• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.11
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• pp.5264-5281
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• 2017

Multihop relay-based cellular networks are attracting much interest because of their throughput enhancement, coverage extension, and low infrastructure cost. In these networks, relay stations (RSs) between a base station (BS) and mobile stations (MSs) drastically increase the overall spectral efficiency, with improved channel quality for MSs located at the cell edge or in shadow areas, and enhanced throughput of MSs in hot spots. These relay-based networks require an advanced radio resource management scheme because the optimal amount of radio resource for a BS-to-RS link should be allocated according to the MS channel quality and distribution, considering the interference among RSs and neighbor BSs. In this paper, we propose optimal resource planning algorithms that maximize the overall utility of relay-based networks under a proportional fair scheduling policy. In the first phase, we determine an optimal scheduling policy for distributing BS-to-RS link resources to RSs. In the second phase, we determine the optimal amount of the BS-to-RS link resources using the results of the first phase. The proposed algorithms efficiently calculate the optimal amount of resource without exhaustive searches, and their accuracy is verified by comparison with simulation results, in which the algorithms show a perfect match with simulations.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2821-2823
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• 2002

In this paper we propose the Multi-FNN (Fuzzy-Neural Networks) for optimal identification modeling of complex system. The proposed Multi-FNNs is based on a concept of FNNs and exploit linear inference being treated as generic inference mechanisms. In the networks learning, backpropagation(BP) algorithm of neural networks is used to updata the parameters of the network in order to control of nonlinear process with complexity and uncertainty of data, proposed model use a HCM(Hard C-Means)clustering algorithm which carry out the input-output dat a preprocessing function and Genetic Algorithm which carry out optimization of model The HCM clustering method is utilized to determine the structure of Multi-FNNs. The parameters of Multi-FNN model such as apexes of membership function, learning rates, and momentum coefficients are adjusted using genetic algorithms. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization abilities of the model. NOx emission process data of gas turbine power plant is simulated in order to confirm the efficiency and feasibility of the proposed approach in this paper.

• International Journal of Naval Architecture and Ocean Engineering
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• v.5 no.3
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• pp.468-477
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• 2013

An automatic pipe routing system is proposed and implemented. Generally, the pipe routing design as a part of the shipbuilding process requires a considerable number of man hours due to the complexity which comes from physical and operational constraints and the crucial influence on outfitting construction productivity. Therefore, the automation of pipe routing design operations and processes has always been one of the most important goals for improvements in shipbuilding design. The proposed system is applied to a pipe routing design in the engine room space of a commercial ship. The effectiveness of this system is verified as a reasonable form of support for pipe routing design jobs. The automatic routing result of this system can serve as a good basis model in the initial stages of pipe routing design, allowing the designer to reduce their design lead time significantly. As a result, the design productivity overall can be improved with this automatic pipe routing system.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.3
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• pp.188-193
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• 2000

This paper presents an approach to obtain a reduced neuro-fuzzy model for a plant. The Neuro-Fuzzy Network are compose of the Radial Basis Function Networks with Gausis membership and learned by using temporal back propagation. The dependency in rough set theory is used to eliminate rules. Dependency between the condition membership value of each rule in a model and the output of the plant can allow us to see how much contribution the rule is to identify the plant. While the reduced model maintains the same performance as the original one, the selection algorithm can minimize its complexity and redundancy of the structure.

• Journal of the Korean Society for Railway
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• v.15 no.2
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• pp.185-192
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• 2012

Line planning is to determine the frequency of trains on each line to satisfy origin-destination demand while minimizing total operation cost. However, different from the line planning in passenger transportation, it is more important at which intermediate stations each train should be stopped and shunted because the freight car handling works like drop-off or(and) pick-up can incur much time and high cost so that the delay deteriorates the quality of rail freight transportation service. We present an optimization model for constructing line plan in rail freight transportation to simultaneously minimize the train operation cost and total transportation time of freights. And we suggest a column generation approach for our problem, which can solve the real network instances in reasonable computation times.

