• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.207-208
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• 2008

In this paper, we propose an efficient algorithm for reducing the complexity of LDPC code decoding by using node monitoring (NM). This NM algorithm is based on a new node-threshold method, and the message passing algorithm. This algorithm was simulated in order to verify its efficiency. Simulation results show that the complexity of our NM algorithm is improved to about 10%, compared with well-known methods.

• The Journal of the Korea institute of electronic communication sciences
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• v.6 no.1
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• pp.20-26
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• 2011

In this paper, we propose an efficient algorithm for reducing the complexity of LDPC code decoding by using node monitoring (NM) and Piecewise Linear Function Approximation (NP). This NM algorithm is based on a new node-threshold method, and the message passing algorithm. Piecewise linear function approximation is used to reduce the complexity for more. This algorithm was simulated in order to verify its efficiency. Simulation results show that the complexity of our NM algorithm is reduced to about 20%, compared with thoes of well-known method.

• Journal of computational fluids engineering
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• v.1 no.1
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• pp.26-34
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• 1996

The performance of the SIMPLE, SIMPLE-C and PISO algorithms for the treatment of the pressure-velocity coupling in fluid flow problems were examined by comparing the computational effort required to obtain the same level of the convergence. Example problems are circular duct and 90-degree bent square-duct. For circular duct case, laminar and turbulent flow were computed. For 90-degree bent square-duct case, laminar flow was simulated by the time-marching method as well as the iterative method. The convergence speed of the other two algorithms are not always superior to SIMPLE algorithm. SIMPLE algorithm is faster than SIMPLE-C algorithm in the simple laminar flow calculations. The application of the PISO algorithm in three dimensional general coordinates is not so effective as in two-dimensional ones. Since computational time of PISO algorithm is increased at each time step(or iterative step) in three dimension, the total convergence speed is not decreased. But PISO algorithm is stable for large time step by using time marching method,.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.1
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• pp.15-24
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• 1998

This paper proposes a new template matching algorithm and its chip design. The CC and SAD algorithms require the massive amount of computation. Hence, several algorithms using quantization schemes have been proposed to reduce the amount of computation and its hardware cost. the proposed algorithm called the EMPPM improves at least 22% of the noise margin compared with the MPPM algorithm. In addition, the proposed architecture can reduce the gate count by more than 60% of that used in the SAD algorithm without usig quantization schemes and 28% of the MPPM algorithm. The VHDL models have been simulated by using the CADANCETEX>$^{TM}$ and logic synthesis has been performed by using the SYNOPSYSTEX>$^{TM}$ with $0.6\mu\textrm{m}$ SOG(sea-of-gate) cell library. The implemented chip consists of 35,829 gates, operates at 100 MHz (worst case 53 MHz) and performs the template maching with the speed of 200 Mpixels/sec.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.7
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• pp.46-58
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• 1998

In this paper, we propose a synthesis algorithm for the design of low powe rcombinational circuits under area constraints. The proposed algorithm partitions a given circuit into several subcircuits such that only a selected subcircuit is activated at a time, hence reduce unnecessary signal transitions. Partitioning of a given circuit is performed through adaptive simulated annealing algorithm employing the cost function reflecting poer consumption under area constraints. Experimental reuslts for the MCNC benchmark circuits show that the proposed algorithm generates the circuits which consume less power by 61.1% and 51.1%, when compared to those generated by the sis 1.2 and the precomputation algorithm, respectively.

• Proceeding of KASS Symposium
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• 2005.05a
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• pp.246-253
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• 2005

The design of structural engineering optimization is to minimize the cost. This problem has many objective functions formulating section and shape as a function of the included discrete variables. simulated annealing, genetic algerian and TABU algorithm are searching methods for optimum values. The object of this reserch is comparing the result of TABU algorithm, and verifying the efficiency of TABU algorithm in structural optimization design field. For the purpose, this study used a solid truss of 25 elements having 10 nodes, and size optimization for each constraint and load condition of Geodesic one, and shape optimization of Cable Dome for verifying spatial structures by the application of TABU algorithm

• The Journal of the Acoustical Society of Korea
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• v.26 no.1E
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• pp.21-26
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• 2007

In this paper, we propose a modified Viterbi algorithm to compensate for endpoint detection error during the decoding phase of an isolated word recognition task. Since the conventional Viterbi algorithm explores only the search space whose boundaries are fixed to the endpoints of the segmented utterance by the endpoint detector, the recognition performance is highly dependent on the accuracy level of endpoint detection. Inaccurately segmented word boundaries lead directly to recognition error. In order to relax the degradation of recognition accuracy due to endpoint detection error, we describe an unconstrained search of word boundaries and present an algorithm to explore the search space with efficiency. The proposed algorithm was evaluated by performing a variety of simulated endpoint detection error cases on an isolated word recognition task. The proposed algorithm reduced the Word Error Rate (WER) considerably, from 84.4% to 10.6%, while consuming only a little more computation power.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.4
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• pp.180-187
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• 2001

This paper proposes an efficient evolutionary programming algorithm for solving a generation expansion planning(GEP) problem known as a highly-nonlinear dynamic problem. Evolutionary programming(EP) is an optimization algorithm based on the simulated evolution (mutation, competition and selection). In this paper, new algorithm is presented to enhance the efficiency of the EP algorithm for solving the GEP problem. By a domain mapping procedure, yearly cumulative capacity vectors are transformed into one dummy vector, whose change can yield a kind of trend in the cost value. To validate the proposed approach, this algorithm is tested on two cases of expansion planning problems. Simulation results show that the proposed algorithm can provide successful results within a resonable computational time compared with conventional EP and dynamic programming.

• Journal of the Korean Society for Precision Engineering
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• v.11 no.6
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• pp.136-142
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• 1994

This study presents an algorithm and related techniques which could satisfy the important properties of check weighers and conveyor scales. The algorithm of Recursive Least Squares Regression is described for te weighing system simulated as a dynamic model of the second order. Using the model and the algorithm, model parameters and then the mass being weighed can be determined from the step input. The performance of the algorithm is illustrated in digital simulation. Discussions are extended to the development of fast converging algorithm. It turns out that the algorithm shows several desirable features suitable for microcomputer assisted real-time signal processing, which are high precision and stability in noisy environment.

• Advances in aircraft and spacecraft science
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• v.10 no.5
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• pp.393-418
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• 2023

Drones are increasingly used in logistics delivery due to their low cost, high-speed and straight-line flight. Considering the small cargo capacity, limited endurance and other factors, this paper optimized the pickup and delivery vehicle routing problem with time windows in the mode of "truck+drone". A mixed integer programming model with the objective of minimizing transportation cost was proposed and an improved adaptive large neighborhood search algorithm is designed to solve the problem. In this algorithm, the performance of the algorithm is improved by designing various efficient destroy operators and repair operators based on the characteristics of the model and introducing a simulated annealing strategy to avoid falling into local optimum solutions. The effectiveness of the model and the algorithm is verified through the numerical experiments, and the impact of the "truck+drone" on the route cost is analyzed, the result of this study provides a decision basis for the route planning of "truck+drone" mode delivery.