• Journal of Institute of Control, Robotics and Systems
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• v.14 no.5
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• pp.444-452
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• 2008

This paper presents an overall image processing approach of defect inspection of patterned TFT-LCD panels for the real manufacturing process. A prototype of AOI(Automatic Optical Inspection) system which is composed of air floating stage and multi line scan cameras is developed. Adjacent pattern comparison algorithm is enhanced and used for pattern elimination to extract defects in the patterned image of TFT-LCD panels. New region merging algorithm which is based on border expansion is proposed to identify defects from the pattern eliminated defect image. Experimental results show that a developed AOI system has acceptable performance and the proposed algorithm reduces environmental effects and processing time effectively for applying to the real manufacturing process.

• Journal of the Korea Society for Simulation
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• v.23 no.2
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• pp.25-33
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• 2014

In this study, the border security problem of the ROK Army is examined by applying the agent-based modeling and simulation (ABMS) concept as well as its platform, MANA. Based on the approximately optimized behavior of the infiltrator obtained using genetic algorithm (GA), we evaluate the GOP border security system which consists of human resources, surveillance, as well as command and control (C2) systems. We use four measures of effectiveness (MOEs) to evaluate its performance, and we apply a near optimal latin hypercube (NOLH) design to deal with the large number of factors of interest in our model. By using a NOLH design, our simulation runs are implemented efficiently. We hope the results of this study provide valuable data for deciding the configuration of the border security system structure and the number of soldiers assigned in the platoon.

• International Journal of Control, Automation, and Systems
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• v.6 no.3
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• pp.453-459
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• 2008

The neural network is currently being used throughout numerous control system fields. However, it is not easy to obtain an input-output pattern when the neural network is used for the system of a single feedback controller and it is difficult to obtain satisfactory performance with when the load changes rapidly or disturbance is applied. To resolve these problems, this paper proposes a new mode to implement a neural network controller by installing a real object for control and an algorithm for this, which can replace the existing method of implementing a neural network controller by utilizing activation function at the output node. The real plant object for controlling of this mode implements a simple neural network controller replacing the activation function and provides the error back propagation path to calculate the error at the output node. As the controller is designed using a simple structure neural network, the input-output pattern problem is solved naturally and real-time learning becomes possible through the general error back propagation algorithm. The new algorithm applied neural network controller gives excellent performance for initial and tracking response and shows a robust performance for rapid load change and disturbance, in which the permissible error surpasses the range border. The effect of the proposed control algorithm was verified in a test that controlled the speed of a motor equipped with a high speed computing capable DSP on which the proposed algorithm was loaded.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.3
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• pp.901-920
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• 2015

Wireless Sensor Networks (WSNs) are widely used in geographically isolated applications like military border area monitoring, battle field surveillance, forest fire detection systems, etc. Uninterrupted power supply is not possible in isolated locations and hence sensor nodes live on their own battery power. Localization of sensor nodes in isolated locations is important to identify the location of event for further actions. Existing localization algorithms consume more energy at sensor nodes for computation and communication thereby reduce the lifetime of entire WSNs. Existing approaches also suffer with less localization coverage and localization accuracy. The objective of the proposed work is to increase the lifetime of WSNs while increasing the localization coverage and localization accuracy. A novel intelligent unmanned aerial vehicle anchor node (IUAN) is proposed to reduce the communication cost at sensor nodes during localization. Further, the localization computation cost is reduced at each sensor node by the proposed intelligent arc selection (IAS) algorithm. IUANs construct the location-distance messages (LDMs) for sensor nodes deployed in isolated locations and reach the Control Station (CS). Further, the CS aggregates the LDMs from different IUANs and computes the position of sensor nodes using IAS algorithm. The life time of WSN is analyzed in this paper to prove the efficiency of the proposed localization approach. The proposed localization approach considerably extends the lifetime of WSNs, localization coverage and localization accuracy in isolated environments.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1349-1353
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• 2003

CEMTool is a command style design and analyzing package for scientific and technological algorithm and a matrix based computation language. In this paper, we present a compiler based approach to the implementation of the command mode generalized PDE solver in CEMTool. In contrast to the existing MATLAB PDE Toolbox, our proposed FEM package can deal with the combination of the reserved words such as "laplace" and "convect". Also, we can assign the border lines and the boundary conditions in a very easy way. With the introduction of the lexical analyzer and the parser, our FEM toolbox can handle the general boundary condition and the various PDEs represented by the combination of equations. That is why we need not classify PDE as elliptic, hyperbolic, parabolic equations. Consequently, with our new FEM toolbox, we can overcome some disadvantages of the existing MATLAB PDE Toolbox.

