• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.1
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• pp.57-62
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• 2015

In this paper, we design fuzzy inference-based deterioration diagnosis system through different image for rapid as well as efficient diagnosis of electrical equipments. When the deterioration diagnosis of the electrical equipment starts, abnormal state of assigned area is detected by comparing with the temperature of the first normal state of the area. Deterioration state of detected area is diagnosed by using fuzzy inference algorithm. In the fuzzy inference algorithm, fuzzy rules are defined by If-then form and are described as look-up table. Both temperature and its ensuing variation are used as input variables. While triangular membership function is used for the fuzzy input variables of fuzzy rules, singleton membership function is used for the output variable of fuzzy rules. The final output is calculated by using the center of gravity of fuzzy inference method. Experimental data acquired from individual electrical equipments is used in order to evaluate the output performance of the proposed system.

• Journal of Biomedical Engineering Research
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• v.32 no.2
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• pp.134-143
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• 2011

The selection of meaningful clinical tests and its reference values from a high-dimensional clinical data with imbalanced class distribution, one class is represented by a large number of examples while the other is represented by only a few, is an important issue for differential diagnosis between similar diseases, but difficult. For this purpose, this study introduces methods based on the concepts of both discernibility matrix and function in rough set theory (RST) with two discretization approaches, equal width and frequency discretization. Here these discretization approaches are used to define the reference values for clinical tests, and the discernibility matrix and function are used to extract a subset of significant clinical tests from the translated nominal attribute values. To show its applicability in the differential diagnosis problem, we have applied it to extract the significant clinical tests and its reference values between normal (N = 351) and abnormal group (N = 101) with either cholecystitis or cholelithiasis disease. In addition, we investigated not only the selected significant clinical tests and the variations of its reference values, but also the average predictive accuracies on four evaluation criteria, i.e., accuracy, sensitivity, specificity, and geometric mean, during l0-fold cross validation. From the experimental results, we confirmed that two discretization approaches based rough set approximation methods with relative frequency give better results than those with absolute frequency, in the evaluation criteria (i.e., average geometric mean). Thus it shows that the prediction model using relative frequency can be used effectively in classification and prediction problems of the clinical data with imbalanced class distribution.

• Journal of Biomedical Engineering Research
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• v.42 no.3
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• pp.125-142
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• 2021

The POCT (point-of-care test) sensing that has been a fast-developing field is expected to be a next generation technology in health care. The POCT sensors for the detection of proteins, small molecules and especially nucleic acids have lately attracted considerable attention. According to the World Health Organization (WHO), the POCT methods are required to follow the ASSURED guidelines (Affordable, Sensitive, Specific, User- friendly, Robust and rapid, Equipment-free, Deliverable to all people who need the test). Recently, several CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) based diagnostic techniques using the sensitive gene recognition function of CRISPR have been reported. CRISPR/Cas (Cas, CRISPR associated protein) systems based detection technology is the most innovative gene analysis technology that is following the ASSURED guidelines. It is being re-emerged as a powerful diagnostic tool that can detect nucleic acids due to its characteristics that enable rapid, sensitive and specific analyses of nucleic acid. The first CRISPR-based diagnosis begins with the discovery of the additional function of Cas13a. The enzymatic cleavage occurs when the conjugate of Cas protein and CRISPR RNA (crRNA) detect a specific complementary sequence of the target sequence. Enzymatic cleavage occurs on not only the target sequence, but also all surrounding non-target single-stranded RNAs. This discovery was immediately utilized as a biosensor, and numerous sensor studies using CRISPR have been reported since then. In this review, the concept of CRISPR, the characteristics of the Cas protein required for CRISPR diagnosis, the current research trends of CRISPR diagnostic technology, and some aspects to be improved in the future are covered.

• The Journal of Korean Medicine
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• v.28 no.3 s.71
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• pp.126-137
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• 2007

Objectives : Standardization of pattern identification for stroke and development of a diagnostic tool for Korean medicine. Methods : We organized a committee for stroke diagnosis standardization of Korean traditional medicine and submitted the Korean standard differentiation of the symptoms and signs for stroke (KSDSS). We collected cases through a multi-center network consisting of twelve university hospitals and one local hospital. We analyzed the data with discriminant function and logistic regression. Results : 321 cases were confirmed by diagnosis of medical specialists and residents. They were divided into qi deficiency 30.84%, dampness & phlegm 25.55%, fire & heat 22.43%, eum deficiency 18.69% and blood stasis 2.49%. The accordance rate between discriminant function and doctor's diagnosis was calculated. Conclusions : To make a stroke diagnostic program, we must raise the accordance rate between doctor's diagnosis and the program.

