• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.237-240
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• 2003

Gas turbines are extensively used in flight propulsion, electrical power generation, and other industrial applications. During its life span, a turbine blade is taken out periodically for repair and maintenance. This includes re-coating the blade surface and re-drilling the cooling holes/channels. A successful laser re-drilling requires the measurement of a hole within the accuracy of ${\pm}0.15mm$ in position and ${\pm}3^{\circ}$ in orientation. Detection of gas turbine blade/vane cooling hole position and orientation thus becomes a very important step for the vane/blade repair process. The industry is in urgent need of an automated system to fulfill the above task. This paper proposes approaches and algorithms to detect the cooling hole position and orientation by using a vision system mounted on a robot arm. The channel orientation is determined based on the alignment of the vision system with the channel axis. The opening position of the channel is the intersection between the channel axis and the surface around the channel opening. Experimental results have indicated that the concept of cooling hole identification is feasible. It has been shown that the reproducible detection of cooling channel position is with +/- 0.15mm accuracy and cooling channel orientation is with +/$-\;3^{\circ}$ with the current test conditions. Average processing time to search and identify channel position and orientation is less than 1 minute.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.10a
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• pp.37-39
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• 2000

The purpose of this paper is to present a neural network model based on change-point detection for the prediction of the U.S. Treasury Securities. Interest rates have been studied by a number of researchers since they strongly affect other economic and financial parameters. Contrary to other chaotic financial data, the movement of interest rates has a series of change points due to the monetary policy of the U.S. government. The basic concept of this proposed model is to obtain intervals divided by change points, to identify them as change-point groups, and to use them in interest rates forecasting. The proposed model consists of three stages. The first stage is to detect successive change points in the interest rates dataset. The second stage is to forecast the change-point group with the backpropagation neural network (BPN). The final stage is to forecast the output with BPN. This study then examines the predictability of the integrated neural network model for interest rates forecasting using change-point detection.

• Steel and Composite Structures
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• v.25 no.4
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• pp.485-496
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• 2017

In this paper, damage detection has been introduced as an optimization problem and a two-step method has been proposed that can detect the location and severity of damage in truss structures precisely and reduce the volume of computations considerably. In the first step, using the residual force vector concept, the suspected damaged members are detected which will result in a reduction in the number of variables and hence a decrease in the search space dimensions. In the second step, the precise location and severity of damage in the members are identified using the genetic algorithm and the results of the first step. Considering the reduced search space, the algorithm can find the optimal points (i.e. the solution for the damage detection problem) with less computation cost. In this step, the Efficient Correlation Based Index (ECBI), that considers the structure's first few frequencies in both damaged and healthy states, is used as the objective function and some examples have been provided to check the efficiency of the proposed method; results have shown that the method is innovatively capable of detecting damage in truss structures.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.41 no.9
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• pp.39-45
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• 2004

A design method is proposed for the sequence detection with fixed decision delay with less hardware complexity using the concept of the Voronoi diagram and its dual, the Delaunay tessellation. This detector design is based on the Fixed Delay Tree Search (FDTS) detection. The FDTS is a computationally efficient sequence detection algerian and has been shown to achieve near-optimal performance in the severe Intersymbol Interference (ISI) channels when combined with decision feedback equalization and the appropriate channel coding. In this approach, utilizing the information contained in the Voronoi diagram or equivalently the Delaunay tessellation, the relative location of the detector input sequence in the multi-dimensional Euclidean space is found without any computational redundancy, which leads to a reduced complexity implementation of the detector.

• The Transactions of the Korean Institute of Power Electronics
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• v.15 no.4
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• pp.327-334
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• 2010

This paper deals with a single-phase dynamic voltage restorer(DVR) with a quasi Z-source topology. The proposed system based on a single-phase quasi Z-source PWM ac-ac converter which have features such as the input voltage and output voltage are sharing ground, and input current operates in continuous current mode(CCM). For the detection of voltage sag-swell, peak voltage detection method is applied. Also, the circuit principles of the proposed system are described. During the 60% severe voltage sag and 30% voltage swell, the proposed system controls the adding or missing voltage and maintains the rated voltage of sinusoidal waveform at the terminals of the critical loads. Finally, PSIM simulation and experimental results are presented to verify the proposed concept and theoretical analysis.

