• Journal of Advanced Navigation Technology
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• v.24 no.2
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• pp.67-72
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• 2020

Satellite communication is not used by many people like mobile communication, but it is a necessary technology for public service and communication services, such as providing the Internet in military, disaster, remote education and medical services, island areas, and infrastructure vulnerable areas. However, on ships and aircraft, mobile communications requiring base stations are either unavailable or restricted in their use. In this paper, we used a Raspberry Pi board as the terminal device to communicate network through satellite modem and PPP protocol, and implemented two-way data link using the text message of the modem to connect to the Thuraya geo-stationary orbit network. In addition, I/O devices were connected to the controller of the terminal equipment to design and implement an IoT device system for ships that can remotely access the system under control and control I/Os and transmit measured data through various sensors.

• Journal of the Korea Society of Computer and Information
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• v.29 no.7
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• pp.21-32
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• 2024

Since leather shoes cannot be washed with water, there is a need for a cleaning method that can remove extraneous substance from the inside and outside of shoes and senitize the inside of shoes without using water. For this purpose, this paper develops a shoes cleaning machine control system that automatically controls the entire process of shoes cleaning in a shoes cleaning machine that quickly cleans the inside and outside of shoes using compressed air, sterilization solution. The developed system uses Rasberry Pi, a general purpose single board computer(SBC), to control various actuators of the shoes cleaning machine. The shoes cleaning machine operated by the developed system shows a sterilization efficiency of more than 99% and an odor removal efficiency of more than 86% in a cleaning time of less than 1 minute.

• Bulletin of the Korean Space Science Society
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• 2004.10b
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• pp.60-60
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• 2004

• Journal of Broadcast Engineering
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• v.25 no.2
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• pp.200-207
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• 2020

Recently, deep neural networks such as CNN are showing excellent performance in various fields such as image classification, object recognition, visual quality enhancement, etc. However, as the model size and computational complexity of deep learning models for most applications increases, it is hard to apply neural networks to IoT and mobile environments. Therefore, neural network compression algorithms for reducing the model size while keeping the performance have been being studied. In this paper, we apply few compression methods to CNN models and evaluate their performances in the embedded environment. For evaluate the performance, the classification performance and inference time of the original CNN models and the compressed CNN models on the image inputted by the camera are evaluated in the embedded board equipped with QCS605, which is a customized AI chip. In this paper, a few CNN models of MobileNetV2, ResNet50, and VGG-16 are compressed by applying the methods of pruning and matrix decomposition. The experimental results show that the compressed models give not only the model size reduction of 1.3~11.2 times at a classification performance loss of less than 2% compared to the original model, but also the inference time reduction of 1.2~2.21 times, and the memory reduction of 1.2~3.8 times in the embedded board.

• Journal of the Korean Applied Science and Technology
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• v.37 no.4
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• pp.755-761
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• 2020

The purpose of this study was to investigate the effects of balance board and whole-body vibration stimulator application on body muscle activities during static squat motion. Twenty adult males(age, 21.90±0.36 years; height, 174.30±1.09 cm; body mass, 66.50±1.00 kg; and BMI, 21.90±0.31 kg/㎡) were participated in this study as subjects. Three types' static squat motions were performed(basic static squat motion, BSSM; static squat motion with balance board, SSBB; static squat motion with whole-body vibration stimulator, SSVS). We measured the right side's body muscle activities of the rectus abdominis(RA), internal oblique(IO), external oblique(EO), rectus femoris(RF), vastus lateralis(VL), and vastus medialis(VM). The research findings were as follows. There was a significant higher RA, IO, and EO muscle activity of SSBB and SSVS(p=.001, p=.004, p=.000). And RF, VL, and VM muscle activities were greatest during SSVS(p=.000). These findings are expected to serve as references for static squat motion applications in training programs for body muscle strengthening.

• Journal of Digital Contents Society
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• v.18 no.6
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• pp.1165-1173
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• 2017

Deep learning method, which is one of the most famous machine learning algorithms, has proven its applicability in various applications and is widely used in digital signal processing. However, it is difficult to apply deep learning technology to IoT devices with limited CPU performance and memory capacity, because a large number of training samples requires a lot of memory and computation time. In particular, if the Arduino with a very small memory capacity of 2K to 8K, is used, there are many limitations in implementing the algorithm. In this paper, we propose a method to optimize the ELM algorithm, which is proved to be accurate and efficient in various fields, on Arduino board. Experiments have shown that multi-class learning is possible up to 15-dimensional data on Arduino UNO with memory capacity of 2KB and possible up to 42-dimensional data on Arduino MEGA with memory capacity of 8KB. To evaluate the experiment, we proved the effectiveness of the proposed algorithm using the data sets generated using gaussian mixture modeling and the public UCI data sets.

• The Journal of the Convergence on Culture Technology
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• v.6 no.3
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• pp.427-430
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• 2020

The multi-channel analog signal to digital signal conversion is increasing in the field of IoT and medical measurement equipments. It has chip area and power consumption constraints to use a few single or 2_channel ADC for multi_channel application. This paper described to design and implement a proposed comb filter for multi-channel, 24bit ADC. The function of proposed comb filter is verified by matlab simulation and the FPGA test board. It was fabricated using SK Hynix 0.35㎛ CMOS standard process. The performance and chip size is compared with the existing design method that uses integrator/differentiator and FIR construction. The proposed comb filter is expected to use the IoT product and medical measurement equipments that require multi-channel, low power consumption and small hardware size.

• Journal of Digital Convergence
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• v.17 no.12
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• pp.503-508
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• 2019

In recent years, among the IoT products that are used in various ways in everyday life, motorized products are increasing. This study aims to develop a microcontroller unit that can easily control multiple motors and develop an application that makes use of this microcontroller unit. The basis of the hardware developed by the research was the Arduino board, and to it, the Bluetooth module, Zigbee module, and a motor driver were connected. To control the device, an application was designed. The final microcontroller unit and its application may be applied to electric curtains, electric blinds, robots, and other various IoT products. Further research will lead to hardware development that can control various types of motors other than stepping motors.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.11
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• pp.1524-1530
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• 2018

In this paper, we designed a product that can prevent environmental pollution, waste of resources, and leakage of foreign currency by commercializing a green IT solution by merging a used smart phone with the IoT object communication technology for the first time in the world. For the experiment of the designed system, various performance and communication condition was experimented by installing it in the actual crop cultivation facility. As a result, when a problem occurs, the alarm sound and video notification are generated by the user's smart phone, and remote control of various installed devices and data analysis in real time are possible. In this study, it is thought that the terminal management board developed for the utilization of the used smart phone can be applied to various fields such as agriculture and environment.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.6
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• pp.115-121
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• 2020

With the spread of IoT technology, various IoT applications using facial recognition are emerging. This paper describes the design and implementation of a remote control system using deep learning-based face recognition and hand gesture recognition. In general, an application system using face recognition consists of a part that takes an image in real time from a camera, a part that recognizes a face from the image, and a part that utilizes the recognized result. Raspberry PI, a single board computer that can be mounted anywhere, has been used to shoot images in real time, and face recognition software has been developed using tensorflow's FaceNet model for server computers and hand gesture recognition software using OpenCV. We classified users into three groups: Known users, Danger users, and Unknown users, and designed and implemented an application that opens automatic door locks only for Known users who have passed both face recognition and hand gestures.