• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.7C
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• pp.677-684
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• 2002

Today's Internet does not have efficient traffic engineering mechanism to support QoS for the explosive increasing internet traffic such as various multimedia traffic. This functional shortage degrades prominently the quality of service, and makes it difficult to provide multi-media service and real-time service. Various technologies are under developed to solve these problems. IETF (Internet Engineering Task Force) developed the MPLS (Multi-Protocol Label Switching) technology that provides a good capabilities of traffic engineering and is independent layer 2 protocol, so MPLS is expected to be used in the Internet backbone network$\^$[1][2]/. The faults occurring in high-speed network such as MPLS, may cause massive data loss and degrade quality of service. So fast network restoration function is essential requirement. Because MPLS is independent to layer 2 protocol, the fault detection and reporting mechanism for restoration should also be independent to layer 2 protocol. In this paper, we present the experimental results of the MPLS OAM function for the performance monitoring and fault detection 'll'&'ll' notification, localization in MPLS network, based on the OPNET network simulator

• Journal of KIISE:Computer Systems and Theory
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• v.31 no.12
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• pp.692-703
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• 2004

Communication induced checkpointing (CIC) is one of the checkpointing techniques to provide fault tolerance for distributed systems. Independent checkpoints that each distributed process produces without coordination are likely to be useless. Useless checkpoints, which cannot belong to any consistent global checkpoint sets, induce nondeterminant rollback. To prevent the useless checkpoints, CIC forces processes to take additional checkpoints at proper moment. The number of those forced checkpoints is the main source of failure-free overhead in CIC. In this paper, we present two new CIC protocols which satisfy 'No Z-Cycle (NZC)'property. The proposed protocols reduce the number of forced checkpoints compared to the existing protocols with the drawback of the increase in message delay. Our simulation results with the synthetic data show that the proposed protocols have lower failure-free overhead than the existing protocols. Additionally, we show that the classical 'index-based checkpointing' protocols are inefficient in constructing the consistent global cut in distributed executions.

• Journal of Wind Energy
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• v.13 no.3
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• pp.33-42
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• 2022

In this paper, a semi-supervised machine learning technique applied to actual field vibration data acquired from Jeju-do wind turbines for predictive diagnosis of abnormal conditions of offshore wind turbines is introduced. Semi-supervised machine learning, which combines un-supervised learning with supervised learning, can be used to perform anomaly detection in situations where sufficient fault data cannot be obtained. The signal processing results using the spectrogram of the original signal were shown, and external data were used to overcome the problem that disturbance reactions easily occurred due to the imbalance between the number of normal and abnormal data. Out of distribution (OOD), which uses external data, is a technology that is regarded as abnormal data that is unlikely to occur in reality, but we were able to use it by expanding it. By rearranging the distribution of data in this way, classification can be performed more robustly. Specifically, by observing the trends of the abnormal score and the change in the feature of the representation layer, continuous learning was performed through a mixture of existing and new data.

• International Journal of Control, Automation, and Systems
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• v.4 no.3
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• pp.372-381
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• 2006

The purpose of this study was to develop a process management system to manage ingot fabrication and improve ingot quality. The ingot is the first manufactured material of wafers. Trace parameters were collected on-line but measurement parameters were measured by sampling inspection. The quality parameters were applied to evaluate the quality. Therefore, preprocessing was necessary to extract useful information from the quality data. First, statistical methods were used for data generation. Then, modeling was performed, using the generated data, to improve the performance of the models. The function of the models is to predict the quality corresponding to control parameters. Secondly, rule extraction was performed to find the relation between the production quality and control conditions. The extracted rules can give important information concerning how to handle the process correctly. The dynamic polynomial neural network (DPNN) and decision tree were applied for data modeling and rule extraction, respectively, from the ingot fabrication data.

• Journal of the Korean Institute of Gas
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• v.18 no.3
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• pp.53-59
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• 2014

In this study, we suggest a system to build the monitoring model for compressed natural gas (CNG) stations, operated in only non-stationary modes, and perform the real-time monitoring and the abnormality diagnosis using principal component analysis (PCA) that is suitable for processing large amounts of multi-dimensional data among multivariate statistical analysis methods. We build the model by the calculation of the new characteristic variables, called as the major components, finding the factors representing the trend of process operation, or a combination of variables among 7 pressure sensor data and 5 temperature sensor data collected from a CNG station at every second. The real-time monitoring is performed reflecting the data of process operation measured in real-time against the built model. As a result of conducting the test of monitoring in order to improve the accuracy of the system and verification, all data in the normal operation were distinguished as normal. The cause of abnormality could be refined, when abnormality was detected successfully, by tracking the variables out of the score plot.

