• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.303-304
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• 2008

For the state estimation problem, the weighted least squares (WLS) method and the fast decoupled method are widely used at present. However, these algorithms can converge to local optimal solutions. Recently, modern heuristic optimization methods such as Particle Swarm Optimization (PSO) have been introduced to overcome the disadvantage of the classical optimization problem. However, heuristic optimization methods based on populations require a lengthy computing time to find an optimal solution. In this paper, we used PSO to search for the optimal solution of state estimation in power systems. To overcome the shortcoming of heuristic optimization methods, we proposed parallel processing of the PSO algorithm based on the PC cluster system. the proposed approach was tested with the IEEE-118 bus systems. From the simulation results, we found that the parallel PSO based on the PC cluster system can be applicable for power system state estimation.

• Journal of the Korean Operations Research and Management Science Society
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• v.30 no.1
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• pp.27-41
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• 2005

This paper considers identical parallel machine scheduling problem. Each job has a processing time. due date. weight and family type. If a different type of job is followed by prior job. a family setup is incurred. A two phased heuristic is presented for minimizing the sum of weighted tardiness. In the first phase. using roiling horizon technique. group each job into same family and schedule each family. In the second phase. assign each job to machines using schedule obtained in the first phase. Extensive computational experiments and comparisons among other algorithms are carried out to show the efficiency of the proposed algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.2A
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• pp.100-107
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• 2011

Algorithms based on the QR decomposition of the equivalent space-time channel matrix have been proved useful in the detection of V-BLAST systems. Especially, the parallel detection (PD) algorithm offers ML approaching performance up to 4 transmit antennas with reasonable complexity. We show that when directly applied to STBCs, the PD algorithm may suffer a rather significant SNR degradation over ML detection, especially at high SNRs. However, simply extending the PD algorithm to allow p ${\geq}$ 2 candidate layers, i.e. p-PD, regains almost all the loss but only at a significant increase in complexity. Here, we propose a simplification to the p-PD algorithm specific to STBCs without a corresponding sacrifice in performance. The proposed algorithm results in significant complexity reductions for moderate to high order modulations.

• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.21-24
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• 2002

This paper presents a new method for the motion detection of vehicles using hierarchical motion estimation and parallel processing. It captures the road image by using a CMOS sensor. It divides the captured image into small blocks and detects the motion of each block by using a block-matching method which is based on a hierarchical motion estimation and parallel processing for the real-time processing. The parallelism is achieved by using the pipeline and the data flow technique. The proposed method has been implemented with an embedded system. Experimental results show that the proposed method detects the motion of vehicles in real-time.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.1
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• pp.65-73
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• 2015

This paper considers a parallel-machine scheduling problem with dedicated and common processing machines using GA (Genetic Algorithm). Non-identical setup times, processing times and order lot size are assumed for each machine. The GA is proposed to minimize the total-tardiness objective measure. In this paper, heuristic algorithms including EDD (Earliest Due-Date), SPT (Shortest Processing Time) and LPT (Longest Processing Time) are compared with GA. The effectiveness and suitability of the GA are derived and tested through computational experiments.

• ETRI Journal
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• v.37 no.4
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• pp.667-676
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• 2015

The third-party FFT IP cores available in today's markets do not provide the desired speed demands for optical communication. This study deals with the design and implementation of a 256-point Radix-4 100 Gbit/s FFT, where computational steps are reconsidered and optimized for high-speed applications, such as radar and fiber optics. Alternative methods for FFT implementation are investigated and Radix-4 is decided to be the optimal solution for our fully parallel FPGA application. The algorithms that we will implement during the development phase are to be tested on a Xilinx Virtex-6 FPGA platform. The proposed FFT core has a fully parallel architecture with a latency of nine clocks, and the target clock rate is 312.5 MHz.

• Journal of Korean Institute of Industrial Engineers
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• v.33 no.3
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• pp.329-338
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• 2007

This paper presents a dispatching algorithm for nonidentical parallel machines problem considering rework, sequence dependent setup times and release times. For each pair of a machine and a job type, rework probability of each job on a machine can be known through historical data acquisition. The heuristic scheduling scheme named by EDDR (Earliest Due Date with Rework probability) algorithm is proposed in this paper making use of the rework probability. The proposed dispatching algorithm is measured by two objective function value: 1) total tardiness and 2) the number of reworked jobs, respectively. The extensive computational results show that the proposed algorithm gives very efficient schedules superior to the existing dispatching algorithms.

• Journal of the Korea Safety Management & Science
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• v.25 no.2
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• pp.1-8
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• 2023

This paper is proposing a novel machine scheduling model for the unrelated parallel machine scheduling problem without setup times to minimize the total completion time, also known as "makespan". This problem is a NP-complete problem, and to date, most approaches for real-life situations are based on the operator's experience or simple heuristics. The new model based on the Memetic Algorithm, which was proposed by P. Moscato in 1989, is a hybrid algorithm that includes genetic algorithm and local search optimization. The new model is tested on randomly generated datasets, and is compared to optimal solution, and four scheduling models; three rule-based heuristic algorithms, and a genetic algorithm based scheduling model from literature; the test results show that the new model performed better than scheduling models from literature.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.32A no.4
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• pp.93-102
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• 1995

This paper introduces a general purpose Associative Processor(AP) which is very efficient for search-oriented applications. The proposed architecture consists of three main functional blocks: Content-Addressable Memory(CAM) arry, row logic, and control section. The proposed AP is a Single-Instruction, Multiple-Data(SIMD) device based on a CAM core and an array of high speed processors. As an application for the proposed hardware, we present a parallel algorithm to solve a global routing problem in the layout process utilizing the processing capabilities of a rudimentary logic and the selective matching and writing capability of CAMs, along with basic algorithms such a minimum(maximum) search, less(greater) than search and parallel arithmetic. We have focused on the simultaneous minimization of the desity of the channels and the wire length by sedking a less crowded channel with shorter wire distance. We present an efficient mapping technique of the problem into the CAM structure. Experimental results on difficult examples, on randomly generated data, and on benchmark problems from MCNC are included.

• Journal of the Korean Data and Information Science Society
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• v.24 no.5
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• pp.989-998
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• 2013

We cannot analyze big data, which attracts recent attentions in industry and academy, by batch processing algorithms developed in data mining because big data, by definition, cannot be uploaded and processed in the memory of a single system. So an imminent issue is to develop various leaning algorithms so that they can be applied to big data. In this paper, we review various algorithms for support vector machines in the literature. Particularly, we introduce online type and parallel processing algorithms that are expected to be useful in big data classifications and compare the strengths, the weaknesses and the performances of those algorithms through simulations for linear classification.