• Nuclear Engineering and Technology
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• v.54 no.10
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• pp.3620-3630
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• 2022

The diagnosis of abnormalities in a nuclear power plant is essential to maintain power plant safety. When an abnormal event occurs, the operator diagnoses the event and selects the appropriate abnormal operating procedures and sub-procedures to implement the necessary measures. To support this, abnormality diagnosis systems using data-driven methods such as artificial neural networks and convolutional neural networks have been developed. However, data-driven models cannot always guarantee an accurate diagnosis because they cannot simulate all possible abnormal events. Therefore, abnormality diagnosis systems should be able to detect their own potential misdiagnosis. This paper proposes a rulebased diagnostic validation algorithm using a previously developed two-stage diagnosis model in abnormal situations. We analyzed the diagnostic results of the sub-procedure stage when the first diagnostic results were inaccurate and derived a rule to filter the inconsistent sub-procedure diagnostic results, which may be inaccurate diagnoses. In a case study, two abnormality diagnosis models were built using gated recurrent units and long short-term memory cells, and consistency checks on the diagnostic results from both models were performed to detect any inconsistencies. Based on this, a re-diagnosis was performed to select the label of the second-best value in the first diagnosis, after which the diagnosis accuracy increased. That is, the model proposed in this study made it possible to detect diagnostic failures by the developed consistency check of the sub-procedure diagnostic results. The consistency check process has the advantage that the operator can review the results and increase the diagnosis success rate by performing additional re-diagnoses. The developed model is expected to have increased applicability as an operator support system in terms of selecting the appropriate AOPs and sub-procedures with re-diagnosis, thereby further increasing abnormal event diagnostic accuracy.

• Journal of radiological science and technology
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• v.45 no.4
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• pp.321-330
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• 2022

Echocardiography and cardiac enzymes test are the tests to assess ischemic heart disease. The purpose of this study was to verify the accuracy by comparing and analyzing two tests for the diagnosis of ischemic heart disease. A retrospective study was conducted on 393 study subjects who underwent echocardiography and cardiac enzymes test. As a result of the study, regional wall motion abnormality (RWMA) increased as the age of the study subjects increased. As a result of ROC analysis, RWMA showed a larger area under the curve (AUC) than cardiac enzymes. RWMA showed the highest accuracy with 81.1% of all cardiac enzymes. Among cardiac enzymes, cTnI showed the highest accuracy. Thus, It was confirmed that RWMA of echocardiography is more accurate than cardiac enzyme is in diagnosing ischemic heart disease.

• Journal of Multimedia Information System
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• v.3 no.2
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• pp.35-42
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• 2016

Electrocardiogram (ECG) signal gives a clear indication whether the heart is at a healthy status or not as the early notification of a cardiac problem in the heart could save the patient's life. Several methods were launched to clarify how to diagnose the abnormality over the ECG signal waves. However, some of them face the problem of lack of accuracy at diagnosis phase of their work. In this research, we present an accurate and successive method for the diagnosis of abnormality through Discrete Wavelet Transform (DWT), QRS complex detection and Support Vector Machines (SVM) classification with overall accuracy rate 95.26%. DWT Refers to sampling any kind of discrete wavelet transform, while SVM is known as a model with related learning algorithm, which is based on supervised learning that perform regression analysis and classification over the data sample. We have tested the ECG signals for 10 patients from different file formats collected from PhysioNet database to observe accuracy level for each patient who needs ECG data to be processed. The results will be presented, in terms of accuracy that ranged from 92.1% to 97.6% and diagnosis status that is classified as either normal or abnormal factors.

• Journal of the korean academy of Pediatric Dentistry
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• v.50 no.4
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• pp.421-433
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• 2023

This study was to examine the developmental dental abnormalities in childhood cancer survivors. Risk factors were assessed for 125 children with radiographic data through a retrospective analysis of medical records and panoramic images. 68.0% of childhood cancer survivors exhibited at least one dental abnormality. The types of abnormalities varied depending on the age at cancer diagnosis and treatment intensity, ranging from microdontia (43.2%), to abnormal root development (39.2%) and tooth agenesis (33.6%). Logistic regression analysis demonstrated that a young age at diagnosis (under 3 years), the use of heavy metal agents, a history of hematopoietic stem cell transplantation (HSCT), and combination treatment of chemotherapy, radiation therapy, and HSCT were associated with a significantly higher risk for overall dental abnormalities. The increased risk ratios were 6.00, 3.06, 3.22, and 7.87, respectively (p < 0.05). The results of this study will predict dental abnormality in permanent dentition according to the diagnosis age and treatment method of childhood cancer.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.801-804
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• 2013

