• Journal of the Korean housing association
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• v.26 no.2
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• pp.67-79
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• 2015

As the Korean construction market in the apartment housing has changed to a housing consumer focused market, interest and importance on efficient use and management on existing buildings has increased rather than demand for new buildings. Interest of housing consumers on apartment house quality has increased in this market paradigm, and this spontaneously is connected to quality flaw related defect disputes and lawsuits that the importance of defect diagnosis has continuously increased. This defect diagnosis is directly connected to maintenance charges in defect dispute and lawsuit processes that rather objective and highly credible progress of duty is required. However, most defect diagnosis firms today that progress defect diagnosis are using different diagnosis methods and depend on the experience of experienced professionals that there is no standardized defect diagnosis process. Therefore, the purpose of this study is to provide common defect diagnosis process model for defect diagnosis firms utilizing the BPMN (Business Process Modeling Notation) modeling method. It is expected that this will contribute to professional and reliable task performances of concerned defect diagnosis workers. Furthermore, it is expected that design lawsuit damage will be substantially reduced by standardizing defect diagnosis processes.

• Journal of KIISE
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• v.42 no.12
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• pp.1575-1583
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• 2015

With the emergence of Internet-of-Things (IoT) computing, it has become possible to acquire users' health-related contexts from various IoT devices and to diagnose their biological aging through analysis of the IoT health contexts. However, previous work on methods of aging diagnosis used a fixed list of aging diagnosis factors, making it difficult to handle the variability of users' IoT health contexts and to dynamically adapt the addition and deletion of aging diagnosis factors. This paper proposes a design and methods for a dynamically adaptable aging diagnosis framework that acquires a set of IoT health contexts from various IoT devices based on a set of aging diagnosis factors of the user. By using the proposed aging diagnosis framework, aging diagnosis methods can be applied without considering the variability of IoT health contexts and aging diagnosis factors can be dynamically added and deleted.

• Investigative Magnetic Resonance Imaging
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• v.23 no.2
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• pp.100-113
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• 2019

Sarcoidosis is a multisystem disease characterized by noncaseating granulomas. Cardiac involvement is known to have poor prognosis because it can manifest as a serious condition such as the conduction abnormality, heart failure, ventricular arrhythmia, or sudden cardiac death. Although early diagnosis and early treatment is critical to improve patient prognosis, the diagnosis of CS is challenging in most cases. Diagnosis usually relies on endomyocardial biopsy (EMB), but its diagnostic yield is low due to the incidence of patchy myocardial involvement. Guidelines for the diagnosis of CS recommend a combination of clinical, electrocardiographic, and imaging findings from various modalities, if EMB cannot confirm the diagnosis. Especially, the role of advanced imaging such as cardiac magnetic resonance (CMR) imaging and positron emission tomography (PET), has shown to be important not only for the diagnosis, but also for monitoring treatment response and prognostication. CMR can evaluate cardiac function and fibrotic scar with good specificity. Late gadolinium enhancement (LGE) in CMR shows a distinctive enhancement pattern for each disease, which may be useful for differential diagnosis of CS from other similar diseases. Effectively, T1 or T2 mapping techniques can be also used for early recognition of CS. In the meantime, PET can detect and quantify metabolic activity and can be used to monitor treatment response. Recently, the use of a hybrid CMR-PET has introduced to allow identify patients with active CS with excellent co-localization and better diagnostic accuracy than CMR or PET alone. However, CS may show various findings with a wide spectrum, therefore, radiologists should consider the possible differential diagnosis of CS including myocarditis, dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy, amyloidosis, and arrhythmogenic right ventricular cardiomyopathy. Radiologists should recognize the differences in various diseases that show the characteristics of mimicking CS, and try to get an accurate diagnosis of CS.

• Journal of Physiology & Pathology in Korean Medicine
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• v.25 no.5
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• pp.894-899
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• 2011

Traditional Korean Medicine (TKM) has been actively researched through various approaches, including computational methods. This paper aims at providing an overview of domestic studies using the computational techniques in TKM field. A literature search was conducted in Korean publications using OASIS system, and major studies of data mining in TKM were identified. A review was presented in six diagnosis fields, including sasang constitution diagnosis, eight constitution diagnosis, tongue diagnosis, pattern diagnosis for stroke, diagnosis based on ontology, diagnosis for cause of disease. They collect clinical data themselves for experiments and primarily applied a algorithm of decision tree, SVM, neural network, case-based reasoning, ontology reasoning, discriminant analysis. In the future, there needs to identify which algorithm is suitable to diagnosis or other fields of TKM.

