• Nuclear Engineering and Technology
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• 제54권10호
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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.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 제39회 하계학술대회
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• pp.1380-1381
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• 2008

By applying the digital sub-station, the change of various protect and measuring equipments has been applying. This paper is analyzed that the development of GIS partial discharge condition monitoring equipment is suited to the electricity IT technology and digital sub-station. It's constitution apply to be able to suit the data to high rank for GIS partial discharge condition monitoring equipment that is suited digital sub-station and Watch-dog module that be able to monitor the inner communication of the GIS partial discharge equipment. And then it also be able to apply of GIS partial discharge equipment when digital sub-station is applied.

• 한국환경과학회지
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• 제27권6호
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• pp.447-456
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• 2018

In this research, the target process was a modified type of a conventional aeration tank with four different influent feeding points and alternated aeration to obtain nitrogen removal. For more accurate switching of influent feeding, the process was operated under a designed control strategy based on monitoring of $NH_4-N$ and $NO_X-N$ concentrations in the tank. However, the strategy did have some limitations. For example, it was not sensitive to detecting the end of each reaction when losing the balance between nitrification and denitrification of each opposite part of biological tank. To overcome the limitations of the existing control strategy, a diagnosis-based control strategy was suggested in this research using the diagnosis results classified as normal (N), ammonia accumulation (AA) and nitrate accumulation (NA). Using the pre-designed rules for control actions, the aeration and volume of the aerated part of the reactor could be increased or decreased at a fixed mode time. In simulations of the suggested diagnosis-based control strategy, the $NH_4-N$ and $NO_X-N$ removal rates in the reactor were maintained at higher levels than those of the existing control strategy.

• 대한기계학회논문집A
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• 제26권4호
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• pp.587-595
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• 2002

Recent trend changes a criterion for evaluation of humors that environmental problems are raised as a global issue. Burners with higher thermal efficiency and lower oxygen in the exhaust gas, evaluated better. To comply with environmental regulations, burners must satisfy the NO/sub x/ and CO regulation. Consequently, 'good burner'means one whose thermal efficiency is high under the constraint of NO/sub x/ and CO consistency. To make existing burner satisfy recent criterion, it is highly recommended to develop a feedback control scheme whose output is the consistency of NO/sub x/ and CO. This paper describes the development of a real time flame diagnosis technique that evaluate and diagnose the combustion states, such as consistency of components in exhaust gas, stability of flame in the quantitative sense. In this paper, it was proposed on the flame diagnosis technique of burner using Neuro-Fuzzy algorithm. This study focuses on the relation of the color of the flame and the state of combustion. Neuro-Fuzzy loaming algorithm is used in obtaining the fuzzy membership function and rules. Using the constructed inference algorithm, the amount of NO/sub x/ and CO of the combustion gas was successfully inferred.

• KEPCO Journal on Electric Power and Energy
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• 제6권4호
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• pp.447-454
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• 2020

Since SF₆ gas was discovered in the early 1900s, it has been widely used as an insulation material for electrical equipment. While various indicators have been developed to diagnose oil-immersed transformers, there are still insufficient indicators for the diagnosis of gas-insulated transformers. When necessary, chemical diagnostic methods can be used for gas-insulated transformers. However, the field suitability and accuracy of those methods for transformer diagnosis have not been verified. In addition, since various types of decomposition gases are generated therein, it is also necessary to establish appropriate analysis methods to cover the variety of gases. In this study, a gas-insulated transformer was diagnosed through the analysis of decomposition gases. Reliability assessments of both simple analysis methods suitable for on-site tests and precise analysis methods for laboratory level tests were performed. Using these methods, a gas analysis was performed for the internal decomposition gases of a 154 kV transformer in operation. In addition, simulated discharge and thermal fault experiments were demonstrated. Each major decomposition gas generation characteristics was identified. The results showed that an approximate diagnosis of the inside of a gas-insulated transformer is possible by analyzing SO₂, SOF₂, and CO using simple analysis methods on-site. In addition, since there are differences in the types of decomposition gas generation patterns with various solid materials of the internal transformer, a detailed examination should be performed by using precise analysis methods in the laboratory.

