• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.31 no.3
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• pp.39-49
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• 2003

The delta operator approach and the singular perturbation technique are introduced. The former reduces the round-off error in the numerical computation. The latter reduces computing time by decoupling the original system into the fast and slow sub-systems. The aircraft dynamics consists of the Phugoid and short-period motions whether its model is longitudinal or lateral. In this paper, an approximated solutions of lateral dynamic model of Beaver obtained by using those two methods in compared with the exact solution. For open-loop system and closed-loop system, and approximated solution gets identical to the exact solution with only one iteration and without iteration, respectively. Therefore, it is shown that implementing those approaches is very effective in the flight dynamic and control.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.2821-2839
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• 2015

Energy consumption by large computing systems has become an important research theme not only because the sources of energy are depleting fast but also due to the environmental concern. Computational grid is a huge distributed computing platform for the applications that require high end computing resources and consume enormous energy to facilitate execution of jobs. The organizations which are offering services for high end computation, are more cautious about energy consumption and taking utmost steps for saving energy. Therefore, this paper proposes a scheduling technique for Minimizing Energy consumption using Adapted Genetic Algorithm (MiE-AGA) for dependent tasks in Computational Grid (CG). In MiE-AGA, fitness function formulation for energy consumption has been mathematically formulated. An adapted genetic algorithm has been developed for minimizing energy consumption with appropriate modifications in each components of original genetic algorithm such as representation of chromosome, crossover, mutation and inversion operations. Pseudo code for MiE-AGA and its components has been developed with appropriate examples. MiE-AGA is simulated using Java based programs integrated with GridSim. Analysis of simulation results in terms of energy consumption, makespan and average utilization of resources clearly reveals that MiE-AGA effectively optimizes energy, makespan and average utilization of resources in CG. Comparative analysis of the optimization performance between MiE-AGA and the state-of-the-arts algorithms: EAMM, HEFT, Min-Min and Max-Min shows the effectiveness of the model.

• Journal of Biomedical Engineering Research
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• v.24 no.5
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• pp.473-480
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• 2003

Iterative reconstruction methods have played a prominent role in emission computed tomography due to their remarkable advantages over the conventional filtered backprojection method. However, since iterative reconstructions typically are comprised of repeatedly projecting and backprojecting the data, the computational load required for reconstructing an image depends highly on the performance of the projector-backprojector pair used in the algorithm. In this work we compare quantitative performance of representative methods for implementing projector-backprojector pairs. To reduce the overall cost for the projection-backprojection operations for each method, we investigate how previously computed results can be reused so that the number of redundant calculations can be minimized. Our experimental results demonstrate that the ray tracing method not only outperforms other methods in computation time, but also provides improved reconstructions with good accuracy.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.33 no.1
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• pp.26-32
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• 2005

In this paper, the effect of various regression models is investigated on structural optimization using the central composite method. Three bar truss and the upper platform of a satellite are optimized using various regression models that are polynomial, exponential and log functions. Response surface method is non-gradient, semi-global, discrete and fast converging in optimization problem. Sampling points are extracted by the design of experiments using the central composite method. Response surface is generated using the various regression functions. Structural analysis for calculating constraints is executed to find static and dynamic responses. From this study, it is verified that the response surface method has advantage in optimum value and computation time in comparison to other optimization methods.

• Journal of Broadcast Engineering
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• v.15 no.2
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• pp.236-247
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• 2010

Recently, mean shift tracking algorithms have been proposed which use the information of color histogram together with some spatial information provided by the kernel. In spite of their fast speed, the algorithms are suffer from an inherent instability problem which is due to the use of an isotropic kernel for spatiality and the use of the Bhattacharyya coefficient as a similarity function. In this paper, we analyze how the kernel and the Bhattacharyya coefficient can arouse the instability problem. Based on the analysis, we propose a novel tracking scheme that uses a new representation of the location of the target which is constrained by the color, the area, and the spatiality information of the target in a more stable way than the mean shift algorithm. With this representation, the target localization in the next frame can be achieved by one step computation, which makes the tracking stable, even in difficult situations such as low-rate-frame environment, and partial occlusion.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.11A
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• pp.979-986
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• 2005

