• Structural Engineering and Mechanics
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• v.87 no.3
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• pp.211-219
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• 2023

The structural health of a pipeline is usually assessed by visual inspection. In addition to the fact that this method is expensive and time consuming, inspection of the whole structure is not possible due to limited access to some points. Therefore, adopting a damage detection method without the mentioned limitations is important in order to increase the safety of the structure. In recent years, vibration-based methods have been used to detect damage. These methods detect structural defects based on the fact that the dynamic responses of the structure will change due to damage existence. Therefore, the location and extent of damage, before and after the damage, are determined. In this study, fuzzy genetic algorithm has been used to monitor the structural health of the pipeline to create a fuzzy automated system and all kinds of possible failure scenarios that can occur for the structure. For this purpose, the results of an experimental model have been used. Its numerical model is generated in ABAQUS software and the results of the analysis are used in the fuzzy genetic algorithm. Results show that the system is more accurate in detecting high-intensity damages, and the use of higher frequency modes helps to increase accuracy. Moreover, the system considers the damage in symmetric regions with the same degree of membership. To deal with the uncertainties, some error values are added, which are observed to be negligible up to 10% of the error.

• Computers and Concrete
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• v.32 no.5
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• pp.475-486
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• 2023

Nondestructive evaluation (NDE) is an important task of civil engineering structure monitoring and inspection, but minor damage such as small cracks in local structure is difficult to observe. If cracks continued expansion may cause partial or even overall damage to the structure. Therefore, monitoring and detecting the structure in the early stage of crack propagation is important. The crack detection technology based on machine vision has been widely studied, but there are still some problems such as bad recognition effect for small cracks. In this paper, we proposed a deep learning method based on sweep signals to evaluate concrete surface crack with a width less than 1 mm. Two convolutional neural networks (CNNs) are used to analyze the one-dimensional (1D) frequency sweep signal and the two-dimensional (2D) time-frequency image, respectively, and the probability value of average damage (ADPV) is proposed to evaluate the minor damage of structural. Finally, we use the standard deviation of energy ratio change (ERVSD) and infrared thermography (IRT) to compare with ADPV to verify the effectiveness of the method proposed in this paper. The experiment results show that the method proposed in this paper can effectively predict whether the concrete surface is damaged and the severity of damage.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.1
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• pp.153-168
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• 2016

This paper introduces a structure, features and programming techniques for the CNG(Cryptography API: Next Generation), which is the substitution of the CAPI(Cryptography API) from Microsoft. The CNG allows to optimize a scope of functions and features because it is comprised of independent modules based on plug-in structure. Therefore, the CNG is competitive on development costs and agility to extend. In addition, the CNG supports various functions for the newest cryptographic algorithm, audit, kernel-mode programming with agility and possible to contribute for core cryptography services in a new environment. Therefore, based on these advantageous functions, we analyze the structure of CNG to extend it for the enterprise and the public office. In addition, we implement an error-detection tool for program which utilizes CNG library.

• Journal of KIISE
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• v.44 no.11
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• pp.1231-1235
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• 2017

Given the network structure in online social network, it is important to determine a way to distinguish spam accounts from the network features. In online social network, the service provider attempts to detect social spamming to maintain their service quality. However the spammer group changes their strategies to avoid being detected. Even though the spammer attempts to act as legitimate users, certain distinguishable structural features are not easily changed. In this paper, we investigate a way to generate meaningful network structure features, and suggest spammer detection method using recursive structural features. From a result of real-world dataset experiment, we found that the proposed algorithm could improve the classification performance by about 8%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.5
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• pp.1221-1227
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• 2015

Edge detection is one of the important technologies to simplify images in the text, lane and object recognition implementation process, and various studies are actively carried out at home and abroad. Existing edge detection methods include a method to detect edge by applying directional gradient masks in spatial space, and a mathematical morphology-based edge detection method. These existing detection methods show insufficient edge detection results in excessively dark or bright images. In this regard, to complement these drawbacks, we proposed an algorithm using the Sobel and histogram equalization among the existing methods.

• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.1
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• pp.35-43
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• 2018

MANET composed of only wireless nodes is increasingly utilized in various fields. However, it is exposed to many security vulnerabilities because it doesn't have any infrastructure and transmits data by using multi-hop method. Therefore, MANET should be applied the intrusion detection technique that can detect efficiently malicious nodes and decrease impacts of various attacks. In this paper, we propose a distributed intrusion detection technique that can detect the various attacks while improving the efficiency of attack detection and reducing the false positive rate. The proposed technique uses the cluster structure to manage the information in the center and monitor the traffic of their neighbor nodes directly in all nodes. We use three parameters for attack detection. We also applied an efficient authentication technique using only key exchange without the help of CA in order to provide integrity when exchanging information between cluster heads. This makes it possible to free the forgery of information about trust information of the nodes and attack nodes. The superiority of the proposed technique can be confirmed through comparative experiments with existing intrusion detection techniques.