• 한방비만학회지
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• 제21권1호
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• pp.32-41
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• 2021

Objectives: We aim to observe the relation of body mass index (BMI) and the indicators of oral health. Methods: 400 subjects participated in the study. The BMI values are calculated from the height and weight. For the tongue diagnosis, we used the tongue imaging device to analyze the color, tongue coating, and tooth marks. We measured the concentration of hydrogen sulfide (H₂S) and methyl mercaptan (CH₃SH) to evaluate the halitosis. The dry mouth was evaluated through the measurement of saliva secretion and with the questionnaire asking the frequency of dry mouth. Results: The BMI values were significantly higher in the group with light-white and blue-purple colored tongue, and significantly lower for lightly-coated tongue. However, the correlation of BMI and the amounts of saliva secretion was not significant as well as in the correlation of BMI and the concentration of H₂S, CH₃SH. In tongue diagnosis, the subjects who had blue-purple colored tongue also had significantly higher H₂S and CH₃SH, but tendency of lower saliva secretion. Conclusion: We obtain data showing that BMI value and the indicators of oral health including tongue diagnosis have meaningful correlation.

• 한국한의학연구원논문집
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• 제13권2호통권20호
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• pp.91-100
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• 2007

In this paper, We tried to analyze the patent trend on constitution diagnosis and treatment related technologies. For this, constitution diagnosis and treatment related technologies divided into 16 sub-technology by the advisory committee. And we analyzed patents applied in Korea, Japan, U.S.A., China, and Europe. The 16 sub-technology consist of pulse analyzer, skin diagnosis, tongue analyzer, face and body detector, face analyzer, voice analyzer, intelligence ontology, meridian diagnosis, infrared thermography, electric stimulation, laser, high and low frequency, physical, magnetic, and ultrasound therapy. As a result we found that patents of constitution diagnosis and treatment in Korea has been growing steadily in both quality and quantity since 1980s. The number of patent the pulse analyzer and low frequency therapy are larger than others. But applied relevant international patents, marketability of the patent and Cites per Patent (CPP) index are shown to be very low. In conclusion, to occupy key original technologies of the Oriental medical device, we need to apply more patent of the whole related technology and international patents.

• 대한방사성의약품학회지
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• 제6권2호
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• pp.92-101
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• 2020

The cyclic Arg-Gly-Asp (cRGD) peptide is well-known as a binding molecule to the integrin α_vβ₃ receptor which is highly expressed on activated endothelial cells and new blood vessels in tumors. Although numerous results have been reported by the usage of cRGD peptide-based ligands for cancer diagnosis and therapy, the distinct mechanisms, and functions of cRGD-integrin binding to cancer cells are still being investigated. In this study, we evaluated the internalization efficacy of different types of cRGD peptides (monomer, dimer and tetramer form) in integrin α_vβ₃ overexpressing cancer cells. Western blot and flow cytometric analysis showed U87MG expresses highly integrin α_vβ₃, whereas CT-26 does not show integrin α_vβ₃ expression. Cytotoxicity assay indicated that all cRGD peptides (0-200 µM) had at least 70-80% of viability in U87MG cells. Fluorescence images showed cRGD dimer peptides have the highest cellular internalization compare to cRGD monomer and cRGD tetramer peptides. Additionally, transmission electron microscope results clearly visualized the endocytic internalization of integrin α_vβ₃ receptors and correlated with confocal microscopic results. These results support the rationale for the use of cRGD dimer peptides for imaging, diagnosis, or therapy of integrin α_vβ₃-rich glioblastoma.

• 산업기술연구
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• 제34권
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• pp.67-70
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• 2014

In this paper, we propose a diagnosis algorithm for 25.8kV $N_2$ insulated Pad-mounted Switchgear in oder to improve reliability by preventing of fault in advance. The proposed algorithm can diagnose the problems of Pad-mounted Switchgear such as gas leakage and VI(Vacuum Interrupter) trouble (contact abrasion, coil aging etc.) by using pressure sensor, stroke sensor and coil current sensor.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권2호
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• pp.284-310
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• 2024

Identifying clinical pathways for disease diagnosis and treatment process recommendations are seriously decision-intensive tasks for health care practitioners. It requires them to rely on their expertise and experience to analyze various categories of health parameters from a health record to arrive at a decision in order to provide an accurate diagnosis and treatment recommendations to the end user (patient). Technological adaptation in the area of medical diagnosis using AI is dispensable; using expert systems to assist health care practitioners in decision-making is becoming increasingly popular. Our work architects a novel knowledge-based recommender system model, an expert system that can bring adaptability and transparency in usage, provide in-depth analysis of a patient's medical record, and prescribe diagnostic results and treatment process recommendations to them. The proposed system uses a set of parallel discrete fuzzy rule-based classifier systems, with each of them providing recommended sub-outcomes of discrete medical conditions. A novel knowledge-based combiner unit extracts significant relationships between the sub-outcomes of discrete fuzzy rule-based classifier systems to provide holistic outcomes and solutions for clinical decision support. The work establishes a model to address disease diagnosis and treatment recommendations for primary lung disease issues. In this paper, we provide some samples to demonstrate the usage of the system, and the results from the system show excellent correlation with expert assessments.