is raspberry pi, beagle bone black, and pcduino3. The result of the study show that one board computer can substitute desktop of the school unless tasks related to require massive data storage and processing. We draw a conclusion that Pcduino3 is well-suited for computational thinking education.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.97-102
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• 2016

In this paper, we propose an Android-based smart LED push notification system. The main feature and contribution of the proposed system are as follows. First, because it notifies messages using LED lights, it is possible to check anywhere in the house without carrying the smartphone. Second, the external control using Web2py can not only manages simple LED control but one can also add notification system indicating various conditions inside the house such as motion recognition sensor, temperature sensor. Performance evaluation of the proposed system is executed by two kinds of view point as: First, how to response instantly according to the incoming signal of LED control and notification in the given networks. Second, how to recognize the change of LED light. The results of experiment show that the efficiency and convenience of the proposed system is verified from the user's point of view.

• Journal of Industrial Convergence
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• v.18 no.1
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• pp.79-85
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• 2020

Recently, the RPAS(Remote Piloted Aircraft System), by remote control and autonomous navigation, has been increasing in interest and utilization in various industries and public organizations along with delivery drones, fire drones, ambulances, agricultural drones, and others. The problems of the stability of unmanned drones, which can be self-controlled, are also the biggest challenge to be solved along the development of the drone industry. drones should be able to fly in the specified path the autonomous flight control system sets, and perform automatically an accurate landing at the destination. This study proposes a technique to check arrival by landing point images and control landing at the correct point, compensating for errors in location data of the drone sensors and GPS. Receiving from the Google Map API and learning from the destination video, taking images of the landing point with a drone equipped with a NAVIO2 and Raspberry Pi, camera, sending them to the server, adjusting the location of the drone in line with threshold, Drones can automatically land at the landing point.

• The Journal of the Korea Contents Association
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• v.19 no.12
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• pp.164-172
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• 2019

In this paper, we implement a wheeled mobile robot that accurately and autonomously finds the optimal route from the starting point to the destination point based on computer vision in a complex indoor environment. We get a number of waypoints from the starting point to get the best route to the target through deep reinforcement learning. However, in the case of autonomous driving, the majority of cases do not reach their destination accurately due to external factors such as surface curvature and foreign objects. Therefore, we propose an algorithm to deepen the waypoints and destinations included in the planned route and then correct the route through the waypoint recognition while driving to reach the planned destination. We built an autonomous wheeled mobile robot controlled by Arduino and equipped with Raspberry Pi and Pycamera and tested the planned route in the indoor environment using the proposed algorithm through real-time linkage with the server in the OSX environment.

• 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.

• Journal of Digital Contents Society
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• v.18 no.8
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• pp.1661-1669
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• 2017

In 2017, Korea has reached 1.3 million elderly people living alone. The government is promoting the basic care service for the elderly by using care workers to check the security of the elderly living alone. However, due to lack of service personnel and service usage rate of elderly care workers, it is difficult to manage. To improve these environmental constraints, this study attempted to construct a monitoring system for elderly people living alone by using sensors such as temperature, humidity, motion detection, and gas leak detection. The sensor periodically collects the current status data of the elderly and sends them to the server, creates a real time graph based on the data, and monitors it through the web. In the monitoring process, when the sensor is out of the range of the specified value, it sends a warning text message to the guardian to inform the current situation, and is designed and implemented so as to support the safety life of the elderly living alone.

• The Journal of the Convergence on Culture Technology
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• v.4 no.1
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• pp.291-297
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• 2018

In this paper, we present an automatic image object tracking system for Studio cameras on the stage. For object tracking, we use the OpenCV-based Python program using PC, Raspberry Pi 3 and mobile devices. There are many methods of image object tracking such as mean-shift, CAMshift (Continuously Adaptive Mean shift), background modelling using GMM(Gaussian mixture model), template based detection using SURF(Speeded up robust features), CMT(Consensus-based Matching and Tracking) and TLD methods. CAMshift algorithm is very efficient for real-time tracking because of its fast and robust performance. However, in this paper, we implement an image object tracking system for studio cameras based CMT algorithm. This is an optimal image tracking method because of combination of static and adaptive correspondences. The proposed system can be applied to an effective and robust image tracking system for continuous object tracking on the stage in real time.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.10
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• pp.1247-1255
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• 2016

The safety and autonomous flight function of micro UAV or drones is crucial to its commercial application. The requirement of own building stable drones is still a non-trivial obstacle for researchers that want to focus on the intelligence function, such vision and navigation algorithm. The paper present a GCS using commercial drone and hardware platforms, and open source software. The system follows modular architecture and now composed of the communication, UI, image processing. Especially, lane-keeping algorithm. are designed and verified through testing at a sports stadium. The designed lane-keeping algorithm estimates drone position and heading in the lane using Hough transform for line detection, RANSAC-vanishing point algorithm for selecting the desired lines, and tracking algorithm for stability of lines. The flight of drone is controlled by 'forward', 'stop', 'clock-rotate', and 'counter-clock rotate' commands. The present implemented system can fly straight and mild curve lane at 2-3 m/s.