• Nuclear Engineering and Technology
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• v.52 no.11
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• pp.2601-2606
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• 2020

In this study, we expanded the performance of the existing EXVol code and performed empirical experiments and calculations. A high-resolution gamma spectroscopy system was constructed, and a standard point source and a standard volume source were measured with an HPGe detector with 43.1% relative efficiency. EXVol was verified by quantitative comparison of the detection efficiencies determined by measurements and calculations. To introduce the concept of the detector scanning that occurs in the actual measurement into the EXVol code, a collimator was placed between the source and detector. The detection efficiency was determined in the asymmetric arrangement of the source and detector with a collimator. A collimator made of lead with a diameter of 15 mm and a thickness of 50 mm was installed between the source and the detector to determine the detection efficiency at a specific location. The calculation result was contour plotted so that the distribution of detection efficiency could be visually confirmed. The relative deviation between the measurements and calculations for the coaxial and asymmetric structures was 10%, and that for the collimation structure was 20%. The results of this study can be applied to research using γ-ray measurements.

• Structural Engineering and Mechanics
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• v.30 no.4
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• pp.445-466
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• 2008

This paper presents a novel structural damage detection method with a new damage index based on the statistical moments of dynamic responses of a structure under a random excitation. After a brief introduction to statistical moment theory, the principle of the new method is put forward in terms of a single-degree-of-freedom (SDOF) system. The sensitivity of statistical moment to structural damage is discussed for various types of structural responses and different orders of statistical moment. The formulae for statistical moment-based damage detection are derived. The effect of measurement noise on damage detection is ascertained. The new damage index and the proposed statistical moment-based damage detection method are then extended to multi-degree-of-freedom (MDOF) systems with resort to the leastsquares method. As numerical studies, the proposed method is applied to both single and multi-story shear buildings. Numerical results show that the fourth-order statistical moment of story drifts is a more sensitive indicator to structural stiffness reduction than the natural frequencies, the second order moment of story drift, and the fourth-order moments of velocity and acceleration responses of the shear building. The fourth-order statistical moment of story drifts can be used to accurately identify both location and severity of structural stiffness reduction of the shear building. Furthermore, a significant advantage of the proposed damage detection method lies in that it is insensitive to measurement noise.

• Nuclear Engineering and Technology
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• v.51 no.1
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• pp.198-206
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• 2019

A new methodology to analyze the nuclear material accountability for pyroprocessing system is developed. The $Pu-to-^{244}Cm$ ratio quantification is one of the methods for Pu accountancy in pyroprocessing. However, an uncertainty in the $Pu-to-^{244}Cm$ ratio due to the non-uniform composition in used fuel assemblies can affect the accountancy of Pu. A random variable, LOPu, is developed to analyze the probability of detection for Pu diversion of hypothetical scenarios at a pyroprocessing facility considering the uncertainty in $Pu-to-^{244}Cm$ ratio estimation. The analysis is carried out by the hypothesis testing and the event tree method. The probability of detection for diversion of 8 kg Pu is found to be less than 95% if a large size granule consisting of small size particles gets sampled for measurements. To increase the probability of detection more than 95%, first, a new Material Balance Area (MBA) structure consisting of more number of Key Measurement Points (KMPs) is designed. This multiple KMP-measurement for the MBA shows the probability of detection for 8 kg Pu diversion is greater than 96%. Increasing the granule sample number from one to ten also shows the probability of detection is greater than 95% in the most ranges for granule and powder sizes.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.625-627
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• 2022

In the construction and development of modern industrial production technology, the traditional technology management mode is faced with many problems such as low qualification rates and high application costs. In the research, an improved workpiece defect detection method based on deep learning is proposed, which can control the application cost and improve the detection efficiency of irregular defects. Based on the research of the current situation of deep learning applications, this paper uses the improved Faster R-CNN network structure model as the core detection algorithm to automatically locate and classify the defect areas of the workpiece. Firstly, the robustness of the model was improved by appropriately changing the depth and the number of channels of the backbone network, and the hyperparameters of the improved model were adjusted. Then the deformable convolution is added to improve the detection ability of irregular defects. The final experimental results show that this method's average detection accuracy (mAP) is 4.5% higher than that of other methods. The model with anchor size and aspect ratio (65,129,257,519) and (0.2,0.5,1,1) has the highest defect recognition rate, and the detection accuracy reaches 93.88%.