• Journal of Institute of Control, Robotics and Systems
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• v.6 no.4
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• pp.254-260
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• 2000

This paper deals with the development of fault tolerant control for a nonlinear boiler system with noise and disturbance. The MCMBPC(Multivariable Constrained Model Based Predictive Control) is adopted for the control of the specific boiler turbin model. The fault detection and diagnosis are accomplished with the Kalman filter and two bias estimators. Once a fault is detected, two Bias estimators are driven to estimate the fault and to discriminate Process fault and sensor fault. In this paper, a fault tolerant control scheme combining MCMBPC with a fault compensation method based on the bias estimator is proposed. The proposed scheme has been applied to the nonlinear boiler system and shown a satisfactory performance through some simulations.

• 대한예방의학회:학술대회논문집
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• 1994.02b
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• pp.142-149
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• 1994

Substitution of graphic representation for extensive lists of numerical statistical data is highly desirable by both editors and readers of medical journals, faced with an exploding abundance of contemporary medical literature. A novel graphic tool. the 'double-ring diagram', is described herein which permits visual representation of information regarding certain statistical variables used to describe the performance of a test or physical sign in the diagnosis of a disease. The diagram is relatively easy to construct on the basis of a number of primary data such as the prevalence and the true positive, true negative. false positive and false negative test results. These values are reflected in the diagram along with the values of other statistical variables derived from them. such as the sensitivity. specificity, predictive values for positive and negative test result. and accuracy. This diagram may be useful in visualizing a test's performance and facilitating visual comparison of performance of two or more tests.

• Journal of Multimedia Information System
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• v.6 no.2
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• pp.87-90
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• 2019

Machine learning techniques have been applied in almost all the domains of human life to aid and enhance the problem solving capabilities of the system. The field of medical science has improved to a greater extent with the advent and application of these techniques. Efficient expert systems using various soft computing techniques like artificial neural network, Fuzzy Logic, Genetic algorithm, Hybrid system, etc. are being developed to equip medical practitioner with better and effective diagnosing capabilities. In this paper, a comparative study to evaluate the predictive performance of subtractive clustering based ANFIS hybrid system (SCANFIS) with Fuzzy C-Means (FCM) based ANFIS system (FCMANFIS) for Alzheimer disease (AD) has been taken. To evaluate the performance of these two systems, three parameters i.e. root mean square error (RMSE), prediction accuracy and precision are implemented. Experimental results demonstrated that the FCMANFIS model produce better results when compared to SCANFIS model in predictive analysis of Alzheimer disease (AD).

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.9
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• pp.5455-5459
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• 2013

Background: The risk of malignancy index (RMI) for the evaluation of adnexal masses is a sensitive tool in certain populations. The best cut off value for RMI 1, 2 and 3 is 200. The cut off value of RMI-4 to differentiate benign from malignant lesions is 450. Our aim was to evaluate the efficiency of four different malignancy indexes (RMI1-4) in a homogeneous population. Materials and Methods: We evaluated a total of 153 non-pregnant women with adnexal masses who did not have a history of malignancy and who were above 18 years of age. Results: A cut-off value of 250 for RMI-1 provided 95.9% inter-observer agreement, yielding 95.9% specificity, 93.5% negative predictive value, 75.0% sensitivity and 82.8% positive predictive value. A cut-off value of 250 for RMI-1 showed high performance in preoperative diagnosis of invasive malignant lesions than cut-off value of 200 in our population. A cut-off value of 350 for RMI-2 provided 94.5% inter-observed agreement, yielding 94.2% specificity, 93.4% negative predictive value, 75.0% sensitivity and 77.4% positive predictive value. RMI-2 showed the higher performance when the cut-off value was set at 350 in our population. A cut-off value of 250 provided 95.2% inter-observer agreement, yielding 95.0% specificity, 93.2% negative predictive value, 75.0% sensitivity, and 88.0% positive predictive value. RMI-3 showed the highest performance to diagnose malignant adnexal masses when the cut-off value was set at 250. In our study, RMI-4 showed similar statistical performance when the cut-off value was set at 400 [(Kappa: 0.684/p=0.000), yielding 93.8% inter-observer agreement, 93.4% specificity, 93.4% negative predictive value, 75.0% sensitivity, and 75.0% negative predictive value]. Conclusions: We showed successful utilization of RMIs in preoperative differentiation of benign from malignant masses. Many studies conducted in Asian and Pacific countries have reported different cut-off values as was the case in our study. We think that it is difficult to determine universally accepted cut-off values for RMIs for common use around the globe.

