• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2272-2275
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• 2002

PCA(Principle Component Analysis) has emerged as a useful tool for process monitoring and fault diagnosis. The general approach requires the user to identify the root cause by interpreting the residual or principle components. This could be tedious and often impossible for a large process. In this paper, PCA scheme is combined with the FCM-based fault diagnostic algorithm to enhance the diagnosistic results. The implementation of the PCA-FCM based fault diagnostic system is done and its application is illustrated on the two-tank system.

• Transactions of The Korea Fluid Power Systems Society
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• v.6 no.2
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• pp.22-30
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• 2009

An in-process method of diagnosing the spool wear of hydraulic servovalves was explored. The diagnostic method discussed in this paper is for force-control hydraulic servo systems. The key principle used is that pressure sensitivity of a servovalve drops as the valve spool wears out so that it is possible to determine the spool condition by monitoring pressure sensitivity. A diagnostic algorithm was developed and evaluated through numerical simulation and experiments. Two major steps of diagnosis are the evaluation of null bias of the servovalve and the approximation of pressure sensitivity, both of which could be successfully done during normal operation of a servo system. The difference between a new servovalve and a worn valve could be clearly detected in-process, and the diagnostic test was found to be repeatable.

• Clinical and Experimental Pediatrics
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• v.49 no.2
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• pp.117-128
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• 2006

Non-traumatic acute abdominal pain in children presents a diagnostic dilemma. Numerous disorders can cause abdominal pain. Although many etiologies are benign, some require a rapid diagnosis and treatment in order to minimize morbidity. This review concentrates on the clinical office evaluation of acute abdominal pain in infants and children and details the clinical guideline for the diagnostic approach to imaging and the salient clinical features of some of the conditions. The clinical outcomes of children presenting with acute abdominal pain and the risk factors of the high risk underlying diseases would be provided for the diagnostic algorithm.

• Journal of Sasang Constitutional Medicine
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• v.32 no.4
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• pp.65-85
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• 2020

Objectives The aim of study was to suggest diagnostic indicator according to Exterior-Interior disease for the Sasang Constitution based on original symptom. Methods We investigated the literature(『Dongeuisusebowon sinchukbon』) and another study(Clinical Practice Guideline for Disease of Sasang Constitutional Medicine: Diagnosis and Algorithm). As a result, we developed diagnostic indicator of original symptom for Exterior-Interior disease in Sasang Constitutional Medicine. Results and Conclusions Diagnosis of Exterior-Interior disease in Sasang Constitution was decided by heat and cold of original symptom. Detailed indicators of diagnosis in Exterior-Interior disease were heat/cold sensitivity, the degree of sweating, the amount of drinking water, thirst, face color and somatalgia.

• Journal of Korea Multimedia Society
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• v.25 no.10
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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.

• Journal of Information Processing Systems
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• v.18 no.6
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• pp.822-829
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• 2022

According to existing study into the remote fault diagnosis procedure, the current diagnostic approach has an imperfect decision model, which only supports communication in a close distance. An Internet of Things (IoT)-based remote fault diagnostic approach for wind power equipment is created to address this issue and expand the communication distance of fault diagnosis. Specifically, a decision model for active power coordination is built with the mechanical energy storage of power generation equipment with a remote diagnosis mode set by decision tree algorithms. These models help calculate the failure frequency of bearings in power generation equipment, summarize the characteristics of failure types and detect the operation status of wind power equipment through IoT. In addition, they can also generate the point inspection data and evaluate the equipment status. The findings demonstrate that the average communication distances of the designed remote diagnosis method and the other two remote diagnosis methods are 587.46 m, 435.61 m, and 454.32 m, respectively, indicating its application value.

