• Journal of the Korea Society of Computer and Information
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• v.20 no.7
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• pp.57-64
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• 2015

In this paper, we propose a design of microwave motion detector using X-band doppler radar sensor to minimize the power consumption. To minimize the power consumption and implement battery operated system, pulse input with 2 KHz, 4% duty cycle is exerted on the doppler radar sensor. In order to simplify the process of working with ATmega2560 microcontroller unit, Arduino compatible board is designed and implemented. Arduino is open source hardware and many library software is published as open source tools. Smartphone app is also proposed and designed as a real-time user interface of the motion detector. The SQLite database on the Android mobile operating system is used for recording raw data of motion detection for post-processing job, such as fast Fourier transform (FFT). Bluetooth interface module is implemented on the motion detection board as a wireless communication interface to the smartphone. The speed of human movement is identified by post-processing FFT.

• Journal of Aerospace System Engineering
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• v.3 no.2
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• pp.31-38
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• 2009

COMS On-board FDIR(Failure Detection, Isolation and Recovery) functions are implemented on the on-board software to satisfy the autonomy and failure tolerance requirements. This paper presents concepts of COMS Failure Management with hierarchical layers and addresses the characteristics of the FDIR layer from low level to high level. It is aimed at giving the reader the understanding how the COMS FDIR was designed and how works. It first recalls what are the system level applicable requirements, which are based on the COMS mission requirements. Then it describes the philosophy and structure of the FDIR and subsequently breaks it down into the several FDIR layers. It could be used as an important and useful reference of the information to design and develop an automatic FDIR mechanism in the future.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.3
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• pp.490-493
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• 2021

This paper proposes an embedded system that detects mask and face recognition based on a microprocessor instead of Nvidia Jetson Board what is popular development kit. We use a class of efficient models called Mobilenets for mobile and embedded vision applications. MobileNets are based on a streamlined architechture that uses depthwise separable convolutions to build light weight deep neural networks. The device used a Maix development board with CNN hardware acceleration function, and the training model used MobileNet_V2 based SSD(Single Shot Multibox Detector) optimized for mobile devices. To make training model, 7553 face data from Kaggle are used. As a result of test dataset, the AUC (Area Under The Curve) value is as high as 0.98.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.5
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• pp.225-231
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• 2023

With the recent surge in the autonomous driving market, the significance of lane detection technology has escalated. Lane detection plays a pivotal role in autonomous driving systems by identifying lanes to ensure safe vehicle operation. Traditional lane detection models rely on engineers manually extracting lane features from predefined environments. However, real-world road conditions present diverse challenges, hampering the engineers' ability to extract adaptable lane features, resulting in limited performance. Consequently, recent research has focused on developing deep learning based lane detection models to extract lane features directly from data. In this paper, we classify lane detection models into four categories: cluster-based, curve-based, information propagation-based, and anchor-based methods. We conduct an extensive analysis of the strengths and weaknesses of each approach, evaluate the model's performance on an embedded board, and assess their practicality and effectiveness. Based on our findings, we propose future research directions and potential enhancements.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.6
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• pp.9-16
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• 2017

An Algorithm for Lane Detection and Lane Departure Warning for a Vehicle Driving on Roads is proposed in This Paper. Using Images Obtained from On-board Cameras for Lane Detection has Some Difficulties, e.g. the Increase of Fault Detection Ratio Due to Symbols on Roads, Missing Yellow Lanes in the Tunnel due to a Similar Color Lighting, Missing Some Lanes in Rainy Days Due to Low Intensity of Illumination, and so on. The Proposed Algorithm has been developed Focusing on Solving These Problems. It also has an Additional Function to Determine How much the Vehicle is leaning to any Side between The Lanes and, If Necessary, to Give a Warning to a Driver. Experiments Using an Image Database Built by Collecting with Vehicle On-board Blackbox in Six Different Situations have been conducted for Validation of the Proposed Algorithm. The Experimental Results show a High Performance of the Proposed Algorithm with Overall 97% Detection Success Ratio.

