• Structural Engineering and Mechanics
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• 제63권6호
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• pp.779-788
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• 2017

System identification and damage detection for structural health monitoring have received considerable attention. Various time domain analysis methodologies based on measured vibration data of structures have been proposed. Among them, recursive least-squares estimation of structural parameters which is also known as parametric Kalman filter (PKF) approach has been studied. However, the conventional PKF requires that all the external excitations (inputs) be available. On the other hand, structural uncertainties are inevitable for civil infrastructures, it is necessary to develop approaches for probabilistic damage detection of structures. In this paper, a parametric Kalman filter with unknown inputs (PKF-UI) is proposed for the simultaneous identification of structural parameters and the unmeasured external inputs. Analytical recursive formulations of the proposed PKF-UI are derived based on the conventional PKF. Two scenarios of linear observation equations and nonlinear observation equations are discussed, respectively. Such a straightforward derivation of PKF-UI is not available in the literature. Then, the proposed PKF-UI is utilized for probabilistic damage detection of structures by considering the uncertainties of structural parameters. Structural damage index and the damage probability are derived from the statistical values of the identified structural parameters of intact and damaged structure. Some numerical examples are used to validate the proposed method.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제16권4호
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• pp.238-245
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• 2016

Inverters are considered the basic building blocks of industrial electrical drive systems that are widely used for various applications; however, the failure of electronic switches mainly affects the constancy of these inverters. For safe and reliable operation of an electrical drive system, faults in power electronic switches must be detected by an efficient system that is capable of identifying the type of faults. In this paper, an open switch fault identification technique for a three-phase inverter is presented. Single, double, and triple switching faults can be diagnosed using this method. The detection mechanism is based on stator current analysis. Discrete wavelet transform (DWT) using Daubechies is performed on the Clarke transformed (-) stator current and features are extracted from the wavelets. An artificial neural network is then used for the detection and identification of faults. To prove the feasibility of this method, a Simulink model of the DWT-based feature extraction scheme using a neural network for the proposed fault detection system in a three-phase inverter with an induction motor is briefly discussed with simulation results. The simulation results show that the designed system can detect faults quite efficiently, with the ability to differentiate between single and multiple switching faults.

• Journal of Electrical Engineering and Technology
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• 제12권4호
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• pp.1406-1416
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• 2017

In complex and large-scale industries, properly designed fault detection and identification (FDI) systems considerably improve safety, reliability and availability of target processes. In thermal power plants (TPPs), generating units operate under very dangerous conditions; system failures can cause severe loss of life and property. In this paper, we propose a bagged auto-associative kernel regression (AAKR)-based FDI approach for steam boilers in TPPs. AAKR estimates new query vectors by online local modeling, and is suitable for TPPs operating under various load levels. By combining the bagging method, more stable and reliable estimations can be achieved, since the effects of random fluctuations decrease because of ensemble averaging. To validate performance, the proposed method and comparison methods (i.e., a clustering-based method and principal component analysis) are applied to failure data due to water wall tube leakage gathered from a 250 MW coal-fired TPP. Experimental results show that the proposed method fulfills reasonable false alarm rates and, at the same time, achieves better fault detection performance than the comparison methods. After performing fault detection, contribution analysis is carried out to identify fault variables; this helps operators to confirm the types of faults and efficiently take preventive actions.

• 항공우주시스템공학회지
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• 제17권4호
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• pp.133-143
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• 2023

ICT의 발전으로 드론(이하, 무인기(Unmanned Aerial Vehicle, UAV)와 동일)이 대중화됨에 따라, 농업, 건축업 등 다양한 분야에서 드론이 유용하게 사용되고 있다. 그러나, 악의적인 공격자는 고도화된 드론을 통해 국가주요기반시설에 위협을 가할 수 있다. 이에, 불법드론의 위협에 대응하기 위해 안티드론 시스템이 개발되어왔다. 특히, 드론이 브로드캐스트하는 원격 식별 데이터(Remote-ID, R-ID) 데이터를 기반으로 불법드론을 탐지·식별하는 R-ID 기반 UAV 탐지·식별 시스템이 개발되어 세계적으로 많이 사용되고 있다. 하지만, 이러한 R-ID 기반 UAV 탐지·식별 시스템은 무선 브로드캐스트 특성으로 인해 보안에 매우 취약하다. 본 논문에서는 R-ID 기반 UAV 탐지·식별 시스템의 대표적인 예인 DJI 사(社) Aeroscope를 대상으로 보안 취약성을 분석하여, 재전송 공격(Replay Attack) 기반 무력화 방식을 제안하였다. 제안된 무력화 방식은 소프트웨어 프로그램으로 구현되어, 실제 테스트 환경에서 4가지 유형의 공격에 대해 검증되었다. 우리는 검증 결과를 통해 제안한 무력화 방식이 R-ID 기반 UAV 탐지·식별 시스템에 실효적인 무력화 방식임을 입증하였다.

• 대한의용생체공학회:의공학회지
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• 제14권2호
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• pp.185-193
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• 1993

In this paper, the peak detection algorithm was developed for the purpose of screening of the target compounds. Algorithm is divided into searching the characteristic ion and peak detection. The heuristic knowledge about analytical chemistry was applied for the searching the characteristic ion. Peak detection was accomplished in comparison with the peak identification strings and pattern strings around the retention time. Pattern strings are composed with the number which generated by pattern identification function. The variables of pattern identification function are the codes which represent the difference of two adjacent abundances Some of the free steroids were selected to demonstrate the proposed algorithm.

