• Smart Structures and Systems
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• 제12권6호
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• pp.619-640
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• 2013

It is highly desirable to explore efficient algorithms for detecting structural damage of large size structural systems with limited input and output measurements. In this paper, a new structural damage detection algorithm based on substructure approach is proposed for large size structural systems with limited input and output measurements. Inter-connection effect between adjacent substructures is treated as 'additional unknown inputs' to substructures. Extended state vector of each substructure and its unknown excitations are estimated by sequential extended Kalman estimator and least-squares estimation, respectively. It is shown that the 'additional unknown inputs' can be estimated by the algorithm without the measurements on the substructure interface DOFs, which is superior to previous substructural identification approaches. Also, structural parameters and unknown excitation are estimated in a sequential manner, which simplifies the identification problem compared with other existing work. Structural damage can be detected from the degradation of the identified substructural element stiffness values. The performances of the proposed algorithm are demonstrated by several numerical examples and a lab experiment. Measurement noise effect is considered. Both the simulation results and experimental data validate that the proposed algorithm is viable for structural damage detection of large size structural systems with limited input and output measurements.

• Journal of Information Processing Systems
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• 제18권5호
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• pp.677-687
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• 2022

We propose a detection algorithm based on tree-structured convolutional neural networks (TsCNNs) that finds pornography, propaganda, or other inappropriate content on a social media network. The algorithm sequentially applies the typical convolutional neural network (CNN) algorithm in a tree-like structure to minimize classification errors in similar classes, and thus improves accuracy. We implemented the detection system and conducted experiments on a data set comprised of 6 ordinary classes and 11 inappropriate classes collected from the Korean military social network. Each model of the proposed algorithm was trained, and the performance was then evaluated according to the images and videos identified. Experimental results with 20,005 new images showed that the overall accuracy in image identification achieved a high-performance level of 99.51%, and the effectiveness of the algorithm reduced identification errors by the typical CNN algorithm by 64.87 %. By reducing false alarms in video identification from the domain, the TsCNNs achieved optimal performance of 98.11% when using 10 minutes frame-sampling intervals. This indicates that classification through proper sampling contributes to the reduction of computational burden and false alarms.

• 대한원격탐사학회지
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• 제40권2호
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• pp.167-177
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• 2024

With advancements in satellite technology, interest in target detection and identification is increasing quantitatively and qualitatively. Synthetic Aperture Radar(SAR) images, which can be acquired regardless of weather conditions, have been applied to various areas combined with machine learning based detection algorithms. However, conventional studies primarily focused on the detection of stationary targets. In this study, we proposed a method to identify moving targets using an algorithm that integrates sub-aperture SAR images and cosine similarity calculations. Utilizing a transformer-based deep learning target detection model, we extracted the bounding box of each target, designated the area as a region of interest (ROI), estimated the similarity between sub-aperture SAR images, and determined movement based on a predefined similarity threshold. Through the proposed algorithm, the quantitative evaluation of target identification capability enhanced its accuracy compared to when training with the targets with two different classes. It signified the effectiveness of our approach in maintaining accuracy while reliably discerning whether a target is in motion.

• Nuclear Engineering and Technology
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• 제17권2호
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• pp.83-97
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• 1985

GLR (Generalized Likelihood Ratio)방식은 최적상태 변수 추정기인 Kalman-Buchy 필터로부터 발생되는 연속 Innovation들에 대해 통계확률적검사를 수행함으로써 시스템 고장 탐지 및 종류를 판별하게 된다. 그러나, 이러한 종전의 GLR방식은 각 경우마다 특별한 고장 형태를 가정해야 하므로, 모든 가능한 고장 형태를 탐지하는 데 많은 어려움이 있다. 이번 논문에서는 이런 난제를 해결할 한 방법을 제시하였다. 그리고, 가압경수형 원자력발전소 일차측 압력을 조절하는 가압기에 적용시켜 본 결과, 어떤 형태의 고장이든 잘 탐지되고 그 종류도 구별할 수 있음을 보여주었으며, 종전방식에 비해 고장 탐지 및 고장 구별에 필요한 컴퓨터처리 시간도 줄일 수가 있었다.

• Mass Spectrometry Letters
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• 제8권4호
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• pp.90-97
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• 2017

Androgenic anabolic steroids (AASs) are synthetic derivatives of testosterone with a common structure containing cyclopentanoperhydrophenanthrene nucleus. Their use enhances the muscle building capacity and is beneficial during performance. The AASs are one of the most abused group of substances in horse doping. Liquid chromatography tandem mass spectrometry ($LC/MS^n$) has been successfully applied to the detection of anabolic steroids in biological samples. However, the saturated hydroxysteroids viz: nandrolone, $5{\alpha}-estrane-3{\beta}$, $17{\alpha}-diol$ exhibit lower detection responses in electrospray ionisation (ESI) because of their poor ionisation efficiency. To overcome this limitation pre-column chemical derivatization has been introduced to enhance their detection responses in $LC-ESI-MS^n$ analysis. The aim of present study was to develop a sensitive method for identification and confirmation of nandrolone and its metabolite in horse urine incorporating pre-column derivatization using picolinic acid. The method consists of extraction of targeted steroid conjugates by solid phase extraction (SPE). The eluted steroid conjugates were hydrolysed by methanolysis and free steroids were recovered with liquid-liquid extraction. The resulting steroids were derivatized to form picolinoyl esters and identification was done using LC-ESI-MS/MS in positive ionization mode. The picolinated steroid adduct enhanced the detection levels in comparison to underivatized steroids.

