• 한국수산과학회지
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• 제52권6호
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• pp.702-708
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• 2019

The range resolution of echo sounders can be improved by enhancing the transducer bandwidth. We designed a bandpass matching network for expanding the bandwidth of a transducer by scaling in both impedance and frequency after transforming a lowpass network into a bandpass configuration for a third-order Bessel filter. We measured the effect of the Bessel matching network for a 50 kHz sandwich type transducer on the transmitting voltage response (TVR), receiving sensitivity (SRT) and figure of merit (FOM), using a chirp echo sounder system. Both the simulation and experimental results indicated that the transducer with a bandpass matching network was capable of producing a symmetrical acoustic output over a wider bandwidth (8.25 kHz) than was the transducer without a matching network (3.75 kHz). By implementing the Bessel matching network, we achieved a 120% bandwidth enhancement.

• 대한임베디드공학회논문지
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• 제16권5호
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• pp.179-186
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• 2021

For stereo matching based on deep learning, the design of network structure is crucial to the calculation of matching cost, and the time-consuming problem of convolutional neural network in image processing also needs to be solved urgently. In this paper, a method of stereo matching using sparse loss volume in parallax dimension is proposed. A sparse 3D loss volume is constructed by using a wide step length translation of the right view feature map, which reduces the video memory and computing resources required by the 3D convolution module by several times. In order to improve the accuracy of the algorithm, the nonlinear up-sampling of the matching loss in the parallax dimension is carried out by using the method of multi-category output, and the training model is combined with two kinds of loss functions. Compared with the benchmark algorithm, the proposed algorithm not only improves the accuracy but also shortens the running time by about 30%.

• ETRI Journal
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• 제42권3호
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• pp.411-419
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• 2020

In real-world intelligent transportation systems, accuracy in vehicle license plate detection and recognition is considered quite critical. Many algorithms have been proposed for still images, but their accuracy on actual videos is not satisfactory. This stems from several problematic conditions in videos, such as vehicle motion blur, variety in viewpoints, outliers, and the lack of publicly available video datasets. In this study, we focus on these challenges and propose a license plate detection and recognition scheme for videos based on a temporal matching prior network. Specifically, to improve the robustness of detection and recognition accuracy in the presence of motion blur and outliers, forward and bidirectional matching priors between consecutive frames are properly combined with layer structures specifically designed for plate detection. We also built our own video dataset for the deep training of the proposed network. During network training, we perform data augmentation based on image rotation to increase robustness regarding the various viewpoints in videos.

• 전자공학회논문지B
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• 제30B권6호
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• pp.59-66
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• 1993

Stereo vision is useful in obtaining three dimensional depth information from two images taken from different view points. Neural network modeling for stereo matching, the key step in stereo vision, is defined by an energy function satisfying with three constraints proposed by Marr and Poggio. Stereo matching is then carried out through the network to find minimum energy corresponding to the optimized solution of the problem. An algorithm for stereo matching using an analog neural network is presented here. The network can reduce errors in initial state an early iteration steps by adoption of continuous sigmoid function in stead of binary state. The experimental results show good matching performance for sparse random dot stereogram and real image.

• 전자공학회논문지
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• 제52권2호
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• pp.55-69
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• 2015

근접 결합 급전 방식을 이용한 마이크로스트립 패치 안테나의 급전 선로에 임피던스 정합기를 집적하여 대역폭을 확장하는 방법에 대하여 연구하였다. 여러 가지 유전상수와 두께를 가지는 기판을 사용한 근접 결합 급전을 이용한 패치 안테나에 임피던스 정합기를 집적한 경우와 임피던스 정합기를 사용하지 않은 경우의 대역폭과 방사특성을 비교하였다. 임피던스 정합기를 사용한 경우가 사용하지 않은 경우에 비하여 방사특성의 저하 없이 대역폭이 크게 증가함을 볼 수 있었다.

• 한국안전학회지
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• 제37권1호
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• pp.41-48
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• 2022

Bridge engineering documents have essential contents that must be referenced continuously throughout a structure's entire life cycle, but research related to the quality of the contents is still lacking. XML schema matching is an excellent technique to improve the quality of stored data; however, it takes excessive computing time when applied to documents with many contents and a deep hierarchical structure, such as bridge engineering documents. Moreover, it requires a manual parametric study for matching elements' weight factors, maintaining a high matching accuracy. This study proposes an efficient weight-factor determination method based on an artificial neural network (ANN) model using the simplified XML schema-matching method proposed in a previous research to reduce the computing time. The ANN model was generated and verified using 580 data of document properties, weight factors, and matching accuracy. The proposed ANN-based schema-matching method showed superiority in terms of accuracy and efficiency compared with the previous study on XML schema matching for bridge engineering documents.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권5호
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• pp.1597-1610
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• 2022