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.135-143
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• 2022

As the world has implemented social distancing and telecommuting due to the spread of COVID-19, real-time streaming sessions based on routing protocols have increased dependence on the Internet due to the activation of video and voice-related content services and cloud computing. BGP is the most widely used routing protocol, and although many studies continue to improve security, there is a lack of visual analysis to determine the real-time nature of analysis and the mis-detection of algorithms. In this paper, we analyze BGP data, which are powdered as normal and abnormal, on a real-world basis, using an anomaly detection algorithm that combines statistical and post-processing statistical techniques with Rule-based techniques. In addition, we present an interactive spatio-temporal analysis plan as an intuitive visualization plan and analysis result of the algorithm with a map and Sankey Chart-based visualization technique.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.9 s.351
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• pp.88-98
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• 2006

VANET has several different characteristics from MANET such as high mobility of nodes and frequent change of node density and network topology. Due to these characteristics, the network topology based protocol, often used in MANET, can not be applied to VANET. In this paper, we propose an emergency warning message broadcast protocol using range based relay node selecting algorithm which determines the minimal waiting time spent by a given node before rebroadcasting the received warning message. Because the time is randomly calculated based on the distance between sender node and receiver node, a node chosen as a relay node is assured to have a minimal waiting time, even though it is not located at the border of radio transmission range. The proposed emergency warning message broadcast protocol has low network traffic because it does not need to exchange control messages for message broadcasting. In addition, it can reduce End-to-End delay under circumstances of low node density and short transmission range in VANET.

• Journal of Radiation Protection and Research
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• v.45 no.3
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• pp.117-129
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• 2020

Background: Radiation portal monitors (RPMs) involving plastic scintillators installed at the border inspection sites can detect illicit trafficking of radioactive sources in cargo containers within seconds. However, RPMs may generate false alarms because of the naturally occurring radioactive materials. To manage these false alarms, we previously suggested an energy-weighted algorithm that emphasizes the Compton-edge area as an outstanding peak. This study intends to evaluate the identification of radioactive sources using an improved energy-weighted algorithm. Materials and Methods: The algorithm was modified by increasing the energy weighting factor, and different peak combinations of the energy-weighted spectra were tested for source identification. A commercialized RPM system was used to measure the energy-weighted spectra. The RPM comprised two large plastic scintillators with dimensions of 174 × 29 × 7 ㎤ facing each other at a distance of 4.6 m. In addition, the in-house-fabricated signal processing boards were connected to collect the signal converted into a spectrum. Further, the spectra from eight radioactive sources, including special nuclear materials (SNMs), which were set in motion using a linear motion system (LMS) and a cargo truck, were estimated to identify the source identification rate. Results and Discussion: Each energy-weighted spectrum exhibited a specific peak location, although high statistical fluctuation errors could be observed in the spectrum with the increasing source speed. In particular, ¹³⁷Cs and ⁶⁰Co in motion were identified completely (100%) at speeds of 5 and 10 km/hr. Further, SNMs, which trigger the RPM alarm, were identified approximately 80% of the time at both the aforementioned speeds. Conclusion: Using the modified energy-weighted algorithm, several characteristics of the energy weighted spectra could be observed when the used sources were in motion and when the geometric efficiency was low. In particular, the discrimination between ⁶⁰Co and ⁴⁰K, which triggers false alarms at the primary inspection sites, can be improved using the proposed algorithm.

• Journal of Science and Technology Studies
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• v.18 no.3
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• pp.153-216
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• 2018

The contemporary practices of Big-Data based automated decision making algorithms are widely deployed not just because we expect algorithmic decision making might distribute social resources in a more efficient way but also because we hope algorithms might make fairer decisions than the ones humans make with their prejudice, bias, and arbitrary judgment. However, there are increasingly more claims that algorithmic decision making does not do justice to those who are affected by the outcome. These unfair examples bring about new important questions such as how decision making was translated into processes and which factors should be considered to constitute to fair decision making. This paper attempts to delve into a bunch of research which addressed three areas of algorithmic application: criminal justice, law enforcement, and national security. By doing so, it will address some questions about whether artificial intelligence algorithm discriminates certain groups of humans and what are the criteria of a fair decision making process. Prior to the review, factors in each stage of data mining that could, either deliberately or unintentionally, lead to discriminatory results will be discussed. This paper will conclude with implications of this theoretical and practical analysis for the contemporary Korean society.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.49 no.1
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• pp.29-38
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• 2012

The noise generated in the process of obtaining the medical image acts as the element obstructing the image interpretation and diagnosis. To restore the true image from the image polluted from the noise, the total variation optimization algorithm was proposed by the R.O. F (L.Rudin, S Osher, E. Fatemi). This method removes the noise by fitting the balance of the regularity and fidelity. However, the blurring phenomenon of the border area generated in the process of performing the iterative operation cannot be avoided. In this paper, we propose the adaptive total variation method by mapping the control parameter to the proposed transfer function for minimizing boundary error. The proposed transfer function is determined by the noise variance and the local property of the image. The proposed method was applied to 464 tooth images. To evaluate proposed method performance, PSNR which is a indicator of signal and noise's signal power ratio was used. The experimental results show that the proposed method has better performance than other methods.