• 유체기계공업학회:학술대회논문집
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• 1999.12a
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• pp.317-323
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• 1999

The necessity of diagnosis of the rotating machinery which is widely used in the industry is increasing. Many research has been conducted to manipulate field vibration signal data for diagnosing the fault of designated machinery. As the pattern recognition tool of that signal, neural network which use usually back-propagation algorithm was used in the diagnosis of rotating machinery. In this paper, self-organizing feature map(SOFM) which is unsupervised learning algorithm is used in the abnormal vibration diagnosis of rotating machinery and then learning vector quantization(LVQ) which is supervised teaming algorithm is used to improve the quality of the classifier decision regions.

• Journal of Korean Academy of Nursing
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• v.44 no.3
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• pp.328-338
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• 2014

Purpose: This study was done to explore experiences of persons living through the periods of cancer diagnosis, treatment, and self-care. Methods: With permission, texts of 29 cancer survival narratives (8 men and 21 women, winners in contests sponsored by two institutes), were analyzed using Kang's Korean-Computerized-Text-Analysis-Program where the commonly used Korean-Morphological-Analyzer and the 21st-century-Sejong-Modern-Korean-Corpora representing laymen's Korean-language-use are connected. Experiences were explored based on words included in 100 highly-used-morphemes. For interpretation, we used 'categorizing words by meaning', 'comparing use-rate by periods and to the 21st-century-Sejong-Modern-Korean-Corpora', and highly-used-morphemes that appeared only in a specific period. Results: The most highly-used-word-morpheme was first-person-pronouns followed by, diagnosis treatment-related- words, mind-expression-words, cancer, persons-in-meaningful-interaction, living and eating, information-related-verbs, emotion-expression- words, with 240 to 0.8 times for layman use-rate. 'Diagnosis-process', 'cancer-thought', 'things-to-come-after-diagnosis', 'physician husband', 'result-related-information', 'meaningful-things before diagnosis-period', and 'locus-of-cause' dominated the life of the diagnosis-period. 'Treatment', 'unreliable-body', 'husband people mother physician', 'treatment-related-uncertainty', 'hard-time', and 'waiting-time represented experiences in the treatment-period. Themes of living in the self-care-period were complex and included 'living-as-a-human', 'self-managing-of-diseased-body', 'positive-emotion', and 'connecting past present future'. Conclusion: The results show that the experience of living for persons with cancer is influenced by each period's own situational-characteristics. Experiences of the diagnosis and treatment-period are negative disease-oriented while that of the self-care period is positive present-oriented.

• Journal of the Korea Society of Computer and Information
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• v.15 no.2
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• pp.27-34
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• 2010

Recently, lots of internet service companies provide on-line health diagnosis systems. But general persons not having expert knowledge are difficult to use, because most of the health diagnosis systems present prescriptions or dietetic treatments for diseases based on western medicine. In this paper, a self health diagnosis system of oriental medicine coinciding with physical characteristics of Korean using fuzzy ART algorithm, is proposed. In the proposed system, three high rank of diseases having high similarity values are derived by comparing symptoms presented by a user with learned symptoms of specific diseases based on treatment records using neural networks. And also the proposed system shows overall symptoms and folk remedies for the three high rank of diseases. Database on diseases and symptoms is built by several oriental medicine books and then verified by a medical specialist of oriental medicine. The proposed self health diagnosis system of oriental medicine showed better performance than conventional health diagnosis systems by means of learning diseases and symptoms using treatment records.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.4123-4141
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• 2019

Autonomous driving technology is divided into 0~5 levels. Of these, Level 5 is a fully autonomous vehicle that does not require a person to drive at all. The automobile industry has been trying to develop Level 5 to satisfy safety, but commercialization has not yet been achieved. In order to commercialize autonomous unmanned vehicles, there are several problems to be solved for driving safety. To solve one of these, this paper proposes 'A Deep Learning Part-diagnosis Platform(DLPP) based on an In-vehicle On-board gateway for an Autonomous Vehicle' that diagnoses not only the parts of a vehicle and the sensors belonging to the parts, but also the influence upon other parts when a certain fault happens. The DLPP consists of an In-vehicle On-board gateway(IOG) and a Part Self-diagnosis Module(PSM). Though an existing vehicle gateway was used for the translation of messages happening in a vehicle, the IOG not only has the translation function of an existing gateway but also judges whether a fault happened in a sensor or parts by using a Loopback. The payloads which are used to judge a sensor as normal in the IOG is transferred to the PSM for self-diagnosis. The Part Self-diagnosis Module(PSM) diagnoses parts itself by using the payloads transferred from the IOG. Because the PSM is designed based on an LSTM algorithm, it diagnoses a vehicle's fault by considering the correlation between previous diagnosis result and current measured parts data.

• 한국ITS학회:학술대회논문집
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• 2008.11a
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• pp.559-564
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• 2008

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.6
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• pp.285-292
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• 2023

With the wide spread of smart farms and the advancements in IoT technology, it is easy to obtain additional data in addition to crop images. Consequently, deep learning-based crop disease diagnosis research utilizing multimodal data has become important. This study proposes a crop disease diagnosis method using multimodal supervised contrastive learning by expanding upon the multimodal self-supervised learning. RandAugment method was used to augment crop image and time series of environment data. These augmented data passed through encoder and projection head for each modality, yielding low-dimensional features. Subsequently, the proposed multimodal supervised contrastive loss helped features from the same class get closer while pushing apart those from different classes. Following this, the pretrained model was fine-tuned for crop disease diagnosis. The visualization of t-SNE result and comparative assessments of crop disease diagnosis performance substantiate that the proposed method has superior performance than multimodal self-supervised learning.

