• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.1033-1039
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• 2005

Parallel and distributed simulation is concerned with the efficient execution of large-scale discrete event simulation models on multiprocessors and distributed platforms. After the development of WWW, many efforts in the parallel and distributed simulation have been made for modeling, particularly building simulation languages and creating model libraries that can be assembled and executed over WWW. However, web-based parallel and distributed simulation is restricted by heterogeneous computing environments. Recently, the advent of XML and web services technology has made these efforts enter upon a new phase. Especially, the web services as a distributed information technology have demonstrated powerful capabilities for scalable interoperation of heterogeneous systems. This paper aims to develop and evaluate the parallel and distributed simulation using the web services technology. In particular, a prototype multi-pass simulation framework is implemented using Java-based web services technology. It focuses on the efficiency of multi-pass simulation used for optimization through the distribution of simulation replication to several simulation service providers. The development of parallel and distributed simulation using web services will help solve efficiently large-scale problems and also guarantee interoperability among heterogeneous networked systems.

• Journal of the Korean Society for information Management
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• v.34 no.4
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• pp.33-57
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• 2017

This study aims to introduce an emerging prescriptive analytics method and suggest its efficient application to a category-based service system. Prescriptive analytics method provides the whole process of analysis and available alternatives as well as the results of analysis. To simulate the process of optimization, large scale journal articles have been collected and categorized by classification scheme. In the process of applying the concept of prescriptive analytics to a real system, we have fused a dynamic automatic-categorization method for large scale documents and intellectual structure analysis method for scholarly subject fields. The test result shows that some optimized scenarios can be generated efficiently and utilized effectively for reorganizing the classification-based service system.

• 유체기계공업학회:학술대회논문집
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• 2006.08a
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• pp.33-36
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• 2006

As the computing environment is rapidly improved, the interests of CFD are gradually focused on large-scale computation over complex geometry. Keeping pace with the trend, essential computational tools to obtain solutions of complex aerospace flow analysis and design problems are examined. An accurate and efficient flow analysis and design codes for large-scale aerospace problem are presented in this work. With regard to original numerical schemes for flow analysis, high-fidelity flux schemes such as RoeM, AUSMPW+ and higher order interpolation schemes such as MLP (Multi-dimensional Limiting Process) are presented. Concerning the grid representation method, a general-purpose basis code which can handle multi-block system and overset grid system simultaneously is constructed. In respect to design optimization, the importance of turbulent sensitivity is investigated. And design tools to predict highly turbulent flows and its sensitivity accurately by fully differentiating turbulent transport equations are presented. Especially, a new sensitivity analysis treatment and geometric representation method to resolve the basic flow characteristics are presented. Exploiting these tools, the capability of the proposed approach to handle complex aerospace simulation and design problems is tested by computing several flow analysis and design problems.

• Journal of the Korean Society for Precision Engineering
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• v.29 no.2
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• pp.139-146
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• 2012

A multi-tasking machine tool for large scale marine engine crankshafts has been developed together with design technologies for its special devices. Since work pieces, that is, crankshafts to be machined are big and heavy; weight of over 100 tons, length of 10 m long, and diameter of over 3.5 m, several special purpose core devices are necessarily developed such as PTD (Pin Turning Device) for machining eccentric pin parts, face place and steady rest for chucking and resting heavy work pieces. PTD is a unique special purpose device of open-and-close ring typed structure equipped with revolving ring spindle for machining eccentric pins apart from journal. In order to achieve high rigidity of the machine tool, structural design optimization using TMSA (Taguch Method based Sequential Algorithm) has been completed with FEM structural analysis, and a hydrostatic bearing system for the PTD has been developed with theoretical hydrostatic analysis.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.12
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• pp.5307-5327
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• 2016

Restricted by finite battery energy, traditional wireless sensor networks (WSNs) can only maintain for a limited period of time, resulting in serious performance bottleneck in long-term deployment of WSN. Fortunately, the advancement in the wireless energy transfer technology provides a potential to free WSNs from limited energy supply and remain perpetual operational. A mobile charger called wireless charging vehicle (WCV) is employed to periodically charge each sensor node and keep its energy level above the minimum threshold. Aiming at maximizing the ratio of the WCV's vocation time over the cycle time as well as guaranteeing the perpetual operation of networks, we propose a feasible and optimal solution to this issue within the context of a real-time large-scale deployed WSN. First, we develop two different types of charging cycles: initialization cycles and renewable cycles and give relevant algorithms to construct these two cycles for each sensor node. We then formulate the optimization problem into an optimal construction algorithm and prove its correctness through theoretical analysis. Finally, we conduct extensive simulations to demonstrate the effectiveness of our proposed algorithms.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.2
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• pp.305-314
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• 2017