We propose a new tongue examination model according to the taste division of tongue. The proposed sytem consists of image acquisition, region segmentation, color distribution analysis and abnormality decision of tongue. Tongue DB which is classified into abnormality is constructed with tongue images captured from oriental medicine hospital inpatients. We divided 4 basic taste(bitter, sweet, salty and sour) regions and performed color distribution analysis targeting each region under HSI(Hue Saturation Intensity) color model. To minimize the influence of illumination, the histograms of H and S components only except I are utilized. The abnormality of taste regions each by comparing the proposed diagnosis model with diagnosis results by a doctor of oriental medicine. We confirmed the 87.5% of classification results of abnormality by proposed algorithm is coincide with the doctor's results.

• Journal of Society of Preventive Korean Medicine
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• v.4 no.2
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• pp.72-92
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• 2000

This research has conducted studies on an Oriental medicine-based method of diagnosing of occupational musculoskeletal system diseases. This researcher has searched through existing relevant medical literature. Also, this researcher has worked on a moire topography using moire topography. In this course, this researcher has reached the following conclusion in relation to the possibility of using a moire topography as a diagnosing device of musculoskeletal system diseases under Oriental medicine . 1 The Western medicine outlines its criteria of screening occupational musculoskeletal system diseases as follows A. The occupational musculoskeletal diseases must clearly include one or more of the subjective symptoms characterized by pain, hypoesthesia dysaesthesia, anaesthesia. etc . B, There should be clinically admitted objective observations and diagnosis outlining that the disease concerned shows symptoms such as tenderness, induration. and edema that can appear with occupational musculoskeletal system diseases. dyscinesia should be admitted with the disease concerned, or there should be observations and diagnosis outlining that abnormality exists in electric muscular or nervous diagnosis and examination . C. It should be admitted that prior to the occurrence of symptoms or observations and diagnosis on musculoskeletal system-related diseases, a patient has been engaged in works with conditions requiring improper work posture or work movement. That is, this is an approach whereby they see abnormality in the musculoskeletal system come from material and structural defect, and adjust and control abnormality in the musculoskeletal system and secreta . 2. The Oriental medicines sees that a patient develops the pain of occupational musculoskeletal diseases as he cannot properly activate the flow of his life force and blood thus not only causing formation of lumps in the body and blocking the flow of life force and blood in some parts of the body. Hence, The Oriental medicine focuses on resolving the cause of weakening the flow of life force and blood, instead of taking material approach of correcting structural abnormality Furthermore , Oriental medicine sees that when muscle tension builds up, this presses blood vessels and nerves passing by, triggering circulation dyscrasia and neurological reaction and thus leading to lesion. Thus, instead of taking skeletal or neurophysiological approach. it seeks to fundamentally resolve the cause of the flow of the life force and blood in muscles not being activated. As a result Oriental medicine attributes the main cause of musculoskeletal system diseases to muscle tension and its build-up that stem from an individual's long formed chronicle habit and work environment. This approach considers not only the social structure aspect including companies owners and work environment that the existing methods have looked at, but also individual workers' responsibility and their environmental factors. Hence, this is a step forward method. 3 The diagnosis of musculoskeletal diseases under Oriental medicine is characterized by the fact that an Oriental medicine doctor uses not only photos taken by himself, but also various detection devices to gather information and pass comprehensive judgment on it. Thus, it is the core of diagnosis under Oriental medicine to develop diagnosing devices matching the characteristics of information to be induced and to interpret information so induced from the views of Oriental medicine. Diagnosis using diagnosing devices values the whole state of a patient and formal abnormality alike, and the whole balance and muscular state of a patient serves as the basis of diagnosis. Hence, this method, instead of depending on the information gathered from devices under Western medicine, requires devices that provide information on the whole state of a patient in addition to the local abnormality information that X-ray. CT, etc., can offer. This method sees muscle as the central part of the abnormality in the musculoskeletal system and thus requires diagnosing devices enabling the muscular state. 4. The diagnosing device using moire topography under Oriental medicine has advantages below and can be used for diagnosing musculoskeletal system diseases with industrial workers . First, the device can Provide information on the body in an unbalanced state. and thus identify the imbalance and difference of height in the left and right stature that a patient can not notice at normal times. Second, the device shows the twisting of muscles or induration regions in a contour map. This is not possible with existing shooting machines such as X-ray, CT, etc., thus differentiating itself from existing machines. Third, this device makes it possible for Oriental medicine to take its unique approach to the abnormality in the musculoskeletal system. Oriental medicine sees the state and imbalance state in muscles as major factors in determining the lesion of musculoskeletal system, and the device makes it possible to shoot the state of muscles in detail. In this respect, the device is significant. Fourth, the device has an advantage as non-aggression diagnosing device.

