• Structural Monitoring and Maintenance
• /
• 제2권3호
• /
• pp.237-252
• /
• 2015

This paper addresses the issue of reliability and performance in wireless sensor networks (WSN) based structural health monitoring (SHM), particularly with decentralized damage identification techniques. Two decentralized damage identification algorithms, namely, the autoregressive (AR) model based damage index and the Wiener filter method are developed for structural damage detection. The ambient and impact testing have been carried out on the steel beam structure in the laboratory. Seven wireless sensors are installed evenly along the steel beam and seven wired sensor are also installed on the beam to monitor the dynamic responses as comparison. The results showed that wireless measurements performed very much similar to wired measurements in detecting and localizing damages in the steel beam. Therefore, apart from the usual advantages of cost effectiveness, manageability, modularity etc., wireless sensors can be considered a possible substitute for wired sensors in SHM systems.

• Mycobiology
• /
• 제50권5호
• /
• pp.382-388
• /
• 2022

White mold (or Sclerotinia stem rot), caused by Sclerotinia species, is a major air, soil, or seed-transmitted disease affecting numerous crops and wild plants. Microscopic or culture-based methods currently available for their detection and identification are time-consuming, laborious, and often erroneous. Therefore, we developed a multiplex quantitative PCR (qPCR) assay for the discrimination, detection, and quantification of DNA collected from each of the three economically relevant Sclerotinia species, namely, S. sclerotiorum, S. minor, and S. nivalis. TaqMan primer/probe combinations specific for each Sclerotinia species were designed based on the gene sequences encoding aspartyl protease. High specificity and sensitivity of each probe were confirmed for sclerotium and soil samples, as well as pure cultures, using simplex and multiplex qPCRs. This multiplex assay could be helpful in detecting and quantifying specific species of Sclerotinia, and therefore, may be valuable for disease diagnosis, forecasting, and management.

• 센서학회지
• /
• 제32권6호
• /
• pp.378-385
• /
• 2023

Human biosignals provide essential information for diagnosing diseases such as dementia and Parkinson's disease. Owing to the shortcomings of current clinical assessments, noninvasive solutions are required. Machine learning (ML) on wearable sensor data is a promising method for the real-time monitoring and early detection of abnormalities. ML facilitates disease identification, severity measurement, and remote rehabilitation by providing continuous feedback. In the context of wearable sensor technology, ML involves training on observed data for tasks such as classification and regression with applications in clinical metrics. Although supervised ML presents challenges in clinical settings, unsupervised learning, which focuses on tasks such as cluster identification and anomaly detection, has emerged as a useful alternative. This review examines and discusses a variety of ML algorithms such as Support Vector Machines (SVM), Random Forests (RF), Decision Trees (DT), Neural Networks (NN), and Deep Learning for the analysis of complex clinical data.

• 한국균학회소식:학술대회논문집
• /
• 한국균학회 2015년도 추계학술대회 및 정기총회
• /
• pp.41-41
• /
• 2015

Oomycetes belong to the kingdom Straminipila, a remarkably diverse group which includes brown algae and planktonic diatoms, although they have previously been classified under the kingdom Fungi. These organisms have evolved both saprophytic and pathogenic lifestyles, and more than 60% of the known species are pathogens on plants, the majority of which are classified into the order Peronosporales (includes downy mildews, Phytophthora, and Pythium). Recent phylogenetic investigations based on DNA sequences have revealed that the diversity of oomycetes has been largely underestimated. Although morphology is the most valuable criterion for their identification and diversity, morphological species identification is time-consuming and in some groups very difficult, especially for non-taxonomists. DNA barcoding is a fast and reliable tool for identification of species, enabling us to unravel the diversity and distribution of oomycetes. Accurate species determination of plant pathogens is a prerequisite for their control and quarantine, and further for assessing their potential threat to crops. The mitochondrial cox2 gene has been widely used for identification, taxonomy and phylogeny of various oomycete groups. However, recently the cox1 gene was proposed as a DNA barcode marker instead, together with ITS rDNA. To determine which out of cox1 or cox2 is best suited as universal oomycete barcode, we compared these two genes in terms of (1) PCR efficiency for 31 representative genera, as well as for historic herbarium specimens, and (2) in terms of sequence polymorphism, intra- and interspecific divergence. The primer sets for cox2 successfully amplified all oomycete genera tested, while cox1 failed to amplify three genera. In addition, cox2 exhibited higher PCR efficiency for historic herbarium specimens, providing easier access to barcoding type material. In addition, cox2 yielded higher species identification success, with higher interspecific and lower intraspecific divergences than cox1. Therefore, cox2 is suggested as a partner DNA barcode along with ITS rDNA instead of cox1. Including the two barcoding markers, ITS rDNA and cox2 mtDNA, the multi-locus phylogenetic analyses were performed to resolve two complex clades, Bremia lactucae (lettuce downy mildew) and Peronospora effuse (spinach downy mildew) at the species level and to infer evolutionary relationships within them. The approaches discriminated all currently accepted species and revealed several previously unrecognized lineages, which are specific to a host genus or species. The sequence polymorphisms were useful to develop a real-time quantitative PCR (qPCR) assay for detection of airborne inoculum of B. lactucae and P. effusa. Specificity tests revealed that the qPCR assay is specific for detection of each species. This assay is sensitive, enabling detection of very low levels of inoculum that may be present in the field. Early detection of the pathogen, coupled with knowledge of other factors that favor downy mildew outbreaks, may enable disease forecasting for judicious timing of fungicide applications.

