• Journal of KSNVE
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• v.10 no.2
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• pp.331-337
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• 2000

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 defect diagnosis of rotating machinery and then learning vector quantization(LVQ) which is supervised learning algorithm is used to improve the quality of the classifier decision regions.

• Proceedings of the KIEE Conference
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• 1988.11a
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• pp.111-114
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• 1988

With the increase of size and complicacy of power systems, distribution system need to operate effectively for high reliability. In order to achieve this purpose, the study which apply expert system to operating plan, restoration on fault and distribution system operating, has developing actively. The essential element of the study is system line state which make a system observe. The development of expert system on power system operation make a system be able to judge state of loading and looping system line, related current direction, substation, and distribution line, atomatically by breaker operation. Finally, this paper developed expert system which decides itself atomatically by rules for deciding system line state.

• Korean Journal of Adult Nursing
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• v.13 no.2
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• pp.200-208
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• 2001

The purpose of this study was to understand the nature and structure of "uncertainty of chronically ill patients" by explaining it more scientifically. This study is based on the unique experiences, which individual uncertainty experiences differ from others. In this sense, Q-methodology which includes self-psychology and abductive logics is applied to the study. The results indicate that there are six types of uncertainty of chronically ill patients : my own fault, self-esteem loss, self-care determination, cure-doubt, reality-restructure, and past-tenacity reality-absence. Thus, "uncertainty of chronically ill patients" is defined from the study as the process in which continuous transition and evaluation of possibility cause changes in human recognition, attitude, action, etc.. The significance of the study is threefold : (1) discovery of six types of uncertainty of chronically ill patients in Korean people, (2) the better understanding of "uncertainty of chronically ill patients", (3) possible developments of nursing concept and assessment and intervention technique based on the new dimension of the understanding in uncertainty for nursing of chronically ill patients from this research.

• Transactions of the Korean Society of Automotive Engineers
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• v.3 no.6
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• pp.262-273
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• 1995

For rotating machinery with high speed and high efficiency, large labor and high expenses are required to conduct machine health monitoring. Therefore, it becomes necessary to develop new diagnosis technique which can detect abnormalities of the rotating machinery effectively. In this paper, it is identified that bispectrum analysis technique can be successfully applied to dectect the abnormalities of the roating machinery through computer simulation, and results of the bispectrum analysis are patterned in griding form. Further, pattern recognition technique using back propagation algorithm, which is one of neural network algorithm, being consisted of patterned input layer and output layer for abnormal status, is applied to detect the abnormalities of simulator which is able to make up various kinds of abnorml conditions(misalignment, unbalance, rubbing etc.) of the rotating machinery.

• Proceedings of the KSR Conference
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• 2008.06a
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• pp.1936-1945
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• 2008

In Korea, TCN has applied to the Korean High-speed Train (HSR350X) through G7 High-speed Train development project. TCN is the most suitable international standard communication network for distributed control systems that is adopted for high-speed of vehicle, safety and flexibility. TCN is the network exclusively for the high-speed train and electrical trains. This TCN satisfies the network standards. The network standards are real time communication, fault tolerance design, integrated data system, resistance of environment, automated recognition for modification of vehicle formation and maintenance. The purpose of this research is applying the development of WTB analyzer which is part of communication network system TCN, to check the communication of high-speed trains and electrical trains.

• Journal of Korea Society of Digital Industry and Information Management
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• v.6 no.2
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• pp.199-207
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• 2010

Recently, large amount of information in IDS(Intrusion Detection System) can be un manageable and also be mixed with false prediction error. In this paper, we propose a data mining methodology for IDS, which contains uncertainty based on training process and post-processing analysis additionally. Our system is trained to classify the existing attack for misuse detection, to detect the new attack pattern for anomaly detection, and to define border patter between attack and normal pattern. In experimental results show that our approach improve the performance against existing attacks and new attacks, from 0.62 to 0.84 about 35%.

• 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.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 1995.11a
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• pp.55-66
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• 1995

Earlier studies indicated clearly that information obtained from conventional statistics cannot provide guidance on detailed casualty analysis. Much more is needed if casualty statistics are to play a proficient and vigorous role in encouraging safety. Specific difficulties originate from making the causal relationships clear. Fault tree analysis and symbolic modeling of functional block diagram are an efficient aid in safety analysis so that these methods are generally used in other industries. This paper describes the application of these tools in shipping casualties and how to evaluate the relationships between different causes of ship casualties. Thesee methods are proved to be an efficient and useful tools to indicate the relationships between individual physical factors. In addition these models permit easy understanding and recognition of the factors leading to individual casualty

• Journal of the Korean Society of Systems Engineering
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• v.18 no.2
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• pp.40-49
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• 2022

Remote-controlled and autonomous driving based on artificial intelligence are key elements required for unmanned combat vehicles. The required capability of such an unmanned combat vehicle should be expressed in reasonable required operational capability(ROC). To this end, in this paper, the requirements of an unmanned combat vehicle operated under a manned-unmanned teaming were analyzed. The functional requirements are remote operation and control, communication, sensor-based situational awareness, field environment recognition, autonomous return, vehicle tracking, collision prevention, fault diagnosis, and simultaneous localization and mapping. Remote-controlled and autonomous driving of unmanned combat vehicles could be achieved through the combination of these functional requirements. It is expected that the requirement analysis results presented in this study will be utilized to satisfy the military operational concept and provide reasonable technical indicators in the system development stage.

• Journal of Korea Multimedia Society
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• v.21 no.8
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• pp.837-848
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• 2018

Monitoring technology of machining has a long history since unmanned machining was introduced. Despite the long history, many researchers have presented new approaches continuously in this area. Sound based machine fault diagnosis is the process consisting of detecting automatically the damages that affect the machines by analyzing the sounds they produce during their operating time. The collected sound is corrupted by the surrounding work environment. Therefore, the most important part of the diagnosis is to find hidden elements inside the data that can represent the error pattern. This paper presents a feature extraction methodology that combines various digital signal processing and pattern recognition methods for the analysis of the sounds produced by tools. The magnitude spectrum of the sound is extracted using the Fourier analysis and the band-pass filter is applied to further characterize the data. Statistical functions are also used as input to the nonlinear classifier for the final response. The results prove that the proposed feature extraction method accurately captures the hidden patterns of the sound generated by the tool, unlike the conventional features. Therefore, it is shown that the proposed method can be applied to a sound based automatic diagnosis system.