• Nuclear Engineering and Technology
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• v.50 no.4
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• pp.582-588
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• 2018

Accident diagnosis is one of the complex tasks for nuclear power plant (NPP) operators. In abnormal or emergency situations, the diagnostic activity of the NPP states is burdensome though necessary. Numerous computer-based methods and operator support systems have been suggested to address this problem. Among them, the recurrent neural network (RNN) has performed well at analyzing time series data. This study proposes an algorithm for accident diagnosis using long short-term memory (LSTM), which is a kind of RNN, which improves the limitation for time reflection. The algorithm consists of preprocessing, the LSTM network, and postprocessing. In the LSTM-based algorithm, preprocessed input variables are calculated to output the accident diagnosis results. The outputs are also postprocessed using softmax to determine the ranking of accident diagnosis results with probabilities. This algorithm was trained using a compact nuclear simulator for several accidents: a loss of coolant accident, a steam generator tube rupture, and a main steam line break. The trained algorithm was also tested to demonstrate the feasibility of diagnosing NPP accidents.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.1
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• pp.97-102
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• 2013

Objective: The aim of this study was to develop an automated computer-aided diagnostic system for diagnosis of thyroid cancer pattern in fine needle aspiration cytology (FNAC) microscopic images with high degree of sensitivity and specificity using statistical texture features and a Support Vector Machine classifier (SVM). Materials and Methods: A training set of 40 benign and 40 malignant FNAC images and a testing set of 10 benign and 20 malignant FNAC images were used to perform the diagnosis of thyroid cancer. Initially, segmentation of region of interest (ROI) was performed by region-based morphology segmentation. The developed diagnostic system utilized statistical texture features derived from the segmented images using a Gabor filter bank at various wavelengths and angles. Finally, the SVM was used as a machine learning algorithm to identify benign and malignant states of thyroid nodules. Results: The SVMachieved a diagnostic accuracy of 96.7% with sensitivity and specificity of 95% and 100%, respectively, at a wavelength of 4 and an angle of 45. Conclusion: The results show that the diagnosis of thyroid cancer in FNAC images can be effectively performed using statistical texture information derived with Gabor filters in association with an SVM.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.2 no.3
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• pp.61-71
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• 2003

This paper suggests a wireless diagnostic technique using SMS (Short Message Service) of mobile telecommunication network for pole transformer. The temperature and current of pole transformer are acquired and the average and standard deviation are transmitted using SMS of mobile telecommunication network when transformer is overloaded The algorithm and protocol is design to fit the wireless diagnostic technique. By using the wireless method, the weak point of accessability can overcome. It is possible to manage, control, and monitor many transformers with just one server.

• Journal of Korean Neurosurgical Society
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• v.66 no.1
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• pp.53-62
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• 2023

Objective : Deep learning is a machine learning approach based on artificial neural network training, and object detection algorithm using deep learning is used as the most powerful tool in image analysis. We analyzed and evaluated the diagnostic performance of a deep learning algorithm to identify skull fractures in plain radiographic images and investigated its clinical applicability. Methods : A total of 2026 plain radiographic images of the skull (fracture, 991; normal, 1035) were obtained from 741 patients. The RetinaNet architecture was used as a deep learning model. Precision, recall, and average precision were measured to evaluate the deep learning algorithm's diagnostic performance. Results : In ResNet-152, the average precision for intersection over union (IOU) 0.1, 0.3, and 0.5, were 0.7240, 0.6698, and 0.3687, respectively. When the intersection over union (IOU) and confidence threshold were 0.1, the precision was 0.7292, and the recall was 0.7650. When the IOU threshold was 0.1, and the confidence threshold was 0.6, the true and false rates were 82.9% and 17.1%, respectively. There were significant differences in the true/false and false-positive/false-negative ratios between the anterior-posterior, towne, and both lateral views (p=0.032 and p=0.003). Objects detected in false positives had vascular grooves and suture lines. In false negatives, the detection performance of the diastatic fractures, fractures crossing the suture line, and fractures around the vascular grooves and orbit was poor. Conclusion : The object detection algorithm applied with deep learning is expected to be a valuable tool in diagnosing skull fractures.