• Journal of Power Electronics
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• v.15 no.2
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• pp.411-418
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• 2015

In this paper, a new method for the online estimation of equivalent series resistances (ESR) of the DC-link capacitors in induction machine (IM) drive systems with a front-end diode rectifier is proposed, where the ESR estimation is conducted during the regenerative operating mode of the induction machine. In the first place, a regulated AC current component is injected into the q-axis current component of the induction machine, which induces the current and voltage ripple components in the DC-link. By processing these AC signals through digital filters, the ESR can be estimated by a recursive least squares (RLS) algorithm. To acquire the AC voltage across the ESR, the DC-link voltage needs to be measured at a double sampling frequency. In addition, the ESR current is simply reconstructed from the stator currents and switching states of the inverter. Experimental results have shown that the estimation error of the ESR is about 1.2%, which is quite acceptable for condition monitoring of the capacitor.

• Proceedings of the Korean Information Science Society Conference
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• 2010.06c
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• pp.211-215
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• 2010

지금까지 병원에서 사용하던 일반 종이차트를 벗어나 전자적으로 환자의 데이터를 기록하고 유전자 데이터를 이용하여 환자의 유사 질병까지 찾아 낼 수 있는 EMR(Electronic Medical Record 전자 의무 기록)이 개발되면서 의료계는 환자에게 더욱 신속하고 정확한 진료를 할 수 있게 되었다. 본 논문은 이에 그리드 환경을 접목하여 더 빠른 데이터 처리와 신뢰성 과 접근성을 높일 수 있는 방법을 제시한다. 첫째, 현재 기 개발된 EMR 시스템의 환경에서 인증된 사용자만이 스토리지에 접근 할 수 있도록 GSI Service를 이용하여 단일 인증 방식으로 보안성을 높이며 동시에 단 한번의 인증절차로 모든 자원을 활용 할 수 있다. 둘째, Replica Service를 이용하여 기존의 스토리지를 복제 하여 중요한 데이터 들을 보호하며 다수의 접근이 발생할 경우 처리를 분산 시킬 수 있는 방법을 제시한다. 그리드 미들웨어인 글로 버스가 스토리지와 서버 상에서 CA인증을 담당하며 파일 전송을 담당하는 RFT는 스토리지의 Replica를 관리하는 RLS서버의 정보를 사용 하여 멀리 떨어져 있는 복제된 데이터와의 관계를 기억하고 접근시 가장 가용성이 뛰어난 머신에서 데이터를 불러온다. 이런 글로버스의 서비스 들은 중요하며 고용량이 데이터를 분산 시킴으로써 데이터의 지역성을 높여 재사용 혹은 동시 접근시 처리 시간을 단축 시킬 수 있다. 본 논문은 그리드 환경을 접목하여 이러한 서비스를 구현할 경우 높은 신뢰성과 접근의 신속성을 보장할 수 있다고 제시한다.

• Journal of the Korean Society for Precision Engineering
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• v.11 no.1
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• pp.138-149
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• 1994

This paper introduces a new monitoring technique which utilizes an adaptive signal processing for feature generation, coupled with a multilayered merual network for pattern recognition. The cutting force signal in face milling operation was modeled by a low order discrete autoregressive model, shere parameters were estimated recursively at each sampling instant using a parameter adaptation algorithm based on an RLS(recursive least square) method with discounted measurements. The influences of the adaptation algorithm parameters as well as some considerations for modeling on the estimation results are discussed. The sensitivity of the extimated model parameters to the tool state(new and worn tool)is presented, and the application of a multilayered neural network to tool state monitoring using the previously generated features is also demonstrated with a high success rate. The methodology turned out to be quite suitable for in-process tool wear monitoring in the sense that the model parameters are effective as tool state features in milling operation and that the classifier successfully maps the sensors data to correct output decision.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.6
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• pp.673-679
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• 2012

Vehicle rollover is a serious kind of accident, particularly for sport utility vehicles, and its occurrence can be minimized by utilizing active rollover prevention systems. The performance of these protection systems is very sensitive to vehicle inertial parameters such as the vehicle's mass and center of mass. These parameters vary with the number of passengers and in different load situations. In this paper, a unified method for vehicle mass estimation is proposed that takes into account the available driving conditions. Three estimation algorithms are developed based on longitudinal, lateral, and vertical vehicle motion, respectively. Then, the three algorithms are combined to extract information on the vehicle's mass during arbitrary vehicle maneuvering. The performance of the proposed vehicle mass estimation method is demonstrated through real-time experiments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.3
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• pp.428-451
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• 2010

Traffic matrix-based anomaly detection and DDoS attacks detection in networks are research focus in the network security and traffic measurement community. In this paper, firstly, a new type of unidirectional flow called IF flow is proposed. Merits and features of IF flows are analyzed in detail and then two efficient methods are introduced in our DDoS attacks detection and evaluation scheme. The first method uses residual variance ratio to detect DDoS attacks after Recursive Least Square (RLS) filter is applied to predict IF flows. The second method uses generalized likelihood ratio (GLR) statistical test to detect DDoS attacks after a Kalman filter is applied to estimate IF flows. Based on the two complementary methods, an evaluation formula is proposed to assess the seriousness of current DDoS attacks on router ports. Furthermore, the sensitivity of three types of traffic (IF flow, input link and output link) to DDoS attacks is analyzed and compared. Experiments show that IF flow has more power to expose anomaly than the other two types of traffic. Finally, two proposed methods are compared in terms of detection rate, processing speed, etc., and also compared in detail with Principal Component Analysis (PCA) and Cumulative Sum (CUSUM) methods. The results demonstrate that adaptive filter methods have higher detection rate, lower false alarm rate and smaller detection lag time.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.2
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• pp.55-64
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• 1990