• Vacuum Magazine
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• v.2 no.1
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• pp.31-34
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• 2015

This article introduces fundamentals of self-diagnosis and predictive (or preventive) maintenance technologies for dry vacuum pumps. The state variables of dry pumps are addressed, such as the pump and motor body temperatures, consumption currents of main and booster pumps, mechanical vibration, and exhaust pressure, etc. The adaptive parametric models of the state variables of the dry pump are exploited to provide dramatic reduction of data size and computation time for self-diagnosis. Two indicators, the Hotelling's $T^2$ and the sum of squares residuals (Q), are illustrated to be quite effective and successful in diagnosing dry pumps used in the semiconductor processes.

• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.2134-2143
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• 2018

This work introduces a fault diagnosis method for transformer based on self-powered radio frequency identification (RFID) sensor tag and improved Hilbert-Huang transform (HHT). Consisted by RFID tag chip, power management circuit, MCU and accelerometer, the developed RFID sensor tag is used to acquire and wirelessly transmit the vibration signal. A customized power management including solar panel, low dropout (LDO) voltage regulator, supercapacitor and corresponding charging circuit is presented to guarantee constant DC power for the sensor tag. An improved band restricted empirical mode decomposition (BREMD) which is optimized by quantum-behaved particle swarm optimization (QPSO) algorithm is proposed to deal with the raw vibration signal. Compared with traditional methods, this improved BREMD method shows great superiority in reducing mode aliasing. Then, a promising fault diagnosis approach on the basis of Hilbert marginal spectrum variations is brought up. The measured results show that the presented power management circuit can generate 2.5V DC voltage for the rest of the sensor tag. The developed sensor tag can achieve a reliable communication distance of 17.8m in the test environment. Furthermore, the measurement results indicate the promising performance of fault diagnosis for transformer.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 2002.10b
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• pp.455-456
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• 2002

Wear is one of the main factors causing breakdown and fault of machine, so ferrography technique analyzing wear particles can be an effective way for condition monitoring and fault diagnosis. On the base of the forward multilayer neural network, a nodes self-deleting neural network model is provided in this paper. This network can itself deletes the nodes to optimize its construction. On the basis of the nodes self-deleting neural network, an intelligent ferrography diagnosis expert system (IFDES) for wear particles recognition and wear diagnosis is described. This intelligent expert system can automatically slim lip knowledge by learning from samples and realize basically the entirely automatic processing from wear particles recognition to wear diagnosis.

• The Korean Nurse
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• v.29 no.1
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• pp.59-71
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• 1990

Although the usefulness and importance of clinical application of nursing diagnosis are well recognized by the academic circle, it is not yet generally practiced. In order to provide data for establishing a policy for clinical nursing diagnosis; a study was made at a seminar, sponsored by the Department of nursing, Severance Hospital, with participation of 190 nurses from 33 hospitals. The objective of the study was to find out; 1) if the nurses agree with the academic community in recognizing the benefits and problems of clinical application of nursing diagnosis; 2) how the nurses evaluate their ability to carry out nursing diagnosis; and 3) if educational programs would help enhance ability of nursing diagnosis among nurses. The summary of findings by the study is as follows; 1. While all nurses responded positively on the question of benefits improving science and quality of nursing, thus elevating credibility and position of nurses, some expressed concern on the practicality of the system in setting up nursing objectiveness, confirming the nursing problems and utilizing patient information. For the 20 questions and the scale of 1~5, the lowest average score was 3.223 and the highest 4.066. 2. The study attempted to find out the opinion of the nurses on the problems that 'would make difficult to adopt the nursing diagnosis in clinics. The result of the study indicates the nurses believe the major problems are the fact that the subject of nursing diagnosis are not well defined and that the form sheets do not match with the ones that are currently being used. However, comparing it with the result of the previous study on the same question (inadequate manpower and insufficienf time allocated for the job were two major problems pointed out then.), it can be said that the opinion of the nurses studied this time was much more positive and it suggests that they believe the system can be adopted without increasing manpower and only by giving additional training and by adjusting the format of nursing record sheets. It suggests that the future for adopting a clinical nursing diagnosis is very bright. 3. As the most urgent problem to be solved for adopting clinical nursing diagnosis, 38. 5% responded that it was "education of nurses, "and 34.2% responded that it was "staffing adequate number of nurses". 4. For the 10 questions asked for self-evaluation of ability to adopt the system, with the scale of 1~5, average score was lower than 3. This indicate that they evaluate their ability to adopt the system is low. 5. The results of study taken before and after the educational programs for clinical nursing diagnosis were compared with overall score in order to determine if such program would cause changes in the response to the effect of clinical application of nursing diagnosis, and it was found that there was statistically significant changes suggesting that the education contributed to positive change in the response. 6. The results of study taken before and after the educational programs for clinical nursing diagnosis were compared with overall score in order to determine how the proble~ ms for adopting nursing system would be effected by such educational programs, and it was found that those problems be not soived with a short course of training. 7. The results of study taken before and after the educational programs for clinical nursing diagnosis were compared with overall score in order to determine if such programs would bring changes in the self-evaluation of nurses on the ability of nursing diagno sis, and it was found that program improve score of self-evaluation their ability of the nursing diagnosis. As seen in the above reports, it was found that the nu'rses are very positive about the clinical nursing diagnosis, that educational program for the clinical nursing diagnosis helps nurses for positively changing their attitude for ,the nursing diagnosis, for their self-confidence on their ability to perform nursing diagnosis. With improved know-how and self"confictence of nurses gained through educational and .training programs, the future of clinical application of nursing diagnosis is very bright.diagnosis is very bright.