• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.4
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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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• v.12 no.4
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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.

• Journal of Aerospace System Engineering
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• v.17 no.4
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• pp.133-143
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• 2023

As drones (also known as UAV) become popular with advanced information and communication technology (ICT), they have been utilized for various fields (agriculture, architecture, and so on). However, malicious attackers with advanced drones may pose a threat to critical national infrastructures. Thus, anti-drone systems have been developed to respond to drone threats. In particular, remote identification data (R-ID)-based UAV detection and identification systems that detect and identify illegal drones with R-ID broadcasted by drones have been developed, and are widely employed worldwide. However, this R-ID-based UAV detection/identification system is vulnerable to security due to wireless broadcast characteristics. In this paper, we analyze the security vulnerabilities of DJI Aeroscope, a representative example of the R-ID-based UAV detection and identification system, and propose a replay-attack-based neutralization method using the analyzed vulnerabilities. To validate the proposed method, it is implemented as a software program, and verified against four types of attacks in real test environments. The results demonstrate that the proposed neutralization method is an effective neutralization method for R-ID-based UAV detection and identification systems.

• Journal of Biomedical Engineering Research
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• v.14 no.2
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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.

• Journal of the Semiconductor & Display Technology
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• v.19 no.4
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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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• v.23 no.1
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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.

• Proceedings of the KIPE Conference
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• 2007.07a
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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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• v.33 no.1
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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.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.18 no.3
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• pp.121-129
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• 2008

This paper presents vulnerability identification method based on meaning which is making use of the concept of atomic vulnerability. Also, we are making use of decomposition and specialization processes which were used in DEVS/SES to get identifiers. This vulnerability representation method is useful for managing and removing vulnerability in organized way. It is helpful to make a relation between vulnerability assessing and intrusion detection rules in lower level. The relation enables security manager to response more quickly and conveniently. Especially, this paper shows a mapping between Nessus plugins and Snort rules using meaning based vulnerability identification method and lists usages based on three goals that security officer keeps in mind about vulnerability. The contribution of this work is in suggestion of meaning based vulnerability identification method and showing the cases of its usage for the rule integration of vulnerability assessment and intrusion detection.

• Structural Engineering and Mechanics
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• v.86 no.6
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• pp.715-730
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• 2023

Broad studies have addressed the issue of structural element damage identification, however, rubber bearing, as a key component of load transmission between the superstructure and substructure, is essential to the operational safety of a bridge, which should be paid more attention to its health condition. However, regarding the limitations of the traditional bearing damage detection methods as well as few studies have been conducted on this topic, in this paper, inspired by the model updating-based structural damage identification, a two-stage bearing damage identification method has been proposed. In the first stage, we deduce a novel bearing damage localization indicator, called element relative MSE, to accurately determine the bearing damage location. In the second one, the prior knowledge of bearing damage localization is combined with sailfish optimization (SFO) to perform the bearing damage estimation. In order to validate the feasibility, a numerical example of a 5-span continuous beam is introduced, also the noise robustness has been investigated. Meanwhile, the effectiveness and engineering applicability are further verified based on an experimental simply supported beam and actual engineering of the I-40 Bridge. The obtained results are good, which indicate that the proposed method is not only suitable for simple structures but also can accurately locate the bearing damage site and identify its severity for complex structure. To summarize, the proposed method provides a good guideline for the issue of bridge bearing detection, which could be used to reduce the difficulty of the traditional bearing failure detection approach, further saving labor costs and economic expenses.