• Journal of Trauma and Injury
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• v.20 no.2
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• pp.130-137
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• 2007

Purpose: Ultrasound is of proven accuracy in abdominal and thoracic trauma and may be useful for diagnosing extremity injury in situations where radiography is not available, such as disasters and military and space applications. However, the diagnosis of fractures is suggested by history and physical examination and is typically confirmed with radiography. As a alternative to radiography, we prospectively evaluated the utility of extremity ultrasound performed by trained residents of emergency medicine (EM) one patient with wrist and ankle extremity injuries. Methods: Initially, residents of EM performed physical examinations for fractures. The emergency ultrasound (EM US) was performed by trained residents, who used a portable ultrasound device with a 10- to 5-MHz linear transducer, on suspected patients before radiography examination. The results of emergency ultrasound and radiography and the final diagnosis were recorded, and correlation among them were determined by using Kappa s test Results: Thirty-nine patients were enrolled in our study. The average age was $36.6\;{\pm}\;19.3$ years. There were radius Fx. (n=21), radius-ulna Fx. (n=1), ulna Fx. (n=1), and contusion (n=2) injuries among the wrist injury and lat.-med. malleolar Fx. (n=13), lat. malleolar Fx. (n=6), and med. malleolar Fx. (n=3) injuries among the ankle injury. Comparing EM US with radiography, we found the sensitivity, specificity, positive predictive value, and negative predictive value of EM US for Fx. diagnosis to be 100%, 66.7%, 97.3%, 100% and those of radiography to be 97.2%, 100%, 100%, and 75%, respectively. Kappa s test for a correlation between the Fx. diagnosis of EM US and the final diagnosis of Fx was performed, and Kappa's value was 0.787 (P = 0.004).Conclusion: EM US for Fx. can be performed quickly and accurately by EM residents with excellent accuracy in remote locations such as disaster areas and in military and aerospace applications. EM US was as useful as radiography in our study and had a high correlation to the final diagnosis of Fx. Therefore, ultrasound should performed on patients with extremity injury to determine whether extremity evaluation should be added to the FAST (focused abdominal sonography trauma) examination.

• Asian Oncology Nursing
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• v.12 no.2
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• pp.156-165
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• 2012

Purpose: This study was to identify predictors of sexual function in gynecologic cancer patients. Methods: The participants were 154 patients treated at a university medical center in A city, Korea. The data collection was performed through a structured questionnaire from July to December, 2010. The instruments used in this study were Female Sexual Function Index (FSFI) perceived health status scale, Eastern Cooperative Oncology Group (ECOG) performance status, body image, and depression. Data were analyzed using descriptive statistics, Mann-Whitney test, Kruskal-Wallis test and stepwise multiple regression with the SPSS 18.0. Results: The mean score of perceived health status was 8.42 and sexual function was 8.42. The lowest score among sexual function was lubrication. The scores of sexual function was significantly different by age, job, marital status, period after diagnosis of cancer and diagnosis. There were significant correlations between sexual function, perceived health status, ECOG performance, body image and depression. In multiple regression analysis, predictors were identified as ECOG performance, age, diagnosis and period after diagnosis of cancer (Adj.$R^2$=.28). The most powerful predictor of female sexual function was ECOG performance (19.0%). Conclusion: The above findings indicate that it is necessary to develop a more effective and personalized sexual function improvement program for gynecologic cancer patient.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.11
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• pp.6241-6243
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• 2013

