• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2004년도 가을 학술발표논문집 Vol.31 No.2 (3)
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• pp.142-144
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• 2004

To provide more qualified data service on the HFC(Hybrid-Fiber Coaxial) access network, the channel characteristics of upstream transmission band should be carefully investigated and analysed. It will be easier to do network management if the monitoring system for noise measurement in the network is available, In this paper, noise analysis method and the frequency selection method in the upstream band for duplex transmission are suggested. And, Data aquisition device for the signal measurement Is implemented. With this network monitoring system, field test and the result from the collected data are described.

• 웰빙융합연구
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• 제4권2호
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• pp.35-41
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• 2021

Purpose: In this study, volatile organic compounds(VOCs) and aldehydes generated from roadside vehicles and other pollutants were measured and analyzed. Research design, data and methodology: As a result of measuring and analyzing three areas near the roadside, Vinyl chloride 0.00 ~ 0.02 ppb, Benzene 2.87 ~ 5.01 ppb. Toluene 4.51 ~ 8.62 ppb, Styrene 0.00 ~ 0.34 ppb, Formaldehyde 8.45 ~ 17.12 ug/m³, Acetaldehyde 7.01 ~ 17.64 ug/m³ were detected. When comparing the analysis results and the 6-month average concentration of the hazardous air monitoring network, the analysis results were about 26 times higher for Benzene, about 5 times for Toluene, and about 3.75 times for Styrene. In the case of vinyl chloride, it was confirmed that it was about 20 times lower than that of the hazardous atmosphere monitoring network. Results: Therefore, it is necessary to reexamine the installation location of the measurement network because people are exposed to pollutants on the actual roadside. It is judged that it is right to build a measurement network that is practically helpful to people by increasing the measurement items in the measurement network.

• 전자공학회논문지
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• 제50권2호
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• pp.35-46
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• 2013

모바일 네트워크 사용량이 급증함에 따라 트래픽 수요 문제를 해결하기 위한 많은 연구가 이뤄지고 있다. 특히 네트워크 환경 측정 분야는 정확한 분석을 통해 네트워크상에 발생되는 문제들의 원인을 찾아냄으로써 트래픽 수요 문제를 해결할 수 있는 기반을 제공한다. 특히 최근 스마트폰의 수요가 늘어남에 따라 모바일 플랫폼 특성이 네트워크에 미치는 영향을 고려한 측정시스템이 필요하다. 이에 본 논문에서는 모바일 플랫폼을 위한 네트워크 환경 측정 시스템을 설계하였다. 설계된 시스템은 클라이언트를 통하여 얻은 패킷의 정보를 통하여 패킷 전송간의 지연시간과 throughput을 실시간으로 계산한다. 그리고 측정시 클라이언트인 모바일 단말기에 요구되는 계산량을 줄임으로써 모바일 단말기에 걸리는 부하를 최소화하였다. 설계한 시스템을 통하여 네트워크 자원을 최대로 사용하였을 시 Wi-Fi 망이 3G 망보다 짧은 전송지연시간, 높은 최대 throughput, 낮은 손실률을 가지고, Android가 iOS보다 짧은 전송지연시간과 높은 최대 throughput을 가지며, UDP가 TCP보다 긴 전송지연시간, 높은 최대 throughput을 가진다는 것을 확인하였다.

• Journal of Biosystems Engineering
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• 제17권1호
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• pp.65-78
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• 1992

When using a computer vision system for a measurement, the geometrically distorted input image usually restricts the site and size of the measuring window. A geometrically distorted image caused by the image sensing and processing hardware degrades the accuracy of the visual measurement and prohibits the arbitrary selection of the measuring scope. Therefore, an image calibration is inevitable to improve the measuring accuracy. A calibration process is usually done via four steps such as measurement, modeling, parameter estimation, and compensation. In this paper, the efficient error calibration technique of a geometrically distorted input image was developed using a neural network. After calibrating a unit pixel, the distorted image was compensated by training CMLAN(Cerebellar Model Linear Associator Network) without modeling the behavior of any system element. The input/output training pairs for the network was obtained by processing the image of the devised sampled pattern. The generalization property of the network successfully compensates the distortion errors of the untrained arbitrary pixel points on the image space. The error convergence of the trained network with respect to the network control parameters were also presented. The compensated image through the network was then post processed using a simple DDA(Digital Differential Analyzer) to avoid the pixel disconnectivity. The compensation effect was verified using known sized geometric primitives. A way to extract directly a real scaled geometric quantity of the object from the 8-directional chain coding was also devised and coded. Since the developed calibration algorithm does not require any knowledge of modeling system elements and estimating parameters, it can be applied simply to any image processing system. Furthermore, it efficiently enhances the measurement accuracy and allows the arbitrary sizing and locating of the measuring window. The applied and developed algorithms were coded as a menu driven way using MS-C language Ver. 6.0, PC VISION PLUS library functions, and VGA graphic functions.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.831-833
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• 2004

