• Journal of Oral Medicine and Pain
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• 제47권2호
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• pp.107-108
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• 2022

Artificial intelligence (AI) refers to the use of machines to mimic intelligent human behavior. It involves interactions with humans in clinical settings, and augmented intelligence is considered as a cognitive extension of AI. The importance of AI in healthcare and medicine has been emphasized in recent studies. Machine learning models, such as genetic algorithms, artificial neural networks (ANNs), and fuzzy logic, can learn and examine data to execute various functions. Among them, ANN is the most popular model for diagnosis based on image data. AI is rapidly becoming an adjunct to healthcare professionals and is expected to be human-independent in the near future. The introduction of AI to the diagnosis and treatment of oral diseases worldwide remains in the preliminary stage. AI-based or assisted diagnosis and decision-making will increase the accuracy of the diagnosis and render treatment more precise and personalized. Therefore, dental professionals must actively initiate and lead the development of AI, even if they are unfamiliar with it.

• Structural Engineering and Mechanics
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• 제65권3호
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• pp.303-314
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• 2018

Shear walls are a typical member under a complex stress state and have complicated mechanical properties and failure modes. The separated-elements model Genetic Evolutionary Structural Optimization (GESO), which is a combination of an elastic-plastic stress method and an optimization method, has been introduced in the literature for designing such members. Although the separated-elements model GESO method is well recognized due to its stability, feasibility, and economy, its adequacy has not been experimentally verified. This paper seeks to validate the adequacy of the separated-elements model GESO method against experimental data and demonstrate its feasibility and advantages over the traditional elastic stress method. Two types of reinforced concrete shear wall specimens, which had the location of an opening in the middle bottom and the center region, respectively, were utilized for this study. For each type, two specimens were designed using the separated-elements model GESO method and elastic stress method, respectively. All specimens were subjected to a constant vertical load and an incremental lateral load until failure. Test results indicated that the ultimate bearing capacity, failure modes, and main crack types of the shear walls designed using the two methods were similar, but the ductility indexes including the stiffness degradation, deformability, reinforcement yielding, and crack development of the specimens designed using the separated-elements model GESO method were superior to those using the elastic stress method. Additionally, the shear walls designed using the separated-elements model GESO method, had a reinforcement layout which could closely resist the actual critical stress, and thus a reduced amount of steel bars were required for such shear walls.

• 전기학회논문지P
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• 제66권4호
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• pp.213-218
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• 2017

The DC electrolytic capacitor for DC-link of power converter is widely used in various power electronic circuits and system application. Its functions include, DC Bus voltage stabilization, conduction of ripple current due to switching events, voltage smoothing, etc. Unfortunately, DC electrolytic capacitors are some of the weakest components in power electronics converters. Many papers have proposed different algorithms or diagnosis method to determinate the ESR and tan ${\delta}$ capacitance C for fault alarm system of the electrolytic capacitor. However, both ESR vary with frequency and temperature. Accurate knowledge of both parameters at the capacitors operating conditions is essential to achieve the best reference data of fault alarm. According to parameter analysis, the capacitance increases with temperature and the initial ESR decreases. Higher frequencies make the reference ESR with the initial ESRo value to decrease. Analysis results show that the proposed DC Bus electrolytic capacitor reference ESR model setting technique can be applied to advanced reference signal of capacitor diagnosis systems successfully.

• International Journal of Naval Architecture and Ocean Engineering
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• 제8권3호
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• pp.243-251
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• 2016

Autonomous Underwater Vehicles (AUVs) generally work in complex marine environments. Any fault in AUVs may cause significant losses. Thus, system reliability and automatic fault diagnosis are important. To address the actuator failure of AUVs, a fault diagnosis method based on the Gaussian particle filter is proposed in this study. Six free-space motion equation mathematical models are established in accordance with the actuator configuration of AUVs. The value of the control (moment) loss parameter is adopted on the basis of these models to represent underwater vehicle malfunction, and an actuator failure model is established. An improved Gaussian particle filtering algorithm is proposed and is used to estimate the AUV failure model and motion state. Bayes algorithm is employed to perform robot fault detection. The sliding window method is adopted for fault magnitude estimation. The feasibility and validity of the proposed method are verified through simulation experiments and experimental data.

• 한국한의학연구원논문집
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• 제14권2호
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• pp.41-46
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• 2008

The study was to investigate the agreement rate between the statistical diagnosis of pattern identification by discriminant analysis and the clinical diagnosis of pattern identification by medical specialist in obese patients with BMI$\geqq$23. The agreement rate of deficiency of the spleen, phlegm-retention, deficiency of Yang, retention of undigested food, stagnation of liver Gi, and blood stagnation are 0.40, 0.33, 0.52, 0.76, 0.71, and 0.66, respectively and accuracy rate and prediction rate using linear discriminant function are 0.59 and 0.61, respectively. Therefore, the complementary management in CRF questionnaires and/or consultation from experts will improve the accuracy and prediction rate, which will be helpful for pattern identification of obesity by clinical experts.

