• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.58-63
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• 1998

B.Widrow established fundamental relations between the least-mean-square (LMS) algorithm and the digital Fourier transform［1］. By extending these relations, we proposed the short time spectra analysis system using the LMS algorithm［2]. In that paper, we used the normal LMS algorithm on the thought of dealing with only real analytical signal. This algorithm minimizes the real mean-square by recursively altering the complex weight vector at each sampling instant. But, the short time spectra analysis sometimes deals with the complex signal that is outputted from complex analog filter. So, in order to optimize and develop this methods, furthermore it is necessary to derive an algorithm for the complex analytical signal. In this paper, we first discuss the new adaptive system for the spectra analysis using the complex LMS algorithm and then derive convergence condition, time constant of coefficient adjustment and frequency resolution by extending the discussion. Finally, the effectiveness of the proposed method is experimentally demonstrated by applying it to the measurement of transfer performance on complex analog filter.

• Parasites, Hosts and Diseases
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• v.61 no.3
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• pp.272-281
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• 2023

Chigger mites are the vector of scrub typhus. This study estimates the infestation status and ecological characteristics of chiggers on the chestnut white-bellied rat Niviventer fulvescens in Southwest China between 2001 and 2019. Chiggers were identified under the microscope, and infestation indices were calculated. The Preston's log-normal model was used to fit the curve of species abundance distribution. A total of 6,557 chiggers were collected in 136 of 342 N. fulvescens rats, showing high overall infestation indices (prevalence=39.8%, mean abundance=19.2, mean intensity=48.2) and high species diversity (S=100, H'=3.0). Leptotrombidium cangjiangense, Neotrombicula japonica, and Ascoschoengastia sifanga were the three dominant chigger species (constituent ratio=42.9%; 2,736/6,384) and exhibited an aggregated distribution among different rat individuals. We identified 100 chigger species, with 3 of them (Leptotrombidium scutellare, Leptotrombidium wenense, and Leptotrombidium deliense) as the main vectors of scrub typhus in China and nine species as potential vectors of this disease. Disease vector occurrence on N. fulvescens may increase the risk of spreading scrub typhus from rats to humans. Chigger infestation on N. fulvescens varied significantly in different environments. The species abundance distribution showed a log-normal distribution pattern. The estimated number of chigger species on N. fulvescens was 126 species.

• Journal of Biomedical Engineering Research
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• v.37 no.4
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• pp.127-133
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• 2016

This study proposes the feasibility for automatic classification of sleep/wakefulness using nasal pressure in patients with sleep-disordered breathing (SDB). First, SDB events were detected using the methods developed in our previous studies. In epochs for normal breathing, we extracted the features for classifying sleep/wakefulness based on time-domain, frequency-domain and non-linear analysis. And then, we conducted the independent two-sample t-test and calculated Mahalanobis distance (MD) between the two categories. As a results, $SD_{LEN}$ (MD = 0.84, p < 0.01), $P_{HF}$ (MD = 0.81, p < 0.01), $SD_{AMP}$ (MD = 0.76, p = 0.031) and $MEAN_{AMP}$ (MD = 0.75, p = 0.027) were selected as optimal feature. We classified sleep/wakefulness based on support vector machine (SVM). The classification results showed mean of sensitivity (Sen.), specificity (Spc.) and accuracy (Acc.) of 60.5%, 89.0% and 84.8% respectively. This method showed the possibilities to automatically classify sleep/wakefulness only using nasal pressure.

• Journal of Applied Reliability
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• v.14 no.4
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• pp.208-212
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• 2014

An experimental gear-box was set-up to simulate the real situation of the wind-turbine. Artificial cracks of different sizes were machined into the gear. Vibration signals were acquired to diagnose the different crack fault conditions. Time-domain features such as root mean square, variance, kurtosis, normalized 6th central moments were used to capture the characteristics of different crack conditions. Normal condition, 1 mm crack condition, 2mm crack condition, 6mm crack condition, and tooth fault condition were compared using ANFIS and DAG-SVM methods, and three different DAG-SVM models were compared. High-pass filtering improved the success rates remarkably in the case of DAG-SVM.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.11-14
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• 1996

Performance of the group cumulative sum(CUSUM) control scheme using multiple univariate CUSUM charts is more sensitive to the change of quality control(QC) characteristics than the control chart scheme based on the Hotelling statistics. We examine three group charts for multivariate normal data sets simulated with various correlation structures and shift directions in the mean vector. These group schemes apply the orginal measurement vectors, the scaled residual vectors from the regression of each variable on all others and the principal component vectors respectively to calculating the CUSUM statistics. They are also compared to the multivariate QC charts based on the Hotelling statistic by estimating average run lengths, coefficients of variation of run length and ranks in signaling order. On the basis of simulation results, we suggest a control chart scheme appropriate for specific quality control environment.

