• Journal of the Korean Physical Society
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• v.73 no.11
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• pp.1644-1649
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• 2018

Aiming at infrared thermal images with different buried depth defects, we study a variety of image segmentation algorithms based on the threshold to develop global search ability and the ability to find the defect area accurately. Firstly, the iterative thresholding method, the maximum entropy method, the minimum error method, the Ostu method and the minimum skewness method are applied to image segmentation of the same infrared thermal image. The study shows that the maximum entropy method and the minimum error method have strong global search capability and can simultaneously extract defects at different depths. However none of these five methods can accurately calculate the defect area at different depths. In order to solve this problem, we put forward a strategy of "divide and conquer". The infrared thermal image is divided into several local thermal maps, with each map containing only one defect, and the defect area is calculated after local image processing of the different buried defects one by one. The results show that, under the "divide and conquer" strategy, the iterative threshold method and the Ostu method have the advantage of high precision and can accurately extract the area of different defects at different depths, with an error of less than 5%.

• Convergence Security Journal
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• v.24 no.3
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• pp.105-115
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• 2024

Since 2015, programming has been included in school curricula to enhance computer literacy and problem-solving skills. C language, widely used for its simplicity, efficiency, and long history, poses significant security risks, particularly in memory vulnerabilities like buffer overflow, pointer errors, format strings, and integer overflow. These vulnerabilities can cause severe system issues and widespread damage. This paper proposes an "Error Detection and Automatic Correction of Memory Vulnerabilities (EDAC)" algorithm to detect and correct these errors, aiming to reduce the impact of C language memory vulnerabilities.

• Computers and Concrete
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• v.25 no.1
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• pp.83-94
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• 2020

The use of fly ash in modern-day concrete technology aiming sustainable constructions is on rapid rise. Fly ash, a spinoff from coal calcined thermal power plants with pozzolanic properties is used for cement replacement in concrete. Fly ash concrete is cost effective, which modifies and improves the fresh and hardened properties of concrete and additionally addresses the disposal and storage issues of fly ash. Soft computing techniques have gained attention in the civil engineering field which addresses the drawbacks of classical experimental and computational methods of determining the concrete compressive strength with varying percentages of fly ash. In this study, models based on soft computing techniques employed for the prediction of the compressive strengths of fly ash concrete are collected from literature. They are classified in a categorical way of concrete strengths such as control concrete, high strength concrete, high performance concrete, self-compacting concrete, and other concretes pertaining to the soft computing techniques usage. The performance of models in terms of statistical measures such as mean square error, root mean square error, coefficient of correlation, etc. has shown that soft computing techniques have potential applications for predicting the fly ash concrete compressive strengths.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.11C
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• pp.1113-1121
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• 2009

In this paper, the channel estimation and pilot sequence design technique of multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems in multi-cell environments are studied for situations in which the inter cell interference (ICI) is the dominant channel impairment. We design pilot sequence aiming at minimizing mean square error and propose the channel estimation technique correspond to the designed pilot sequences. The proposed pilot sequences employ the sequences with good correlation properties such as Chu sequence and through simulations, it is shown that channel estimation algorithm using designed pilot sequence is effective for mitigating the ICI.

• Nuclear Engineering and Technology
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• v.51 no.3
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• pp.702-708
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• 2019

This paper deals with break size estimation of loss of coolant accidents (LOCA) using a nonlinear autoregressive with exogenous inputs (NARX) neural network. Previous studies used static approaches, requiring time-integrated parameters and independent firing algorithms. NARX neural network is able to directly deal with time-dependent signals for dynamic estimation of break sizes in real-time. The case studied is a LOCA in the primary system of Bushehr nuclear power plant (NPP). In this study, number of hidden layers, neurons, feedbacks, inputs, and training duration of transients are selected by performing parametric studies to determine the network architecture with minimum error. The developed NARX neural network is trained by error back propagation algorithm with different break sizes, covering 5% -100% of main coolant pipeline area. This database of LOCA scenarios is developed using RELAP5 thermal-hydraulic code. The results are satisfactory and indicate feasibility of implementing NARX neural network for break size estimation in NPPs. It is able to find a general solution for break size estimation problem in real-time, using a limited number of training data sets. This study has been performed in the framework of a research project, aiming to develop an appropriate accident management support tool for Bushehr NPP.

