• ETRI Journal
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• v.38 no.3
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• pp.435-443
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• 2016

In this paper, an efficient beam tracking algorithm for a regularized zero-forcing (RZF) approach in slowly fading multiple-input and single-output (MISO) broadcast channels is considered. By modifying an RZF equation, an RZF beam tracking algorithm is proposed using matrix perturbation theory. The proposed algorithm utilizes both beams from the previous time step and channel difference (between the previous and current time steps) to calculate the RZF beams. The tracking performance of the proposed algorithm is analyzed in terms of the mean square error (MSE) between a tracking approach and an exact recomputing approach, and in terms of the additional MSE caused by the beam tracking error at the receiver. Numerical results show that the proposed algorithm has almost the same performance as the exact recomputing approach in terms of the sum rate.

• Korean Management Science Review
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• v.14 no.1
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• pp.77-85
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• 1997

This paper proposes an approach for individual selection in genetic algorithms to improve problem solving efficiency and effectiveness. To investigate the utility of combining simulated annealing with genetic algorithm, two experiment are conducted that compare both the conventional genetic algorithm and suggested approach. Result indicated that suggested approach significantly reduced the required time to find optimal solution in moderate-sized problems under the conditions studied. It is also found that quality of the solutions generated by suggested approach in large- sized problems is greatly improved.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.94-96
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• 2021

In this paper, we develop a new navigation algorithm for industrial mobile robots to arrive at the destination in unknown environment. To achieve this, we suggest a navigation algorithm that combines Dynamic Window Approach (DWA) and Dijkstra path planning algorithm. We compare Local Dynamic Window Approach (LDWA), Global Dynamic Window Approach(GDWA), Rapidly-exploring Random Tree (RRT) Algorithm. The navigation algorithm using Dijkstra algorithm combined with LDWA and GDWA makes mobile robots to reach the destination. and obstacles faced during the path planning process of LDWA and GDWA. Then, we compare on time taken to arrive at the destination, obstacle avoidance and computation complexity of each algorithm. To overcome the limitation, we seek ways to use the optimized navigation algorithm for industrial use.

• Journal of information and communication convergence engineering
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• v.11 no.4
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• pp.298-306
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• 2013

The scale invariant feature transform (SIFT) is an effective algorithm used in object recognition, panorama stitching, and image matching. However, due to its complexity, real-time processing is difficult to achieve with current software approaches. The increasing availability of parallel computers makes parallelizing these tasks an attractive approach. This paper proposes a novel parallel approach for SIFT algorithm implementation using a block filtering technique in a Gaussian convolution process on the SIMD Pixel Processor. This implementation fully exposes the available parallelism of the SIFT algorithm process and exploits the processing and input/output capabilities of the processor, which results in a system that can perform real-time image and video compression. We apply this implementation to images and measure the effectiveness of such an approach. Experimental simulation results indicate that the proposed method is capable of real-time applications, and the result of our parallel approach is outstanding in terms of the processing performance.

• Journal of Advanced Marine Engineering and Technology
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• v.22 no.5
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• pp.687-692
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• 1998

Test pattern generation is one of most difficult problems encountered in automating the design of logic circuits. The goal is to obtain the highest fault coverage with the minimum number of test patterns for a given circuit and fault set. although there have been many deterministic algorithms and heuristics the problem is still highly complex and time-consuming. Therefore new approach-es are needed to augment the existing techniques. This paper considers the problem of test pattern improvement for combinational circuits as a restricted subproblem of the test pattern generation. The problem is to maximize the fault coverage with a fixed number of test patterns for a given cir-cuit and fault set. We propose a new approach by use of a genetic algorithm. In this approach the genetic algorithm evolves test patterns to improve their fault coverage. A fault simulation is used to compute the fault coverage of the test patterns Experimental results show that the genetic algorithm based approach can achieve higher fault coverages than traditional techniques for most combinational circuits. Another advantage of the approach is that the genetic algorithm needs no detailed knowledge of faulty circuits under test.

