• Journal of KIISE:Information Networking
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• v.36 no.1
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• pp.50-56
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• 2009

This paper proposes a new algorithm of clustering similar nodes defined as nodes having similar characteristic values in pure P2P environment. To compare similarity between nodes, we introduce a fitness function whose return value depends only on the two nodes' characteristic values. The higher the return value is, the more similar the two nodes are. We propose a GORGFM algorithm newly in conjunction with the fitness function to recommend and exchange nodes' characteristic values for an interest group formation and maintenance. With the GORGFM algorithm, the interest groups are formed dynamically based on the similarity of users, and all nodes will highly satisfy with the information recommended and received from nodes of the interest group. To evaluate of performance of the GORGFM algorithm, we simulated a matching rate by the total number of nodes of network and the number of iterations of the algorithm to find similar nodes accurately. The result shows that the matching rate is highly accurate. The GORGFM algorithm proposed in this paper is highly flexible to be applied for any searching system on the web.

• ETRI Journal
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• v.39 no.1
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• pp.51-61
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• 2017

This paper demonstrates a systematic approach for the design of broadband, high efficiency, high power, Class-AB RF amplifiers with high gain flatness. It is usually difficult to simultaneously achieve a high gain flatness and high efficiency in a broadband RF power amplifier, especially in a high power design. As a result, the use of a computer-aided simulation is most often the best way to achieve these goals; however, an appropriate initial value and a systematic approach are necessary for the simulation results to rapidly converge. These objectives can be accomplished with a minimum of trial and error through the following techniques. First, signal gain variations are reduced over a wide bandwidth using a proper pre-matching network. Then, the source and load impedances are satisfactorily obtained from small-signal and load-pull simulations, respectively. Finally, two high-order Chebyshev low-pass filters are employed to provide optimum input and output impedance matching networks over a bandwidth of 100 MHz-500 MHz. By using an EM simulation for the substrate, the simulation results were observed to be in close agreement with the measured results.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.9
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• pp.62-66
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• 2016

This paper presents a MedRadio-band low power low noise amplifier for Medical Devices. A proposed MedRadio-band low power low noise amplifier adopts a current-reuse resistive feedback topology to increase overall gm and reduce power consumption. The gain of the LNA increases by the Q-factor of the additional series RLC input matching network, and its noise figure is minimized by the similar factor. Furthermore, it consumes low power because of low supply voltage and current reuse technique. By exploiting the $g_m$-booting and matching network property, the proposed MedRadio-band low noise amplifier achieves a noise figure of 0.85 dB, a voltage gain of 30 dB, and IIP3 of -7.9 dBm while consuming 0.18 mA from a 1 V supply voltage in $0.13{\mu}m$ CMOS technology.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.7
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• pp.1339-1346
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• 2015

The traffic identification is a preliminary and essential step for stable network service provision and efficient network resource management. While a number of identification methods have been introduced in literature, the payload signature-based identification method shows the highest performance in terms of accuracy, completeness, and practicality. However, the payload signature-based method's processing speed is much slower than other identification method such as header-based and statistical methods. In this paper, we first classifies signatures by matching type based on range, order, and direction of packet in a flow which was automatically extracted. By using this classification, we suggest a novel method to improve processing speed of payload signature-based identification by reducing searching space.

• Journal of the Korea Society of Computer and Information
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• v.15 no.4
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• pp.47-55
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• 2010

In this paper, we proposed a recognition system of smile facial expression for smile treatment training. The proposed system detects face candidate regions by using Haar-like features from camera images. After that, it verifies if the detected face candidate region is a face or non-face by using SVM(Support Vector Machine) classification. For the detected face image, it applies illumination normalization based on histogram matching in order to minimize the effect of illumination change. In the facial expression recognition step, it computes facial feature vector by using PCA(Principal Component Analysis) and recognizes smile expression by using a multilayer perceptron artificial network. The proposed system let the user train smile expression by recognizing the user's smile expression in real-time and displaying the amount of smile expression. Experimental result show that the proposed system improve the correct recognition rate by using face region verification based on SVM and using illumination normalization based on histogram matching.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.6
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• pp.1231-1238
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• 2021

Recently, large online shopping malls are providing image search services as well as text or category searches. However, in the case of an image search service, there is a problem in that the search service cannot be used in the absence of an image. This paper describes the development of a system that allows users to find the clothes they want through hand-drawn images of the style of clothes when they search for clothes in an online clothing shopping mall. The hand-drawing data drawn by the user increases the accuracy of matching through neural network learning, and enables matching of clothes using various object detection algorithms. This is expected to increase customer satisfaction with online shopping by allowing users to quickly search for clothing they are looking for.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.4
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• pp.419-426
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• 1999

A novel motion analysis system is presented in this paper. The proposed system is inspired by processing functions observed in the fly visual system, which detects changes in input light intensities, determines motion on both the local and the wide-field levels. The system has several differences from conventional motion analysis system. First, conventional systems usually focused on matching similar feature or optical flow, but neural network is applied in this system. Back propagation is used by learning method, and Tine Delay Neural Network (TDNN) is also used as analysis method. Second, while conventional systems usually limited on only two frames of sequence, the proposed system accept multiple frames of sequence. The experimental results showed a 94.7% correct rate with a speed of 71.47 milli seconds for real and synthetic images.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.52 no.1
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• pp.35-41
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• 2003

In this paper, a machine vision system for recognizing and classifying the patterns and marks engraved by die molding or laser marking on the glass panels of computer monitors is suggested and evaluated experimentally. The vision system is equipped with a neural network and an NGC pattern classifier including searching process based on normalized grayscale correlation and adaptive binarization. This system is found to be applicable even to the cases in which the segmentation of the pattern area from the background using ordinary blob coloring technique is quite difficult. The inspection process is accomplished by the use of the NGC hypothesis and ANN verification. The proposed pattern recognition system is composed of three parts: NGC matching process and the preprocessing unit for acquiring the best quality of binary image data, a neural network-based recognition algorithm, and the learning algorithm for the neural network. Another contribution of this paper is the method of generating the training patterns from only a few typical product samples in place of real images of all types of good products.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.102.3-102
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• 2001

In this paper, we propose a novel method that can detect face region effectively with fuzzy theory and neural network We make fuzzy rules and membership functions to describe the face color. In this algorithm, we use a perceptually uniform color space to increase the accuracy and stableness of the nonlinear color information. We use this model to extract the face candidate, and then scan it with the pre-built sliding window by using a neural network-based pattern-matching method to find eye. A neural network examines small windows of face candidate, and decides whether each window contains eye. We can standardize the face candidate geometrically with detected eyes.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.95-98
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• 2003

To provide high-quality media streaming service over the best-effort Internet, an efficient streaming system with a network-adaptive transport is essential. By adopting FEC-based adaptive transport and by matching it to the priority of media packets, in this paper, an enhanced MPEG-4 streaming system is implemented. The implemented adaptive transport can complement the existing MPEG-4 streaming server with components such as elementary stream provider, sync layer and DMIF layer. Sync layer packets of each MPEG-4 elementary stream are prioritized and then selectively dropped at the sender to match the currently available bandwidth. Also, with the proposed adaptive transport module, the level of FEC protection is dynamically adjusted based on the underlying network condition. With preliminary verification over the emulated network testbed, it is shown that the proposed implementation can mitigate the impact of network fluctuation and thus improve the quality of streaming.