• 반도체디스플레이기술학회지
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• 제19권4호
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• pp.112-115
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• 2020

Trump cards are used in variable games in households such as poker and blackjack. In many cases, it is able to be helpful to algorithmically identify the playing cards from camera views. In this paper, we provide an approach that detects and identifies the playing card using template matching scheme, and evaluate the results of the provided implementation. For ideal cases, the implemented system provides a 100% success rate for card identification correct. However, non-ideal case of perspective distortion is estimated with 70% success ratio. This work aims to evaluate the effectiveness of augmented reality user interface for an entertainment application like playing card games.

• Journal of Veterinary Science
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• 제23권1호
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• pp.17.1-17.10
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• 2022

Background: Inspection of livestock farms using surveillance cameras is emerging as a means of early detection of transboundary animal disease such as African swine fever (ASF). Object tracking, a developing technology derived from object detection aims to the consistent identification of individual objects in farms. Objectives: This study was conducted as a preliminary investigation for practical application to livestock farms. With the use of a high-performance artificial intelligence (AI)-based 3D depth camera, the aim is to establish a pathway for utilizing AI models to perform advanced object tracking. Methods: Multiple crossovers by two humans will be simulated to investigate the potential of object tracking. Inspection of consistent identification will be the evidence of object tracking after crossing over. Two AI models, a fast model and an accurate model, were tested and compared with regard to their object tracking performance in 3D. Finally, the recording of pig pen was also processed with aforementioned AI model to test the possibility of 3D object detection. Results: Both AI successfully processed and provided a 3D bounding box, identification number, and distance away from camera for each individual human. The accurate detection model had better evidence than the fast detection model on 3D object tracking and showed the potential application onto pigs as a livestock. Conclusions: Preparing a custom dataset to train AI models in an appropriate farm is required for proper 3D object detection to operate object tracking for pigs at an ideal level. This will allow the farm to smoothly transit traditional methods to ASF-preventing precision livestock farming.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2007년도 하계학술대회 논문집
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• pp.325-327
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• 2007

This paper proposes a novel sensorless fault detection algorithm for a brushless DC(BLDC) motor drive system. This proposed method is configured without the additional sensor for fault detection and identification. The fault detection and identification are achieved by a simple algorithm using the operating characteristic of the BLDC motor. This proposed method can also be embedded into existing BLDC motor drive systems as a subroutine without excessive computational effort. The feasibility of a novel sensorless fault detection algorithm is validated in simulation.

• Journal of Microbiology and Biotechnology
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• 제33권1호
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• pp.83-95
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• 2023

These days, bacterial detection methods have some limitations in sensitivity, specificity, and multiple detection. To overcome these, novel detection and identification method is necessary to be developed. Recently, NGS panel method has been suggested to screen, detect, and even identify specific foodborne pathogens in one reaction. In this study, new NGS panel primer sets were developed to target 13 specific virulence factor genes from five types of pathogenic Escherichia coli, Listeria monocytogenes, and Salmonella enterica serovar Typhimurium, respectively. Evaluation of the primer sets using singleplex PCR, crosscheck PCR and multiplex PCR revealed high specificity and selectivity without interference of primers or genomic DNAs. Subsequent NGS panel analysis with six artificially contaminated food samples using those primer sets showed that all target genes were multi-detected in one reaction at 10⁸-10⁵ CFU of target strains. However, a few false-positive results were shown at 10⁶-10⁵ CFU. To validate this NGS panel analysis, three sets of qPCR analyses were independently performed with the same contaminated food samples, showing the similar specificity and selectivity for detection and identification. While this NGS panel still has some issues for detection and identification of specific foodborne pathogens, it has much more advantages, especially multiple detection and identification in one reaction, and it could be improved by further optimized NGS panel primer sets and even by application of a new real-time NGS sequencing technology. Therefore, this study suggests the efficiency and usability of NGS panel for rapid determination of origin strain in various foodborne outbreaks in one reaction.

• 정보보호학회논문지
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• 제18권3호
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• pp.121-129
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• 2008

본 논문은 소프트웨어의 취약점을 표현하기 위한 방법으로 단위 취약점을 기반으로 한 의미기반 취약점 식별자 부여 방법을 제안하고 있다. 의미기반 취약점 식별자 부여를 위해 기존의 취약점 단위를 DEVS 모델링 방법론의 SES 이론에서 사용되는 분할 및 분류(Decomposition/Specialization) 절차를 적용하였다. 의미기반 취약점 식별자는 취약점 점검 규칙 및 공격 탐지 규칙과 연관 관계를 좀 더 낮은 레벨에서 맺을 수 있도록 해주고, 보안 관리자의 취약점에 대한 대응을 좀더 편리하고 신속하게 하는 데 활용될 수 있다. 특히, 본 논문에서는 Nessus와 Snort의 규칙들이 의미기반 취약점 식별자와 어떻게 맵핑되는 지를 제시하고, 보안 관리자 입장에서 어떻게 활용 될 수 있는 지를 3가지 관점에서 정리하였다. 본 논문의 기여점은 의미기반 취약점 식별자 개념 정의 및 이를 기반으로 한 취약점 표현과 활용 방법의 제안에 있다.