• Korean Journal of Fisheries and Aquatic Sciences
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• v.52 no.6
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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.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.5
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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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• v.42 no.3
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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.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.6
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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.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.2
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• pp.55-69
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• 2015

Bandwidth enhancement technique for a proximity coupled patch antenna is investigated. The bandwidth and radiation characteristics of a proximity coupled patch antenna with an integrated impedance matching network printed on substrates with various dielectric constants and thicknesses are compared to those of a proximity coupled patch antenna without an impedance matching network. The bandwidth of a proximity coupled patch antenna with an integrated impedance matching network is greatly increased than that of a proximity coupled patch antenna without an impedance matching network without the degradation of radiation characteristics.

• Journal of the Korean Society of Safety
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• v.37 no.1
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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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• v.16 no.5
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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.

• The KIPS Transactions:PartA
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• v.12A no.2 s.92
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• pp.87-94
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• 2005

Pattern matching is one of critical parts of Network Intrusion Prevention Systems (NIPS) and computationally intensive. To handle a large number of attack signature fattens increasing everyday, a network intrusion prevention system requires a multi pattern matching method that can meet the line speed of packet transfer. In this paper, we analyze Snort, a widely used open source network intrusion prevention/detection system, and its pattern matching characteristics. A multi pattern matching method for NIPS should efficiently handle a large number of patterns with a wide range of pattern lengths and case insensitive patterns matches. It should also be able to process multiple input characters in parallel. We propose a multi pattern matching hardware accelerator based on Shift-OR pattern matching algorithm. We evaluate the performance of the pattern matching accelerator under various assumptions. The performance evaluation shows that the pattern matching accelerator can be more than 80 times faster than the fastest software multi-pattern matching method used in Snort.

• Journal of the Semiconductor & Display Technology
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• v.22 no.2
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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.

• Journal of Cadastre & Land InformatiX
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• v.44 no.1
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• pp.127-138
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• 2014

It can be effective to extract and apply the updated information from the newly updated map data for updating road data of topographic map. In this study, update target data and update reference data are overlaid and the update objects are explored using network matching technique. And the network objects are classified into five matching and update cases and the update processes for each case are applied to the test data. For this study, road centerline data of digital topographic map is used as an update target data and road data of Korean Address Information System is used as an update reference data. The buffer-based network matching method is applied to the two data and the matching and update cases are classified after calculating the overlaid ratio of length. The newly updated road centerline data of digital topographic map is generated from the application of update process for each case. As a result, the update information can be extracted from the different map dataset and applied to the road network data updating.