• Journal of Welding and Joining
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• v.25 no.6
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• pp.64-70
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• 2007

Recently, several models to control weld quality, productivity and weld properties in arc welding process have been developed and applied. Also, the applied model to make effective use of the robotic GMA(Gas Metal Arc) welding process should be given a high degree of confidence in predicting the bead dimensions to accomplish the desired mechanical properties of the weldment. In this study, a development of the on-line learning neural network models that investigate interrelationships between welding parameters and bead width as well as apply for the on-line quality control system for the robotic GMA welding process has been carried out. The developed models showed an excellent predicted results comparing with the predicted ability using off-line learning neural network. Also, the system will extend to other welding process and the rule-based expert system which can be incorporated with integration of an optimized system for the robotic welding system.

• Journal of Internet Computing and Services
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• v.7 no.6
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• pp.21-40
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• 2006

As the knowledge-based society hot been constructed, the size of work process that has to be done grows big and the amount of the information that has to be analyzed increases. In the paper business process can be accurately executed by the rule agent to manage precise rules and definitions to be needed by execution of process for management of business process. And individuals con accurately measure and manage personal schedules to execute process through supporting the agent of schedule management. The model is designed and implemented for organization to improve process of control, flexibleness, agility, reliability, and reuse through business process automation and removing process overlapping and for personal to improve measuring personal process capacity and distinguishing process weak points.

• Journal of the Korea Society of Computer and Information
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• v.7 no.1
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• pp.174-183
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• 2002

In this paper a control chart pattern recognition methodology based on the back propagation algorithm and Multi layer perceptron, a neural computing theory, is presented. This pattern recognition algorithm, suitable for real time statistical process control. evaluates observations routinely collected for control charting to determine whether a Pattern, such as a cycle. trend or shift, which is exists in the data. This approach is promising because of its flexible training and high speed computation with low-end workstation. The artificial neural network methodology is developed utilizing the delta learning rule, sigmoid activation function with two hidden layers. In a computer integrated manufacturing environment, the operator need not routinely monitor the control chart but, rather, can be alerted to patterns by a computer signal generated by the proposed system.

• Transactions of the Korean Society of Mechanical Engineers
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• v.16 no.7
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• pp.1223-1233
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• 1992

Chemical and mechanical properties of workpieces and tools are important factors for selecting machining parameters in machining process planning. As there is no universal rule representing the machinability defined by metal removal rate, the selection of machining parameters still requires experience-oriented methods. In this paper, a new approach is presented to develop mathematical models for generating optimum machinability in turning processes based on chemical and mechanical properties of workpieces. Neural-Network methodology is introduced to identify mathematical models for machinability. It is confirmed by simulations that the proposed methodology can be used for developing numerical controllers with adaptive control performance.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.3
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• pp.210-216
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• 2006

In the rule based modeling, data partitioning plays crucial role be cause partitioned sub data set implies particular information of the given data set or system. In this paper, we present an empirical study result of the data pattern estimation to find underlying data patterns of the given data. Presented method performs crisp type clustering with given n number of data samples by means of the sequential agglomerative hierarchical nested model (SAHN). In each sequence, the average value of the sum of all inter-distance between centroid and data point. In the sequel, compute the derivation of the weighted average distance to observe a pattern distribution. For the final step, after overall clustering process is completed, weighted average distance value is applied to estimate range of the number of clusters in given dataset. The proposed estimation method and its result are considered with the use of FCM demo data set in MATLAB fuzzy logic toolbox and Box and Jenkins's gas furnace data.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.3
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• pp.853-858
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• 2010

DRAM package substrate has been demonstrated using a thick alumina layer produced by aluminum anodization process. To apply a transmission-based design methodology, 2 dimensional electromagnetic simulation is performed. The design parameters including signal line width/spacing and alumina's thickness are optimized based on the simulation analysis and are verified with the fabrication and the measurement of the test patterns on the anodic alumina substrate. DDR2 DRAM package is chosen as a design vehicle. Aluminum anodization technique has been applied successfully to fabricate new DRAM package substrate.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39B no.6
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• pp.387-396
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• 2014

Recently, there have been continuous efforts and progresses regarding the research on diverse network control and management platforms for SDN (Software Defined Networking). SDN is defined as a new technology to enable service providers/network operators easily to control and manage their networks by writing a simple application program. In SDN, incomplete or malicious programmable entities could cause break-down of underlying networks shared by heterogeneous devices and stake-holders. In this sense, any misunderstanding or diverse interpretations should be completely avoided. This paper proposes a new framework for SDN application verification and a prototype based on the formal method, especially with process algebra called pACSR which is an extended version of Algebra of Communicating Shared Resources (ACSR).

