• Structural Monitoring and Maintenance
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• v.11 no.3
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• pp.203-217
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• 2024

Electro-mechanical impedance (EMI) technique is a low-cost structural damage detection method. It reflects structural damage through the change in admittance signal which contains the structural mechanical impedance information. The ambient temperature greatly affects the admittance signal, which hides the changes caused by structural damage and reduces the accuracy of damage identification. This study introduces a convolutional neural network to compensate for the temperature effect. The proposed method uses a framework that consists of a feature extraction network and a decoding network, and the original admittance signal with temperature information is used as the input. The output admittance signal is eliminated from the temperature effect, improving damage identification robustness. The admittance data simulated by the finite element model of the spatial grid structure is used to verify the effectiveness of the proposed method. The results show that the proposed method has advantages in identification accuracy compared with the damage index minimization method and the principal component analysis method.

• The Journal of Engineering Geology
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• v.7 no.2
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• pp.139-149
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• 1997

Electrical resistivity monitoring methods were adopted to detect groundwater table change in alluvium. Numerical modelling test using finite element method(FEM) and field resisfivity monitoring were conducted in the study. The field monitoring data were acquired in the alluvium deposit site in Jeong-Dong Ri, Geum River where pumping test had been conducted continuously for 20 days to make artificial changes of groundwater table. The unit distance of the electrode array was 4m and 21 fixed electrodes were applied in numerical calculation and field data acquisition. "Modified Wenner" and dipole-dipole array configurations were used in the study. The models used in two-dimensional numerical test were designed on the basis of the simplifving geological model of the alluvium in Jeong Dong Ri, Geum River. Numerical test results show that the apparent resistivity pseudosections were changed in the vicinity of the pootion where groundwater table was changed. Furthermore, there are some apparent resistivity changes in the boundary between aquifer and crystalline basement rock which overlays the aquifer. The field monitoring data also give similar results which were observed in numerical tests. From the numerical test using FEM and field resistivity monitoring observations in alluvium site of Geum River, the electrical monitoring method is proved to be a useful tool for detecting groundwater behavior including groundwater table change. There are some limitations, however, in the application of the resistivity method only because the change of groundwater table does not give enough variations in the apparent resistivity pseudosections to estimate the amount of groundwater table change. For the improved detection of groundwater table changes, it is desirable to combine the resistivity method with other geophysical methods that reveal the underground image such as high-resolution seismic and/or ground penetrating radar surveys.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.2
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• pp.40-46
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• 2011

This paper proposes a real-time face detection and tracking system that uses neural oscillators which can be applied to access regulation system or control systems of user authentication as well as a new algorithm. We study a way to track faces using the neural oscillatory network which imitates the artificial neural net of information handing ability of human and animals, and biological movement characteristic of a singular neuron. The system that is suggested in this paper can broadly be broken into two stages of process. The first stage is the process of face extraction, which involves the acquisition of real-time RGB24bit color video delivering with the use of a cheap webcam. LEGION(Locally Excitatory Globally Inhibitory)algorithm is suggested as the face extraction method to be preceded for face tracking. The second stage is a method for face tracking by discovering the leader neuron that has the greatest connection strength amongst neighbor neuron of extracted face area. Along with the suggested method, the necessary element of face track such as stability as well as scale problem can be resolved.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.30 no.1
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• pp.63-70
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• 2017

In this paper, a magnetic flux leakage(MFL) based steel bar damage detection was first researched to quantify the signals from damages on the wire rope. Though many researches inspecting damages using a MFL method was proceeded until the present, the researches are at the level that diagnose whether damages are or not. This has limitation to take measures in accordance with the damage level. Thus, a MFL inspection system was modeled using a finite element analysis(FEM) program dealing with electromagnetism problems, and a steel bar specimen was adopted as a ferromagnetic object. Then, an experimental study was also carried out to verify the simulation results with a steel bar which has same damage conditions as the simulation. The MFL signals was nearly not affected by the increase of the inspection velocity, and the magnitudes of the signals are not identical according to the change of the defect width even the defects have same depth. On the basis of the analysis, the signal properties from the damages were extracted to classify the type of damages, and it could be confirmed that classification of damages using extracted signal properties is feasible.

