• Communications for Statistical Applications and Methods
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• v.8 no.2
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• pp.543-556
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• 2001

This study suggests integrated neural network modes for he stock price index forecasting using change-point detection. The basic concept of this proposed model is to obtain significant intervals occurred by change points, identify them as change-point groups, and reflect them in stock price index forecasting. The model is composed of three phases. The first phase is to detect successive structural changes in stock price index dataset. The second phase is to forecast change-point group with various data mining classifiers. The final phase is to forecast the stock price index with backpropagation neural networks. The proposed model is applied to the stock price index forecasting. This study then examines the predictability of integrated neural network models and compares the performance of data mining classifiers.

• Korean Journal of Hydrosciences
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• v.4
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• pp.1-9
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• 1993

It is a major objective for the management and operation of water resources system to forecast streamflows. The applicability of artificial neural network model to hydrologic system is analyzed and the performance is compared by statistical method with observed. Multi-layered perception was used to model rainfall-runoff process at Pyung Chang River Basin in Korea. The neural network model has the function of learning the process which can be trained with the error backpropagation (EBP) algorithm in two phases; (1) learning phase permits to find the best parameters(weight matrix) between input and output. (2) adaptive phase use the EBP algorithm in order to learn from the provided data. The generalization results have been obtained on forecasting the daily and hourly streamflows by assuming them with the structure of ARMA model. The results show validities in applying to hydrologic forecasting system.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.45 no.1
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• pp.113-122
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• 1996

As integrity of semiconductor device is increased, accurate and efficient modeling and recipe generation of semiconductor fabrication procsses are necessary. Among the major semiconductor manufacturing processes, dry etc- hing process using gas plasma and accelerated ion is widely used. The process involves a variety of the chemical and physical effects of gas and accelerated ions. Despite the increased popularity, the complex internal characteristics made efficient modeling difficult. Because of difficulty to determine the control input for the desired output, the recipe generation depends largely on experiences of the experts with several trial and error presently. In this paper, the optimal control of the etching is carried out in the following two phases. First, the optimal neural network models for etching process are developed with genetic algorithm utilizing the input and output data obtained by experiments. In the second phase, search for optimal control inputs in performed by means of using the optimal neural network developed together with genetic algorithm. The results of study indicate that the predictive capabilities of the neural network models are superior to that of the statistical models which have been widely utilized in the semiconductor factory lines. Search for optimal control inputs using genetic algorithm is proved to be efficient by experiments. (author). refs., figs., tabs.

• Journal of the Korean Society of Systems Engineering
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• v.13 no.2
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• pp.9-17
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• 2017

Concept development of a real-time Field Programmable Gate Array (FPGA)-based switched Ethernet data communication network for the Man-Machine Interface System (MMIS) is presented in this paper. The proposed design discussed in this research is based on the systems engineering (SE) approach. The design methodology is effectively developed by defining the concept development stage of the life-cycle model consisting of three successive phases, which are developed and discussed: needs analysis; concept exploration; and concept definition. This life-cycle model is used to develop an FPGA-based time-triggered Ethernet (TTE) switched data communication network for the non-safety division of MMIS system to provide real-time data transfer from the safety control systems to the non-safety division of MMIS and between the non-safety systems including control, monitoring, and information display systems. The original IEEE standard 802.3 Ethernet networks were not typically designed or implemented for providing real-time data transmission, however implementing a network that provides both real-time and on-demand data transmission is achievable using the real-time Ethernet technology. To develop the design effectively, context diagrams are implied. Conformance to the stakeholders needs, system requirements, and relevant codes and standards together with utilizing the TTE technology are used to analyze, synthesize, and develop the MMIS non-safety data communication network of the APR1400 nuclear power plant.

