• The Journal of Korean Institute of Communications and Information Sciences
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• v.15 no.3
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• pp.177-186
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• 1990

This paper present an analysis of filter network using bondgraph technique for the determination of cofficients of filter function not by means of Computations operation. The proposed bondgraph technique is confirmed to be suitable for obtaining the frequency response characteristics of filter network.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.04a
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• pp.785-787
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• 2014

본 논문에서는 화산 분출 초저음파의 식별을 위해서 FSPNNC(Frequency Sub-bands Parallel Neural NetworkClassification)을 선택한다. FSPNNC 는 각기 다른 주파수 영역에서 독립적으로 추출한 특징벡터를 병렬 구조의 신경망에 학습하는 구조를 가지며 하나의 신경망은 하나의 분류 및 하나의 주파수 부대역만을 학습하고 다른 신경망들은 해당 특징벡터를 분류하지 않도록 학습된다. 실험은 단일 신경망 및 PNNCB(Parallel Neural Network Classifier Bank)와의 비교실험을 통하여 식별 성능을 제시한다.

• Proceedings of the KIEE Conference
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• 1998.07f
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• pp.1984-1986
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• 1998

This paper presents an optimal efficiency control for the inverter fed induction machine system using neural network. The motor speed and the load torque vary the efficiency characteristics of an induction motor. The optimal slip frequency has nonlinearity varied by the load torque as well as the motor speed. The induction motor is driven using the inverter system and the indirect vector control method which input is slip frequency. The neural network for estimating the optimal slip frequency has two input layer(the motor speed and the load torque) and one output layer(the optimal slip frequency that minimize the input power). Learning algorithm of the neural network is the back-propagation. Using the equivalent circuit including the nonlinearity of the induction motor, the loss reduction is analyzed quantitatively. Experimental results are shown noticeable power savings by proposed scheme in high speed and light load conditions.

• ETRI Journal
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• v.29 no.5
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• pp.569-595
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• 2007

Particularly in wireless communications, link errors severely affect the quality of the services due to the high error probability and the specific error characteristics (burst errors) in the radio access part of the network. In this work, we show that thorough analysis and appropriate modeling of radio-link error behavior are essential to evaluate and optimize higher layer protocols and services. They are also the basis for finding network-aware cross-layer processing algorithms which are capable of exploiting the specific properties of the link error statistics, such as predictability. This document presents the analysis of the radio link errors based on measurements in live Universal Mobile Telecommunication System (UMTS) radio access networks as well as new link error models originating from that analysis. It is shown that the knowledge of the specific link error characteristics leads to significant improvements in the quality of streamed video by applying the proposed novel network- and content-aware cross-layer scheduling algorithms. Although based on live UMTS network experience, many of the conclusions in this work are of general validity and are not limited to UMTS only.

• Herbal Formula Science
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• v.30 no.3
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• pp.145-154
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• 2022

Objective : This study aims to study core herbs and formulas in Internal medicine on Spleen system, to enhance efficiency in teaching Internal medicine on Spleen system, Herbalogy, Formula science, and to increase integration of the courses. Methods : Frequency notion, which was generally used in previous studies, was used in this study along with network analysis. Results : Frequently used herbs, herbs with high centrality, frequently combined herbs and core formula were found in this study. The herb with the highest frequency and centrality was 'Citri Unshius Pericarpium', and 'Atractylodis Rhizoma Alba - Citri Unshius Pericarpium' was the most frequent herb combination. The results of network analysis showed a total of 5 herbal communities of combination. Conclusion : Core herbs were found based on the frequency notion, which is a traditional analysis method. Also, core herbs, herbal combinations, formulas that can that may be overlooked when using frequency notions were found by using network analysis. The results may lead to enhancing efficiency in the education of Internal medicine on Spleen system, Herbalogy, Formula science courses and the integration of courses.

