• The Journal of the Convergence on Culture Technology
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• v.7 no.1
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• pp.668-673
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• 2021

As the fourth industrial revolution begins in earnest, related technologies are becoming a hot topic. Hardware development is accelerating to make the most of technologies such as high-speed wireless communication, and related companies are growing rapidly. Artificial intelligence often uses desktops in general for related research, but it is mainly used for the learning process of deep learning and often transplants the generated models into devices to be used by including them in programs, etc. However, it is difficult to produce results for devices that do not have sufficient power or performance due to excessive learning or lack of power due to the use of models built to the desktop's performance. In this paper, we analyze efficiency using boards with several Neural Process Units on sale before developing the performance of deep learning to match embedded boards, and deep learning accelerators that can increase deep learning performance with USB, and present a simple development direction possible using embedded boards.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2008.11a
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• pp.264-265
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• 2008

While the reliability of bulk insulation has become important particularly in multilayer boards and embedded boards, electronics are to be used under various environments such as at high temperature and in high humidity. We observed internal space charge behavior for two types of epoxy composites under dc electric fields to investigate the influence of water at high temperature. In the case of glass/epoxy specimen, homocharge is observed at water-treated specimen, and spatial oscillations become clearer in the water-treated specimens. Electric field in the vicinity of the electrodes shows the injection of homocharge. In aramid/epoxy specimens, heterocharge is observed at water-treated specimens, i.e. negative charge accumulates near the anode, while positive charge accumulates near the cathode. Electric field is enhanced just before each electrode. In order to further examine the mechanism of space charge formation, we have developed a new system that allows in situ space charge observation during ion migration tests at high temperature and high humidity. Using this in situ system.

• Journal of Software Assessment and Valuation
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• v.17 no.2
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• pp.161-172
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• 2021

Neuromorphic architecture is drawing attention as a next-generation computing that supports artificial intelligence technology with low energy. However, FPGA embedded boards based on Neuromorphic architecturehave limited resources due to size and power. In this paper, we compared and evaluated the image reduction method using the interpolation method that rescales the size without considering the feature points and the DCT (Discrete Cosine Transform) method that preserves the feature points as much as possible based on energy. The scaled images were compared and analyzed for accuracy through CNN (Convolutional Neural Networks) in a PC environment and in the Nengo framework of an FPGA embedded board.. As a result of the experiment, DCT based classification showed about 1.9% higher performance than that of interpolation representation in both CNN and FPGA nengo environments. Based on the experimental results, when the DCT method is used in a limited resource environment such as an embedded board, a lot of resources are allocated to the expression of neurons used for classification, and the recognition rate is expected to increase.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2007.06a
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• pp.232-233
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• 2007

We manufactured Liquid Crystal Polymer (LCP) and (1-x)CaTiO3-xLaAlO3 (CT-LA) ceramic composites and investigated dielectric properties to use as embedded capacitor in printed circuit boards and replace LTCC substrate. The dielectric properties of these composites are varied with volume fraction of CT-LA and ratios of CT/LA. Dielectric constants are in the range of 3~15. In addition, we could get low TCC and High Q value that could not achieve in other ceramic-polymer composites. Especially, in composite with x=0.01 and 30 vol% CT-LA, the dieletric constant and Q-value are 10 and 200, respectively. And more TCC is $-28{\sim}300ppm/^{\circ}C$ in the temperature range of $-55{\sim}125^{\circ}C$. We think that this composites can be used high-Q substrate material like LTCC and embedded temperature compensation capacitor in printed circuit boards.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.651-653
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• 2015

Common Embedded boards like the Arduino, Raspberry Pi, BeagleBone Black, leverages smart home systems, machine tools and various products in our day to day life. Managing and dealing frequent large scale incidents involving recent reagents and hazardous materials can be dangerous and difficult to detect in advance like in an event of an accidents or fires. In this paper I have done research by utilizing an Embedded (BeagleBone Black) boards sensors and RFID management system to detect a hazardous situation like fire in real time and avoiding it by sending out an alert message to the admin user to minimizing the risk. This system provides immediate information to the administrator of any hazardous situation and prevents any accidents from occurring.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2008.06a
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• pp.60-60
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• 2008

