• Journal of the Korean Institute of Gas
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• v.8 no.2 s.23
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• pp.28-34
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• 2004

Enhanced methodologies for process diagnosis and abnormal situation management have been developed for the last two decades. However, there is no single method that always shows better performance over all kinds of diagnostic problems. In this paper, a framework of message-passing, cooperative, intelligent diagnostic agents is presented for improved on-line fault diagnosis through cooperative problem solving of different expertise. A group of diagnostic agents in charge of different process functional perform local diagnoses in parallel; exchange related information with other diagnostic agents; and cooperatively solve the global diagnostic problem of the whole process plant or business units just like human experts would do. For their better understanding, sharing and exchanging of process knowledge and information, we also suggest a way of remodeling processes and protocols, taking into account semantic abstracts of process information and data. The benefits of the suggested multi-agents-based approach are demonstrated by the implementations for solving the diagnostic problems of various chemical processes.

• Journal of Korea Multimedia Society
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• v.18 no.11
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• pp.1342-1350
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• 2015

Due to rapid development of mobile device technologies, the mobile banking through Internet has become a major service of banking information systems as a security-critical information systems. Recently, lots of mobile banking information systems which handle personal and transaction information have been exposed to security threats in vulnerable security control and management processes, mainly software systems. Therefore, in this paper, we propose a process model for software security improvements in mobile banking information system by application of fault tree analysis(FTA) and failure modes and effects analysis(FMEA) on the most important activities such as 'user authentication' and 'access control' and 'virus detection and control' processes which security control and management of mobile banking information systems are very weak.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.10C
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• pp.887-901
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• 2002

Software management model divided into the software project model and design estimation model, software matrices model, reliability growth model, process improvement model(or process maturity model) etc. Among these software management models, software complexity model make an estimated of the product software. For a practice of software managed, need to guideline of the static analysis of software. Especially, Software complexity model introduced for the estimation of software quantity and program complexity. In case of measurement the software matrices, its need for us to analysis of software quality and products. On the other hand, we known that complexity program include many defects and consuming of source cost. So, we apply to complexity model using of the program complexity, control structure and volume matrices, interface metrics, process complexity metrics method. In this paper, we represent that the analysis of fault data detected during the system test. Also, we analysis of program control structure and interface, volume matrices in various aspect of switching software. Others, their results utilized similar of project and system development.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.9
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• pp.29-35
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• 2019

The structure of the machinery industry due to the 4th industrial revolution is changing from precision and durability to intelligent and smart machinery through sensing and interconnection(IoT). There is a growing need for research on prognostics and health management(PHM) that can prevent abnormalities in processing machines and accurately predict and diagnose conditions. PHM is a technology that monitors the condition of a mechanical system, diagnoses signs of failure, and predicts the remaining life of the object. In this study, the vibration generated during machining is measured and a classification algorithm for normal and fault signals is developed. Arbitrary fault signal is collected by changing the conditions of un stable supply cutting oil and fixing jig. The signal processing is performed to apply the measured signal to the learning model. The sampling rate is changed for high speed operation and performed machine learning using raw signal without FFT. The fault classification algorithm for 1D convolution neural network composed of 2 convolution layers is developed.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.4
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• pp.194-201
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• 2012

Because the difference between products and services are getting less and less, service and manufacturing companies' efforts are increasingly focused on utilizing services to satisfy customers' needs under today's competitive market environment. The value of services depends on service reliability that is identified by satisfaction derived from the relationship between customer needs and service providers. In this paper, we extend concepts from the fault tree analysis for reliability analysis of tangible systems to services. We use an event-based process model to facilitate service design and represent the relationships between functions and failures in a service. The objective of this research is to propose a method for evaluating service reliability based on service processes using service blueprint and FTA. We can identify the failure mode of service in a service delivery process with a service blueprint. The fuzzy membership function is used to characterize the probability of failure based on linguistic terms. FTA is employed to estimate the reliability of service delivery processes with risk factors that are represented as potential failure causes. To demonstrate implementation of the proposed method, we use a case study involving a typical automotive service operation.

