• Proceedings of the KSR Conference
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• 2011.10a
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• pp.607-613
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• 2011

This paper reviews search methods of multiple reasonable paths to implement multimodal public transportation network of Seoul. Such a large scale multimodal public transportation network as Seoul, the computation time of path finding algorithm is a key and the result of path should reflect route choice behavior of public transportation passengers. Search method of alternative path is divided by removing path method and deviation path method. It analyzes previous researches based on the complexity of algorithm for large-scale network. Applying path finding algorithm in public transportation network, transfer and loop constraints must be included to be able to reflect real behavior. It constructs the generalized cost function based on the smart card data to reflect travel behavior of public transportation. To validate the availability of algorithm, experiments conducted with Seoul metropolitan public multimodal transportation network consisted with 22,109 nodes and 215,859 links by using the deviation path method, suitable for large-scale network.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.29A no.11
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• pp.70-77
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• 1992

Generally, the conventional tracking algorithms are very limited in the practical applications because of that the computation load is exponentially increased as the number of targets being tracked is increase. Recently, to overcome this kind of limitation, some new tracking methods based on neural network algorithms which have learning and parallel processing capabilities are introduced. By application of neural networks to multi-target tracking problems, the tracking system can be made computationally independent of the number of objects being tracked, through their characteristics of massive parallelism and dense interconnectivity. In this paper, a new neural network tracking algorithm, which has capability of adaptive target tracking with little increase of the amount of calculation under the clutter and noisy environments, is suggested and the possibility of real-time multi-target tracking system based on neural networks is also demonstrated through some good computer simulation results.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.11a
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• pp.421-426
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• 2006

We propose a new algorithm for K-medoids clustering which runs like the K-means clustering algorithm and test several methods for selecting initial medoids. The proposed algorithm calculates similarity matrix once and uses it for finding new medoids at every iterative step. To evaluate the proposed algorithm we use real and artificial data and compare with the clustering results of other algorithms in terms of three performance measures. Experimental results show that the proposed algorithm takes the reduced time in computation with comparable performance as compared to the Partitioning Around Medoids.

• Journal of information and communication convergence engineering
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• v.10 no.2
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• pp.205-209
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• 2012

To manufacture a real time digital holographic display system capable of being applied to next-generation television, it is important to rapidly generate a digital hologram. In this paper, we analyze digital hologram rendering based on a computer computation scheme. We analyze previous recursive methods to identify regularity between the depth-map image and the digital hologram.

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.278-281
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• 2000

Matching pursuit algorithm is a signal expansion technique whose efficiency for motion compensated residual image has already been demonstrated in the MPEG-4 framework. However, one of the practical concerns related to applying matching pursuit algorithm to real-time scalable video coding is its massive computation required for finding dictionary elements. In this respective, this paper proposes a fast algorithm, which is composed of three sub-methods. The first method utilizes the property of symmetry in 1-D dictionary element and the second uses mathematical elimination of inner product calculation in advance, and the last one uses frequency property of 2-D dictionary. Experimental results show that our algorithm needs about 30% computational load compared to the conventional fast algorithm using separable property of 2-D gabor dictionary with negligible quality degradation.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.2
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• pp.429-435
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• 2010

Three dimensional (3-D) object detection and pose estimation from a single view query image has been an important issue in various fields such as medical applications, robot vision, and manufacturing automation. However, most of the existing methods are not appropriate in a real time environment since object detection and pose estimation requires extensive information and computation. In this paper, we present a fast 3-D object detection and pose estimation scheme based on surrounding camera view-changed images of objects. Our scheme has two parts. First, we detect images similar to the query image from the database based on the shape feature, and calculate candidate poses. Second, we perform accurate pose estimation for the candidate poses using the scale invariant feature transform (SIFT) method. We earned out extensive experiments on our prototype system and achieved excellent performance, and we report some of the results.

• Journal of Information Processing Systems
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• v.7 no.1
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• pp.63-74
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• 2011

As a powerful and flexible processor, the Graphic Processing Unit (GPU) can offer a great faculty in solving many high-performance computing applications. Sweep3D, which simulates a single group time-independent discrete ordinates (Sn) neutron transport deterministically on 3D Cartesian geometry space, represents the key part of a real ASCI application. The wavefront process for parallel computation in Sweep3D limits the concurrent threads on the GPU. In this paper, we present multi-dimensional optimization methods for Sweep3D, which can be efficiently implemented on the finegrained parallel architecture of the GPU. Our results show that the overall performance of Sweep3D on the CPU-GPU hybrid platform can be improved up to 4.38 times as compared to the CPU-based implementation.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.472-477
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• 2010

자동유량측정시설은 연속적인 유량자료를 측정하기 위해 운영되는 실시간-무인화 유량측정 시스템이다. 현재 국내 외에서 유량측정을 위해 설치 및 운영되고 있는 자동유량측정시설은 대부분 초음파유속계를 이용하고 있는는데, 초음파유속계는 유속을 측정하는 방식에 따라 크게 도플러방식 초음파유속계(ADVM, Acoustic Doppler Velocity Meter)와 이동시간차방식 초음파유속계(UVM, Ultrasonic Velocity Meter)로 분류된다. 본 연구에서는 각 방식별로 유량산정방법을 개선하기 위해 설치 운영 중인 자동유량측정시설 중 고령교 지점의 ADVM 방식과 여주, 적성 지점의 UVM 방식을 대상으로 측정성과에 대한 문제점을 분석하고 통계적인 방법을 통해 오측유속을 제거하거나 방식별 또는 지점별 특성을 고려하여 적절한 유량산정방법을 적용하였다. 그 결과 3개 지점에 대한 검증유량과의 평균 상대오차율은 모두 10% 이내로 나타났다.

• Nuclear Engineering and Technology
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• v.54 no.5
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• pp.1747-1753
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• 2022

Compton imaging is the main method for locating radioactive hot spots emitting high-energy gamma-ray photons. In particular, this imaging method is crucial when the photon energy is too high for coded-mask aperture imaging methods to be effective or when a large field of view is required. Reconstruction of the photon source requires advanced Compton event processing algorithms to determine the exact position of the source. In this study, we introduce a novel method based on a Deep Learning algorithm with a Convolutional Neural Network (CNN) to perform Compton imaging. This algorithm is trained on simulated data and tested on real data acquired with Caliste, a single planar CdTe pixelated detector. We show that performance in terms of source location accuracy is equivalent to state-of-the-art algorithms, while computation time is significantly reduced and sensitivity is improved by a factor of ~5 in the Caliste configuration.

• Journal of Korea Water Resources Association
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• v.48 no.10
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• pp.845-855
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• 2015

Studies of optimizing pump operation in water distribution networks (WDN) are receiving spotlight in recent days. However, the water networks are quite complex including thousands of or even tens of thousands of nodes and pipes, thus simulation time is an issue. In some cases, implementing a computer model for pump operation decisions is restrictive due to intensive computation time. To that end, it is necessary to reduce the simulation time of water networks by simplifying the network layout. In this study, WDN skeletonization approaches were suggested and applied to a real water transmission network in South Korea. In skeletonizing the original network, it was constrained to match the water pressure and water age in the same junction locations to maintain the hydraulic and water quality characteristics in the skeletonized network. Using the skeletonization approaches suggested in this study, it is expected to reduce the simulation time of WDN and apply for developing a computer module of WDN real-time optimal operation.