• Journal of the Korean Institute of Gas
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• v.8 no.3 s.24
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• pp.31-36
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• 2004

Electrofusion(EF) joints have been widely used as they are easy to fuse and suitable for high-quality joints for polyethylene(PE) pipes. This paper studies the cause of defects and classifies 5 types of defects. The defect detection technique for electrofusion joints of polyethylene piping is utilized by the ultrasonic phased array technique to obtain ultrasonic images of electrofusion joints. Test sample joints have been designed and fabricated using artificial defects which were made using paper. Finally, we studied the condition of electrofusion in the field and analyzed the main causes of defects. And we classified the defect types as local lack of fusion, sand inclusion, voids or air inclusion, short stab, excess penetration or excess bead.

• Journal of Electrical Engineering and Technology
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• v.9 no.4
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• pp.1369-1374
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• 2014

Most accidents of medium-voltage cables installed in nuclear power plants result from the initial defect of internal insulators or the initial failure due to poor construction. However, as the service years of plants increase, the possibility of cable accidents is also rapidly increases. This is primarily caused by electric, mechanical, thermal, and radiation stresses. Recently, much attention is paid to the study of cable diagnoses. To date, partial discharge and Tan${\delta}$ measurements are known as reliable methods to diagnose the aging of medium-voltage cables. High frequency partial discharge measurement techniques have been widely used to diagnose cables in transmission and distribution systems. However, the on-line high frequency partial discharge technique has not been used in the nuclear power plants because of the plant shutdown risk, degraded measurement sensitivity, and application problems. In this paper, the partial discharge measurement with a portable device was tried to evaluate the integrity of the 4.16kV and 13.8kV cable lines. The test results show that the high detection sensitivity can be achieved by the high frequency partial discharge technique. The present technique is highly attractive to diagnose medium voltage cables in nuclear power plants.

• Analytical Science and Technology
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• v.28 no.4
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• pp.255-259
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• 2015

A technique on the determination of Co(II) in aqueous samples with quinalizarin-β-cyclodextrin inclusion complex was developed. The conditions such as pH of the sample solution, the concentration of quinalizarin and the concentration of β-CD were optimized to 11.3(±0.2), 1.2×10⁻⁶ M and 8.0×10⁻⁶ M, respectively. Under these optimum conditions, the calibration curve of Co(II) was obtained over concentration range of 5.0×10⁻⁹ ~ 1.2×10⁻⁷ M. The detection limit was 4.2×10⁻⁹ M (0.25 ng/mL). For validating this proposed technique, aqueous samples(stream water, reservoir water and tap water) were used. Recovery yields of 95~105% were obtained. The relative standard deviation(%) in aqueous samples were less than 7.0%. Based on experimental results, it is proposed that this technique can be applied to the practical determination of Co(II).

• Smart Media Journal
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• v.5 no.2
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• pp.77-83
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• 2016

Today, the great revolution in all financial industrial such as bank have been progressing through the utilization of IT technology actively, it is called the fintech. In this paper, we draw the draft design of NFC-based electronic payment and coupon system using FIDO framework to apply the 2 factor authentication technique for strength security. In detailed, we will study that the terminal device in front-end will be applied the 2 factor authentication and electric signature, and cloud-based payment gateway in back-end will be applied malicious code detection technique of distributed avoidance type.

• Nuclear Engineering and Technology
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• v.53 no.6
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• pp.1846-1857
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• 2021

Ultrasonic nondestructive testing is important for monitoring the structural integrity of dissimilar metal welds (DMWs) in pressure vessels and piping in nuclear power plants. However, there is a low probability of crack detection via inspection of DMWs using ultrasonic waves because the grain structures (grain orientations) of the weld area cause distortion and splitting of ultrasonic beams propagating in anisotropic media. To overcome this issue, the grain orientation should be known, and a precise ultrasonic wave simulation technique in anisotropic media is required to model the distortion and splitting of the waves accurately. In this study, a method for nondestructive prediction of the DMW grain orientations is presented for accurate simulation of ultrasonic wave propagation behavior in the weld area. The ultrasonic wave propagation behavior in anisotropic media is simulated via finite-element analysis when ultrasonic waves propagate in a transversely isotropic material. In addition, a methodology to predict the DMW grain orientation is proposed that employs a simulation technique for ultrasonic wave propagation behavior calculation and an optimization technique. The simulated ultrasonic wave behaviors with the grain orientations predicted via the proposed method demonstrate its usefulness. Moreover, the method can be used to determine the focal law in DMWs.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.1-10
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• 2022

Nowadays software defect prediction (SDP) is most active research going on in software engineering. Early detection of defects lowers the cost of the software and also improves reliability. Machine learning techniques are widely used to create SDP models based on programming measures. The majority of defect prediction models in the literature have problems with class imbalance and high dimensionality. In this paper, we proposed Centroid and Nearest Neighbor based Class Imbalance Reduction (CNNCIR) technique that considers dataset distribution characteristics to generate symmetry between defective and non-defective records in imbalanced datasets. The proposed approach is compared with SMOTE (Synthetic Minority Oversampling Technique). The high-dimensionality problem is addressed using Ant Colony Optimization (ACO) technique by choosing relevant features. We used nine different classifiers to analyze six open-source software defect datasets from the PROMISE repository and seven performance measures are used to evaluate them. The results of the proposed CNNCIR method with ACO based feature selection reveals that it outperforms SMOTE in the majority of cases.