• Journal of IKEEE
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• v.18 no.4
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• pp.485-494
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• 2014

Traffic Sign Recognition(TSR) is an important element in an Advanced Driver Assistance System(ADAS). However, many studies related to TSR approaches only in normal daytime environment because a sign's unique color doesn't appear in poor environment such as night time, snow, rain or fog. In this paper, we propose a new TSR algorithm based on machine learning for daytime as well as poor environment. In poor environment, traditional methods which use RGB color region doesn't show good performance. So we extracted sign characteristics using HoG extraction, and detected signs using a Support Vector Machine(SVM). The detected sign is recognized by a decision tree based on 25 reference points in a Normalized RGB system. The detection rate of the proposed system is 96.4% and the recognition rate is 94% when applied in poor environment. The testing was performed on an Intel i5 processor at 3.4 GHz using Full HD resolution images. As a result, the proposed algorithm shows that machine learning based detection and recognition methods can efficiently be used for TSR algorithm even in poor driving environment.

• Journal of the Korea institute for structural maintenance and inspection
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• v.21 no.6
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• pp.140-146
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• 2017

It is laser induced breakdown spectroscopy(LIBS) that enables qualitative and quantitative analysis of the elements contained in unknown specimen by comparing the wavelength characteristics of each element obtained from the spectral analysis of the standard specimen with the wavelength analysis results from unknown specimens. In this study, the applicability of LIBS to the analysis of major deterioration factors affecting concrete durability was experimentally analyzed. That is, the possibility of applying LIBS to the diagnosis of concrete deterioration by studying the quantitative detection of harmful deteriorating factors on chloride, sulfate and carbonated mortar specimens using LIBS was studied. As a result of LIBS test for each chloride and sulfate specimen, the LIBS spectral wavelength intensity of chlorine and sulfur ions increased linearly with increasing concentration. Carbon ion LIBS spectral wave intensities of carbonated specimens increased nonlinearly over the duration of carbonation exposure. From the above results, it can be partially confirmed that LIBS can be applied to the diagnosis of concrete deterioration. In case of concrete carbonation, it is presumed that carbon content is contained in the cement itself and is different from the detection of chloride and sulfate specimen. Therefore, it is considered that more various parameter studies should be performed to apply LIBS to concrete carbonation.

• Geophysics and Geophysical Exploration
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• v.9 no.4
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• pp.291-298
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• 2006

Resistivity method is a practical and effective geophysical technique to detect leakage zones in embankment dams. Generally, resistivity survey conducted along the crest assumes that the embankment dam has a 2D structure. However, the 3D topography of embankments distorts significantly resistivity data measured on anywhere of the dam. In this study, we analyse the influence from 3D effects created by specific dam geometry through the 3D finite element modeling technique. We compared 3D effects when resistivity surveys are carried out on the upstream slope, left edge of the crest, center of the crest, right edge of the crest and downstream slope. We ensure that 3D effect is greatly different according to the location of the survey line and data obtained on the downstream slope are most greatly influenced by 3D dam geometry. Also, resistivity data are more influenced by the electrical resistivity of materials constituting reservoir than 3D effects due to specific dam geometry. Furthermore, using resistivity data synthesized with 3D modeling program for an embankment dam model with leakage zone, we analyse the possibility of leakages detection from 2D resistivity surveys performed along the embankment dam.

• Journal of the Korea Society for Simulation
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• v.29 no.3
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• pp.57-72
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• 2020

To respond to the threat of Long range artillery of North Korea, it is necessary to establish the Korean counter long range artillery intercept system(CLRAIS). The purpose of this study is to study the operational concept of the CLRAIS against the threat of long range artillery of North Korea, and to develop the operational effectiveness process of the CLRAIS. First, we set up the operating concept of the CLRAIS and established the concept of an effectiveness in a many-to-many engagement situation and a process to derive it. Based on this, a tool was developed to analyze the actual effectiveness. In order to find out the factors influencing the effectiveness in many-to-many engagement situations, simulation experiments were performed by combining various variables such as detection assets, engagement control, and launchpad performance. As a result, it was found that in addition to the missile performance, the performance of the detection assets and the engagement control center had a significant impact on the intercept rate and the defense success rate. These findings can be used to understand important indicators in terms of effectiveness in many-to-many engagement situations in the future development of weapon system, and to determine the development direction and target value of each element necessary for the level of defense success rate to be achieved.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.112-119
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• 2023