At present, the main method of high-speed train chassis detection is using computer vision technology to extract keypoints from two related chassis images firstly, then matching these keypoints to find the pixel-level correspondence between these two images, finally, detection and other steps are performed. The quality and accuracy of image matching are very important for subsequent defect detection. Current traditional matching methods are difficult to meet the actual requirements for the generalization of complex scenes such as weather, illumination, and seasonal changes. Therefore, it is of great significance to study the high-speed train image matching method based on deep learning. This paper establishes a high-speed train chassis image matching dataset, including random perspective changes and optical distortion, to simulate the changes in the actual working environment of the high-speed rail system as much as possible. This work designs a convolutional neural network to intensively extract keypoints, so as to alleviate the problems of current methods. With multi-level features, on the one hand, the network restores low-level details, thereby improving the localization accuracy of keypoints, on the other hand, the network can generate robust keypoint descriptors. Detailed experiments show the huge improvement of the proposed network over traditional methods.

• 정보처리학회논문지A
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• 제12A권2호
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• pp.87-94
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• 2005

패턴 매칭(Pattern Matching)은 네트워크 침입방지 시스템에서 가장 중요한 부분의 하나며 많은 연산을 필요로 한다. 날로 증가되는 많은 수의 공격 패턴을 다루기 위해, 네트워크 침입방지 시스템에서는 회선 속도로 들어오는 패킷을 처리 할 수 있는 다중 패턴 매칭 방법이 필수적이다. 본 논문에서는 현재 많이 사용되고있는 네트워크 침입방지 및 탐지 시스템인 Snort와 이것의 패턴 매칭 특성을 분석한다. 침입방지 시스템을 위한 패턴 매칭 방법은 다양한 길이를 갖는 많은 수의 패턴과 대소문자 구분 없는 패턴 매칭을 효과적으로 다룰 수 있어야 한다. 또한 여러 개의 입력 문자들을 동시에 처리 할 수 있어야 한다. 본 논문에서 Shift-OR 패턴 매칭 알고리즘에 기반을 둔 다중 패턴 매칭 하드웨어 가속기를 제시하고 여러 가지 가정 하에서 성능 측정을 하였다. 성능 측정에 따르면 제시된 하드웨어 가속기는 현재 Snort에서 사용되는 가장 빠른 소프트웨어 다중 패턴 매칭 보다 80배 이상 빠를 수 있다.

• 반도체디스플레이기술학회지
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• 제22권2호
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• pp.64-71
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• 2023

If an impedance mismatch occurs between the source and load in a Radio Frequency transmission system, reflected power is generated. This results in incomplete power transmission and the generation of Reflected Power, which returns to the Radio Frequency generator. To minimize this Reflected Power, Impedance matching is performed. Fast and efficient Impedance matching, along with converging reflected power towards zero, is advantageous for achieving desired plasma characteristics in semiconductor processes. This paper explores Impedance matching by adjusting the Vacuum Variable Capacitor of an L-type Matching Module based on the trends observed in the voltage of the Phase Sensor and Electromotive Force voltage. After assessing the impedance matching characteristics, the findings are described.

• 지적과 국토정보
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• 제44권1호
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• pp.127-138
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• 2014

수치지도의 도로 데이터를 효율적으로 갱신하기 위해서는 최신 갱신된 지도자료로부터 갱신된 정보만을 추출하여 반영하는 것이 효율적이다. 본 연구에서는 갱신 대상이 되는 네트워크 데이터와 갱신 참조 데이터를 중첩한 후 네트워크 매칭기법을 적용하여 갱신 대상 객체를 탐색한 후, 해당 대상 객체의 갱신 케이스를 분류하고 케이스에 따라 갱신하는 일련의 프로세스를 개발, 적용하고자 하였다. 이를 위해 수치지형도 도로중심선 데이터를 갱신 대상으로 하고 도로중심선 전자지도의 도로구간 데이터를 갱신 참조 데이터로 하여 버퍼 기반 네트워크 매칭 기법을 적용한 후, 각 객체의 중첩길이비를 계산하여 매칭 및 갱신 케이스에 따라 객체그룹을 분류하였으며 분류된 객체그룹에 갱신 프로세스를 적용하여 갱신된 수치지형도 도로중심선 데이터를 도출하였다. 적용 결과, 이종의 데이터로부터 갱신정보를 추출하여 도로 네트워크 데이터의 갱신과정에 이를 활용할 수 있을 것으로 판단된다.