• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.9
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• pp.1199-1208
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• 2013

In recent years, the development of application programs for distribution system analysis and control has been essential part for distribution management system (DMS). In this paper, we propose the common database for application programs of distribution management system. The proposed database model has several characteristics as followings. First, the proposed database model is designed for the common use of almost the whole distribution application software. The static equipment model and dynamic type tables are mixed and the parallel table structure is applied. Second, the linked list structure of database are used for the fast processing of applications. The database model includes the hierarchy and non-hierarchy distribution system structure. Third, the reduction method of distribution database is applied. For this, we present the network reduction rules. The basic concept of reduction rules are the electrical unification of successive line section which has not lateral branches and the removal of simple lateral branches which has no devices and other laterals. Proposed database model is tested for the Jeju system of Korea Electric Power Corporation (KEPCO). Through the test, we verified that the proposed database structure can be effectively used to accomplish the distribution system operation.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.133-141
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• 2001

Since the computing environment changes very rapidly, the estimation of software effort is very difficult because it is not easy to collect a sufficient number of relevant cases from the historical data. If we pinpoint the cases, the number of cases becomes too small. However is we adopt too many cases, the relevance declines. So in this paper we attempt to balance the number of cases and relevance. Since many researches on software effort estimation showed that the neural network models perform at least as well as the other approaches, so we selected the neural network model as the basic estimator. We propose a search method that finds the right level of relevant cases for the neural network model. For the selected case set. eliminating the qualitative input factors with the same values can reduce the scale of the neural network model. Since there exists a multitude of combinations of case sets, we need to search for the optimal reduced neural network model and corresponding case, set. To find the quasi-optimal model from the hierarchy of reduced neural network models, we adopted the beam search technique and devised the Case-Set Selection Algorithm. This algorithm can be adopted in the case-adaptive software effort estimation systems.

• Korean Journal of Cognitive Science
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• v.10 no.3
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• pp.1-15
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• 1999

Based on known physiological and psychophysical results, a neural network model for visual selection, called FeaureGate is proposed. The model consists of a hierarchy of spatial maps. and the flow of information from each level of the hierarchy to the next is controlled by attentional gates. The gates are jointly controlled by a bottom-up system favoring locations with unique features. and a top-down mechanism favoring locations with features designated as target features. The present study focuses on the top-down mechanism of the FeatureGate model that produces results similar to Moran and Desimone's (1985), which many current models have failed to explain, The FeatureGate model allows a consistent interpretation of many different experimental results in visual attention. including parallel feature searches and serial conjunction searches. attentional gradients triggered by cuing, feature-driven spatial selection, split a attention, inhibition of distractor locations, and flanking inhibition. This framework can be extended to produce a model of shape recognition using upper-level units that respond to configurations of features.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.10
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• pp.3950-3957
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• 2010

Wireless sensor network is composed of sensor node with limited sources, and to maintain and repair is vexatious once made up. Accordingly it is important matter to maximize the network lifetime by minimizing the energy consumption in wireless sensor network, and utilizing the limited sources efficiently. In this paper, I propose a technique arranging the cluster number with efficiency in clustering method to optimize the energy consumption. The energy usage needed for wireless transmission varies in distance(threshold). This technique reduces the energy consumption considering the threshold when arranging the cluster number. I verify that the clustering method organized through the valid processes outperform the LEACH(Low-Energy Adaptive Clustering Hierarchy) in total energy consumption.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.1
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• pp.35-57
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• 2014

Multi-criteria decision making (MCDM) algorithms play an important role in ensuring quality of service in an integrated HetNets (Heterogeneous Networks). The primary objective of this paper is to develop a multi-criteria vertical handoff decision algorithm (VHDA) for best access network selection in an integrated Wireless Local Area Network (WLAN)/Universal Mobile Telecommunications System (UMTS)/Worldwide Interoperability for Microwave Access (WiMAX) system. The proposed design consists of two parts, the first part is the evaluation of an Analytic Hierarchy Process (AHP) to decide the relative weights of handoff decision criteria and the second part computes the final score of the weights to rank network alternatives using Simple Additive Weighting (SAW). SAW ranks the network alternatives in a faster and simpler manner than AHP. The AHP-SAW mathematical model has been designed, evaluated and simulated for streaming video type of traffic. For other traffic type, such as conversational, background and interactive, only simulation results have been discussed and presented in brief. Simulation results reveal that the hierarchical modelling and computing provides optimum solution for access network selection in an integrated environment as obtained results prove to be an acceptable solution to what could be expected in real life scenarios.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.1910-1914
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• 2007