• Journal of Korea Society of Industrial Information Systems
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• v.14 no.3
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• pp.22-29
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• 2009

In previous work, anomaly-based intrusion detection techniques have been widely used to effectively detect various intrusions into a computer. This is because the anomaly-based detection techniques can effectively handle previously unknown intrusion methods. However, most of the previous work assumed that the normal network connections are fixed. For this reason, a new network connection may be regarded as an anomalous event. This paper proposes a new anomaly detection method based on an association-mining algorithm. The proposed method is composed of two phases: intra-packet association mining and inter-packet association mining. The performances of the proposed method are comparatively verified with JAM, which is a conventional representative intrusion detection method.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.04b
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• pp.71-75
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• 1993

The application of artificial neural network forcondition monitoring and diagnosis of milling tools was introduced. To detect the conditions of milling tools, the monitoring wywtem consists of three phases: "preparation phase", "learning phase", "production phase". The conditions of milling tools were categorized into the three states. "normal", "warning", "abnormal". The dectection of tool condition, in this paper, could be successfully performed by monitoring the variation of power spectrum on Y dirctional cutting force.

• Journal of Internet Computing and Services
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• v.8 no.5
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• pp.21-33
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• 2007

An overlay network is a virtual network which is constructed on top of a physical computer network. A node in the overlay network is connected through virtual or logical links, where each link corresponds to a path of the links in the underlying physical network. Overlay networks are suitable for sharing heterogeneous resources in distributed environments, However, overlay networks are limited for achieving reliable communication that failure detection in overlay networks is a very important issue. In this paper, we review conditions of conventional failure detection and propose a new approach to failure detection and resilience which can be applied to HRing (Hierarchical Ring) overlay networks. The proposed method consists of the failure detection and the failure resilience phases. Because it utilizes the characteristics of the HRing overlay network for failure detection, it can reduce unnecessary network traffic and provide better scalability and flexibility. We also analyzed and evaluated the performance of the proposed approach through simulations.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.153-154
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• 2008

This paper presents a window-based software package for the education and training for the reduction of power system by using locational marginal price (LMP), clustering, and similarity indices of each bus. The developed package consists of three modules: 1) the LMP module, 2) the Clustering module and 3) the Reduction module. Each module has a separated and interactive interface window. First of all, LMPs are created in the LMP module, and then the Clustering module carries out clustering based on the results of the LMP module. Finally, groups created in this Clustering module are reduced by using the similarity indices of each bus. The developed package displays a variety of tables for results of the LMPs of base network, voltages, phases and power flow of reduced network so that the user can easily understand the reduction of network. To demonstrate the performance of the developed package, it is tested for the IEEE 39-bus power system.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.3
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• pp.221-229
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• 2003

In the early phases of the product life cycle, Life Cycle Assessment (LCA) is recently used to support the decision-making fer the conceptual product design and the best alternative can be selected based on its estimated LCA and its benefits. Both the lack of detailed information and time for a full LCA fur a various range of design concepts need the new approach fer the environmental analysis. This paper suggests a novel approximate LCA methodology for the conceptual design stage by grouping products according to their environmental characteristics and by mapping product attributes into impact driver index. The relationship is statistically verified by exploring the correlation between total impact indicator and energy impact category. Then a neural network approach is developed to predict an approximate LCA of grouping products in conceptual design. Trained learning algorithms for the known characteristics of existing products will quickly give the result of LCA for new design products. The training is generalized by using product attributes for an ID in a group as well as another product attributes for another IDs in other groups. The neural network model with back propagation algorithm is used and the results are compared with those of multiple regression analysis. The proposed approach does not replace the full LCA but it would give some useful guidelines fer the design of environmentally conscious products in conceptual design phase.

• The Transactions of the Korean Institute of Power Electronics
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• v.9 no.3
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• pp.246-252
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• 2004

This paper presents a position sensorless control system of Switched Reluctance Motor (SRM) using neural network. The control of SRM depends on the commutation of the stator phases in synchronism with the rotor position. The position sensing requirement increases the overall cost and complexity. In this paper, the current-flux-rotor position lookup table based position sensorless operation of SRM is presented. Neural network is used to construct the current-flux-rotor position lookup table, and is trained by sufficient experimental data. Experimental results for a 1-hp SRM is presented for the verification of the proposed sensorless algorithm.