• Journal of the Korean Institute of Gas
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• v.23 no.4
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• pp.19-27
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• 2019

The consumption of liquefied natural gas (LNG) has increased annually due to the strengthening of international environmental regulations. In order to produce stable and efficient LNG, it is essential to divide the global (overall) operating condition and construct a quick and accurate monitoring system for each operation condition. In this study, multi-mode monitoring system is proposed to the LNG plant fractionation process. First, global normal operation data is divided to local (subdivide) normal operation data using global principal component analysis (PCA) and k-means clustering method. And then, the data to be analyzed were matched with the local normal mode. Finally, it is determined the state of process abnormality through the local PCA. The proposed method is applied to 45 fault case and it proved to be more than 5~10% efficient compared to the global PCA and univariate monitoring.

• Journal of KIISE:Software and Applications
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• v.33 no.7
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• pp.605-614
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• 2006

The success of a software development project can be determined by the use of QCD. And as a software's size and complexity increase, the importance of early quality assurance rises. Therefore, more effort should be given to prevention, as opposed to correction. In order to provide a framework for the prevention of defects, defect detection activities such as peer review and testing, along with analysis of previous defects, is required. This entails a systematization and use of quality data from previous development efforts. FMEA, which is utilized for system safety assurance, can be applied as a means of software defect prevention. SW-FMEA (Software Failure Mode Effect Analysis) attempts to prevent defects by predicting likely defects. Presently, it has been applied to requirement analysis and design. SW-FMEA utilizes measured data from development activities, and can be used for defect prevention on both the development and management sides, for example, in planning, analysis, design, peer reviews, testing, risk management, and so forth. This research discusses about related methodology and proposes defect prevention model based on SW-FMEA. Proposed model is extended SW-FMEA that focuses on system analysis and design. The model not only supports verification and validation effectively, but is useful for reducing defect detection.

• Journal of Korea Multimedia Society
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• v.13 no.7
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• pp.950-959
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• 2010

The Election paradigm can be used as a building block in many practical problems such as group communication, atomic commit and replicated data management where a protocol coordinator might be useful. The problem has been widely studied in the research community since one reason for this wide interest is that many distributed protocols need an election protocol. However, mobile ad hoc systems are more prone to failures than conventional distributed systems. Solving election in such an environment requires from a set of mobile nodes to choose a unique node as a leader based on its priority despite failures or disconnections of mobile nodes. In this paper, we describe a solution to the election problem from mobile ad hoc computing systems and it was proved by temporal logic. This solution is based on the Group Membership Detection algorithm.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.9
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• pp.449-456
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• 2005

The preventive diagnostic technique prevents transformers from power failure through giving alarm and observing transformers in service. And it helps to establish the plan for optimum maintenance of the transformer as well as to find location or cause of fault using accumulated data. Data detection and experience of the preventive diagnostic system need to establish the preventive diagnostic algorithm regarding interrelationship between detected data and deterioration of equipment. Therefore in-depth analysis about the preventive diagnosis system is required. KEPCO has adopted the preventive diagnostic system at nine 345kV substations since 1997. Techniques for component sensors of the preventive diagnosis system were settled but diagnosis algorithm, diagnostic criteria and practical use of accumulated data are not yet established. This paper, to build up the base of preventive diagnostic algorithm for the Power transformer. investigated the preventive diagnostic criteria for the power transformer.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.7
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• pp.878-882
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• 2004

In this paper, we propose a hierarchical hybrid neural network for detecting faults of induction motor. Implementing the classifier based on the input and output data, we apply appropriate transform and classification method at each step. In the proposed method, after obtaining the current of state of motor for each period, we transform it by Principle Component Analysis(PCA) to reduce its dimension. Before the training process, we use the conditional Fuzzy C-means(FCM) for obtaining the initial parameters of neural network for more effective learning procedure. From the various simulations, we find that the proposed method shows better performance to detect and diagnosis of induction motor and compare than other methods.