• Journal of the Korean Society of Visualization
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• v.17 no.2
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• pp.48-57
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• 2019

A straightforward strategy for particle image velocimetry (PIV) interrogation and post-processing has been proposed, aiming at reducing errors and clarifying vortex structures. The interrogation window size should be kept small to reduce bias error and improve spatial resolution. A spatial filter is then applied to the velocity field to reduce random error and clarify flow structure. The performance of three popular spatial filters were assessed: box filter, median filter, and local quadratic polynomial regression filter. In order to quantify random uncertainty, the image matching (IM) method is applied to an experimental dataset of homogeneous and isotropic turbulence (HIT) obtained by 2D-PIV. We statistically analyze the uncertainty propagation through the spatial filters, and verify the reduction in random uncertainty. Moreover, we illustrate that the spatial filters help clarify vortex structures using vortex identification criteria. As a result, PIV random uncertainty was reduced and the vortex structures became clearer by spatial filtering.

• Journal of the Korean Society of Clothing and Textiles
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• v.48 no.2
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• pp.233-253
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• 2024

This study analyzed differences between three-dimensional (3D) body scanning and manual measurements, aiming to assess whether 3D scanning can replace traditional anthropometric tools, such as tape measures and calipers. Data from 4,478 participants in the 8th Size Korea Project were analyzed, covering 43 measurement items. Since Given that the 3D and manual measurements were performed on the same subjects in the 8th Size Korea Project, it was possible to determine the correlation more accurately between the two measurement methods more accurately. Using Applying ISO 20685-1(2018) standards, 15 out of the 43 items fell within allowable error limits. When classified into six types, "small circumferences" and "segment lengths" showed averages of 3.35 mm and 3.10 mm, respectively, within acceptable range. "Body heights" and "body depths" slightly exceeded the limit, with averages of 5.28 mm and 6.58 mm. "Body widths" and "large circumferences" surpassed the limit, with means of 16.77 mm and 16.18 mm. The study offers an objective basis to for validate validating 3D measurements' measurements' reliability and accuracy, addressing various industries' needs for information on the human body's dimensions information.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.146-157
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• 2024

Despite numerous air mattresses marketed to prevent Pressure Ulcers (PU), none have fully succeeded due to residual pressure surpassing critical levels. We introduces an innovative medical bed system aiming at complete PU prevention. This system employs a unique 4-bar link mechanism, moving keys up and down to manage body pressure. Each of the 17 keys integrates a sensor controller, reading pressure from 10 sensors. By regulating motor input, we maintain body pressure below critical levels. Keys are equipped with a servo drive and sensor controller, linked to the main controller via two CAN series. Using fuzzy or PI/IP controllers, we adjust keys to minimize total error, dispersing body pressure and ensuring comfort. In case of controller failure, keys alternate swiftly, preventing ulcer development. Through experimental tests under varied conditions, the fuzzy controller with tailored membership functions demonstrated swift performance. PI control showed rapid convergence, while IP control exhibited slower convergence and oscillations near zero error. Our specialized medical robot bed, incorporating 4-bar links and pressure sensors, underwent testing with three controllers-fuzzy, PI, and IP-showcasing their effectiveness in keeping body pressure below critical ulcer levels. Experimental results validate the proposed approach's efficacy, indicating potential for complete PU prevention.

• ETRI Journal
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• v.22 no.2
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• pp.20-31
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• 2000

Much research on natural language processing (NLP), computational linguistics and lexicography has relied and depended on linguistic corpora. In recent years, many organizations around the world have been constructing their own large corporal to achieve corpus representativeness and/or linguistic comprehensiveness. However, there is no reliable guideline as to how large machine readable corpus resources should be compiled to develop practical NLP software and/or complete dictionaries for humans and computational use. In order to shed some new light on this issue, we shall reveal the flaws of several previous researches aiming to predict corpus size, especially those using pure regression or curve-fitting methods. To overcome these flaws, we shall contrive a new mathematical tool: a piecewise curve-fitting algorithm, and next, suggest how to determine the tolerance error of the algorithm for good prediction, using a specific corpus. Finally, we shall illustrate experimentally that the algorithm presented is valid, accurate and very reliable. We are confident that this study can contribute to solving some inherent problems of corpus linguistics, such as corpus predictability, compiling methodology, corpus representativeness and linguistic comprehensiveness.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2612-2616
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• 2003

Method for performance monitoring and diagnosis of a MIMO control system has been studied aiming at application to model predictive control (MPC) for industrial processes. The performance monitoring part is designed on the basis of the traditional SPC/SQC method. To meet the underlying premise of Schwart chart observation that the observed variable should be univariate and independent, the process variables are decorrelated temporally as well as spatially before monitoring. The diagnosis part was designed to identify the root of performance degradation among the controller, process, and disturbance. For this, a method to estimate the model-error and disturbance signal has been devised. The proposed methods were evaluated through numerical examples.