• Journal of KIISE:Computer Systems and Theory
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• v.31 no.5_6
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• pp.257-267
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• 2004

Most algorithms for handling distributed deadlock problem in the generalized request model use the diffusing computation technique where propagation of probes and backward propagation of replies carrying dependency information between processes are both required to detect deadlock Since fast deadlock detection is critical, we propose an algorithm that lets probes rather than replies carry the information required for deadlock detection. This helps to remove the backward propagation of replies and reduce the time cost for deadlock detection to almost half of that of the existing algorithms. Moreover, the proposed algorithm is extended to deal with concurrent executions, which achieves further improvement of deadlock detection time, whereas the current algorithms deal only with a single execution. We compare the performance of the proposed algorithm with that of the other algorithms through simulation experiments.

• The Journal of Korea Robotics Society
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• v.17 no.3
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• pp.373-380
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• 2022

This paper considers a method of fast correspondence matching for iterative closest point (ICP) algorithm. In robotics, the ICP algorithm and its variants have been widely used for pose estimation by finding the translation and rotation that best align two point clouds. In computational perspectives, the main difficulty is to find the correspondence point on the reference point cloud to each observed point. Jump-table-based correspondence matching is one of the methods for reducing computation time. This paper proposes a method that corrects errors in an existing jump-table-based correspondence matching algorithm. The criterion activating the use of jump-table is modified so that the correspondence matching can be applied to the situations, such as point-cloud registration problems with highly curved surfaces, for which the existing correspondence-matching method is non-applicable. For demonstration, both hardware and simulation experiments are performed. In a hardware experiment using Hokuyo-10LX LiDAR sensor, our new algorithm shows 100% correspondence matching accuracy and 88% decrease in computation time. Using the F1TENTH simulator, the proposed algorithm is tested for an autonomous driving scenario with 2D range-bearing point cloud data and also shows 100% correspondence matching accuracy.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.7
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• pp.673-679
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• 2012

This paper introduces robust tracking algorithm for fast and erratic moving object. CAMSHIFT algorithm has less computation and efficient performance for object tracking. However, the method fails to track a object if it moves out of search window by fast velocity and/or large movement. The size of the search window in CAMSHIFT algorithm should be selected manually also. To solve these problems, we propose an efficient prediction technique for fast movement of object using Kalman Filter with automatic initial setting and variable configuration technique for search window. The proposed method is compared to the traditional CAMSHIFT algorithm for searching and tracking performance of objects on test image frames.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2013.06a
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• pp.173-175
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• 2013

In this paper, we propose a fast and low-complexity Motion Estimation (ME) algorithm for High Efficiency Video Coding (HEVC). Motion estimation occupies 77~81% of the amount of computation in HEVC. After all, the main key of codec implementation is to find a fast and low-complexity motion estimation algorithm and architecture. The proposed algorithm uses only 1% of the amount of operations compared to full search algorithm while maintaining compression performance with slight loss of 0.6% (BDBR).

• Journal of information and communication convergence engineering
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• v.12 no.2
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• pp.128-134
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• 2014

In this paper, we present a graphics processing unit (GPU)-based matching technique for the purpose of fast feature matching between different images. The scale invariant feature transform algorithm developed by Lowe for various feature matching applications, such as stereo vision and object recognition, is computationally intensive. To address this problem, we propose a matching technique optimized for GPUs to perform computations in less time. We optimize GPUs for fast computation of keypoints to make our system quick and efficient. The proposed method uses a self-organizing map feature matching technique to perform efficient matching between the different images. The experiments are performed on various image sets to examine the performance of the system under varying conditions, such as image rotation, scaling, and blurring. The experimental results show that the proposed algorithm outperforms the existing feature matching methods, resulting in fast feature matching due to the optimization of the GPU.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.172.2-172
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• 2001

We introduce a new stable feedback controller eventually converges to a conventional SDRE(State Dependent Riccati Equation) based optimal (suboptimal) controller. On conventional SDRE, the optimal control input should be obtained by backward integration of the SDRE at each control point. The proposed controller is given by direct forward integration of a proposed SDRE. This fact enables fast computation and easy implementation. On concerning a state dependent system, the proposed controller may be a candidate to the conventional SDRE based optimal controller if the system is slow varying with states. Though the controller is fast and easy to implement it is not able to cope with a fast varying system. We introduce an optimality index, which indicates how far the proposed controller is deviated from the solution of the convectional SDRE. If the index escapes a ...

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.9
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• pp.77-83
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• 1997

We present a fast adaptive block matching algorithm using variable search area and subsampling to estimate motion vector more exactly. In the presented method, the block is classified into one of three motion categories: zero motion vector block, medium-motion bolck or high-motion block according to mean absolute difference of the block. By the simulation, the computation amount of the presented methoe comparable to three step search algorithm and new three step search algorithm. In the fast image sequence, the PSNR of our algorithm increased more than TSS and NTSS, because our algorithm estimated motion vector more accurately.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.20-22
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• 2007

The H.264/AVC standard developed by the joint Video Team (JVT) provides better coding efficiency than previous standards. The new emerging H.264/AVC employs variable block size motion estimation using multiple reference frame with 1/4-pel MV(Motion Vector) accuracy. These techniques are a important feature to accomplish higher coding efficiency. However, these techniques are increased overall computational complexity. To overcome this problem, this paper proposes advanced fast mode decision suited for variable block size by classifying inter mode based on Rate Distortion Optimization(RDO) technique. Proposed algorithm is going to use to implement H/W structure for fast mode decision. The experimental results shows that the proposed algorithm provides significant reduction computational complexity without any noticeable coding loss and additional computation. Entire computational complexity is decreased about 30%.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.9
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• pp.540-546
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• 2003

A fast pattern classification algorithm with Cellular Nonlinear Network-based dynamic programming is proposed. The Cellular Nonlinear Networks is an analog parallel processing architecture and the dynamic programing is an efficient computation algorithm for optimization problem. Combining merits of these two technologies, fast pattern classification with optimization is formed. On such CNN-based dynamic programming, if exemplars and test patterns are presented as the goals and the start positions, respectively, the optimal paths from test patterns to their closest exemplars are found. Such paths are utilized as aggregating keys for the classification. The algorithm is similar to the conventional neural network-based method in the use of the exemplar patterns but quite different in the use of the most likely path finding of the dynamic programming. The pattern classification is performed well regardless of degree of the nonlinearity in class borders.

• Nuclear Engineering and Technology
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• v.42 no.5
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• pp.520-545
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• 2010

This paper presents the neutronics modeling capabilities of the fast reactor simulation system SHARP, which ANL is developing as part of the U.S. DOE's NEAMS program. We discuss the three transport solvers (PN2ND, SN2ND, and MOCFE) implemented in the UNIC code along with the multigroup cross section generation code $MC^2$-3. We describe the solution methods and modeling capabilities, and discuss the improvement needs for each solver, focusing on massively parallel computation. We present the performance test results against various benchmark problems and ZPR-6 and ZPPR critical experiments. We also discuss weak and strong scalability results for the SN2ND solver on the ZPR-6 critical assembly benchmarks.