Traditionally used for networking computers and communications, the Internet has been evolving from the beginning. Internet is the backbone for many things on the web including social media. The concept of social networking which started in the early 1990s has also been growing with the internet. Social Networking Sites (SNSs) sprung and stayed back to an important element of internet usage mainly due to the services or provisions they allow on the web. Twitter and Facebook have become the primary means by which most individuals keep in touch with others and carry on substantive conversations. These sites allow the posting of photos, videos and support audio and video storage on the sites which can be shared amongst users. Although an attractive option, these provisions have also culminated in issues for these sites like posting offensive material. Though not always, users of SNSs have their share in promoting hate by their words or speeches which is difficult to be curtailed after being uploaded in the media. Hence, this article outlines a process for extracting user reviews from the Twitter corpus in order to identify instances of hate speech. Through the use of MPCA (Modified Principal Component Analysis) and ECNN, we are able to identify instances of hate speech in the text (Enhanced Convolutional Neural Network). With the use of NLP, a fully autonomous system for assessing syntax and meaning can be established (NLP). There is a strong emphasis on pre-processing, feature extraction, and classification. Cleansing the text by removing extra spaces, punctuation, and stop words is what normalization is all about. In the process of extracting features, these features that have already been processed are used. During the feature extraction process, the MPCA algorithm is used. It takes a set of related features and pulls out the ones that tell us the most about the dataset we give itThe proposed categorization method is then put forth as a means of detecting instances of hate speech or abusive language. It is argued that ECNN is superior to other methods for identifying hateful content online. It can take in massive amounts of data and quickly return accurate results, especially for larger datasets. As a result, the proposed MPCA+ECNN algorithm improves not only the F-measure values, but also the accuracy, precision, and recall.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.9
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• pp.4469-4475
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• 2012

Background: The potential use of hypomethylation of Long INterspersed Element 1 (LINE-1) and Alu elements (Alu) as a biomarker has been comprehensively assessed in several cancers, including head and neck squamous cell carcinoma (HNSCC). Failure to detect occult metastatic head and neck tumors on radical neck lymph node dissection can affect the therapeutic measures taken. Objective: The aim of this study was to investigate the LINE-1 and Alu methylation status and determine whether it can be applied for detection of occult metastatic tumors in HNSCC cases. Methods: We used the Combine Bisulfite Restriction Analysis (COBRA) technique to analyse LINE-1 and Alu methylation status. In addition to the methylation level, LINE-1 and Alu loci were classified based on the methylation statuses of two CpG dinucleotides in each allele as follows: hypermethylation ($^mC^mC$), hypomethylation ($^uC^uC$), and 2 forms of partial methylation ($^mC^uC$ and $^uC^mC$). Sixty-one lymph nodes were divided into 3 groups: 1) non-metastatic head and neck cancer (NM), 2) histologically negative for tumor cells of cases with metastatic head and neck cancer (LN), and 3) histologically positive for tumor cells (LP). Results: Alu methylation change was not significant. However, LINE-1 methylation of both LN and LP was altered, as demonstrated by the lower LINE-1 methylation levels (p<0.001), higher percentage of $^mC^uC$ (p<0.01), lower percentage of $^uC^mC$ (p<0.001) and higher percentage of $^uC^uC$ (p<0.001). Using receiver operating characteristic (ROC) curve analysis, $%^uC^mC$ and $%^mC^uC$ values revealed a high level of AUC at 0.806 and 0.716, respectively, in distinguishing LN from NM. Conclusion: The LINE-1 methylation changes in LN have the same pattern as that in LP. This epigenomic change may be due to the presence of occult metastatic tumor in LN cases.