• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.6
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• pp.400-407
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• 2016

The purpose of this study is to propose a useful method of solving the problem of thermal stability in surge protection devices (SPDs). First of all, the existence of the problem in the developed SPDs was confirmed by experiment. After analyzing the problem, a useful method of solving it is proposed and implemented. An experiment is performed to verify the performance of the implemented device. The results of this study are as follows; it is revealed that the problem of the thermal stability results from the varistor, one of the components in the SPD. A varistor with a built-in thermal fuse is applied to the SPD for the purpose of solving the problem. The experimental results confirmed that the thermal stability was improved by replacing the varistor. As a result of this study, the reliability of radar control units is enhanced and the probability of malfunction is reduced.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.6
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• pp.809-817
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• 2020

In the design and development process of Small-Sat power distribution and transmission module, the stability of dynamic resources was evaluated by a deep learning algorithm. The requirements for the stability evaluation consisted of the power distribution function of the power distribution module and demand module to the SAR radar in Small-Sat. To verify the performance of the switching power components constituting the power module PDM, the reliability was verified using a dynamic neural network. The adoption material of deep learning for reliability verification is the power distribution function of the payload to the power supplied from the small satellite main body. Modeling targets for verifying the performance of this function are output voltage (slew rate control), voltage error, and load power characteristics. First, to this end, the Coefficient Structure area was defined by modeling, and PCB modules were fabricated to compare stability and reliability. Second, Levenberg-Marquare based Two-Way NARX neural network Sigmoid Transfer was used as a deep learning algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.10B
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• pp.1777-1784
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• 2000

In this paper we suggested the chirp stitching algorithm and transmitter/receiver channel to a spaceborne high resolution SAR which enables wideband signal generation and processing with minimum hardware requirement. The transmitter channel generates two sub-band signals and then generate a wideband signal using chirp stitching algorithm and the receiver channel divides a wideband signal into two sub-band signals in order to overcome the high speed data handling capability of this spaceborne systems. We generated and processed a 100 MHz wideband signal evaluated the performance and verified the feasibility of the application of this chirp stitching algorithm and transmitter/receiver channel to spaceborne high resoultion SAR.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.3
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• pp.121-127
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• 2020

The small tracking radar targets the target in a real-time, fast-moving, fast-moving target against aircraft with a large RCS that is maneuvering at low speed and a small RCS aircraft maneuvering at high speed (fighters, drones, helicopters, etc.) It is a pulsed radar that detects and tracks. Performing a performance test on a tracking radar in a real environment is expensive, and it is difficult to quantitatively measure performance in a real environment. Describes the composition of the laboratory environment's comprehensive performance test facility and the main requirements and implementation of each configuration.Anechoic chambers to simulate the room environment, simulation target generator to simulate the signal of the room target, target It is composed of a horn antenna driving device to simulate the movement of a vehicle and a Flight Motion Simulatior (FMS) to simulate the flight environment of a tracking radar, and each design and implementation has been described.

• Journal of Internet Computing and Services
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• v.21 no.4
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• pp.117-126
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• 2020

A maritime object detection system is an intelligent assistance system to maritime autonomous surface ship(MASS). It detects automatically floating debris, which has a clash risk with objects in the surrounding water and used to be checked by a captain with a naked eye, at a similar level of accuracy to the human check method. It is used to detect objects around a ship. In the past, they were detected with information gathered from radars or sonar devices. With the development of artificial intelligence technology, intelligent CCTV installed in a ship are used to detect various types of floating debris on the course of sailing. If the speed of processing video data slows down due to the various requirements and complexity of MASS, however, there is no guarantee for safety as well as smooth service support. Trying to solve this issue, this study conducted research on the minimization of computation volumes for video data and the increased speed of data processing to detect maritime objects. Unlike previous studies that used the Hough transform algorithm to find the horizon and secure the areas of interest for the concerned objects, the present study proposed a new method of optimizing a binarization algorithm and finding areas whose locations were similar to actual objects in order to improve the speed. A maritime object detection system was materialized based on deep learning CNN to demonstrate the usefulness of the proposed method and assess the performance of the algorithm. The proposed algorithm performed at a speed that was 4 times faster than the old method while keeping the detection accuracy of the old method.