• Journal of the Korea Society for Simulation
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• v.17 no.2
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• pp.83-90
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• 2008

We introduce the coordination among the intrusion detection agents by BBA(BlackBoard Architecture) that belongs to the field of distributed artificial intelligence. The system which uses BBA for the coordination can be easily expanded by adding new agents and increasing the number of BB(BlackBoard) levels. Several simulation tests performed on the targer network will illustrate our techniques. And this paper applies PBN(Policy-Based Network) to reduce the false positives that is one of the main problems of IDS. The performance obtained from the coordination of intrusion detection agent with PBN is compared against the corresponding non PBN type intrusion detection agent. The application of the research results lies in the experimentation of the various security policies according to the network types in selecting the best security policy that is most suitable for a given network.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.10
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• pp.100-106
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• 2009

In this study, methods of overheat detection at the coupling or wire in electrical facility are investigated, operating characteristic about the optic temperature sensor for continuous on-line temperature monitoring in diagnostics system of electrical facility is analyzed. Heating sources in the experiment for operating characteristics of optic temperature sensor use black body and hot plate, output voltage of optic temperature sensor in accordance with temperature variation is analyzed. Overheat generation due to poor contact at the circuit breaker in panel board detects using a thermocouple, infrared thermal camera and optic temperature sensor, and experiment results are analyzed. The effect of optic temperature sensor is the same that of other methods. These results expect to use basic research material for adjusting field of electrical diagnostics system using RFID type optic temperature sensor in the near future.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.866-869
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• 2015

In this paper, we propose a device for detect front object using low-price the CW Doppler sensor to prevent safety accident such as a bicycle, an electric wheelchair users. For this propose, we develop a signal process board and the object motion detect algorithm using to analyzing output signal of the CW Doppler sensor. Output signal from CW Doppler sensor is analog I and analog Q. The CW Doppler sensor shows phase I and phase Q of object differently when the object approach, stop, drop by sensor. We develop an algorithm that can detect object by discrimination information of phase using the CW Doppler sensor. The verification use firmware of applied hardware and algorithm. Then, the motion information can be confirming output depending on motion object by experiment normally. As a result, we check that the sensing information output by following motion of object and confirm an algorithm and motion of signal processing board.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2002.09a
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• pp.468-470
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• 2002

We have designed X-ray detection system and multi-channel data acquisition system for Spiral CT application. X-ray detection system consists of scintillator and photodiode. Scintillator converts X-ray into visible light. Photodiode converts visible light into electrical signal. The multi-channel data acquisition system consists of analog, digital, master and backplane board. Analog board detects electrical signal and amplifies signal by 140dB. Digital board consists of MUX(Multiplex) which routes multi-channel analog signal to preamplifier, and ADC(Analog to Digital Converter) which converts analog signal into digital signal. Master board supplies the synchronized clock and transmits the digital data to image reconstructor. Backplane provides electrical power, analog output and clock signal. The system converts the projected X-ray signal over the detector array with large gain, samples the data in each channel sequentially, and the sampled data are transmitted to host computer in a given time frame. To meet the timing limitation, this system is very flexible since it is implemented by FPGA(Field Programmable Gate Array). This system must have a high-speed operation with low noise and high SNR(signal to noise ratio), wide dynamic range to get a high resolution image.

• Journal of information and communication convergence engineering
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• v.21 no.1
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• pp.24-31
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• 2023

The solid-state drive (SSD) possesses higher input and output speeds, more resistance to physical shock, and lower latency compared with regular hard disks; hence, it is an increasingly popular storage device. However, tiny components on an internal printed circuit board (PCB) hinder the manual detection of malfunctioning components. With the rapid development of artificial intelligence technologies, automatic detection of components through convolutional neural networks (CNN) can provide a sound solution for this area. This study proposes applying the YOLOv5 model to SSD PCB component detection, which is the first step in detecting defective components. It achieves pioneering state-of-the-art results on the SSD PCB dataset. Contrast experiments are conducted with YOLOX, a neck-and-neck model with YOLOv5; evidently, YOLOv5 obtains an mAP@0.5 of 99.0%, essentially outperforming YOLOX. These experiments prove that the YOLOv5 model is effective for tiny object detection and can be used to study the second step of detecting defective components in the future.