• 한국멀티미디어학회논문지
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• 제7권2호
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• pp.156-163
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• 2004

본 논문에서는 적응적 얼굴영역 검출과 얼굴 특징자 평가함수를 사용한 실시간 얼굴인식 알고리즘을 제안하였다. 제안한 알고리즘은 명암도 정보와 타원마스킹 기법뿐만 아니라 인종별 얼굴피부색을 사용하여 정확한 얼굴영역을 적응적으로 검출 가능하다. 또한 제안한 알고리즘은 얼굴 특징자 및 얼굴특징자간 기하학적 평가함수를 사용하여 얼굴 인식 효율을 개선하였다. 제안한 알고리즘은 생체인증 및 보안 시스템 분야에 사용 가능하다. 실험에서는 제안한 방법의 우수성을 입증하기 위해 실 영상을 사용하였으며 실험 결과 기존의 방법보다 얼굴 영역 검출뿐만 아니라 얼굴인식 성능을 개선하였다.

• Structural Monitoring and Maintenance
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• 제4권4호
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• pp.365-379
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• 2017

This paper presents a comparison of the black-box and the physics based derived gray-box models for subspace identification for structures subjected to support-excitation. The study compares the damage detection capabilities of both these methods for linear time invariant (LTI) systems as well as linear time-varying (LTV) systems by extending the gray-box model for time-varying systems using short-time windows. The numerically simulated IASC-ASCE Phase-I benchmark building has been used to compare the two methods for different damage scenarios. The efficacy of the two methods for the identification of stiffness parameters has been studied in the presence of different levels of sensor noise to simulate on-field conditions. The proposed extension of the gray-box model for LTV systems has been shown to outperform the black-box model in capturing the variation in stiffness parameters for the benchmark building.

• International Journal of Contents
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• 제2권3호
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• pp.27-32
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• 2006

We present an effective algorithm for automatic tracing of retinal vessel structure and vascular landmark extraction of bifurcations and ending points. In this paper we deal with vascular patterns from RGB images for personal identification. Vessel tracing algorithms are of interest in a variety of biometric and medical application such as personal identification, biometrics, and ophthalmic disorders like vessel change detection. However eye surface vasculature tracing in RGB images has many problems which are subject to improper illumination, glare, fade-out, shadow and artifacts arising from reflection, refraction, and dispersion. The proposed algorithm on vascular tracing employs multi-stage processing of ten-layers as followings: Image Acquisition, Image Enhancement by gray scale retinal image enhancement, reducing background artifact and illuminations and removing interlacing minute characteristics of vessels, Vascular Structure Extraction by connecting broken vessels, extracting vascular structure using eight directional information, and extracting retinal vascular structure, and Vascular Landmark Extraction by extracting bifurcations and ending points. The results of automatic retinal vessel extraction using jive different thresholds applied 34 eye images are presented. The results of vasculature tracing algorithm shows that the suggested algorithm can obtain not only robust and accurate vessel tracing but also vascular landmarks according to thresholds.

• 항공우주기술
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• 제6권2호
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• pp.21-28
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• 2007

본 논문의 목표는 고장허용제어시스템에 대한 기반 연구로서 무인비행기 안전성 향상을 위하여 조종면 작동불능과 같은 고장에 대해 검출 방법을 제시하는 것이다. 조종면 고장검출을 위한 실시간 시스템식별 알고리듬은 퓨리에 변환기법을 사용하였으며 프로그램 성능 및 검증을 위해 HILS 시험과 비행시험을 수행하였다. 엘리베이터 조종면 고장은 피칭모멘트에 대한 조종면 효과를 나타내는 조종미계수를 실시간 추정하여 정상상태의 값과 비교함으로써 검출된다. 비행시험 결과를 통해 고장상태의 조종미계수 값은 정상상태의 값보다 작다는 것을 확인하였다.

• Smart Structures and Systems
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• 제23권1호
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• pp.15-29
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• 2019

This paper addresses the problem of damage detection in suspension bridge hangers, with an emphasis on the modal flexibility method. It aims at evaluating the capability and the accuracy of the modal flexibility method to detect and locate single and multiple damages in suspension bridge hangers, with different level of severity and various locations. The study is conducted numerically and experimentally on a laboratory suspension bridge mock-up. First, the covariance-driven stochastic subspace identification is used to extract the modal parameters of the bridge from experimental data, using only output measurements data from ambient vibration. Then, the method is demonstrated for several damage scenarios and compared against other classical methods, such as: Coordinate Modal Assurance Criterion (COMAC), Enhanced Coordinate Modal Assurance Criterion (ECOMAC), Mode Shape Curvature (MSC) and Modal Strain Energy (MSE). The paper demonstrates the relative merits and shortcomings of these methods which play a significant role in the damage detection ofsuspension bridges.