• Title/Summary/Keyword: data network

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On the Design of a Big Data based Real-Time Network Traffic Analysis Platform (빅데이터 기반의 실시간 네트워크 트래픽 분석 플랫폼 설계)

  • Lee, Donghwan;Park, Jeong Chan;Yu, Changon;Yun, Hosang
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.23 no.4
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    • pp.721-728
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    • 2013
  • Big data is one of the most spotlighted technological trends in these days, enabling new methods to handle huge volume of complicated data for a broad range of applications. Real-time network traffic analysis essentially deals with big data, which is comprised of different types of log data from various sensors. To tackle this problem, in this paper, we devise a big data based platform, RENTAP, to detect and analyse malicious network traffic. Focused on military network environment such as closed network for C4I systems, leading big data based solutions are evaluated to verify which combination of the solutions is the best design for network traffic analysis platform. Based on the selected solutions, we provide detailed functional design of the suggested platform.

The Study Active-based for Improvement of Reliablity In Mobile Ad-hoc Network (이동 애드혹 네트워크에서 신뢰성 향상을 위한 액티브 기반연구)

  • 박경배;강경인;유재휘;김진용
    • Journal of the Korea Society of Computer and Information
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    • v.7 no.4
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    • pp.188-198
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    • 2002
  • In this paper, we propose an active network to support reliable data transmission in the mobile ad-hoc network. The active network uses DSR(Dynamic Source Routing) protocol as its basic routing protocol, and uses source and destination nodes as key active nodes. For reliable improvement the source node is changed to source active node to add function that its buffer to store the last data with the flow control for data transmission per destination node. The destination node is changed to destination active node to add function that it requests the re-transmission for data that was not previously received by the destination active node with the flow control for data reception per source active node As the result of evaluation. we found the proposed active network guaranteed reliable data transmission with almost 100% data reception rate for slowly moving mobile ad-hoc network and with more 95% data reception rate, which is improvement of 3.5737% reception rate compared with none active network, for continuously fast moving mobile ad-hoc network.

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Recognition of Virtual Written Characters Based on Convolutional Neural Network

  • Leem, Seungmin;Kim, Sungyoung
    • Journal of Platform Technology
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    • v.6 no.1
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    • pp.3-8
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    • 2018
  • This paper proposes a technique for recognizing online handwritten cursive data obtained by tracing a motion trajectory while a user is in the 3D space based on a convolution neural network (CNN) algorithm. There is a difficulty in recognizing the virtual character input by the user in the 3D space because it includes both the character stroke and the movement stroke. In this paper, we divide syllable into consonant and vowel units by using labeling technique in addition to the result of localizing letter stroke and movement stroke in the previous study. The coordinate information of the separated consonants and vowels are converted into image data, and Korean handwriting recognition was performed using a convolutional neural network. After learning the neural network using 1,680 syllables written by five hand writers, the accuracy is calculated by using the new hand writers who did not participate in the writing of training data. The accuracy of phoneme-based recognition is 98.9% based on convolutional neural network. The proposed method has the advantage of drastically reducing learning data compared to syllable-based learning.

Experiments and Measurements of Public Switching Telephone Network(PSTN) for the Purpose of Opening it to Data Communication (데이터 통신에 공중교환전화망을 개방하기 위한 망의 전송품질의 특성 실험 및 특정)

  • 조규심;박규태
    • Journal of the Korean Institute of Telematics and Electronics
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    • v.21 no.1
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    • pp.13-24
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    • 1984
  • This paper presents the measurements and analyses of transmission qualities of the existing urban and toll public switching telephone network (PSTN) for the purllose of opening it to data transmission. In the tests, random noise and impulsive noise occuring on the telephone network are investigated and bit error rate and block error rate representing the transmission qualities on the existing telephone network are measured. We recommend the fastest practical data transmission speed on the network and investigate a possibility of opening the public switching telephone network (PSIN) to the data transmission based on the above measurements.

