• 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 Korea Multimedia Society
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• v.14 no.9
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• pp.1190-1201
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• 2011

The purpose of this research paper is to develop Mobile application-based Nursing Process Programs on 'Nursing Diagnosis', 'Nursing Interventions' and 'Nursing Outcomes Classification' targeting nurses and nurse students. To achieve it, this paper uses 'standard classification-focused research data' on the basis of Nursing Diagnosis Classification established by NANDA (North American Nursing Diagnosis Association), NIC (Nursing Interventions Classification) and NOC (Nursing Outcomes Classification) mainly developed by Iowa State University. The existing research methods are difficult to be applied to patients, since such methods put a restriction on choosing, developing, and generalizing 'Nursing Process Programs' in clinical spheres. But, this research thesis focuses on developing guidelines applicable to any clinical experiences, with the use of the framework in mutual links with all the nursing diagnosisoutcomes- interventions. In this regard, the Korean version programs were developed and registered in App store in March. Thus, it is expected that these programs would be wildly-available as tools for nursing education.

• Journal of Korean Academy of Nursing Administration
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• v.7 no.2
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• pp.315-347
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• 2001

The purpose of this study was to validate the linkage of nursing diagnosis(NANDA) and Nursing Interventions Classification(NIC) for implementing the Nursing Diagnosis and Nursing interventions in Korea. 36 nurse experts with over the bacculate degree and over 15 years experiences working in tertiary hospitals participated in this study. 5 point Likert scales on each NIC linked 136 NANDA diagnoses were adopted. The results were as follows: 1. In a validity of linkage of nursing diagnosis and nursing interventions classification, the highest score is in 'Chronic low self esteem'(4.66), the lowest score is in 'sensory/Perceptual alterations; Auditory'(3.34) and the average validity score of the total items is 4.27. 2. There was significant differences by educational level and experience in validity score. 3. The nurses who have master degree have higher score than bachelor degree in the diagnoses; 'fatigue', 'health seeking behaviors', 'nutrition: potential for more than body requirements, altered', 'powerlessness'. 4. The nurses with experience over 20 years have higher validity score than less 15 years in 'breast-feeding, effective'. In conclusion, this research indicates that the linkage of NANDA diagnoses and NIC with high validity score can be applied to nursing practice in Korea. And further studies of nursing intervention are needed in Korean culture.

• The Journal of Bigdata
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• v.3 no.2
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• pp.11-18
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• 2018

This study is a study on domain automatic classification for domain - based quality diagnosis which is a key element of big data quality diagnosis. With the increase of the value and utilization of Big Data and the rise of the Fourth Industrial Revolution, the world is making efforts to create new value by utilizing big data in various fields converged with IT such as law, medical, and finance. However, analysis based on low-reliability data results in critical problems in both the process and the result, and it is also difficult to believe that judgments based on the analysis results. Although the need of highly reliable data has also increased, research on the quality of data and its results have been insufficient. The purpose of this study is to shorten the work time to automizing the domain classification work which was performed from manually to using machine learning in the domain - based quality diagnosis, which is a key element of diagnostic evaluation for improving data quality. Extracts information about the characteristics of the data that is stored in the database and identifies the domain, and then featurize it, and automizes the domain classification using machine learning. We will use it for big data quality diagnosis and contribute to quality improvement.

• The Journal of Korean Medicine
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• v.35 no.3
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• pp.1-14
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• 2014

Objectives: The purpose of this study was to evaluate the achievements of Ryodoraku and to suggest new diagnostic ideas. Methods: A search of six Korean databases and one Japanese was performed using the terms such as Ryodoraku, diagnosis, foreigner, etc. and the search results classified and summarized. Results: From the initial search results, 21 Korean papers and 26 Japanese papers were selected and classified into 4 categories, that is, classification by pattern, classification by physiological limit, classification by setting various sections using the average Ryodoraku score, and classification by formula. Conclusions: Each of the 4 methods has its own benefits; however, it is hard to find disease-specific common characteristics from the Ryodoraku diagnostic values with the methods. Further studies which use the number of Pyesaek and Gyeokcha are necessary.

• Imaging Science in Dentistry
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• v.45 no.2
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• pp.123-131
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• 2015

Throughout the years, various classifications have evolved for the diagnosis of vascular anomalies. However, it remains difficult to classify a number of such lesions. Because all hemangiomas were previously considered to involute, if a lesion with imaging and clinical characteristics of hemangioma does not involute, then there is no subclass in which to classify such a lesion, as reported in one of our cases. The recent classification proposed by the International Society for the Study of Vascular Anomalies (ISSVA, 2014) has solved this problem by including non-involuting and partially involuting hemangioma in the classification. We present here five cases of vascular anomalies and discuss their diagnosis in accordance with the ISSVA (2014) classification. A non-involuting lesion should not always be diagnosed as a vascular malformation. A non-involuting lesion can be either a hemangioma or a vascular malformation depending upon its clinicopathologic and imaging characteristics.

