• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.644-646
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• 2016

Deaf are it is difficult to communicate to represent the voice heard, so theay use mostly using the speech, sign language, writing, etc. to communicate. It is the best way to use sign language, in order to communicate deaf and normal people each other. But they must understand to use sign language. In this paper, we designed and implementated finger language translation system to support communicate between deaf and normal people. We used leap motion as input device that can track finger and hand gesture. We used raspberry pi that is low power sing board computer to process input data and translate finger language. We implemented application used Node.js and MongoDB. The client application complied with HTML5 so that can be support any smart device with web browser.

• 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 IKEEE
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• v.26 no.1
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• pp.10-18
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• 2022

Accurate SOC estimation is an important indicator of battery operation strategies, and many studies have been conducted. The simulation method which was mainly used in previous studies, is difficult to conduct real-time SOC estimation like real BMS environment. Therefore, this paper aims to implement a real-time battery SOC estimation embedded system and analyze problems that can arise during the verification process. In environment consisting of two Raspberry Pi boards, SOC estimation with the EKF uses data measured by the Simscape battery model. Considering that the operating characteristics of the battery vary depend on the temperature, the results were analyzed at various ambient temperatures. It was confirmed that accurate SOC estimation was performed even when offset fault and packet loss occurred due to communication or sensing problems. This paper proposes a guide for embedded system strategies that enable real-time SOC estimation with errors within 5%.

• Journal of the Korea Society of Computer and Information
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• v.22 no.8
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• pp.47-53
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• 2017

In this paper, we present open hardware platforms for Internet of Things (IoTs) emphasizing their strengths and weaknesses. We introduce six representative platforms, Raspberry PI, Arduino, Garileo, Edison, Beagle board and Artik. We define important performance issues for open hardware platforms for IoTs and analyze recent platforms according to the performance issues. We present recent research project using open hardware platforms introduced in this paper. We believe that this paper provide wise view and necessary information for open hardware platforms for Internet of Things (IoT).

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.12
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• pp.1742-1745
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• 2019

Hidden flame detection after allegedly extinguishing a fire cannot be emphasized enough. There are a few commercial hidden fire detection equipments which are imported, but the cost is relatively high. In this contribution, we propose a development of a low-cost, high-performance hidden flame detector using open-source hardware/software. We use Raspberry-pi based hardware board equipped with a TFT touch-screen LCD, a 3G modem, and an attachable battery device altogether integrated in a plastic case fabricated with a 3D printer. The proposed hidden flame detector shows the same performance of a commercial product FLIR E5 while consuming less than a half of the cost.

• 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.

• ETRI Journal
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• v.40 no.3
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• pp.396-409
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• 2018

Elliptic curve cryptography (ECC) can achieve relatively good security with a smaller key length, making it suitable for Internet of Things (IoT) devices. DNA-based encryption has also been proven to have good security. To develop a more secure and stable cryptography technique, we propose a new hybrid DNA-encoded ECC scheme that provides multilevel security. The DNA sequence is selected, and using a sorting algorithm, a unique set of nucleotide groups is assigned. These are directly converted to binary sequence and then encrypted using the ECC; thus giving double-fold security. Using several examples, this paper shows how this complete method can be realized on IoT devices. To verify the performance, we implement the complete system on the embedded platform of a Raspberry Pi 3 board, and utilize an active sensor data input to calculate the time and energy required for different data vector sizes. Connectivity and resilience analysis prove that DNA-mapped ECC can provide better security compared to ECC alone. The proposed method shows good potential for upcoming IoT technologies that require a smaller but effective security system.

• International Journal of Advanced Culture Technology
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• v.4 no.4
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• pp.81-86
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• 2016

In this paper, we propose a simple and effective vision-based tracking controller design for autonomous object tracking using multicopter. The multicopter based automatic tracking system usually unstable when the object moved so the tracking process can't define the object position location exactly that means when the object moves, the system can't track object suddenly along to the direction of objects movement. The system will always looking for the object from the first point or its home position. In this paper, PID control used to improve the stability of tracking system, so that the result object tracking became more stable than before, it can be seen from error of tracking. A computer vision and control strategy is applied to detect a diverse set of moving objects on Raspberry Pi based platform and Software defined PID controller design to control Yaw, Throttle, Pitch of the multicopter in real time. Finally based series of experiment results and concluded that the PID control make the tracking system become more stable in real time.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.37-40
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• 2021

This paper presents a design of a speech to sign language converter for deaf and hard of hearing people. The device is low-cost, low-power consumption, and it can be able to work entirely offline. The speech recognition is implemented using an open-source API, Pocketsphinx library. In this work, we proposed a context-oriented language model, which measures the similarity between the recognized speech and the predefined speech to decide the output. The output speech is selected from the recommended speech stored in the database, which is the best match to the recognized speech. The proposed context-oriented language model can improve the speech recognition rate by 21% for working entirely offline. A decision module based on determining the similarity between the two texts using Levenshtein distance decides the output sign language. The output sign language corresponding to the recognized speech is generated as a set of sequential images. The speech to sign language converter is deployed on a Raspberry Pi Zero board for low-cost deaf assistive devices.

• Journal of the Korea Society of Computer and Information
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• v.27 no.3
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• pp.99-104
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• 2022

In this paper, we first intend to develop and introduce a smart laundry drying system for verandas that controls the drying rack by actively responding to climate change. The developed smart laundry drying system receives laundry location information through the app, then detects climate change in real time through data from the Korea Meteorological Administration such as temperature and humidity according to the location information, and automatically controls the laundry on the drying rack in case of rain. It acquires weather information through the Arduino humidity sensor and the Korea Meteorological Administration Open-API, which is used to control the switch bot by the Raspberry Pi. The user interface uses Blynk, and the switch bot controls the laundry. Our proposed system can detect bad weather and automatically control the laundry at a remote location to prevent damage to the laundry.