• Structural Engineering and Mechanics
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• v.77 no.4
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• pp.495-508
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• 2021

This study devises a novel approach, namely quadruple 1D convolutional neural network, for detecting connection stiffness reduction in steel truss bridge structure using experimental and numerical modal data. The method is developed based on expertise in two domains: firstly, in Structural Health Monitoring, the mode shapes and its high-order derivatives, including second, third, and fourth derivatives, are accurate indicators in assessing damages. Secondly, in the Machine Learning literature, the deep convolutional neural networks are able to extract relevant features from input data, then perform classification tasks with high accuracy and reduced time complexity. The efficacy and effectiveness of the present method are supported through an extensive case study with the railway Nam O bridge. It delivers highly accurate results in assessing damage localization and damage severity for single as well as multiple damage scenarios. In addition, the robustness of this method is tested with the presence of white noise reflecting unavoidable uncertainties in signal processing and modeling in reality. The proposed approach is able to provide stable results with data corrupted by noise up to 10%.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.2
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• pp.74.2-75
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• 2021

We demonstrate that a deep learning classifier that only uses to gravitational wave (GW) detectors auxiliary channel data can distinguish various types of non-Gaussian noise transients (glitches) with significant accuracy, i.e., ≳ 80%. The classifier is implemented using the multi-scale neural networks (MSNN) with PyTorch. The glitches appearing in the GW strain data have been one of the main obstacles that degrade the sensitivity of the gravitational detectors, consequently hindering the detection and parameterization of the GW signals. Numerous efforts have been devoted to tracking down their origins and to mitigating them. However, there remain many glitches of which origins are not unveiled. We apply the MSNN classifier to the auxiliary channel data corresponding to publicly available GravitySpy glitch samples of LIGO O1 run without using GW strain data. Investigation of the auxiliary channel data of the segments that coincide to the glitches in the GW strain channel is particularly useful for finding the noise sources, because they record physical and environmental conditions and the status of each part of the detector. By only using the auxiliary channel data, this classifier can provide us with the independent view on the data quality and potentially gives us hints to the origins of the glitches, when using the explainable AI technique such as Layer-wise Relevance Propagation or GradCAM.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.1
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• pp.16-22
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• 2012

In this paper, we proposed a method of baseline correction for analysis of Raman spectra of platelets from Alzheimer's disease (AD) transgenic mice. Measured Raman spectra include the meaningful information and unnecessary noise which is composed of baseline and additive noise. The Raman spectrum is divided into the local region including several peaks and the spectrum of the region is modeled by curve fitting using Gaussian model. The additive noise is clearly removed from the process of replacing the original spectrum with the fitted model. The baseline correction after interpolating the local minima of the fitted model with linear, piecewise cubic Hermite and cubic spline algorithm. The baseline corrected models extract the feature with principal component analysis (PCA). The classification result of support vector machine (SVM) and maximum $a$ posteriori probability (MAP) using linear interpolation method showed the good performance about overall number of principal components, especially SVM gave the best performance which is about 97.3% true classification average rate in case of piecewise cubic Hermite algorithm and 5 principal components. In addition, it confirmed that the proposed baseline correction method compared with the previous research result could be effectively applied in the analysis of the Raman spectra of platelet.

• The Journal of the Acoustical Society of Korea
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• v.40 no.5
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• pp.479-487
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• 2021

In this paper, we propose an Sound Event Detection (SED) model using self-training based on a noisy student model. The proposed SED model consists of two stages. In the first stage, a mean-teacher model based on an Residual Convolutional Recurrent Neural Network (RCRNN) is constructed to provide target labels regarding weakly labeled or unlabeled data. In the second stage, a self-training-based noisy student model is constructed by applying different noise types. That is, feature noises, such as time-frequency shift, mixup, SpecAugment, and dropout-based model noise are used here. In addition, a semi-supervised loss function is applied to train the noisy student model, which acts as label noise injection. The performance of the proposed SED model is evaluated on the validation set of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2020 Challenge Task 4. The experiments show that the single model and ensemble model of the proposed SED based on the noisy student model improve F1-score by 4.6 % and 3.4 % compared to the top-ranked model in DCASE 2020 challenge Task 4, respectively.

• Korean Journal of Remote Sensing
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• v.34 no.6_2
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• pp.1273-1282
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• 2018

Recently, active research on the Arctic Ocean has been conducted due to the influence of global warming and new Arctic ship route. Although previous studies already calculated quantitative extent of sea ice using passive microwave radiometers, melting at the edge of sea ice and surface roughness were hardly considered due to low spatial resolution. Since Sentienl-1A/B data in Extended Wide (EW) mode are being distributed as free of charge and bulk data for Arctic sea can be generated during a short period, the entire Arctic sea ice data can be covered in high spatial resolution by mosaicking bulk data. However, Sentinel-1A/B data in EW mode, especially in HV polarization, needs significant radiometric correction for further classification. Thus, in this study, we developed algorithms that can correct thermal noise and scalloping effects, and confirmed that Arctic sea ice and open-water were well classified using the corrected dual-polarization SAR data.

