• The Transactions of the Korea Information Processing Society
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• v.6 no.4
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• pp.1120-1128
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• 1999

The model-based object recognition is defined as a graph matching process between model images and an input image. In this paper, a graph matching problem is modeled as a n optimization problems and a genetic algorithm is proposed to solve the problems. For this work, fitness function, data structured and genetic operators are developed The simulation results are shown that the proposed genetic algorithm can match feature points between model image and input image for recognition of partially occluded two-dimensional objects. The performance fo the proposed technique is compare with that of a neural network technique.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.8 no.4
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• pp.67-73
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• 2008

In the Markov network which models disparity map with the Markov Random Field(MRF), the belief propagation algorithm is operated by message passing between nodes corresponding to each pixels. Belief propagation algorithm required much iteration for accurate result. In this paper, we propose the stereo matching algorithm using belief propagation in multi-resolution domain. Multi-resolution method based on wavelet or lifting can reduce the search area, therefore this algorithm can generate disparity map with fast speed.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.833-836
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• 1999

In this paper, a wideband MMIC LNA was designed using low Q matching network. Gains of 9.8~12.2 ㏈, and noise figures of 1.7~2.1 ㏈ were obtained from the fabricated wideband MMIC LNA in the frequency ranges of 1.5~2.5㎓. And maximum output power of 10.83 ㏈m were obtained at the center frequency of 2 ㎓. The chip size of the fabricated wideband MMIC low noise amplifier is 1.4 mm$\times$1.4 mm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.13 no.6
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• pp.501-509
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• 1988

A new type toroidal core transformer for impedance matching for 30MHz~90MHz has been designed and fabricated. It is shown to be reasonably good characteristics that insertind loss was 1.2[dB], VSWR was 1:2.0 and 100[%] bandwidthe was achieved. It is expected that resulting transformer can be used to replace existing frequency selector type matching network because it is much more compact and relatively easy to contruct.

• ETRI Journal
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• v.30 no.4
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• pp.597-599
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• 2008

This letter presents the design of a small and low-profile RFID tag antenna in the UHF band that can be mounted on metallic objects. The designed tag antenna, which uses a ceramic material as a substrate, consists of a radiating patch and a microstrip line with two shorting pins for a proximity-coupled feeding structure. Using this structure, impedance matching can be simply obtained between the antenna and tag chip without a matching network. The fractional impedance bandwidth for $S_{11}$ <3 dB and radiation efficiency are about 1.4% and 56% at 911 MHz, respectively. The read range is approximately from 5 m to 6 m when the tag antenna is mounted on a metallic surface.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.311-326
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• 2024

The rapid development of neural network technology promotes the neural network model driven by big data to overcome the texture effect of complex objects. Due to the limitations in complex scenes, it is necessary to establish custom template matching and apply it to the research of many fields of computational vision technology. The dependence on high-quality small label sample database data is not very strong, and the machine learning system of deep feature connection to complete the task of texture effect inference and speculation is relatively poor. The style transfer algorithm based on neural network collects and preserves the data of patterns, extracts and modernizes their features. Through the algorithm model, it is easier to present the texture color of patterns and display them digitally. In this paper, according to the texture effect reasoning of custom template matching, the 3D visualization of the target is transformed into a 3D model. The high similarity between the scene to be inferred and the user-defined template is calculated by the user-defined template of the multi-dimensional external feature label. The convolutional neural network is adopted to optimize the external area of the object to improve the sampling quality and computational performance of the sample pyramid structure. The results indicate that the proposed algorithm can accurately capture the significant target, achieve more ablation noise, and improve the visualization results. The proposed deep convolutional neural network optimization algorithm has good rapidity, data accuracy and robustness. The proposed algorithm can adapt to the calculation of more task scenes, display the redundant vision-related information of image conversion, enhance the powerful computing power, and further improve the computational efficiency and accuracy of convolutional networks, which has a high research significance for the study of image information conversion.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.1
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• pp.100-106
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• 2017

In this paper, we propose a parallel optimization method of Aho-Corasick (AC) algorithm and Parallel Failureless Aho-Corasick (PFAC) algorithm using Open Computing Language (OpenCL) on Field Programmable Gate Array (FPGA). The low throughput of string matching engine causes the performance degradation of network process. Recently, many researchers have studied the string matching engine using parallel computing. FPGA's vendors offer a parallel computing platform using OpenCL. In this paper, we apply the AC and PFAC algorithm on DE1-SoC board with Cyclone V FPGA, where the optimization that considers FPGA architecture is performed. Experiments are performed considering global id, local id, local memory, and loop unrolling optimizations using PFAC algorithm. The performance improvement using loop unrolling is 129 times greater than AC algorithm that not adopt loop unrolling. The performance improvements using loop unrolling are 1.1, 0.2, and 1.5 times greater than those using global id, local id, and local memory optimizations mentioned above.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.24 no.11
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• pp.1098-1103
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• 2013

A low-noise amplifier(LNA) using series RLC matching network and resistive feedback at 8 GHz is presented. Inductive degeneration is used for the input matching with which the proposed LNA shows quite a wide bandwidth in terms of $S_{21}$. An equivalent circuit model is deduced for input matching by conversion from parallel circuit to series resonant circuit. By exploiting the resistive feedback and series RLC input matching, fully integrated LNA achieves maximum $S_{21}$ of 8.5 dB(peak to -3 dB bandwidth is about 3.5 GHz) noise figure of 5.9 dB, and IIP3 of 1.6 dBm while consuming 7 mA from 1.2 V supply.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.10a
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• pp.434-437
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• 2010

NIPS(Network Intrusion Prevention System) is in line at the end of the external and internal networks which performed two kinds of action: Signature-based filtering and anomaly detection and prevention-based on self-learning. Among them, a signature-based filtering is well known to defend against attacks. By using signature-based filtering, intrusion prevention system passing a payload of packets is compared with attack patterns which are signature. If match, the packet is discard. However, when there is packet delay, it will increase the required pattern matching time as the number of signature is increasing whenever there is delay occur. Therefore, to ensure the performance of IPS, we needed more efficient pattern matching algorithm for high-performance ISP. To improve the performance of pattern matching the most important part is to reduce the number of comparisons signature rules and the packet whenever the packets arrive. In this paper, we propose an improve signature hashing-based pattern matching method. We use tuple pruning algorithm with Bloom filters, which effectively remove unnecessary tuples. Unlike other existing signature hashing-based IPS, our proposed method to improve the performance of IPS.

• Journal of Digital Convergence
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• v.10 no.4
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• pp.341-346
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• 2012

Social network service is service that enables the human network to be built up on web. It is important to record users' information simply and establish the network with people based on the information to provide with the social network service effectively. But it is very troublesome work for the user to input his or her own information on the mobile environment. In this paper we suggested a system which classifies users' behavior using context and creates blogging sentences automatically after inferring the destination. For this, users' behavior is classified and the destination is inferred with the sequence matching method using Naive Bayes classification. Then sentences which are suitable for situation is created by arranging the processed context using the structure of 5W1H. The system was evaluated satisfaction degree by comparing the created sentences based on actually collected data with users' intension and got accuracy rate of 88.73%.