• Journal of the Korea Safety Management & Science
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• v.8 no.1
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• pp.27-42
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• 2006

Considering in view of China's low price and Japan's high technology, the most important point is the quality of product. To increase the comparative power internationally all workingmen have to fulfill the responsibility to maximize the quality, and this is possible practiced under 'Zero Defect' spirit. To reduce the defect ratio to 'Zero' the workingmen have to recognize own fault, all the manufacturing process should be conducted under 'Fool Proof System', If done under this circumstances, then labor force can work comfortably and safely without nervousness and tiresome. The productivity and quality can increase ultimately and even foreign labor, imbecile child, old-aged labor can participate in work, which can reduce shortage of labor source. Therefore, in this study all defects in manufacturing process, it would be recognized the errors and mistakes caused by human.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.3
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• pp.306-311
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• 2016

Software reliability in the software development process is an important issue. Infinite failure NHPP software reliability models presented in the literature exhibit either constant, monotonic increasing or monotonic decreasing failure occurrence rates per fault. In this paper, reliability software cost model considering logarithmic and exponential fault detection rate based on observations from the process of software product testing was studied. Adding new fault probability using the Goel-Okumoto model that is widely used in the field of reliability problems presented. When correcting or modifying the software, finite failure non-homogeneous Poisson process model. For analysis of software reliability model considering the time-dependent fault detection rate, the parameters estimation using maximum likelihood estimation of inter-failure time data was made. The logarithmic and exponential fault detection model is also efficient in terms of reliability because it (the coefficient of determination is 80% or more) in the field of the conventional model can be used as an alternative could be confirmed. From this paper, the software developers have to consider life distribution by prior knowledge of the software to identify failure modes which can be able to help.

• Journal of the Korean Institute of Gas
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• v.7 no.3 s.20
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• pp.1-6
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• 2003

This study has suggested a decision method which determine optimum investment level for safety management by process risk assessment at gas governor station. Hazard and operability study(HAZOP), fault tree analysis(FTA) and consequence analysis(CA) were carried out and potential accident cost and benefit for safety management were estimated. As a result, we could be found the trend of safety cost and benefit by the nonlinear regression method and could be determined the optimum investment level for safety management from analysis of safety management cost and potential accident cost.

• Journal of the Korea Safety Management & Science
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• v.6 no.3
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• pp.237-247
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• 2004

The condition of the manufacturing process in a factory should be diagnosed and maintained efficiently because any unexpected disorder in the process will be reason to decrease the efficiency of the overall system. However, if an expert experienced in this system leaves, there will be a problem for the efficient process diagnosis and maintenance, because disorder diagnosis within the process is normally dependent on the expert's experience. This paper suggests a process diagnosis using data mining based on the collected data from the coil-spring manufacturing process. The rules are generated for the relations between the attributes of the process and the output class of the product using a decision tree after selecting the effective attributes. Using the generated rules from decision tree, the condition of the current process is diagnosed and the possible maintenance actions are identified to correct any abnormal condition. Then, the appropriate maintenance action is recommended using the decision network.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.3
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• pp.186-197
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• 2023

In this study, we focus on the improvement of data quality transmitted from a weather buoy that guides a route of ships. The buoy has an Internet-of-Thing (IoT) including sensors to collect meteorological data and the buoy's status, and it also has a wireless communication device to send them to the central database in a ground control center and ships nearby. The time interval of data collected by the sensor is irregular, and fault data is often detected. Therefore, this study provides a framework to improve data quality using machine learning models. The normal data pattern is trained by machine learning models, and the trained models detect the fault data from the collected data set of the sensor and adjust them. For determining fault data, interquartile range (IQR) removes the value outside the outlier, and an NGBoost algorithm removes the data above the upper bound and below the lower bound. The removed data is interpolated using NGBoost or long-short term memory (LSTM) algorithm. The performance of the suggested process is evaluated by actual weather buoy data from Korea to improve the quality of 'AIR_TEMPERATURE' data by using other data from the same buoy. The performance of our proposed framework has been validated through computational experiments based on real-world data, confirming its suitability for practical applications in real-world scenarios.