Most of the existing researches on systemwide optimization of generator maintenance scheduling do not consider the equivalent operating hours(EOHs) mainly due to the difficulties of calculating the EOHs of the CCGTs in the large scale system. In order to estimate the EOHs not only the operating hours but also the number of start-up/shutdown during the planning period should be estimated, which requires the mathematical model to incorporate the economic dispatch model and unit commitment model. The model is inherently modelled as a large scale mixed-integer nonlinear programming problem and the computation time increases exponentially and intractable as the system size grows. To make the problem tractable, this paper proposes an EOH calculation based on demand grouping by K-means clustering algorithm. Network congestion is also considered in order to improve the accuracy of EOH calculation. This proposed method is applied to the actual Korean electricity market and compared to other existing methods.

• Journal of the Korean Society for Precision Engineering
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• v.11 no.4
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• pp.138-147
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• 1994

Performance management of computer networks is intended to improve a given network performance in order for more efficient information exchange between subsystems of an integrated large-scale system. Improtance of performance management is growing as many function of the large- scale system depend on the quality of communication services provided by the network. The role of performance management is to manipulate the adjustable protocol parameters on line so that the network can adapt itself to a dynamic environment. This can be divided into two subtasks : performance evaluation to find how changes in protocol parameters affect the network performance and decision making to detemine the magnitude and direction of parameter adjustment. This paper is the second part of the two papers focusing on conceptual design, development, and evaluation of performance management for token bus networks. This paper specifically deals with the task of decision making which utilizes the principles of stochastic optimization and learning automata. The developed algorithm can adjuxt four timer settings of a token bus protocol based on the result of performance evaluation. The overall performance management has been evaluated for its efficacy on a network testbed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.4
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• pp.1110-1127
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• 2022

To improve the training efficiency and generalization performance of a support vector machine (SVM) in a large-scale set, an optimal SVM learning method based on adaptive sparse sampling and the granularity shift factor is presented. The proposed method combines sampling optimization with learner optimization. First, an adaptive sparse sampling method based on the potential function density clustering is designed to adaptively obtain sparse sampling samples, which can achieve a reduction in the training sample set and effectively approximate the spatial structure distribution of the original sample set. A granularity shift factor method is then constructed to optimize the SVM decision hyperplane, which fully considers the neighborhood information of each granularity region in the sparse sampling set. Experiments on an artificial dataset and three benchmark datasets show that the proposed method can achieve a relatively higher training efficiency, as well as ensure a good generalization performance of the learner. Finally, the effectiveness of the proposed method is verified.

• Smart Structures and Systems
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• v.30 no.6
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• pp.583-599
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• 2022

In this paper, a multi-objective wireless sensor network configuration optimization method is proposed. The proposed method aims to determine the optimal information and lifespan wireless sensor network for structural health monitoring of large-scale infrastructures. In particular, cluster-based wireless sensor networks with multi-type of sensors are considered. To optimize the lifetime of the wireless sensor network, a cluster-based network optimization algorithm that optimizes the arrangement of cluster heads and base station is developed. On the other hand, based on the Bayesian inference, the uncertainty of the estimated parameters can be quantified. The coefficient of variance of the estimated parameters can be obtained, which is utilized as a holistic measure to evaluate the estimation accuracy of sensor configurations with multi-type of sensors. The proposed method provides the optimal wireless sensor network configuration that satisfies the required estimation accuracy with the longest lifetime. The proposed method is illustrated by designing the optimal wireless sensor network configuration of a cable-stayed bridge and a space truss.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.33 no.8
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• pp.34-41
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• 2005

A parallel computing technique was applied to large scale structure analysis or aerodynamic design and it is a essential element in reducing the huge computation time for large scale design problem. We can use a many computers for reducing the analysis time of multidisciplinary design optimization. But previous MDO frameworks can not support a parallel design process technique so still existing which calls an analysis program continuously. In this paper, We developed a MDO framework(MLR) which supports a parallel design process to solve sequential analysis call. Finally, three sample cases are presented to show the efficiency of design time using the suggested MDO framework.