• Proceedings of the KIEE Conference
• /
• 1995.07b
• /
• pp.488-490
• /
• 1995

This paper presents an optimization technique using genetic algorithms(GA) for loss minimization in the distribution network reconfiguration. Determining switch position to be opened for loss minimization in the radial distribution system is a discrete optimization problem. GA is appropriate to solve the multivariable optimization problem and it uses population, not a solution. For this reason, GA is attractive to solve this problem. In this paper, we aimed at finding appropriate open sectionalizing switch position using GA, which can lead to minimum transmission losses.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 2000.10a
• /
• pp.124-132
• /
• 2000

Distributed processing approach for structural optimization is presented in this study. It is implemented on network of personal computers. The validity and efficiency of this approach are demonstrated and verified by test model of truss. Repeated structural analysis algorithm, which spend a lot of overall structural optimization processes, are based on substructuring scheme with domain-wise parallelism and converted to be adapted to hardware and software environments. The design information data are modularized and assigned to each computer in order to minize the communication cost. The communications between nodes are limited to static condensation and constraint-related data collection.

• ETRI Journal
• /
• v.37 no.3
• /
• pp.502-511
• /
• 2015

Intrusion detection plays a key role in detecting attacks over networks, and due to the increasing usage of Internet services, several security threats arise. Though an intrusion detection system (IDS) detects attacks efficiently, it also generates a large number of false alerts, which makes it difficult for a system administrator to identify attacks. This paper proposes automatic fuzzy rule generation combined with a Wiener filter to identify attacks. Further, to optimize the results, simplified swarm optimization is used. After training a large dataset, various fuzzy rules are generated automatically for testing, and a Wiener filter is used to filter out attacks that act as noisy data, which improves the accuracy of the detection. By combining automatic fuzzy rule generation with a Wiener filter, an IDS can handle intrusion detection more efficiently. Experimental results, which are based on collected live network data, are discussed and show that the proposed method provides a competitively high detection rate and a reduced false alarm rate in comparison with other existing machine learning techniques.

• Journal of computational fluids engineering
• /
• v.14 no.4
• /
• pp.31-40
• /
• 2009

Although optimization by sequentially refining metamodels is known to be computationally very efficient, the metamodel that can be used for this purpose is limited to Kriging method due to the difficulties related with sample points selections. The present study suggests a novel method for sequentially refining metamodels using Pareto Frontiers, which can be used independent of the type of metamodels. It is shown from the examples that the present method yields more accurate metamodels compared with full-factorial optimization and also guarantees global optimum irrespective of the initial conditions. Finally, in order to prove the generality of the present method, it is applied to a 2D transonic airfoil optimization problem, and the successful design results are obtained.

• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.32 no.12
• /
• pp.954-962
• /
• 2008

This study presents a numerical procedure to optimize the shape of cylindrical cooling hole to enhance film-cooling effectiveness. The RBNN method is used as an optimization technique with Reynolds-averaged Navier-Stokes analysis of fluid flow and heat transfer with shear stress transport turbulent model. The hole length-to-diameter ratio and injection angle are chosen as design variables and film-cooling effectiveness is considered as objective function which is to be maximized. Twelve training points are obtained by Latin Hypercube Sampling for two design variables. In the sensitivity analysis, it is found that the objective function is more sensitive to the injection angle of hole than the hole length-to diameter ratio. Optimum shape gives considerable increase in film-cooling effectiveness.

• Proceedings of the IEEK Conference
• /
• 2002.06a
• /
• pp.371-374
• /
• 2002

The optimal AP placement for wireless LAN is important factor for improving service quality and reducing cost. Logical area property, which is user's frequently posed place, must be considerated for flexible design. This paper proposes optimal multiple AP placement method based on path loss model which is one of radio prediction tool. The proposed method can got flexibility in multiple AP placement using user's defined parameter and tile optimization design uses Hopfield network algorithm The result of simulation shows that the proposed optimization design of multiple AP placement can improve service quality for wireless LAN.

• Journal of Communications and Networks
• /
• v.18 no.4
• /
• pp.629-638
• /
• 2016

In this paper, we investigate an energy-efficient resource allocation problem in a two-way relay (TWR) network consisting of multiple user pairs and an amplify-and-forward (AF) relay. As the users and relay have individual energy efficiencies (EE), we formulate a multi-objective optimization problem (MOOP). A single-objective optimization problem (SOOP) of the MOOP is introduced using a weighted-sum method, which achieves a single Pareto optimal point of the MOOP. To derive the algorithm for the SOOP, we propose a more tractable equivalent problem using the Karush-Kuhn-Tucker conditions of the SOOP, which guarantees convergence at the local optimal points. The proposed equivalent problem can be efficiently solved by the proposed iterative algorithm. Numerical results demonstrate the effectiveness of the proposed algorithm in achieving the optimal EE in multi-user AF TWR networks.

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.54 no.4
• /
• pp.193-198
• /
• 2005

As the electrical power industry is restructured, the electrical power exchange is extended. One of the key information used to determine how much power can be transferred through the network is known as available transfer capability (ATC). To calculate ATC, traditional deterministic approach is based on the severest case, but the approach has the complexity of procedure. Therefore, novel approach for ATC calculation is proposed using cost optimization in this paper Cheju Island interconnected HVDC system with mainland in KEPCO (Korean Electric Power Corporation) systems, and the demand of Cheju Island increases about 10 ($\%$) every year. To supply for increasing demand, the supply of HVDC system must be increased. This paper proposed the optimal transfer capability of HVDC system between Haenam in mainland and Cheju in Chju Island through cost optimization. The cost optimization is considered production cost in Cheju Island, wheeling charge through Haenam-Cheju HVDC system and outage cost with one depth (N-1 contingency)

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.4
• /
• pp.1738-1764
• /
• 2019

With the advent of big data, deep learning technology has become an important research direction in the field of machine learning, which has been widely applied in the image processing, natural language processing, speech recognition and online advertising and so on. This paper introduces deep learning techniques from various aspects, including common models of deep learning and their optimization methods, commonly used open source frameworks, existing problems and future research directions. Firstly, we introduce the applications of deep learning; Secondly, we introduce several common models of deep learning and optimization methods; Thirdly, we describe several common frameworks and platforms of deep learning; Finally, we introduce the latest acceleration technology of deep learning and highlight the future work of deep learning.

• Asian Journal of Innovation and Policy
• /
• v.8 no.1
• /
• pp.122-140
• /
• 2019

Providing free primary care to everyone is an important goal pursued by many countries under universal health care programs. Countries like India need to efficiently utilize their limited capacities towards this purpose. Unfortunately, due to a variety of reasons, patients incur substantial travel and out-of-pocket expenses for getting primary care from publicly-funded facilities. We propose a set-covering optimization model to assist health policy-makers in managing existing capacity in a better way. Decision-making should consider upgrading centers with better potential to reduce patient expenses and reallocating capacities from less preferred facilities. A multinomial logit choice model is used to predict the preferences. In this article, a brief background and literature survey along with the mixed integer linear programming (MILP) optimization model are presented. The working of the model is illustrated with the help of numerical experiments.