• Nuclear Engineering and Technology
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• v.52 no.9
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• pp.2009-2016
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• 2020

A nuclear power plant is a large complex system with tens of thousands of components. To ensure plant safety, the early and accurate diagnosis of abnormal situations is an important factor. To prevent misdiagnosis, operating procedures provide the anticipated symptoms of abnormal situations. While the more severe emergency situations total less than ten cases and can be diagnosed by dozens of key plant parameters, abnormal situations on the other hand include hundreds of cases and a multitude of parameters that should be considered for diagnosis. The tasks required of operators to select the appropriate operating procedure by monitoring large amounts of information within a limited amount of time can burden operators. This paper aims to develop a system that can, in a short time and with high accuracy, select the appropriate operating procedure and sub-procedure in an abnormal situation. Correspondingly, the proposed model has two levels of prediction to determine the procedure level and the detailed cause of an event. Simulations were conducted to evaluate the developed model, with results demonstrating high levels of performance. The model is expected to reduce the workload of operators in abnormal situations by providing the appropriate procedure to ultimately improve plant safety.

• Nuclear Engineering and Technology
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• v.55 no.2
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• pp.493-505
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• 2023

Comprehensive condition monitoring of large industry systems such as nuclear power plants (NPPs) is essential for safety and maintenance. In this study, we developed novel system-scale diagnostic technology based on deep-learning and IR thermography that can efficiently and cost-effectively classify system conditions using compact Raspberry Pi and IR sensors. This diagnostic technology can identify the presence of an abnormality or accident in whole system, and when an accident occurs, the type of accident and the location of the abnormality can be identified in real-time. For technology development, the experiment for the thermal image measurement and performance validation of major components at each accident condition of NPPs was conducted using a thermal-hydraulic integral effect test facility with compact infrared sensor modules. These thermal images were used for training of deep-learning model, convolutional neural networks (CNN), which is effective for image processing. As a result, a proposed novel diagnostic was developed that can perform diagnosis of components, whole system and accident classification using thermal images. The optimal model was derived based on the modern CNN model and performed prompt and accurate condition monitoring of component and whole system diagnosis, and accident classification. This diagnostic technology is expected to be applied to comprehensive condition monitoring of nuclear power plants for safety.

• Journal of Advanced Marine Engineering and Technology
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• v.38 no.6
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• pp.744-750
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• 2014

The monitoring and abnormality warning of marine diesel engine are important to take appropriate responses for safety navigation. If maintenance engineers do not take appropriate response because of diagnosis mistakes, it will occur a nasty accident. Therefore, we need integrated monitoring and diagnosis system for supporting a diagnosis objectively. In this paper, we analyze time-series data which measured by real-time, monitor the changing of conditions and trends of the analyzed data. Furthermore, we design and implement a monitoring and diagnosis system for objective supporting of real-time diagnosis. When the integrated monitoring and diagnosis system is adopted, it can help to improve stability of marine diesel engine by providing abnormality warning alarm with appropriate responses.

• Proceedings of the PSK Conference
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• 2002.10a
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• pp.237-237
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• 2002

Creatine phosphokinase (CPK) was a marker in diagnosis of rhabdomyolysis. The CPK abnormality could be induced by intake of HMG CoA reductase inhibitors (statins). The objective of this study was to estimate the incidence rate of CPK abnormality by each statin. (omitted)