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The Partial Fault Detection of an hir-Conditioning System by the Neural Network Algorithm using Normalized Input Data (정규화 입력을 사용한 신경망 알고리즘에 의한 냉동기의 부분 고장 검출)

  • 한도영;황정욱
    • Korean Journal of Air-Conditioning and Refrigeration Engineering
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    • v.15 no.3
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    • pp.159-165
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    • 2003
  • The fault detection and diagnosis technology may be applied in order to decrease the energy consumption and the maintenance cost of the air-conditioning system. To detect partial faults of the air-conditioning system, a neural network algorithm may be used. In this study, the neural network algorithm using normalized input data by the standard deviation was applied. And the [7$\times$10$\times$10$\times$1] neural network structure was selected. Test results showed that the neural network algorithm using normalized input data was very effective to detect the condenser fouling and the evaporator fan fault of an air-conditioning system.

The Study on Implementation of IPv6 Testbed and Transition Model for Next Generation Power Communication Networks (차세대 전력통신망을 위한 IPv6 테스트베드 구축 및 이행 모델에 관한 연구)

  • Kim, Jin-Chol;Lim, Yong-Hun;Kim, Yon-Soo;Lee, Ki-Dong;Woo, Hee-Gon
    • Proceedings of the IEEK Conference
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    • 2007.07a
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    • pp.171-172
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    • 2007
  • The need for open architecture based IP network is becoming increasingly critical because the electric power industry has begun to upgrade to digital systems. In this paper, we implemented IPv6 testbed and experimented IPv6 performance actually for examination on IPv6 applications to electric power communication network. We achieved preliminary tests relevant to IPv6 to solve expected problems before.

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A Network Partition Approach for MFD-Based Urban Transportation Network Model

  • Xu, Haitao;Zhang, Weiguo;zhuo, Zuozhang
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.14 no.11
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    • pp.4483-4501
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    • 2020
  • Recent findings identified the scatter and shape of MFD (macroscopic fundamental diagram) is heavily influenced by the spatial distribution of link density in a road network. This implies that the concept of MFD can be utilized to divide a heterogeneous road network with different degrees of congestion into multiple homogeneous subnetworks. Considering the actual traffic data is usually incomplete and inaccurate while most traffic partition algorithms rely on the completeness of the data, we proposed a three-step partitioned algorithm called Iso-MB (Isoperimetric algorithm - Merging - Boundary adjustment) permitting of incompletely input data in this paper. The proposed algorithm was implemented and verified in a simulated urban transportation network. The existence of well-defined MFD in each subnetwork was revealed and discussed and the selection of stop parameter in the isoperimetric algorithm was explained and dissected. The effectiveness of the approach to the missing input data was also demonstrated and elaborated.

Game Theoretic based Distributed Dynamic Power Allocation in Irregular Geometry Multicellular Network

  • Safdar, Hashim;Ullah, Rahat;Khalid, Zubair
    • 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.

Multi-temporal Remote-Sensing Imag e ClassificationUsing Artificial Neural Networks (인공신경망 이론을 이용한 위성영상의 카테고리분류)

  • Kang, Moon-Seong;Park, Seung-Woo;Lim, Jae-Chon
    • Proceedings of the Korean Society of Agricultural Engineers Conference
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    • 2001.10a
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    • pp.59-64
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    • 2001
  • The objectives of the thesis are to propose a pattern classification method for remote sensing data using artificial neural network. First, we apply the error back propagation algorithm to classify the remote sensing data. In this case, the classification performance depends on a training data set. Using the training data set and the error back propagation algorithm, a layered neural network is trained such that the training pattern are classified with a specified accuracy. After training the neural network, some pixels are deleted from the original training data set if they are incorrectly classified and a new training data set is built up. Once training is complete, a testing data set is classified by using the trained neural network. The classification results of Landsat TM data show that this approach produces excellent results which are more realistic and noiseless compared with a conventional Bayesian method.

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Comparing Accuracy of Imputation Methods for Incomplete Categorical Data

  • Shin, Hyung-Won;Sohn, So-Young
    • Proceedings of the Korean Statistical Society Conference
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    • 2003.05a
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    • pp.237-242
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    • 2003
  • Various kinds of estimation methods have been developed for imputation of categorical missing data. They include modal category method, logistic regression, and association rule. In this study, we propose two imputation methods (neural network fusion and voting fusion) that combine the results of individual imputation methods. A Monte-Carlo simulation is used to compare the performance of these methods. Five factors used to simulate the missing data are (1) true model for the data, (2) data size, (3) noise size (4) percentage of missing data, and (5) missing pattern. Overall, neural network fusion performed the best while voting fusion is better than the individual imputation methods, although it was inferior to the neural network fusion. Result of an additional real data analysis confirms the simulation result.

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