This paper concerned about a study on the direct pole placement PID self-tuning controller design for DC servo motor control system. The method of a direct pole placement self-tuning PID control for a DC servo motor of Robot manipulator tracks a reference velocity in spite of the parameters uncertainties in nonminimum phase system. In this scheme, the parameters of classical controller are estimated by the recursive least square (RLS)identification algorithm, the pole placement method and diophantine equation. A series of simulation in which minimum phase system and nonminimum phase system are subjected to a pattern of system parameter changes is presented to show some of the features of the proposed control algorithm. The proposed control algorithm which shown are effective for the practical application, and experiments of DC servo motor speed control for Robot manipulator by a microcomputer IBM-PC/AT are performed and the results are well suited.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.40 no.4
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• pp.264-274
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• 2003

This paper presents a direct generalized minimum-variance self tuning controller with a PID structure using neural network which adapts to the changing parameters of the nonlinear system with nonminimum phase behavior, noises and time delays. The self-tuning controller with a PID structure is a combination of the simple structure of a PID controller and the characteristics of a self-tuning controller that can adapt to changes in the environment. The self-tuning control effect is achieved through the RLS (recursive least square) algorithm at the parameter estimation stage as well as through the Robbins-Monro algorithm at the stage of optimizing the design parameter of the controller. The neural network control effect which compensates for nonlinear factor is obtained from the learning algorithm which the learning error between the filtered reference and the auxiliary output of plant becomes zero. Computer simulation has shown that the proposed method works effectively on the nonlinear nonminimum phase system with time delays and changed system parameter.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.sup3
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• pp.143-147
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• 2016

Breast and cervical cancers are among leading causes of morbidity and mortality in women worldwide. Regular screening is very important for early detection of these cancers, but studies indicate low rates of screening participation. In this survey we studied the rate of screening participation among women 18-64 years old referred to urban health centers in Kerman, Iran in 2015. A cross-sectional study was carried out on 240 women who were selected using a multistage sampling method. Data collected using a questionnaire covered demographics and questions about common cancer screening status in women. Analysis was by SPSS 19. The mean age of participants was $31.7{\pm}7$. Most (97.1%) were married, housewives (83.3%), had high school diploma (43.8%) and a monthly income more than ten million Rls. The frequency of the Pap test performance was higher in women who were employed and with a university degree (p<0.05). The frequency of mammography performance in women over 40 years was also higher in women with university degree (p<0.05). There was no statistically significant difference in the frequency of pelvic examination, and self and clinical breast examinations based on education, household income and employment (p>0.05). Our study found that the rate of screening participation among women is low. Investigation of the barriers, increasing the awareness of women about the importance and advantages of screening and also more incentives for health personnel especially family physicians to pay more attention to preventive programs could be effective.

• Journal of the Korea Convergence Society
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• v.8 no.9
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• pp.285-292
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• 2017

This paper describes a recursive least-squares based convergent algorithm for driving characteristic classification for personalized autonomous driving. Recently, various researches on autonomous driving technology have been conducted for level 4 fully autonomous driving. In order for commercialization of the autonomous vehicle, personalized autonomous driving is required to minimize passenger's insecureness to the autonomous vehicle. To address this problem. this study proposes mathematical model that represents driving characteristics and recursive least-squares based algorithm that can estimate the defined characteristics. The actual data of two drivers has been used to derive driving characteristics and the hypothesis testing method has been used to classify two drivers. It is shown that the proposed algorithms can derive driving characteristics and classify two drivers reasonably.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.3 no.2 s.5
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• pp.19-28
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• 2004

This paper shows how to compensate the skew from the traffic sign included in the natural image and extract the text. The research deals with the Process related to the array image. Ail the process comprises four steps. In the first fart we Perform the preprocessing and Canny edge extraction for the edge in the natural image. In the second pan we perform preprocessing and postprocessing for Hough Transform in order to extract the skewed angle. In the third part we remove the noise images and the complex lines, and then extract the candidate region using the features of the text. In the last part after performing the local binarization in the extracted candidate region, we demonstrate the text extraction by using the differences of the features which appeared between the tett and the non-text in order to select the unnecessary non-text. After carrying out an experiment with the natural image of 100 Pieces that includes the traffic sign. The research indicates a 82.54 percent extraction of the text and a 79.69 percent accuracy of the extraction, and this improved more accurate text extraction in comparison with the existing works such as the method using RLS(Run Length Smoothing) or Fourier Transform. Also this research shows a 94.5 percent extraction in respect of the extraction on the skewed angle. That improved a 26 percent, compared with the way used only Hough Transform. The research is applied to giving the information of the location regarding the walking aid system for the blind or the operation of a driverless vehicle