In this paper, an obstacle avoidance algorithm is proposed for a network-based robot considering network delay by distribution. The proposed algorithm is based on the VFH(Vector Field Histogram) algorithm, and for the network-based robot system, in which it is assumed robot localization information is transmitted through network communication. In this paper, target vector for the VFH algorithm is estimated through the robot localization information and the measurement of its delay by distribution. The delay measurement is performed by time-stamp method. To synchronize all local clocks of the nodes distributed on the network, a global clock synchronization method is adopted. With the delay measurement, the robot localization estimation is performed by calculating the kinematics of the robot. The validation of the proposed algorithm is performed through the performance comparison of the obstacle avoidance between the proposed algorithm and the existing VFH algorithm on the network-based autonomous mobile robot.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권12호
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• pp.5948-5971
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• 2019

Estimating loss rates with the packet traces captured from some point in the middle of the network has received much attention within the research community. Meanwhile, existing intermediate-point methods like [1] require the capturing system to capture all the TCP traffic that crosses the border of an access network (typically Gigabit network) destined to or coming from the Internet. However, limited to the performance of current hardware and software, capturing network traffic in a Gigabit environment is still a challenging task. The uncaptured packets will affect the total number of captured packets and the estimated number of packet losses, which eventually affects the accuracy of the estimated loss rate. Therefore, to obtain more accurate loss rate, a method of strengthening packet loss measurement from the network intermediate point is proposed in this paper. Through constructing a series of heuristic rules and leveraging the binomial distribution principle, the proposed method realizes the compensation for the estimated loss rate. Also, experiment results show that although there is no increase in the proportion of accurate estimates, the compensation makes the majority of estimates closer to the accurate ones.

• 한국인공지능학회지
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• 제11권2호
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• pp.19-27
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• 2023

In recent times, an exponential increase in Internet traffic has been observed as a result of advancing development of the Internet of Things, mobile networks with sensors, and communication functions within various devices. Further, the COVID-19 pandemic has inevitably led to an explosion of social network traffic. Within this context, considerable attention has been drawn to research on network traffic analysis based on machine learning. In this paper, we design and develop a new machine learning framework for network traffic analysis whereby normal and abnormal traffic is distinguished from one another. To achieve this, we combine together well-known machine learning algorithms and network traffic analysis techniques. Using one of the most widely used datasets KDD CUP'99 in the Weka and Apache Spark environments, we compare and investigate results obtained from time series type analysis of various aspects including malicious codes, feature extraction, data formalization, network traffic measurement tool implementation. Experimental analysis showed that while both the logistic regression and the support vector machine algorithm were excellent for performance evaluation, among these, the logistic regression algorithm performs better. The quantitative analysis results of our proposed machine learning framework show that this approach is reliable and practical, and the performance of the proposed system and another paper is compared and analyzed. In addition, we determined that the framework developed in the Apache Spark environment exhibits a much faster processing speed in the Spark environment than in Weka as there are more datasets used to create and classify machine learning models.

• 한국멀티미디어학회논문지
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• 제21권8호
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• pp.864-875
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• 2018

In this paper, we propose a measurement method for height of white light scanning interferometer using deep learning. In order to measure the fine surface shape, a three-dimensional surface shape measurement technique is required. A typical example is a white light scanning interferometer. In order to calculate the surface shape from the measurement image of the white light scanning interferometer, the height of each pixel must be calculated. In this paper, we propose a neural network for height calculation and use virtual data generation method to train this neural network. The accuracy was measured by inputting 57 actual data to the neural network which had completed the learning. We propose two new functions for accuracy measurement. We have analyzed the cases where there are many errors among the accuracy calculation values, and it is confirmed that there are many errors when there is no interference fringe or outside the learned range. We confirmed that the proposed neural network works correctly in most cases. We expect better results if we improve the way we generate learning data.

• 대한임베디드공학회논문지
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• 제3권1호
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• pp.42-48
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• 2008

The architecture of WSN based Vehicle Speed Measurement System is presented in this paper from Telematics Sensor Network(TSN) to Management System. To verify the feasibility of the system, we implemented the vehicle speed measurement system and evaluated the accuracy of velocity measured by the system in our testbed, an old highway located near Kyungbu highway. The system performed over 95% of accuracy at 80kmph from the measurement. In addition, the battery life time of the sensor node was evaluated by simulation analysis with real measured current consumption profiles. Assuming the maximum average daily traffic in 2005, the battery life time is expected to be over 1.6 year from the simulation result.

• 한국산업정보학회논문지
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• 제12권1호
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• pp.61-71
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• 2007

본 연구는 현재 인력기반 중심의 측정장비인 사이고미터가 가지고 있는 제약성을 개선할 목적으로 유비쿼터스 센서 네트워크를 이용하여 수목의 수세활력도를 측정할 수 있는 자동화 시스템을 개발하였다. 본 연구를 통하여 개발된 시스템의 신뢰성을 검증하기 위하여 사이고미터와 측정값을 비교한 결과 거의 차이가 없었으며, 배터리 수명은 약 1,844일 정도 유지되는 것으로 나타났다. 그리고 센서 네트워크 안정성 검증 결과 데이터 전송이 가능한 최대 거리는 130m로 나타나 산림지역이나 가로수 관리에 적용할 경우 조사 및 관리비용의 절감과 노동생산성을 향상 시킬 수 있을 것으로 사료된다.