• Korean Journal of Remote Sensing
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• v.29 no.1
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• pp.105-114
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• 2013

Desertification of the East Asia area induced by human's indiscriminate activities and natural causes has gradually expanded and demanded scientific research for monitoring and predicting land cover condition. Therefore, this research classified land types which were compared to MODIS land cover and analyzed the extent of barren zone effecting Korea through yellow dust using S10-DAY MVC NDVI from SPOT between 1999 and 2011. This study used unsupervised classification after processing NDVI Correction and Water Mask for eliminating noise values included in the data for enhancement of classification accuracy. The results of analysis are that there are active variations near the borders of desert, especially the Mongolian steppe and the Gobi Desert in central Asia. In addition, the extent of entire desert has been decreased in the middle of the last decade, although desertification is in going on in East Asia.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.4
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• pp.107-114
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• 2008

In this paper, we constructed a speech emotion recognition system that classifies four emotions - neutral, happy, sad, and anger from speech based on male/female gender discrimination. At first, the proposed system distinguish between male and female from a queried speech, then the system performance can be improved by using separate optimized feature vectors for each gender for the emotion classification. As a emotion feature vector, this paper adopts ZCPA(Zero Crossings with Peak Amplitudes) which is well known for its noise-robustic characteristic from the speech recognition area and the features are optimized using SFS method. For a pattern classification of emotion, k-NN and SVM classifiers are compared experimentally. From the computer simulation results, the proposed system was proven to be highly efficient for speech emotion classification about 85.3% regarding four emotion states. This might promise the use the proposed system in various applications such as call-center, humanoid robots, ubiquitous, and etc.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.5
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• pp.871-879
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• 2020

Perceptual Ad-Blocking is a new advertising blocking technique that detects online advertising by using an artificial intelligence-based advertising image classification model. A recent study has shown that these Perceptual Ad-Blocking models are vulnerable to adversarial attacks using adversarial examples to add noise to images that cause them to be misclassified. In this paper, we prove that existing perceptual Ad-Blocking technique has a weakness for several adversarial example and that Defense-GAN and MagNet who performed well for MNIST dataset and CIFAR-10 dataset are good to advertising dataset. Through this, using Defense-GAN and MagNet techniques, it presents a robust new advertising image classification model for adversarial attacks. According to the results of experiments using various existing adversarial attack techniques, the techniques proposed in this paper were able to secure the accuracy and performance through the robust image classification techniques, and furthermore, they were able to defend a certain level against white-box attacks by attackers who knew the details of defense techniques.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.8
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• pp.216-223
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• 2019

Fault detection during production processes is one of the required operational tasks to run production processes both safely and consistently. Unexpected operational events or undetected process faults can have a serious impact on the production systems and subsequently on the final products' quality. In addition, such situations may lead to malfunctions or breakdowns of production processes. To reliably detect such abnormalities, a new one-class classification-based detection scheme has recently been developed The proposed method consists of four steps:1) noise filtering, 2) feature selection, 3) nonlinear representation and 4) outlier detection. The performance of the proposed scheme was demonstrated using the multivariate data obtained from a simulation process. The results have shown that the proposed method produced reliable monitoring results and outperforms any existing methods with an average improvement of 25.4%. The use of proper feature selection in the proposed framework yielded better detection performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2168-2187
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• 2021

The smart grid replaces the traditional power structure with information inventiveness that contributes to a new physical structure. In such a field, malicious information injection can potentially lead to extreme results. Incorrect, FDI attacks will never be identified by typical residual techniques for false data identification. Most of the work on the detection of FDI attacks is based on the linearized power system model DC and does not detect attacks from the AC model. Also, the overwhelming majority of current FDIA recognition approaches focus on FDIA, whilst significant injection location data cannot be achieved. Building on the continuous developments in deep learning, we propose a Deep Learning based Locational Detection technique to continuously recognize the specific areas of FDIA. In the development area solver gap happiness is a False Data Detector (FDD) that incorporates a Convolutional Neural Network (CNN). The FDD is established enough to catch the fake information. As a multi-label classifier, the following CNN is utilized to evaluate the irregularity and cooccurrence dependency of power flow calculations due to the possible attacks. There are no earlier statistical assumptions in the architecture proposed, as they are "model-free." It is also "cost-accommodating" since it does not alter the current FDD framework and it is only several microseconds on a household computer during the identification procedure. We have shown that ANN-MLP, SVM-RBF, and CNN can conduct locational detection under different noise and attack circumstances through broad experience in IEEE 14, 30, 57, and 118 bus systems. Moreover, the multi-name classification method used successfully improves the precision of the present identification.