• 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%.

• 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 the Korean Institute of Gas
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• v.12 no.3
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• pp.1-6
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• 2008

The safety management of the LNG industry which shows huge growth recently, become an essential element. So the necessity of development for the pre-existing LNG storage/transport facility has been shown up and the improvement of information technology (IT) of these days make it possible to synthesize several models for integrated LNG facilitiy safety management system. This system will contains risk analysis/assessment technology, explosion, leakage and diffusion model construction technology, real-time monitoring and fault diagnosis technology, and reliability progression technology of process information through data reconciliation. The final integrated safety management system will contribute the increase of LNG industry's safety and exportation of technique.

• Proceedings of the SAREK Conference
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• 2007.11a
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• pp.149-154
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• 2007

Now, in Korea, the performances of the building service equipment relay on the individual superintendent's share for the assessment of performance, fault detection, deterioration diagnosis of the building service equipment. As the result, very different quality of the performance or the durability of equipment is being obtained with his skill and effort and it is also not easy to assess that quality. This finally lead to the waste of labor force and the operating cost due to the high-cost, low-efficiency system. How to construct the N-BMS was considered to save energy, resource and to conserve performance of building service equipment. The FEMIS, facility, energy/environmental management & information system, for building service offer management process integrated with BAS, FMS and EMS and so on.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.1
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• pp.75-84
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• 2018

Increasing the operation rate of components and stabilizing the operation through timely management of the core parts are crucial for improving the efficiency of the railroad maintenance industry. The demand for diagnosis technology to assess the condition of rolling stock components, which employs history management and automated big data analysis, has increased to satisfy both aspects of increasing reliability and reducing the maintenance cost of the core components to cope with the trend of rapid maintenance. This study developed a big data platform-based system to manage the rolling stock component condition to acquire, process, and analyze the big data generated at onboard and wayside devices of railroad cars in real time. The system can monitor the conditions of the railroad car component and system resources in real time. The study also proposed a machine learning technique that enabled the distributed and parallel processing of the acquired big data and automatic component fault diagnosis. The test, which used the virtual instance generation system of the Amazon Web Service, proved that the algorithm applying the distributed and parallel technology decreased the runtime and confirmed the fault diagnosis model utilizing the random forest machine learning for predicting the condition of the bearing and wheel parts with 83% accuracy.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.11B
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• pp.956-966
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• 2004

Nowadays, NGN(Next Generation Network) is evolving into network with packet based QoS.\ulcorner This paper presents the recovery method as a selecting way of more efficient path in MPLS network that reduces the service interruption time. In order to increase the efficiency of usage of recovering resource which is one of major functions of disorder recovering, it opens the state information of each Node both to fault detector and bandwidth broker so that it reduces message transmission time for recovery when functional disorder occurs. in addition, by effective management and adjustment of required bandwidth of entire network depending on the changes of traffic load, it improves the efficiency of bandwidth usage. and in MPLS network supporting priority selection function, it allocates domains depending on the traffic relating to priority order to the entire network so that\ulcorner it efficiently allocates proper resources to the restoring process between the entry and the end for the traffic of high priority and for low priority traffic, it restores path oかy inside disordered domain. by doing this, it reduces resetting time and unnecessary massage transmission.

• Journal of Digital Convergence
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• v.11 no.1
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• pp.275-281
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• 2013

This space is for the of your study in English. If simple menu item changes or the addition of check items are necessary on GUI menu of existing test equipments for military facilities that are programmed by using RAD tools such as Visual C++, they should go through complex steps, such as numerous conducting steps, coding, flash design modification, recompiling and distribution. It is cumbersome process and waste much time. Also, on implementing them, it was worried about leaking secrets because a number of military security considerations were included. To solve such as the above problem, we proposed commercial RIA technologies and a COTS fault diagnostic knowledge-based system that implemented by the XML data design technique in this research. The proposed approach solves the problem of existing methods, reduced inspection time, and improved performance, usability, and maintainability.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.403-409
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• 2024

In this paper, the guidelines for the design of an Artificial Intelligence(AI) based Integrated Process Safety Management(PSM) system to enhance workplace safety using data from process safety reports submitted by hazardous and risky facility operators in accordance with the Occupational Safety and Health Act is proposed. The system composed of the proposed guidelines is to be implemented separately by individual facility operators and specialized process safety management agencies for single or multiple workplaces. It is structured with key components and stages, including data collection and preprocessing, expansion and segmentation, labeling, and the construction of training datasets. It enables the collection of process operation data and change approval data from various processes, allowing potential fault prediction and maintenance planning through the analysis of all data generated in workplace operations, thereby supporting decision-making during process operation. Moreover, it offers utility and effectiveness in time and cost savings, detection and prediction of various risk factors, including human errors, and continuous model improvement through the use of accurate and reliable training data and specialized datasets. Through this approach, it becomes possible to enhance workplace safety and prevent accidents.

• Proceedings of the Korean Vacuum Society Conference
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• 2015.08a
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• pp.142.2-142.2
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• 2015

Plasma etch endpoint detection (EPD) of SiO2 and PR layer is demonstrated by plasma impedance monitoring in this work. Plasma etching process is the core process for making fine pattern devices in semiconductor fabrication, and the etching endpoint detection is one of the essential FDC (Fault Detection and Classification) for yield management and mass production. In general, Optical emission spectrocopy (OES) has been used to detect endpoint because OES can be a simple, non-invasive and real-time plasma monitoring tool. In OES, the trend of a few sensitive wavelengths is traced. However, in case of small-open area etch endpoint detection (ex. contact etch), it is at the boundary of the detection limit because of weak signal intensities of reaction reactants and products. Furthemore, the various materials covering the wafer such as photoresist (PR), dielectric materials, and metals make the analysis of OES signals complicated. In this study, full spectra of optical emission signals were collected and the data were analyzed by a data-mining approach, modified K-means cluster analysis. The K-means cluster analysis is modified suitably to analyze a thousand of wavelength variables from OES. This technique can improve the sensitivity of EPD for small area oxide layer etching processes: about 1.0 % oxide area. This technique is expected to be applied to various plasma monitoring applications including fault detections as well as EPD.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.6
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• pp.267-276
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• 2021

The automotive industry and academic research have been continuously conducting research on standardization such as AUTOSAR (AUTomotive Open System ARchitecture) and ISO26262 to solve problems such as safety and efficiency caused by the complexity of electric/electronic architecture of automotive. AUTOSAR is an automotive standard software platform that has a layered structure independent of MCU (Micro Controller Unit) hardware, and improves product reliability through software modularity and reusability. And, ISO26262, an international standard for automotive functional safety and suggests a method to minimize errors in automotive ECU (Electronic Control Unit)s by defining the development process and results for the entire life cycle of automotive electrical/electronic systems. These design methods are variously applied in representative automotive safety-critical systems. However, since the functional and safety requirements are different according to the characteristics of the safety-critical system, it is essential to research the AUTOSAR functional safety design method specialized for each application domain. In this paper, a software functional safety mechanism design method using AUTOSAR is proposed, and a new failure management framework is proposed to ensure the high reliability of the product. The AUTOSAR functional safety mechanism consists of memory partitioning protection, timing monitoring protection, and end-to-end protection. The fault management framework is composed of several safety SWCs to maintain the minimum function and performance even if a fault occurs during the operation of a safety-critical system. Finally, the proposed method is applied to the Shift-by-Wire system design to prove the validity of the proposed method.