• Proceedings of the Korean Information Science Society Conference
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• 2004.10c
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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.

• Journal of Wellbeing Management and Applied Psychology
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• v.4 no.2
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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.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.2
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• pp.35-46
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• 2013

As a rapid increase of mobile network usage, many studies on solution for network traffic's demand problem have been done. Especially network environment measurement area provides basis for solving network traffic's demand problem by finding causes of problems through accurate network analysis. However, as increase of demand for smartphone, we should consider effects of mobile platform's property measuring mobile network. In this paper, we design a network traffic measurement system considering mobile platform. Through the information from packets, this system calculates packet transmission delay and throughput. We minimize computation cost required for a mobile device that is a client in this system. When fully using network resources, we found that Wi-Fi has shorter transmission delay, higher maximum throughput and lower loss rate than 3G, Android has shorter transmission delay and higher maximum throughput than iOS, and UDP has longer transmission delay and higher maximum throughput through this system.

• Journal of Biosystems Engineering
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• v.17 no.1
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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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• 2004.08a
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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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• v.13 no.12
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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.

• Korean Journal of Artificial Intelligence
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• v.11 no.2
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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.

• Journal of Korea Multimedia Society
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• v.21 no.8
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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.

• IEMEK Journal of Embedded Systems and Applications
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• v.3 no.1
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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.

• Journal of Korea Society of Industrial Information Systems
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• v.12 no.1
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• pp.61-71
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• 2007

The main purpose of this study was to develop Automatic Measurement System for monitoring tree's vigor using Ubiquitous Sensor Network This study also focused on presenting an alternative for monitoring automatically tree's vigor due to Shigometer's limits. Application test of the system in comparison with Shigometer showed that the measurement values were not different to choose between the two, and battery lasted about 1,844 days in this system. To test the sensor network the possible transmission distance using the sensor network in maximum was 130m. Investigation and management expenses can be reduced and labor productivity will also be improved in the forest and street trees.