This paper employs a relatively new technique of Artificial Neural Network (ANN) to forecast water demand of Daegu city. The ANN model used in this study is a single hidden layer hierarchy model. About seventeen sets of historical water demand records and the values of their socioeconomic impact factors are used to train the model. Also other regression and time serious models are investigated for comparison purpose. The results present the ANN model can better perform the issue of urban water demand forecasting, and obtain the correlation coefficient of $R^2$ with a value of 0.987 and the relative difference less than 4.4% for this study.

• 한국IT서비스학회:학술대회논문집
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• 2003.11a
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• pp.616-623
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• 2003

Since the computing environment changes very rapidly, the estimation of software effort is very difficult because it is not easy to collect a sufficient number of relevant cases from the historical data. If we pinpoint the cases, the number of cases becomes too small. However if we adopt too many cases, the relevance declines. So in this paper we attempt to balance the number of cases and relevance. Since many researches on software effort estimation showed that the neural network models perform at least as well as the other approaches, so we selected the neural network model as the basic estimator. We propose a search method that finds the right level of relevant cases for the neural network model. For the selected case set, eliminating the qualitative input factors with the same values can reduce the scale of the neural network model. Since there exists a multitude of combinations of case sets, we need to search for the optimal reduced neural network model and corresponding case set. To find the quasi-optimal model from the hierarchy of reduced neural network models, we adopted the beam search technique and devised the Case-Set Selection Algorithm. This algorithm can be adopted in the case-adaptive software effort estimation systems.

• Journal of The Korean Society of Agricultural Engineers
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• v.56 no.1
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• pp.11-20
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• 2014

Many classification studies for the community of densely-connected nodes are limited to the comprehensive analysis for detecting the communities in probabilistic networks with nodes and edge of the probabilistic distribution because of the difficulties of the probabilistic operation. This study aims to use convolution method for operating nodes and edge of probabilistic distribution. For the probabilistic hierarchy network with nodes and edges of the probabilistic distribution, the model of this study detects the communities of nodes to make the new probabilistic distribution with two distribution. The results of our model was verified through comparing with Monte-carlo Simulation and other community-detecting methods.

• Journal of information and communication convergence engineering
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• v.22 no.1
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• pp.1-6
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• 2024

Energy efficiency in wireless sensor networks (WSNs) is a critical issue because batteries are used for operation and communication. In terms of scalability, energy efficiency, data integration, and resilience, WSN-cluster-based routing algorithms often outperform routing algorithms without clustering. Low-energy adaptive clustering hierarchy (LEACH) is a cluster-based routing protocol with a high transmission efficiency to the base station. In this paper, we propose an energy consumption model for LEACH and compare it with the existing LEACH, advanced LEACH (ALEACH), and power-efficient gathering in sensor information systems (PEGASIS) algorithms in terms of network lifetime. The energy consumption model comprises energy-sensitive cluster formation and a cluster head selection technique. The setup and steady-state phases of the proposed model are discussed based on the cluster head selection. The simulation results demonstrated that a low-energy-consumption network was introduced, modeled, and validated for LEACH.

• Convergence Security Journal
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• v.2 no.2
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• pp.49-58
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• 2002

RBAC(Role Based Access Control) is an access control method based on the application concept of role instead of DAC(Discretionary Access Control) or MAC(Mandatory Access Control) based on the abstract basic concept. Model provides more flexibility and applicability on the various computer and network security fields than the limited 1functionality of kernel access control orginated from BLP model. In this paper, we propose $ERBAC_0$ (Extended $RBAC_0$ ) model by considering subject's and object's roles and the role hierarchy result from the roles additionally to $RBAC_0$ base model. The proposed $ERBAC_0$ model assigns hierarchically finer role on the base of subject and object level and provides flexible access control services than traditional $RBAC_0$ model.