• Journal of the Korean Operations Research and Management Science Society
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• v.23 no.3
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• pp.49-62
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• 1998

In the ring loading problem, traffic demands are given for each pair of nodes in an undirected ring network with n nodes and a flow is routed in either of the two directions, clockwise and counter-clockwise. The load of a link is the sum of the flows routed through the link and the objective of the Problem is to minimize the maximum load on the ring. In the ring loading problem with integer demand splitting, each demand can be split between the two directions and the flow routed in each direction is restricted to integers. Recently, Vachani et al. ［INFORMS J. Computing 8 (1996) 235-242］ have developed an Ο(n$^3$) algorithm for solving this integer version of the ring loading problem and independently, Schrijver et al. ［to appear in SIAM J. Disc. Math.］ have presented an algorithm which solves the problem with ｛0,1｝ demands in Ο（n$^2$｜K｜ ) time where K denotes the index set of the origin-desㅇtination pairs of nodes having flow demands. In this paper, we develop an algorithm which solves the problem in Ο(n ｜K｜) time.

• Proceedings of the IEEK Conference
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• summer
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• pp.15-18
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• 2004

In this paper we present a linear discrete bit-loading algorithm that maximizes the transmit bit rate using the channel informations to optimize the performance of the very high-speed digital subscriber line(VDSL) system. It will be useful under the constraint of a maximum transmit power for each user. When the level of crosstalk is high, the power allocation of a user changes the noise experienced by the other users in the same binder. In this case, the performance of DSL modems can be improved by jointly considering the bit and power allocation of all users.

• Nuclear Engineering and Technology
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• v.56 no.5
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• pp.1672-1678
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• 2024

We introduce a creative approach combining machine learning with optimization techniques to enhance the optimization of the loading pattern (LP). Finding the optimal LP is a critical decision that impacts both the reload safety and the economic feasibility of the nuclear fuel cycle. While simulated annealing (SA) is a widely accepted technique to solve the LP optimization problem, it suffers from the drawback of high computational cost since LP optimization requires three-dimensional depletion calculations. In this note, we introduce a technique to tackle this issue by leveraging neural networks to filter out inappropriate patterns, thereby reducing the number of SA evaluations. We demonstrate the efficacy of our novel approach by constructing a machine learning-based optimization model for the LP data of the Korea Standard Nuclear Power Plant (OPR-1000).

• Journal of Broadcast Engineering
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• v.19 no.1
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• pp.14-23
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• 2014

This paper proposed controlling methods for SDN(Software Defined Network) based multiple radio access technology as the solutions of following two issues which were mainly occurred by explosive increasing of video traffic. The first one is a requirement for traffic off-loading caused by 3rd-party video service providers from the mobile network operator's viewpoint. The other one is a provision of high-speed video contents transmission services with low price. Furthermore, the performance evaluation was also conducted on the real test-bed which is composed of OpenStack cloud and SDN technology such as OpenFlow and Open vSwitch. A virtual machine running on the OpenStack provide a video service and the terminal which is able to use multiple radio access technology supports two 2.4GHz WLANs(Wireless Local Area Network) and three 5GHz WLANs, concurrently. Finally, we can get 820Mbps of the maximum transmission speed by using that five WLAN links for the single service at the same time.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.7
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• pp.815-823
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• 1999

This study considers an implementation of artificial neural networks to the receding horizon optimal control and is applications to power systems. The Generalized Backpropagation-Through-Time (GBTT) algorithm is presented to deal with a quadratic cost function defined in a finite-time horizon. A decentralized approach is used to control the complex global system with simpler local controllers that need only local information. A Neural network based Receding horizon Optimal Control (NROC) 1aw is derived for the local nonlinear systems. The proposed NROC scheme is implemented with two artificial neural networks, Identification Neural Network (IDNN) and Optimal Control Neural Network (OCNN). The proposed NROC is applied to a power system to improve the damping of the low-frequency oscillation. The simulation results show that the NROC based power system stabilizer performs well with good damping for different loading conditions and fault types.

• 한국방재학회:학술대회논문집
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• 2008.02a
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• pp.469-472
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• 2008

In general, structures in service gradually lose original performance according to time due to initial defects in design and construction, or exposure to unfavorable external conditions such as repeated loading or deteriorating environment, and in extreme cases, may collapse in large disaster. Therefore, in order to maintain the serviceability of structures at optimal level, advanced structure measuring system which can inform optimal time point and method of maintenance is required in addition to accurate prediction of residual life the structure by periodic inspection. To guarantee the safety level of bridge structure and to prevent from disaster, the integration of safety network for bridge structures are needed. Therefore in this study, to enhance the effectiveness of safety network for bridge, the connection methodologies between safety network and pre-installed bridge monitoring system are investigated.

• Proceedings of the KIEE Conference
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• 1995.11a
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• pp.55-58
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• 1995

An optimum design method of electric machines using neural network is presented. In this method, two multi - layer perceptrons of analysis and design neural network are used in optimizing process. A preliminary model of linear induction motor for subway is designed by the electric and magnetic loading distribution method and then optimized by presented method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.346-348
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• 2022

A monitoring system was constructed to identify the cause of occurrence based on data on the analysis of the ignition factors of fermentation heat generated from loading waste. Universal Middleware was used to provide a real-time run-time environment for the configuration and speed of scenarios for each type of fire early warning. It is necessary to dynamically recognize the loading height and pressure of the loading waste, the drying of wood, batteries, and plastic waste, which are representative compositional wastes, and the carbonization changes on the surface. Therefore, this IoT situation recognition platform for analyzing low-temperature-fired fire possibility data was dynamically configured and presented.

• Transactions of Materials Processing
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• v.9 no.6
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• pp.644-652
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• 2000

Tube hydroforming is recently drawing attention of automotive industries due to its several advantages over conventional methods. It can produce wide range of products such as subframes, engine cradles, and exhaust manifolds with cheaper production cost by reducing overall number of processes. h successful tube hydroforming depends on the reasonable combination of the internal pressure and axial load at the tube ends. This paper deals with the optimal process design of hydroforming process using the genetic algorithm and neural network. An optimization technique is used in order to minimize the tube thickness variation by determining the optimal loading path in the tube expansion forming and the tube T-shape forming process.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5669-5684
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• 2018

The new mobile edge network architecture has been required for an increasing amount of traffic, quality requirements, advanced driver assistance system for autonomous driving and new cloud computing demands on highway. This article proposes a hierarchical cloud computing architecture to enhance performance by using adaptive data load distribution for buses that play the role of edge computing server. A vehicular dynamic cloud is based on wireless architecture including Wireless Local Area Network and Long Term Evolution Advanced communication is used for data transmission between moving buses and cars. The main advantages of the proposed architecture include both a reduction of data loading for top layer cloud server and effective data distribution on traffic jam highway where moving vehicles require video on demand (VOD) services from server. Through the description of real environment based on NS-2 network simulation, we conducted experiments to validate the proposed new architecture. Moreover, we show the feasibility and effectiveness for the connected car media service on highway.