• IEMEK Journal of Embedded Systems and Applications
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• v.9 no.3
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• pp.157-163
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• 2014

This paper demonstrates the benefit of using MOOS-IvP in the development of control system for Unmanned Underwater Vehicles(UUV). The demand for autonomy in UUVs has significantly increased due to the complexity in missions to be performed. Furthermore, the increased number of sensors and actuators that are interconnected through a network has introduced a need for a middleware platform for UUVs. In this context, MOOS-IvP, which is an open source software architecture, has been developed by several researchers from MIT, Oxford University, and NUWC. The MOOS software is a communication middleware based on the publish-subscribe architecture allowing each application to communicate through a MOOS database. The IvP Helm, which is one of the MOOS modules, publishes vehicle commands using multi-objective optimization in order to implement autonomous decision making. This paper explores the benefit of MOOS-IvP in the development of control software for UUVs by using a case study with an auto depth control system based on self-organizing fuzzy logic control. The simulation results show that the design and verification of UUV control software based on MOOS-IvP can be carried out quickly and efficiently thanks to the reuse of source codes, modular-based architecture, and the high level of scalability.

• Journal of the Korea Convergence Society
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• v.9 no.2
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• pp.13-18
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• 2018

This paper is based on a simple approach that the most efficient learning of a multi-layered network is the process of finding the optimal set of weight vectors. To overcome the disadvantages of general learning problems, the proposed model uses a combination of features of EBP and OVSSA. In other words, the proposed method can construct a single model by taking advantage of each algorithm so that it can escape to the probability theory of OVSSA in order to reinforce the property that EBP falls into local minimum value. In the proposed algorithm, methods for reducing errors in EBP are used as energy functions and the energy is minimized to OVSSA. A simple experimental result confirms that two algorithms with different properties can be combined.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.47-50
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• 2000

In this paper, an optimal identification method using Multi-FNN(Fuzzy-Neural Network) is proposed for model ins of nonlinear complex system. In order to control of nonlinear process with complexity and uncertainty of data, proposed model use a HCM clustering algorithm which carry out the input-output data preprocessing function and Genetic Algorithm which carry out optimization of model. The proposed Multi-FNN is based on Yamakawa's FNN and it uses simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rules. HCM clustering method which carry out the data preprocessing function for system modeling, is utilized to determine the structure of Multi-FNN by means of the divisions of input-output space. Also, the parameters of Multi-FNN model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. Also, a performance index with a weighting factor is presented to achieve a sound balance between approximation and generalization abilities of the model, To evaluate the performance of the proposed model, we use the time series data for gas furnace and the numerical data of nonlinear function.

• Human Ecology Research
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• v.54 no.3
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• pp.239-250
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• 2016

This study identifies the aspects of a managers' perceived work experience in a Multicultural Family Support Center and analyzes how work experience backgrounds are formulated. In-depth interviews were conducted with 10 managers from March to September 2015 to understand managers' work experience. Descriptions from the interviews were analyzed using phenomenological research methods. The result show that their work experience can be categorized into 73 meanings, 10 subcategories, and five themes. The five themes are 'program development,' 'employees assignment optimization,' 'use and control network resources,' 'deal with changes in needs of multicultural families,' and 'supports vs. controls.' The analysis of the themes and subcategories from each theme allows us to first interpret that managers consider their significant and diversified work tasks overwhelming. Second, they find their jobs fit them and feel personally interested with a sense of duty from their work to overcome stress from heavy workloads. Third, managers put a high value on their work as a hands-on experience that is an officially authorized position from the government. Fourth, they are proud that they contribute to offering welfare services to multicultural families as members of Multicultural Family Support Centers.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.3
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• pp.310-315
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• 2007

When a mail was given to users, each user's response could be different according to his or her preference. This paper presents a solution for this situation by constructing a u!;or preferred ontology for anti-spam systems. To define an ontology for describing user behaviors, we applied associative classification mining to study preference information of users and their responses to emails. Generated classification rules can be represented in a formal ontology language. A user preferred ontology can explain why mail is decided to be spam or non-spam in a meaningful way. We also suggest a nor rule optimization procedure inspired from logic synthesis to improve comprehensibility and exclude redundant rules.