We manufactured Liquid Crystal Polymer (LCP) and (1-x)$BaTiO_3-xSrTiO_3$(BST) ceramic composites and investigated dielectric properties to use as embedded capacitor in printed circuit boards and replace LTCC substrates. The dielectric properties of these composites are varied with volume fraction of BST and ratios of BT/ST. Dielectric constants are in the range of 3~28. In addition, we could get low TCC and High Q value that could not achieve in other ceramic-polymer composites. Especially, in composite with x=0.4 and 50vol% BST, the dieletric constant and Q-value are 27 and 300, respectively. And more TCC is -116~145ppm/$^{\circ}C$ in the temperature range of -55~$125^{\circ}C$. We think that this composites can be used high-Q substrate material like LTCC and embedded temperature compensation capacitor in printed circuit boards.

• Journal of Advanced Marine Engineering and Technology
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• v.28 no.5
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• pp.811-817
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• 2004

Recently embedded systems are widely used in various industrial fields as supervisory controller because they have many merits. One of merits seems to be that operating environment of embedded system is the same as development environment using PC. That makes developing and manufacturing period shorten and also proper time to market Most of all machinery have sequential control system for their maneuvering which is composed of relays. contacts. timers. etc. In this paper. software sequential control system is proposed to be able to replace hardware sequential control system by using embedded system A lot of merits by the software sequential control system can be expected in the respect of economic reproduction, intelligent technologies and utilities, And porting of LINUX operating system to embedded system is carried out and device drivers and interface boards for LINUX 05 are designed for controlling air compressor by software Internet remote control and monitoring system of air compressor is implemented with Java script and CGI for these purposes. The experiment for operating air compressor system is taken through internet networks. The results show that developed system can be used for real plant.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.2
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• pp.171-177
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• 2004

Many instrumentations have been used to acquire the performance data of military systems fer many years. But they could not satisfy environmental specifications(vibration, shock, temperature) and processing speed to apply for the performance test of military systems because of having developed as common vehicles/fixed installation equipments. Thus a new rugged embedded measurement system is required to process large data in high processing speed(Maximum sample rate:1.25Mhz/ch) with rugged environmental specifications. We have developed embedded measurement systems by using PXI(PCI extension for Instrumentation)bus interface composed of a stand alone controller and versatile data acquisition boards(analog, digital, vision, temperature and small signal conditioner) on PC-based environment to solve these problems. Operation programs have been developed using Lab_View and the performances have been validated experimentally.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.3
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• pp.141-147
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• 2005

In the field of electrical industry, embedded computing environment including hardware and software is getting more important as the industry shifts to the knowledge-based one. Java could play a great role as bridging technology in such a transition because it provides a lot of benefits like dynamic application download, compatibility of cross platform, and its own security solution. However, the Java technology has a limitation of real-time problem when it is applied to the embedded computing system of the electrical industry. To solve the problem, a novel java-native combination model has been proposed and designed to a firmware level. This scheme has been employed in four kinds of control boards. The result shows that the proposed model has great potential to implement the real-time processing in control of the devices.

• The Journal of Korea Robotics Society
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• v.17 no.2
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• pp.133-141
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• 2022

This paper introduces model compression algorithms which make a deep neural network smaller and faster for embedded systems. The model compression algorithms can be largely categorized into pruning, quantization and knowledge distillation. In this study, gradual pruning, quantization aware training, and knowledge distillation which learns the activation boundary in the hidden layer of the teacher neural network are integrated. As a large deep neural network is compressed and accelerated by these algorithms, embedded computing boards can run the deep neural network much faster with less memory usage while preserving the reasonable accuracy. To evaluate the performance of the compressed neural networks, we evaluate the size, latency and accuracy of the deep neural network, DenseNet201, for image classification with CIFAR-10 dataset on the NVIDIA Jetson Xavier.