• The Korean Journal of Nuclear Medicine Technology
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• v.16 no.1
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• pp.115-118
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• 2012

Purpose: The Levey-Jennings chart controlled measurement values that deviated from the tolerance value (mean ${\pm}2SD$ or ${\pm}3SD$). On the other hand, the upgraded Westgard Multi-Rules are actively recommended as a more efficient, specialized form of hospital certification in relation to Internal Quality Control. To apply Westgard Multi-Rules in quality control, credible quality control substance and target value are required. However, as physical examinations commonly use quality control substances provided within the test kit, there are many difficulties presented in the calculation of target value in relation to frequent changes in concentration value and insufficient credibility of quality control substance. This study attempts to improve the professionalism and credibility of quality control by applying Westgard Multi-Rules and calculating credible target value by using a commercialized quality control substance. Materials and Methods : This study used Immunoassay Plus Control Level 1, 2, 3 of Company B as the quality control substance of Total T3, which is the thyroid test implemented at the relevant hospital. Target value was established as the mean value of 295 cases collected for 1 month, excluding values that deviated from ${\pm}2SD$. The hospital quality control calculation program was used to enter target value. 12s, 22s, 13s, 2 of 32s, R4s, 41s, $10\bar{x}$, 7T of Westgard Multi-Rules were applied in the Total T3 experiment, which was conducted 194 times for 20 days in August. Based on the applied rules, this study classified data into random error and systemic error for analysis. Results: Quality control substances 1, 2, and 3 were each established as 84.2 ng/$dl$, 156.7 ng/$dl$, 242.4 ng/$dl$ for target values of Total T3, with the standard deviation established as 11.22 ng/$dl$, 14.52 ng/$dl$, 14.52 ng/$dl$ respectively. According to error type analysis achieved after applying Westgard Multi-Rules based on established target values, the following results were obtained for Random error, 12s was analyzed 48 times, 13s was analyzed 13 times, R4s was analyzed 6 times, for Systemic error, 22s was analyzed 10 times, 41s was analyzed 11 times, 2 of 32s was analyzed 17 times, $10\bar{x}$ was analyzed 10 times, and 7T was not applied. For uncontrollable Random error types, the entire experimental process was rechecked and greater emphasis was placed on re-testing. For controllable Systemic error types, this study searched the cause of error, recorded the relevant cause in the action form and reported the information to the Internal Quality Control committee if necessary. Conclusions : This study applied Westgard Multi-Rules by using commercialized substance as quality control substance and establishing target values. In result, precise analysis of Random error and Systemic error was achieved through the analysis of 12s, 22s, 13s, 2 of 32s, R4s, 41s, $10\bar{x}$, 7T rules. Furthermore, ideal quality control was achieved through analysis conducted on all data presented within the range of ${\pm}3SD$. In this regard, it can be said that the quality control method formed based on the systematic application of Westgard Multi-Rules is more effective than the Levey-Jennings chart and can maximize error detection.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.8
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• pp.664-674
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• 2001

In this paper, as a new category of fuzzy-neural networks architecture, we propose Fuzzy Polynomial Neural Networks (FPNN) and discuss a comprehensive design methodology related to its architecture. FPNN dwells on the ideas of fuzzy rule-based computing and neural networks. The FPNN architecture consists of layers with activation nodes based on fuzzy inference rules. Here each activation node is presented as Fuzzy Polynomial Neuron(FPN). The conclusion part of the rules, especially the regression polynomial, uses several types of high-order polynomials such as linear, quadratic and modified quadratic. As the premise part of the rules, both triangular and Gaussian-like membership functions are studied. It is worth stressing that the number of the layers and the nods in each layer of the FPNN are not predetermined, unlike in the case of the popular multilayer perceptron structure, but these are generated in a dynamic manner. With the aid of two representative time series process data, a detailed design procedure is discussed, and the stability is introduced as a measure of stability of the model for the comparative analysis of various architectures.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1137-1140
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• 1993

A dynamic-structure system is one that has the flexibility to change the system configuration automatically so as to operate in an optimal manner. A conceptural model for a dynamic-structure system is presented in this paper. In this model, the interchangeable components of the overall system are grouped together. Their activity levels are evaluated by an intelligent preprocessor that is associated with the group. A knowledge-based task distribution system evaluates the activity levels and makes decisions as to how the components operating below capacity should be shared with workcells that have similar components that are overloaded. Associated decision making can be effected through fuzzy logic and particularly the compositional rule of inference. A simulation example is given to illustrate the application of dynamic structuring.