• 한국지능시스템학회논문지
• /
• 제22권3호
• /
• pp.328-333
• /
• 2012

현재 운영 중인 위성항법보정시스템(Differential GPS)은 기준국(Reference Station), 감시국(Integrity Monitor), 그리고 제어국(Control Station)으로 구성되어 있다. 기준국(RS)에서는 의사거리 보정정보(Pseudorange Correction)를 계산하고 RTCM 국제표준 메시지를 생성하여 사용자에게 방송한다. 감시국(IM)에서는 기준국으로부터 보정정보를 수신하여 보정정보가 허용치 이내인지를 검사한다. 그리고 제어국(CS)에서는 기준국과 감시국의 기능 및 성능 파라미터 제어, 상태 감시를 수행한다. DGPS 무결성 감시국의 핵심 기능은 보정정보의 검사와 기준국으로 피드백 메시지를 전송하는 것이다. 하지만 무결성 감시를 위한 현재의 알고리즘은 위성 이상이 발생할 경우 그 무결성 기능에 한계가 있다. 그러므로 본 논문에서는 해상 DGPS RSIM을 위한 위성 이상 검출 및 식별기법에 중점을 둔다. 먼저 현재 운영 중인 DGPS RSIM의 기능 분석을 토대로 DGPS RSIM을 위한 무결성 기능의 한계를 분석하고, 다음으로 위성시계 이상을 검출하고 이상위성을 식별하기 위한 기법을 제안한다. 위성이상 검출 및 식별 기법을 실제 위성시계 이상사례에 적용하여 그 실험 결과를 제시한다.

• Structural Engineering and Mechanics
• /
• 제58권6호
• /
• pp.1087-1107
• /
• 2016

A method of damage detection based on the moving harmonic excitation and continuous wavelet transforms is presented. The applied excitation is used as a moving actuator and its frequency and speed parameters can be adjusted for an amplified response. The continuous wavelet transforms, CWT, is used for cracks detection based on the resulting amplified signal. It is demonstrated that this identification procedure is largely better than the classical ones based on eigenfrequencies or on the eigenmodes wavelet transformed. For vibration responses, free and forced vibration analyses of multi-cracked beams are investigated based on both analytical and numerical methodological approaches. Cracks are modeled through rotational springs whose compliances are evaluated using linear elastic fracture mechanics. Based on the obtained forced responses, multi-cracks positions are accurately identified and the CWT identification can be highly improved by adjusting the frequency and the speed excitation parameters.

• Asian-Australasian Journal of Animal Sciences
• /
• 제16권3호
• /
• pp.425-429
• /
• 2003

Staphylococcus aureus is a major pathogen for cattle, causing various forms of subclinical and clinical mastitis and could be a causative agent of food poisoning, it produces various superantigenic exotoxins which have a great public health significance. A total of 72 S. aureus clinical isolates from dairy farms located in Kyunggi Province Korea were examined for the species identification by biochemical method, and for the detection of $\beta$-lactamase, enterotoxin and other exotoxins genes by PCR. The results of species identification by biochemical method agreed with those of PCR done with species specific primer STA-AU. $\beta$-lactamase is an enzyme closely associated with the resistance to antibiotic penicillin, which is an important means of treatment of mastitis, all the isolates were positive for the presence of genes encoding $\beta$-lactamase, which were reproduced in penicillin susceptibility disc assay. Six types of toxin genes, Staphylococcal enterotoxin (SE)A, SEB, SEC, SEE, toxic shock syndrome toxin (TSST-1) and exfoliative toxin A (ET A) were detected in 72 isolates by PCR associated genotypic method in this study, none of the isolates carried the genes for enterotoxin D (SED) and exfoliative toxin B (ETB). The occurrence rate of exotoxin genes rated as 12.5%, and the precision of the PCR identification results has been confirmed using the reference strains.

• Smart Structures and Systems
• /
• 제5권4호
• /
• pp.457-468
• /
• 2009

A novel approach for integrating active sensing data interrogation algorithms for structural health monitoring (SHM) applications is presented. These algorithms cover Lamb wave propagation, impedance methods, and sensor diagnostics. Contrary to most active-sensing SHM techniques, which utilize only a single signal processing method for damage identification, a suite of signal processing algorithms are employed and grouped into one package to improve the damage detection capability. A MATLAB-based user interface, referred to as HOPS, was created, which allows the analyst to configure the data acquisition system and display the results from each damage identification algorithm for side-by-side comparison. By grouping a suite of algorithms into one package, this study contributes to and enhances the visibility and interpretation of the active-sensing methods related to damage identification. This paper will discuss the detailed descriptions of the damage identification techniques employed in this software and outline future issues to realize the full potential of this software.

• 한국정밀공학회:학술대회논문집
• /
• 한국정밀공학회 1993년도 추계학술대회 논문집
• /
• pp.363-368
• /
• 1993

Hilbert Transform has been used for detection of nonlinearity in modal analysis. HTD(Hilbert Transform Describers) are used to quantify and identify nonlinearity. Mottershead and Stanway method for identification of N-th power velocity nonlinear damping are extended to P-th power displacement stiffness, N-th power velocity damping and dry friction. Time domain and frequency domain data are used and HTD and Mottershead methods are combined for identification of nonlinear parameters in this paper. Computer simulations and experimental results are shown to verify nonlinear structure identification methods.

• 응용통계연구
• /
• 제25권6호
• /
• pp.1019-1026
• /
• 2012

This paper presents a method for the identification of "edge observations" located on a boundary area constructed by a truncation variable as well as for the identification of outliers and the after fit of multivariate skew $t$-distribution(MST) to asymmetric data. The detection of edge observation is important in data analysis because it provides information on a certain critical area in observation space. The proposed method is applied to an Australian Institute of Sport(AIS) dataset that is well known for asymmetry in data space.