• Journal of Creative Information Culture
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• v.5 no.2
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• pp.145-156
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• 2019

In this study, we have developed and evaluated an information extracting module based on the named entity recognition technique. For the given purpose in this paper, the module was designed to apply to the problem dealing with extraction of postal address information from arbitrary documents without any prior knowledge on the document layout. From the perspective of information technique practice, our approach can be said as a probabilistic n-gram (bi- or tri-gram) method which is a generalized technique compared with a uni-gram based keyword matching. It is the main difference between our approach and the conventional methods adopted in natural language processing that applying sentence detection, tokenization, and POS tagging recursively rather than applying the models sequentially. The test results with approximately two thousands documents are presented at this paper.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.5
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• pp.1466-1488
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• 2022

Recently, the importance and necessity of artificial intelligence (AI), especially machine learning, has been emphasized. In fact, studies are actively underway to solve complex and challenging problems through the use of AI systems, such as intelligent CCTVs, intelligent AI security systems, and AI surgical robots. Information security that involves analysis and response to security vulnerabilities of software is no exception to this and is recognized as one of the fields wherein significant results are expected when AI is applied. This is because the frequency of malware incidents is gradually increasing, and the available security technologies are limited with regard to the use of software security experts or source code analysis tools. We conducted a study on MalDC, a technique that converts malware into images using machine learning, MalDC showed good performance and was able to analyze and classify different types of malware. MalDC applies a preprocessing step to minimize the noise generated in the image conversion process and employs an image augmentation technique to reinforce the insufficient dataset, thus improving the accuracy of the malware classification. To verify the feasibility of our method, we tested the malware classification technique used by MalDC on a dataset provided by Microsoft and malware data collected by the Korea Internet & Security Agency (KISA). Consequently, an accuracy of 97% was achieved.

• Nuclear Engineering and Technology
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• v.56 no.2
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• pp.688-706
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• 2024

Austenitic stainless steels (ASS) are extensively employed in various sectors such as nuclear, power, petrochemical, oil and gas because of their excellent structural strength and resistance to corrosion. SS304 and SS316 are the predominant choices for piping, pressure vessels, heat exchangers, nuclear reactor core components and support structures, but they are susceptible to stress corrosion cracking (SCC) in chloride-rich environments. Over the course of several decades, extensive research efforts have been directed towards evaluating SCC using diverse methodologies and models, albeit some uncertainties persist regarding the precise progression of cracks. This review paper focuses on the application of Acoustic Emission Technique (AET) for assessing SCC damage mechanism by monitoring the dynamic acoustic emissions or inelastic stress waves generated during the initiation and propagation of cracks. AET serves as a valuable non-destructive technique (NDT) for in-service evaluation of the structural integrity within operational conditions and early detection of critical flaws. By leveraging the time domain and time-frequency domain techniques, various Acoustic Emission (AE) parameters can be characterized and correlated with the multi-stage crack damage phenomena. Further theories of the SCC mechanisms are elucidated, with a focus on both the dissolution-based and cleavage-based damage models. Through the comprehensive insights provided here, this review stands to contribute to an enhanced understanding of SCC damage in stainless steels and the potential AET application in nuclear industry.

• The Journal of the Korea Contents Association
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• v.14 no.4
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• pp.19-28
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• 2014

Recent advancements in the IT industry have made daily life more convenient than ever before. In particular, studies have focused on the position detection of individuals using the GPS in smartphones, and this application has been utilized actively in emergency rescue organizations. However, existing methods send the location information of a user to a predetermined guardian set by the user or to a control center when the user enters into or deviates from a predetermined space. Such spaces are created by an arbitrary radius, thereby making it difficult to set a detailed area by using an existing radius-area creation method in an unstructured space and path with a specific road, such as for trekking, amusement parks, or mountaineering. This study proposes a novel method for setting up an area by connecting multiple radii to improve the existing radius-area creation method in order to easily set a detailed area in smart devices or on the Internet. In addition, an optimization method for resource use is proposed by comparing the operation results in which a user's location is detected by using the proposed location-based area setup method and deviation detection.