• 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.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.12
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• pp.1545-1551
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• 1995

As the modern industrial processes become more complex, it is getting more difficult to model and control the processes. Naturally, an advanced type of DCS(Distributed Control System) with higher level functions is being sought. Advanced DCS is a DCS with advanced functions such as fault diagnosis, GPC(Generalized Predictive Control), NN(Neural Network), and Fuzzy Control. In this thesis, we have studied a fuzzy control algorithm for realizing an advanced DCS. Its algorithm is implemented in a form of function code which is a process control language, being used by the industrial engineers. To verify the realized function code of the fuzzy control, the function code is applied to a continuous casting process of the Pohang Iron & Steel Works in Kwangyang. The rules of the fuzzy control were collected via interviews of the field operators and their operation documents. Finally under a real-time operating system environment, usability of the function code of the fuzzy control is shown via simulation for the continuous casting process.

• Journal of the Korean Society of Laryngology, Phoniatrics and Logopedics
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• v.6 no.1
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• pp.9-15
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• 1995

Bowing of the vocal cords may be due to aging. atrophy. bilateral superior laryngeal nerve paralysis. injudicious vocal cord surgery, of an idiopathic cause. The bowing usually produces a dysphonia characterized by breathiness due to air escape : however, it can produce aphonia. This report reviews vocal function after surgical correction of bowing of the vocal cords for diagnosis and management. The vocal function of 13 patients with sulcus vocalis and 12 patients with vocal cord atrophy was evaluated with the use of a test battery of multidimensional evaluation items. The voice was improved postoperatively in most patients. The voice improvement was reflected objectively in maximum phonation time, mean air flow rate during phonation, stroboscopic findings. sound pressure level range and fundamental frequency range of phonation, and results of acoustic analyses of tape-recorded voice. The vocal function after surgical correction of the sulcus vocalis and vocal cord atrophy was improved postoperatively in most patient, but the results were not satisfactory.

• Structural Engineering and Mechanics
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• v.16 no.5
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• pp.581-595
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• 2003

Since the conventional direct approaches are hard to be applied for damage diagnosis of complex large-scale structures, a two-step approach for diagnosing the joint damage of framed structures is presented in this paper by using artificial neural networks. The first step is to judge the damaged areas of a structure, which is divided into several sub-areas, using probabilistic neural networks with natural Frequencies Shift Ratio inputs. The next step is to diagnose the exact damage locations and extents by using the Radial Basis Function (RBF) neural network with the second Element End Strain Mode of the damaged sub-area input. The results of numerical simulation show that the proposed approach could diagnose the joint damage of framed structures induced by earthquake action effectively and has reliable anti-jamming abilities.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.11a
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• pp.401-406
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• 2006

This paper presents the fault diagnosis of induction motor based on support vector machine(SVMs). SVMs are well known as intelligent classifier with strong generalization ability. Application SVMs using kernel function is widely used for multi-class classification procedure. In this paper, the algorithm of SVMs will be combined with feature extraction and reduction using component analysis such as independent component analysis, principal component analysis and their kernel(KICA and KPCA). According to the result, component analysis is very useful to extract the useful features and to reduce the dimensionality of features so that the classification procedure in SVM can perform well. Moreover, this method is used to induction motor for faults detection based on vibration and current signals. The results show that this method can well classify and separate each condition of faults in induction motor based on experimental work.

• Journal of Electrical Engineering and Technology
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• v.13 no.6
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• pp.2504-2510
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• 2018

Actuator failures and the failures of controlled objects are often considered together. To overcome this limitation, a class of sliding mode observers for the fault diagnosis of nonlinear systems is designed in this paper. Due to the influence of the sliding mode function, the control strategy and the residual change of the observer exhibit certain trends governed by specific relations. Therefore, according to the changes in the control strategy and the observer residuals, the sensor and actuator faults in nonlinear systems can be determined. Finally, the effectiveness of the proposed method is verified based on simulations of a DC motor system.