• 전기학회논문지
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• 제57권10호
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• pp.1847-1853
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• 2008

This paper presents stochastic methodology based fault detection algorithm for induction motor systems. We measure current of healthy induction motors by means of hall sensor systems and then establish its probability distribution. We propose online probability density estimation which is effective in real-time implementation due to its simplicity and low computational burden. In addition, we accomplish theoretical analysis to demonstrate convergence property of the proposed estimation by using statistical convergence and system stability theory. We apply our fault diagnosis approach to three-phase induction motors and achieve real-time experiment for evaluating its reliability and practicability in industrial fields.

• Journal of the Korean Data and Information Science Society
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• 제18권2호
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• pp.429-438
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• 2007

Headache is one of the most common reasons for neurological consultation. Headache as many causes and symptoms. Therefore, screening method using questionaire is helpful in diagnosis of headache. This paper is to propose a medical diagnostic method to grasp patient's diseases using the relations between symptoms and diseases. For this purpose, we develop an interview chart assigned IF(intuitionistic fuzzy) grade with the relation among symptoms and three labels of headache. The method can be used to classify patient's tone of diseases with certain degrees of belief and its concerned symptoms.

• 산업경영시스템학회지
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• 제32권1호
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• pp.20-25
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• 2009

Mahalanobis Taguchi System (MTS) is a pattern information technology, which has been used in different diagnostic applications to make quantitative decisions by constructing a multivariate measurement scale using data analytic methods without any assumption regarding statistical distribution. The MTS performs Taguchi's fractional factorial design based on the Mahahlanobis Distance (MS) as a performance metric. In this work, MTS is used for analyzing Wisconsin Breast Cancer data which has ten attributes. Ten different tests are conducted for the data to determine if the patient has cancer or not. Also, MTS is used for reducing the number of test to define the relationship between each attribute and diagnosis result. The accuracy of diagnosis is compare with two different previous research.

• Journal of Multimedia Information System
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• 제3권2호
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• pp.1-8
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• 2016

In this paper, we propose a method to automatically diagnose various diseases. The input data consists of electrocardiograph (ECG) recordings. We extract R-to-R interval (RRI) signals from ECG recordings, which are preprocessed to remove trends and ectopic beats, and to keep the signal stationary. After that, we perform some prospective analysis to extract time-domain parameters, frequency-domain parameters, and nonlinear parameters of the signal. Those parameters are unique for each disease and can be used as the statistical symptoms for each disease. Then, we perform feature selection to improve the performance of the diagnosis classifier. We utilize the selected features to diagnose various diseases using machine learning. We subsequently measure the performance of the machine learning classifier to make sure that it will not misdiagnose the diseases. The first two steps, which are R-to-R extraction and preprocessing, have been successfully implemented with satisfactory results.

• 전기학회논문지
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• 제63권11호
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• pp.1545-1550
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• 2014

Fault detection and diagnosis (FDD) of photovoltaic (PV) power systems is one of significant techniques for reducing economic loss due to abnormality occurred in PV modules. This paper presents a new FDD method against PV power systems by using statistical comparison. This comparative approach includes deviation signals between the outputs of two neighboring PV modules. We first define a binary hypothesis testing under such deviation and make use of a generalized likelihood ratio testing (GLRT) theory to derive its FDD algorithm. Additionally, a recursive computational mechanism for our proposed FDD algorithm is presented for improving a computational effectiveness in practice. We carry out a real-time experiment to test reliability of the proposed FDD algorithm by utilizing a lab based PV test-bed system.

• 품질경영학회지
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• 제38권2호
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• pp.150-160
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• 2010

Statistics Korea has been diagnosis national statistics every year since 2006. They diagnosis over 200 kinds of national statistics. They have 7 quality dimension used for quality diagnosis. That is relevance, accuracy, Timeliness, Comparability, Coherence, Accessibility. Since we are interest in how well they produce national statistics, comparability has become the most important dimension recently. In this reason, Statistics Korea try to rating quality level and development comparability index for national statistics. This paper propose the practical method of grading the quality level and developing the comparability index of the national statistics.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 하계학술대회 논문집 B
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• pp.748-750
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• 1995

An early detection of cancer is very important for the complete cure of cancer. Therefore, it is considered a diagnosis of cancer via the detection of an abrupt change from the healthy state to the cancerous state. It includes the development of algorithm for the detection of parameter change for conditionally-linear stochastic systems for the cancer diagnosis. The statistical testing is proposed to implement a parameter change algorithm. The detection algorithm studied in this research is based on sequential hypotheses testing in a so-called local asymptotic framework. Here a simple numerical example is provided to highlight some of the concepts and to provide a basis for further investigation. Despite its simplicity this research may have practical application in clinical oncology.

• 한국멀티미디어학회논문지
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• 제25권10호
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• pp.1512-1523
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• 2022

Artificial intelligence application in digital health care has been increasing with its development of artificial intelligence. The convergence of the healthcare industry and information and communication technology makes the diagnosis of diseases more simple and comprehensible. From the perspective of medical services, its practice as an initial test and a reference indicator may become widely applicable. Therefore, analyzing the factors that are the basis for existing diagnosis protocols also helps suggest directions using artificial intelligence beyond previous regression and statistical analyses. This paper conducts essential diagnostic prediction learning based on the analysis of blood cancer factors reported previously. Blood cancer diagnosis predictions based on artificial intelligence contribute to successfully achieve more than 90% accuracy and validation of blood cancer factors as an alternative auxiliary approach.

• 한국정밀공학회지
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• 제14권11호
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• pp.147-153
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• 1997

Fault diagnosis technique in machining system which is one of engineering techniques absolutely necessary to automation of manufacturing system has been proposed. As a whole, diagnosis process is explained by two steps: sensor data acquisition and reasoning current state of system with the given sensor data. Flexible disk grinding process implemented in milling machine was employed in order to obtain empirical manufacturing process information. Resistance force data during machining were acquired using tool dynamometer known as sensor which is comparably accurate and reliable in operation. Tool status during the process was analyzed using influnece diagram assigning probability from the statistical analysis procedure.

• Asian Pacific Journal of Cancer Prevention
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• 제13권9호
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• pp.4769-4772
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• 2012

Aims: The purpose of the present study was to investigate healthy lifestyle changes during the period before and after breast cancer diagnosis in Taiwan. Materials and Method: Lifestyle changes during the period before and after cancer diagnosis were assessed by convenience sampling with a structured questionnaire for breast cancer survivors. Results: A total of 235 breast cancer survivors completed the healthy lifestyle scale. The mean values before and after breast cancer diagnosis of the participants were 3.27 and 3.73. The final five dimensions for the period before breast cancer diagnosis were: had not experienced stress; had exercised; had maintained sleep quality; had maintained body weight; and had maintained relationships. The final five dimensions for the period after breast cancer diagnosis were: sleep quality; had not experienced stress; relationship; had exercised; and had maintained body weight. A paired-t test was applied to examine the differences before and after cancer diagnosis, revealing that the total average scores of the participants on the healthy lifestyle scale clearly differed statistically (t= -17.20, p<0.01); and the nine dimensions before and after testing also demonstrate a marked statistical difference (p<0.01). Conclusions: These findings are helpful in understanding the healthy lifestyle changes during the period before and after cancer diagnosis among breast cancer survivors. It is expected that these results can offer references of self-care for this group of patients.