• Nuclear Engineering and Technology
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• v.55 no.5
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• pp.1527-1532
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• 2023

The noise reduction algorithm using the non-local means (NLM) approach is very efficient in nuclear medicine imaging. In this study, the applicability of the NLM noise reduction algorithm in single-photon emission computed tomography (SPECT) images with a brain phantom and the optimization of the NLM algorithm by changing the smoothing factors according to various reconstruction methods are investigated. Brain phantom images were reconstructed using filtered back projection (FBP) and ordered subset expectation maximization (OSEM). The smoothing factor of the NLM noise reduction algorithm determined the optimal coefficient of variation (COV) and contrast-to-noise ratio (CNR) results at a value of 0.020 in the FBP and OSEM reconstruction methods. We confirmed that the FBP- and OSEM-based SPECT images using the algorithm applied with the optimal smoothing factor improved the COV and CNR by 66.94% and 8.00% on average, respectively, compared to those of the original image. In conclusion, an optimized smoothing factor was derived from the NLM approach-based algorithm in brain SPECT images and may be applicable to various nuclear medicine imaging techniques in the future.

• Transactions of Materials Processing
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• v.5 no.4
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• pp.269-280
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• 1996

A new approach to die shape optimal design in extrusion is presented. The approach consists of a FEM analysis model to predict the value of the objective function a design model to relate the die profile with the design variables and a genetic algorithm based optimaization procedure. The approach was described in detail with emphasis on our modified micro genetic algorithm. Comparison with theoretical solutions was made to examine the validity of the predicted optimal die shapes. The approach was then applied to revealing the optimal die shapes with regard to various objective functions including those for which the design sensitivities can not be deter-mined analytically.

• Proceedings of the IEEK Conference
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• 2000.11d
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• pp.251-254
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• 2000

This paper proposes an enhanced algorithm for person identification system utilizing hand vein pattern. The conventional algorithm does not cope with distortion caused by image rotation caused by misplaced hands on the imaging device. A straightforward approach to consider the rotaional compensation required too much computational load, thus, we devised an approach to expect the rotation direction along with image translation, reducing the compuational requirement dramatically In this paper, we present the details of the algorithm with experimental results with the new algorithm.

• Journal of the Korea Institute of Military Science and Technology
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• v.24 no.1
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• pp.61-69
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• 2021

Recently, autonomous navigation technology is actively being developed due to the increasing demand of an unmanned surface vehicle(USV). Local planning is essential for the USV to safely reach its destination along paths. the dynamic window approach(DWA) algorithm is a well-known navigation scheme as a local path planning. However, the existing DWA algorithm does not consider path line tracking, and the fixed weight coefficient of the evaluation function, which is a core part, cannot provide flexible path planning for all situations. Therefore, in this paper, we propose a new DWA algorithm that can follow path lines in all situations. Fixed weight coefficients were trained using reinforcement learning(RL) which has been actively studied recently. We implemented the simulation and compared the existing DWA algorithm with the DWA algorithm proposed in this paper. As a result, we confirmed the effectiveness of the proposed algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.1454-1466
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• 2016

This paper implements an estimated ranking algorithm of closeness centrality measures in large-scale workflow-supported social networks. The traditional ranking algorithms for large-scale networks have suffered from the time complexity problem. The larger the network size is, the bigger dramatically the computation time becomes. To solve the problem on calculating ranks of closeness centrality measures in a large-scale workflow-supported social network, this paper takes an estimation-driven ranking approach, in which the ranking algorithm calculates the estimated closeness centrality measures by applying the approximation method, and then pick out a candidate set of top k actors based on their ranks of the estimated closeness centrality measures. Ultimately, the exact ranking result of the candidate set is obtained by the pure closeness centrality algorithm [1] computing the exact closeness centrality measures. The ranking algorithm of the estimation-driven ranking approach especially developed for workflow-supported social networks is named as RankCCWSSN (Rank Closeness Centrality Workflow-supported Social Network) algorithm. Based upon the algorithm, we conduct the performance evaluations, and compare the outcomes with the results from the pure algorithm. Additionally we extend the algorithm so as to be applied into weighted workflow-supported social networks that are represented by weighted matrices. After all, we confirmed that the time efficiency of the estimation-driven approach with our ranking algorithm is much higher (about 50% improvement) than the traditional approach.