Objective: To explore the predictive value of tumor markers, including cancer antigen 72-4 (CA72-4), cancer antigen 15-3 (CA15-3) and cancer antigen 125 (CA125), in single or combined detection, for the diagnosis of ovarian cancer. Methods: 120 patients diagnosed with ovarian cancer from August 2011 to March 2013 and 80 patients diagnosed with benign ovarian tumors were enrolled in this test, along with 50 health examination women randomly selected from the database as controls. Serum levels of CA72-4, CA15-3 and CA125 in this study were determined by electrochemiluminescence (ECL). Results: Serum levels of CA72-4, CA15-3 and CA125 in ovarian cancer were higher than those in healthy group and benign group (P<0.01).The sensitivity of combined detection of those three tumor markers for diagnosis of ovarian cancer was obviously higher than with single detection with each marker (P<0.01). Conclusions: CA72-4, CA15-3 and CA125 could be a good combination in the diagnosis of ovarian cancer. Patients whose tumor markers continue to increase should be highly suspected of malignancy.

• The Korean Journal of Cytopathology
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• v.10 no.1
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• pp.43-53
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• 1999

We retrospectively reviewed the results of 1,850 fine needle aspiration cytology(FNAC) of thyroid nodules performed from 1990 to 1991 in the Department of Pathology, Seoul National University Hospital. Among 1,528 cases and 322 cases aspirated by clinicians and a pathologist, 465 cases(30.4%) and 13 cases(4.0%) of the aspirates were inadequate, respectively. In 227 cases, correlation of the FNAC diagnosis and histologic diagnosis was done. Excluding the inadequate cases, the sensitivity nor the detection of neoplasm(malignancy together with follicular adenoma) was 86.4% and the specificity was 70.7%. The overall diagnostic accuracy was 79.0%. There were 16 false-positive cases(7.0%), and 19 false-negative cases(8.4%). The predictive value of each cytologic diagnosis was 92% in papillary carcinoma, and 100%, in Hashimoto's thyroiditis. The expectancy of malignancy was 52.8% in "suspicious malignancy" and 26.7% in "atypical lesion".

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.1
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• pp.159-163
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• 2016

Background: Colorectal cancer (CRC) is the third most common cancer in the world, and the fourth in Iran in both genders. The aim of this study was to find predictive factors for CRC survival. Materials and Methods: Medical records of 570 patients referred to the radiotherapy oncology department of Shiraz Namazi hospital from 2005 to 2010 were retrospectively analysed. Data were collected by reviewing medical records, and by telephone interviews with patients. Survival analysis was performed using the Cox's regression model with survival probability estimated with Kaplan-Meier curve. The log-rank test was used to compare survival between strata. Data was analyzed with Stata 12. Results: The five-year survival rate and the mean survival time after cancer diagnosis were 58.5% and $67{\pm}4months$. On multivariate analysis, age of diagnosis, disease stage and primary tumor site, lymphovascular invasion and type of treatment (in colon cancer) were significant factors for survival. Conclusions: Age of diagnosis and type of treatment (adjuvant therapy in patients with colon cancer) were two modifiable factors related to survival of CRC patients. Therefore earlier diagnosis might help increase survival.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.1
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• pp.57-64
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• 2017

In order to reduce damages to major railroad components, which have the potential to cause interruptions to railroad services and safety accidents and to generate unnecessary maintenance costs, the development of rolling stock maintenance technology is switching from preventive maintenance based on the inspection period to predictive maintenance technology, led by advanced countries. Furthermore, to enhance trust in accordance with the speedup of system and reduce maintenances cost simultaneously, the demand for fault diagnosis and prognostic health management technology is increasing. The objective of this paper is to propose a highly reliable learning model using various machine learning algorithms that can be applied to critical rolling stock components. This paper presents a model for railway rolling stock component fault diagnosis and conducts a mechanical failure diagnosis of motor components by applying the machine learning technique in order to ensure efficient maintenance support along with a data preprocessing plan for component fault diagnosis. This paper first defines a failure diagnosis model for rolling stock components. Function-based algorithms ANFIS and SMO were used as machine learning techniques for generating the failure diagnosis model. Two tree-based algorithms, RadomForest and CART, were also employed. In order to evaluate the performance of the algorithms to be used for diagnosing failures in motors as a critical railroad component, an experiment was carried out on 2 data sets with different classes (includes 6 classes and 3 class levels). According to the results of the experiment, the random forest algorithm, a tree-based machine learning technique, showed the best performance.