• ETRI Journal
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• v.43 no.1
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• pp.152-162
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• 2021

In the last few years, detection has become a powerful methodology for network protection and security. This paper presents a new detection scheme for data recorded over a computer network. This approach is applicable to the broad scientific field of information security, including intrusion detection and prevention. The proposed method employs bidimensional (time-frequency) data representations of the forms of the short-time Fourier transform, as well as the Wigner distribution. Moreover, the method applies matrix factorization using singular value decomposition and principal component analysis of the two-dimensional data representation matrices to detect intrusions. The current scheme was evaluated using numerous tests on network activities, which were recorded and presented in the KDD-NSL and UNSW-NB15 datasets. The efficiency and robustness of the technique have been experimentally proved.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.199-205
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• 2022

The extensive growth in data rate demand by the smart gadgets and mobile broadband application services in wireless cellular networks. To achieve higher data rate demand which leads to aggressive frequency reuse to improve network capacity at the price of Inter Cell Interference (ICI). Fractional Frequency Reuse (FFR) has been recognized as an effective scheme to get a higher data rate and mitigate ICI for perfect geometry network scenarios. In, an irregular geometric multicellular network, ICI mitigation is a challenging issue. The purpose of this paper is to develop distributed dynamic power allocation scheme for FFR based on game theory to mitigate ICI. In the proposed scheme, each cell region in an irregular multicellular scenario adopts a self-less behavior instead of selfish behavior to improve the overall utility function. This proposed scheme improves the overall data rate and mitigates ICI.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.3
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• pp.941-955
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• 2015

In dual-channel networks (DCNs), all frequency hopping (FH) sequences used for data channels are chosen from the original FH sequence used for the control channel by shifting different initial phases. As the number of data channels increases, the hitting frequency point problem becomes considerably serious because DCNs is non-orthogonal synchronization network and FH sequences are non-orthogonal. The increasing severity of the hitting frequency point problem consequently reduces the resource utilization efficiency. To solve this problem, we propose a novel hitting frequency point collision avoidance method, which consists of a sequence-selection strategy called sliding correlation (SC) and a collision avoidance strategy called keeping silent on hitting frequency point (KSHF). SC is used to find the optimal phase-shifted FH sequence with the minimum number of hitting frequency points for a new data channel. The hitting frequency points and their locations in this optimal sequence are also derived for KSHF according to SC strategy. In KSHF, the transceivers transmit or receive symbol information not on the hitting frequency point, but on the next frequency point during the next FH period. Analytical and simulation results demonstrate that unlike the traditional method, the proposed method can effectively reduce the number of hitting frequency points and improve the efficiency of the code resource utilization.

• Journal of Electrical Engineering and Technology
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• v.6 no.6
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• pp.776-785
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• 2011

Since the Internet-based real-time Global Positioning System(GPS) synchronized widearea power system frequency monitoring network (FNET) was proposed in 2001, it has been monitoring the power system frequency in interconnected United States power systems and numerous interesting behaviors have been observed, including frequency excursion propagation. We address the consistency of a frequency excursion detection order of frequency disturbance recorders in FNET in relation to the same generation trip, as well as the ability to recreate by power systems dynamic simulation. We also propose a new method, as an application of FNET measurement, to locate a tripped generator using power systems dynamic simulation and wide-area frequency measurement. The simulation database of all the possible trips of generators in the interconnected power systems is created using the off-line power systems dynamic simulation. When FNET detects a sudden drop in the monitoring frequency, which is most likely due to a generation trip in power systems, the proposed algorithm locates a tripped generator by finding the best matching case of the measured frequency excursion in the simulation database in terms of the frequency drop detection order and the time of monitoring points.

• Journal of Intelligence and Information Systems
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• v.5 no.1
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• pp.103-123
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• 1999

Input filtering as a preprocessing method is so much crucial to get good performance in time series forecasting. There are a few preprocessing methods (i.e. ARMA outputs as time domain filters, and Fourier transform or wavelet transform as time-frequency domain filters) for handling time series. Specially, the time-frequency domain filters describe the fractal structure of financial markets better than the time domain filters due to theoretically additional frequency information. Therefore, we, first of all, try to describe and analyze specially some issues on the effectiveness of different filtering methods from viewpoint of the performance of a neural network based forecasting. And then we discuss about neural network model architecture issues, for example, what type of neural network learning architecture is selected for our time series forecasting, and what input size should be applied to a model. In this study an input selection problem is limited to a size selection of the lagged input variables. To solve this problem, we simulate on analyzing and comparing a few neural networks having different model architecture and also use an embedding dimension measure as chaotic time series analysis or nonlinear dynamic analysis to reduce the dimensionality (i.e. the size of time delayed input variables) of the models. Throughout our study, experiments for integration methods of joint time-frequency analysis and neural network techniques are applied to a case study of daily Korean won / U. S dollar exchange returns and finally we suggest an integration framework for future research from our experimental results.