• Nuclear Engineering and Technology
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• 제27권5호
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• pp.670-686
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• 1995

본 논문의 저자는 원자력 발전소와 같은 복잡한 대규모의 시스템의 실시간 고장진단 방법을 1994년 IEEE TNS Vol. 41, No. 4 호[1］에 발표하였다. 이번 논문에서는 고장전파모델(FPM)로서 같은 'Timed SDG Model' 를 사용하고 있으나 고장전파시간( FPT)을 에메논리 개념을 이용하여 정확하게 구하기 어려운 FPT을 실질적으로 이용할 수 있도록 했으며, 또한 고장전파확율(FPP)개념을 도입하여 하나이상의 고장원인 절점 (Node)들을 절점고장율과 더불어, 보다 효과적으로 판별할 수 있도록 했다. 또 FPM내에서 고장의 전파확율를 고려함으로서 보다 실질적인 고장 진단방법을 제시하였으며 본 제안된 방법을 고리 원전 2호기 1차계통에 적용하여 1차계통 FPM내의 각 FPP이 ‘1’인 경우에 한하여 그 성능을 입증하여 보았다.

• 디지털융복합연구
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• 제9권2호
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• pp.141-152
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• 2011

오늘날 데이터센터는 비즈니스에서 핵심영역으로 인식되고 있으며, 이들에 대한 많은 서비스 요구는 보다 많은 에너지 소비를 낳고 있다. 따라서 데이터센터의 높은 수준의 에너지 효율성은 구축, 운영, 폐기 등 그들의 생명주기에 있어서 핵심이슈로 등장하였다. 본 연구에서는 이러한 생명기상에서 데이터센터의 그린화 수준을 진단할 수 있도록 성숙도모델에 기초한 그린데이터센터 수준진단모델을 제시한다. 본 연구에서 제시한 수준진단 모델은 엑션추어 GMM, DCEEF 등 기존 에너지평가모델 및 그린데이터센터 성숙도평가 모델을 참조하였으며, 최종적으로 건축, 공조, 전기, IT, 조직 등 데이터센터의 5개 핵심영역에 대한 진단지표를 제안하였다.

• Smart Structures and Systems
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• 제33권4호
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• pp.253-262
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• 2024

Current-based mechanical fault diagnosis is more convenient and low cost since additional sensors are not required. However, it is still challenging to achieve this goal due to the weak fault information in current signals. In this paper, a dual-loss convolutional neural network (DLCNN) is proposed to implement the intelligent bearing fault diagnosis via current signals. First, a novel similarity loss (SimL) function is developed, which is expected to maximize the intra-class similarity and minimize the inter-class similarity in the model optimization operation. In the loss function, a weight parameter is further introduced to achieve a balance and leverage the performance of SimL function. Second, the DLCNN model is constructed using the presented SimL and the cross-entropy loss. Finally, the two-phase current signals are fused and then fed into the DLCNN to provide more fault information. The proposed DLCNN is tested by experiment data, and the results confirm that the DLCNN achieves higher accuracy compared to the conventional CNN. Meanwhile, the feature visualization presents that the samples of different classes are separated well.

• Journal of Power Electronics
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• 제14권5호
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• pp.859-865
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• 2014

This study presents an electromagnetic model for predicting shaft voltages in a 2-pole field-excited synchronous generator. After the first observations on shaft voltages were made more than a century ago, extensive work has been conducted on eliminating, mitigating, and integrating the aforementioned phenomena. Given that emphasis has been placed on modeling shaft- and bearing-induced voltages in AC motors driven by variable frequency drives, similar efforts toward a model that is dedicated to generators are insubstantial. This work endeavors to improve current physical interpretation and prediction methods for shaft-induced voltages in generators through semi-analytical derivation. Aside from the experimental validation of the model, investigations regarding the behavior of shaft voltages under varying machine complexities and operating conditions clarify previous uncertainties regarding these phenomena. The performance of the numerical method is also assessed for application in eccentricity fault diagnosis.

• Nuclear Engineering and Technology
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• 제47권2호
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• pp.176-186
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• 2015

Pattern classifications have become important tools for fault diagnosis in nuclear power plants (NPP). However, it is often difficult to obtain training data under fault conditions to train a supervised classification model. By contrast, normal plant operating data can be easily made available through increased deployment of supervisory, control, and data acquisition systems. Such data can also be used to train classification models to improve the performance of fault diagnosis scheme. In this paper, a fault diagnosis scheme based on semisupervised classification (SSC) scheme is developed. In this scheme, new measurements collected from the plant are integrated with data observed under fault conditions to train the SSC models. The trained models are subsequently applied to new measurements for fault diagnosis. In comparison with supervised classifiers, the proposed scheme requires significantly fewer data collected under fault conditions to train the classifier. The developed scheme has been validated using different fault scenarios on a desktop NPP simulator as well as on a physical NPP simulator using a graph-based SSC algorithm. All the considered faults have been successfully diagnosed. The results have demonstrated that SSC is a promising tool for fault diagnosis in NPPs.