• Management Science and Financial Engineering
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• v.5 no.1
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• pp.51-71
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• 1999

Performance of the group cumulative sum (CUSUM) control scheme using multiple univariate CUSUM charts is more sensitive to the change of quality control (QC) characteristics than the control chart schemes based on the Hotelling statistic We vexamine three group charts for multivariate normal data sets simulated with various correlation structures and shift directions in the mean vector. These group schemes apply the original measurement vectors, the scaled residual vectors from the re-gression of each variable on all others and the principal component vectors respectively to calculat-ing the CUSUM statistics. They are also compared to the multivariate QC charts based on the Ho-telling statistic by estimating average run lengths, coefficients of variation of run length and ranks in signaling order. On the basis of simulation results, we suggest a control chart scheme appropriate for specific quality control environment.

• Journal of the Korean Operations Research and Management Science Society
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• v.20 no.3
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• pp.123-146
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• 1995

Several quality control schemes have been extensively compared using multivariate normal data sets simulated with various correlation structures. They include multiple univariate CUSUM charts, multivariate EWMA charts, multivariate CUSUM charts and Shewhart T$^{3}$ chart. This paper considers a new approach of the multivariate EWMA chart, in which the smoothing matrix has full elements instead of only diagonal elements. Performance of the schemes is measured by avaerage run length (ARL), coefficient of variation of run length (CVRL) and rank in order of signaling of off-target shifts in the process mean vector. The schemes are also compared by noncentrality parameter. The multiple univariate CUSUM charts are generally affected by the correlation structure. The multivariate EWMA charts provide better ARL performance. Especially, the new EWMA chart shows remarkable results in small shifts.

• Proceedings of the Safety Management and Science Conference
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• 1999.11a
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• pp.565-574
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• 1999

In reverse engineering area, it is rapidly developing reconstruction of surfaces from scanning or digitizing data, but geometric models of existing objects unavailable many industries. This paper describes new methodology of reverse engineering area, good strategies and important algorithms in reverse engineering area. Furthermore, proposing reconstruction of surface technique is presented. A method find base geometry and blending surface between them. Each based geometry is divided by triangular patch which are compared their normal vector for face grouping. Each group is categorized analytical surface such as a part of the cylinder, the sphere, the cone, and the plane that mean each based geometry surface. And then, each based geometry surface is implemented infinitive surface. Infinitive average surface's intersections are trimmed boundary representation model reconstruction. This method has several benefits such as the time efficiency and automatic functional modeling system in reverse engineering. Especially, it can be applied 3D scanner and 3D copier.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.3
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• pp.246-252
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• 2016

Since thermal power plant (TPP) equipment is operated under very high pressure and temperature, failures of the equipment give rise to severe losses of life and property. To prevent the losses, fault detection method is, therefore, absolutely necessary to identify abnormal operating conditions of the equipment in advance. In this paper, we present Mahalanobis distance (MD) based fault detection method for steam boiler tube in TPP. In the MD-based method, it is supposed that abnormal data samples are far away from normal samples. Using multivariate samples collected from normal target system, mean vector and covariance matrix are calculated and threshold value of MD is decided. In a test phase, after calculating the MDs between the mean vector and test samples, alarm signals occur if the MDs exceed the predefined threshold. To demonstrate the performance, a failure case due to boiler tube leakage in 200MW TPP is employed. The experimental results show that the presented method can perform early detection of boiler tube leakage successfully.

• Journal of Preventive Medicine and Public Health
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• v.31 no.4 s.63
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• pp.875-884
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• 1998

Missing observations are common in medical research and health survey research. Several statistical methods to handle the missing data problem have been proposed. The EM algorithm (Expectation-Maximization algorithm) is one of the ways of efficiently handling the missing data problem based on sufficient statistics. In this paper, we developed statistical models and methods for survey data with multivariate missing observations. Especially, we adopted the EM algorithm to handle the multivariate missing observations. We assume that the multivariate observations follow a multivariate normal distribution, where the mean vector and the covariance matrix are primarily of interest. We applied the proposed statistical method to analyze data from a health survey. The data set we used came from a physician survey on Resource-Based Relative Value Scale(RBRVS). In addition to the EM algorithm, we applied the complete case analysis, which uses only completely observed cases, and the available case analysis, which utilizes all available information. The residual and normal probability plots were evaluated to access the assumption of normality. We found that the residual sum of squares from the EM algorithm was smaller than those of the complete-case and the available-case analyses.