• Journal of Physiology & Pathology in Korean Medicine
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• v.19 no.6
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• pp.1483-1490
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• 2005

This study was written in order to help understanding of visible diagnosis of appearance(形). Visible diagnosis of appearance(形) is a very important factor of diagnosis and a first step of visible diagnosis. appearance(形) is closely connection with spirit(神), so is house of spirit(神). If we make a visible diagnosis of appearance(形), we know the prosperousness of energy and the relative seriousness of an illness. Spirit(神) is understood by appearances and movements of patient, and influenced by seasons, lands, human's relationship and the grade of age. By visible diagnosis of appearance(形), we can conclude existence or nonexistence of spirit(神), As comparing spirit(神) with appearance(形), we can decide good or bad prognoses. One man's own appearance(形) is determined by the five human type(五形人). There are very various points of changing form. As divided into principal groups, there are three main groups, that is, sky(天), earth(地) and man(人). The age and sex belong 治 the factor of sky(天), a direction and configuration of the ground(地形) belong to the factor of earth(地), the five human type(五形人) and white fatness(肥白) and black emaciation(黑瘦) belong to the factor of man(人).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.5
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• pp.1186-1207
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• 2024

Bearings are one of the main components of mechanical equipment and one of the primary components prone to faults. Therefore, conducting fault diagnosis on bearings is a key issue in mechanical equipment research. Belief rule base (BRB) is essentially an expert system that effectively integrates qualitative and quantitative information, demonstrating excellent performance in fault diagnosis. However, class imbalance often occurs in the diagnosis task, which poses challenges to the diagnosis. Models with interpretability can enhance decision-makers' trust in the output results. However, the randomness in the optimization process can undermine interpretability, thereby reducing the level of trustworthiness in the results. Therefore, a hierarchical BRB model based on extreme gradient boosting (XGBoost) feature selection with interpretability (HFS-IBRB) is proposed in this paper. Utilizing a main BRB alongside multiple sub-BRBs allows for the conversion of a multi-classification challenge into several distinct binary classification tasks, thereby leading to enhanced accuracy. By incorporating interpretability constraints into the model, interpretability is effectively ensured. Finally, the case study of the actual dataset of bearing fault diagnosis demonstrates the ability of the HFS-IBRB model to perform accurate and interpretable diagnosis.

• Journal of Advanced Marine Engineering and Technology
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• v.27 no.1
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• pp.42-48
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• 2003

In general many engineers can diagnose the fault condition using the abnormal ones among data monitored from a diesel engine, but they don't need the system modelling or identification for the work. They check the abnormal data and the relationship and then catch the fault condition of the engine. This paper proposes the construction of a fault diagnosis engine through malfunction data gained from the data fault detection system of neural networks for diesel generator engine, and the rule inference method to induce the rule for fuzzy inference from the malfunction data of diesel engine like a site engineer with a fuzzy system. The proposed fault diagnosis system is constructed in the sense of the Malfunction Diagnosis Engine(MDE) and Hierarchy of Malfunction Hypotheses(HMH). The system is concerned with the rule reduction method of knowledge base for related data among the various interactive data.

• Journal of Biomedical Engineering Research
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• v.14 no.1
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• pp.73-80
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• 1993

This paper describes the implementation of a computerized radial pulse diagnosis by Elds of a clinical expert. On this base, we composed of the radial pulse diagnosis system in korean traditional medicine. The system composed of a radial pulse wave detection system and a radial pulse diag nosis system. With a detection system, we detected Inyoung and Cheongu radial pulse wave and processed It. Then, we have got the characteristic parameters of radial pulse wave and also quantified thats according to the method of Inyoung-Cheongu Comparison Radial Pulse Diagnosis. We defined the jugement standard of radial Pulse diagnosis system and then we confirmed the PossibiliLy for realization of automatic radial Pulse diagnosis in korean traditional medicine.

• Journal of Biomedical Engineering Research
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• v.7 no.2
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• pp.147-150
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• 1986

This paper describes the implementation of a computerized radial pulse diagnosis by aids of a clinical expert. On this base, we composed of the radial pulse diagnosis system in korean traditional medicine. The system composed of a radial pulse wave detection system and a radial pulse diagnosis system. With a detection system, we detected Inyoung and Cheongu radial pulse wave and processed it. Then, we have got the characteristic parameters of radial pulse wave and also quantified that according to the method of Inyoung-Cheongu Comparison Radial Pulse Diagnosis. We defined the jugement standard of radial pulse diagnosis system and then we confirmed the possibility for realization of automatic radial pulse diagnosis in korean traditional medicine.

• 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.