• Nuclear Engineering and Technology
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• v.56 no.8
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• pp.3188-3198
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• 2024

Efficient and secure decommissioning of nuclear facilities demands advanced technologies. In this context, gamma-ray detection and imaging are crucial in identifying radioactive hotspots and monitoring radiation levels. Our study is dedicated to developing a gamma-ray detection system tailored for integration into robotic platforms for nuclear decommissioning, offering a safe and automated solution for this intricate task and ensuring the safety of human operators by mitigating radiation exposure and streamlining hotspot localization. Our approach integrates a Compton camera based 3D reconstruction algorithm with a single Timepix3 detector. This eliminates the need for a second detector and significantly reduces system weight and cost. Additionally, combining a 3D camera with the setup enhances hotspot visualization and interpretation, rendering it an ideal solution for practical nuclear decommissioning applications. In a proof-of-concept measurement utilizing a 137Cs source, our system accurately localized and visualized the source in 3D with an angular error of 1° and estimated the activity with a 3% relative error. This promising result underscores the system's potential for deployment in real-world decommissioning settings. Future endeavors will expand the technology's applications in authentic decommissioning scenarios and optimize its integration with robotic platforms. The outcomes of our study contribute to heightened safety and accuracy for nuclear decommissioning works through the advancement of cost-effective and efficient gamma-ray detection systems.

• The KIPS Transactions:PartC
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• v.11C no.6 s.95
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• pp.719-724
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• 2004

Recently, since the number of internet users is increasing rapidly and, by using the public hacking tools, general network users can intrude computer systems easily, the hacking problem is getting more serious. In order to prevent the intrusion, it is needed to detect the sign in advance of intrusion in a positive prevention by detecting the various foms of hackers' intrusion trials to know the vulnerability of systems. The existing network-based anomaly detection algorithms that cope with port- scanning and the network vulnerability scans have some weakness in intrusion detection. they can not detect slow scans and coordinated scans. therefore, the new concept of algorithm is needed to detect effectively the various forms of abnormal accesses for intrusion regardless of the intrusion methods. In this paper, SPAD(Session Pattern Anomaly Detector) is presented, which detects the abnormal service patterns by comparing them with the ordinary normal service patterns.

• Journal of Internet Computing and Services
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• v.21 no.1
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• pp.79-85
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• 2020

Today, to block spam texts on smartphone, a simple string comparison between text messages and spam keywords or a blocking spam phone numbers is used. As results, spam text is sent in a gradually hanged way to prevent if from being automatically blocked. In particular, for words included in spam keywords, spam texts are sent to abnormal words using special characters, Chinese characters, and whitespace to prevent them from being detected by simple string match. There is a limit that traditional spam filtering methods can't block these spam texts well. Therefore, new technologies are needed to respond to changing spam text messages. In this paper, we propose a knowledge graph-based new words detection mechanism that can detect new words frequently used in spam texts and respond to changing spam texts. Also, we show experimental results of the performance when detected Korean new words are applied to the Naive Bayes algorithm.

• Informatization Policy
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• v.26 no.4
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• pp.85-106
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• 2019

Recently, there have been increasing cases of collective petitions filed in the electronic petitions system. However, there is no efficient management system, raising concerns on side effects such as increased administrative workload and mass production of social conflicts. Aimed at suggesting a methodology for estimating electronic collective petitions using anomaly detection and corpus linguistics-based content analysis, this study conducted the followings: i) a theoretical review of the concept of collective petitions, ii) estimation of electronic collective petitions using anomaly detection based on nonparametric unsupervised learning, iii) a content similarity analysis on petitions using n-gram cosine angle distance, and iv) a case study on the Voice of Citizens of Changwon City, through which the utility of the proposed methodology, policy implications and future tasks were reviewed.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.10
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• pp.349-360
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• 2015

In recent year, phishing attacks are flooding with services based on the web technology. Phishing is affecting online security significantly day by day with the vulnerability of web pages. To prevent phishing attacks, a lot of anti-phishing techniques has been made with their own advantages and dis-advantages respectively, but the phishing attack has not been eradicated completely yet. In this paper, we have studied phishing in detail and categorize a process of phishing attack in two parts - Landing-phase, Attack-phase. In addition, we propose an phishing detection methodology based on web sites heuristic. To extract web sites features, we focus on URL and source codes of web sites. To evaluate performance of the suggested method, set up an experiment and analyze its results. Our methodology indicates the detection accuracy of 98.9% with random forest algorithm. The evaluation of proof-of-concept reveals that web site features can be used for phishing detection.