• Journal of Power Electronics
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• v.16 no.3
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• pp.1097-1109
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• 2016

Fault detection and isolation are related to system monitoring, identifying when a fault has occurred, and determining the type of fault and its location. Fault detection is utilized to determine whether a problem has occurred within a certain channel or area of operation. Fault detection and diagnosis have become increasingly important for many technical processes in the development of safe and efficient advanced systems for supervision. This paper presents an integrated technique for fault diagnosis and classification for open- and short-circuit faults in three-phase inverter circuits. Discrete wavelet transform and principal component analysis are utilized to detect the discontinuity in currents caused by a fault. The features of fault diagnosis are then extracted. A fault dictionary is used to acquire details about transistor faults and the corresponding fault identification. Fault classification is performed with a fuzzy logic system and relevance vector machine (RVM). The proposed model is incorporated with a set of optimization techniques, namely, evolutionary particle swarm optimization (EPSO) and cuckoo search optimization (CSO), to improve fault detection. The combination of optimization techniques with classification techniques is analyzed. Experimental results confirm that the combination of CSO with RVM yields better results than the combinations of CSO with fuzzy logic system, EPSO with RVM, and EPSO with fuzzy logic system.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.5
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• pp.3000-3005
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• 2015

Reliable monitoring and diagnosis of industrial processes is quite important for in terms of quality and safety. The goal of fault diagnosis is to find process variables responsible for causing specific abnormalities of the process. This work presents a classification-based diagnostic scheme based on nonlinear representation of process data. The use of a nonlinear kernel technique is able to reduce the size of the data considered and provides efficient and reliable representation of the measurement data. As a filtering stage a preprocessing is performed to eliminate unwanted parts of the data with enhanced performance. The case study of an industrial batch process has shown that the performance of the scheme outperformed other methods. In addition, the use of a nonlinear representation technique and filtering improved the diagnosis performance in the case study.

• Journal of Korea Multimedia Society
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• v.22 no.10
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• pp.1168-1177
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• 2019

In order to diagnose and prevent Alzheimer's Disease (AD), it is becoming increasingly important to develop a CAD(Computer-aided Diagnosis) system for AD diagnosis, which provides effective treatment for patients by analyzing 3D MRI images. It is essential to apply powerful deep learning algorithms in order to automatically classify stages of Alzheimer's Disease and to develop a Alzheimer's Disease support diagnosis system that has the function of detecting hippocampus and CSF(Cerebrospinal fluid) which are important biomarkers in diagnosis of Alzheimer's Disease. In this paper, for AD diagnosis, we classify a given MRI data into three categories of AD, mild cognitive impairment, and normal control according by applying 3D brain MRI image to the Faster R-CNN model and detect hippocampus and CSF in MRI image. To do this, we use the 2D MRI slice images extracted from the 3D MRI data of the Faster R-CNN, and perform the widely used majority voting algorithm on the resulting bounding box labels for classification. To verify the proposed method, we used the public ADNI data set, which is the standard brain MRI database. Experimental results show that the proposed method achieves impressive classification performance compared with other state-of-the-art methods.

• KEPCO Journal on Electric Power and Energy
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• v.4 no.2
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• pp.89-99
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• 2018

Downtime and malfunction of industrial rotor machines represents a crucial cost burden and productivity loss. Fault diagnosis of this equipment has recently been carried out to detect their fault(s) and cause(s) by using fault classification methods. However, these methods are of limited use in detecting rotor faults because of their hypersensitivity to unexpected and different equipment conditions individually. These limitations tend to affect the accuracy of fault classification since fault-related features calculated from vibration signal are moved to other regions or changed. To improve the limited diagnosis accuracy of existing methods, we propose a new approach for fault diagnosis of rotor machines based on the model generated by supervised learning. Our work is based on feature residual values from vibration signals as fault indices. Our diagnostic model is a robust and flexible process that, once learned from historical data only one time, allows it to apply to different target systems without optimization of algorithms. The performance of the proposed method was evaluated by comparing its results with conventional methods for fault diagnosis of rotor machines. The experimental results show that the proposed method can be used to achieve better fault diagnosis, even when applied to systems with different normal-state signals, scales, and structures, without tuning or the use of a complementary algorithm. The effectiveness of the method was assessed by simulation using various rotor machine models.