In optical networks, traffic demands often demonstrate periodic nature for which time-overlapping property can be utilized in routing and wavelength assignment (RWA). A RWA problem for scheduled lightpath demands (SLDs) has been solved by combinatorial optimal solution (COS) and graph coloring, or heuristic sequential RWA (sRWA). Such methods are very complex and incurs large computational overhead. In this paper, we propose an efficient RWA algorithm to utilize the time disjoint property as well as space disjoint property through fast grouping of SLDs. The computer simulation shows that our proposed algorithm indeed achieves up to $54\%$ faster computation with similar number of wavelengths than the existing heuristic sRWA algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.11C
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• pp.917-924
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• 2008

Motion estimation and compensation in the spatial domain check the searching area of specified size in the previous frame and search block to minimize the difference with current block. When we check the searching area, it consumes the most encoding times due to increasing the complexity. We can solve this fault by means of motion estimation using shifting matrix in the transform domain instead of the spatial domain. We derive so the existed shifting matrix to a new recursion equation that we decrease more computations. We modify simply vertical shifting matrix and horizontal shifting matrix in the transform domain for motion estimation of half-pixel accuracy. So, we solve increasing computation due to bilinear interpolation in the spatial domain. Simulation results prove that motion estimation by the proposed algorithm in DCT-based transform domain provides higher PSNR using fewer bits than results in the spatial domain.

• Journal of Electrical Engineering and Technology
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• v.1 no.4
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• pp.435-447
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• 2006

This paper presents an application of the parallel Adaptive Evolutionary Algorithm (AEA) to search an optimal solution of the service restoration in electric power distribution systems, which is a discrete optimization problem. The main objective of service restoration is, when a fault or overload occurs, to restore as much load as possible by transferring the de-energized load in the out of service area via network reconfiguration to the appropriate adjacent feeders at minimum operational cost without violating operating constraints. This problem has many constraints and it is very difficult to find the optimal solution because of its numerous local minima. In this investigation, a parallel AEA was developed for the service restoration of the distribution systems. In parallel AEA, a genetic algorithm (GA) and an evolution strategy (ES) in an adaptive manner are used in order to combine the merits of two different evolutionary algorithms: the global search capability of the GA and the local search capability of the ES. In the reproduction procedure, proportions of the population by GA and ES are adaptively modulated according to the fitness. After AEA operations, the best solutions of AEA processors are transferred to the neighboring processors. For parallel computing, a PC cluster system consisting of 8 PCs was developed. Each PC employs the 2 GHz Pentium IV CPU and is connected with others through switch based fast Ethernet. To show the validity of the proposed method, the developed algorithm has been tested with a practical distribution system in Korea. From the simulation results, the proposed method found the optimal service restoration strategy. The obtained results were the same as that of the explicit exhaustive search method. Also, it is found that the proposed algorithm is efficient and robust for service restoration of distribution systems in terms of solution quality, speedup, efficiency, and computation time.

• Journal of the Korean Institute of Telematics and Electronics
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• v.25 no.9
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• pp.1115-1124
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• 1988

A two-dimensional systolic array for fast Fourier transform, which has a regular and recursive VLSI architecture is presented. The array is constructed with identical processing elements (PE) in mesh type, and due to its modularity, it can be expanded to an arbitrary size. A processing element consists of two data routing units, a butterfly arithmetic unit and a simple control unit. The array computes FFT through three procedures` I/O pipelining, data shuffling and butterfly arithmetic. By utilizing parallelism, pipelining and local communication geometry during data movement, the two-dimensional systolic array eliminates global and irregular commutation problems, which have been a limiting factor in VLSI implementation of FFT processor. The systolic array executes a half butterfly arithmetic based on a distributed arithmetic that can carry out multiplication with only adders. Also, the systolic array provides 100% PE activity, i.e., none of the PEs are idle at any time. A chip for half butterfly arithmetic, which consists of two BLC adders and registers, has been fabricated using a 3-um single metal P-well CMOS technology. With the half butterfly arithmetic execution time of about 500 ns which has been obtained b critical path delay simulation, totla FFT execution time for 1024 points is estimated about 16.6 us at clock frequency of 20MHz. A one-PE chip expnsible to anly size of array is being fabricated using a 2-um, double metal, P-well CMOS process. The chip was layouted using standard cell library and macrocell of BLC adder with the aid of auto-routing software. It consists of around 6000 transistors and 68 I/O pads on 3.4x2.8mm\ulcornerarea. A built-i self-testing circuit, BILBO (Built-In Logic Block Observation), was employed at the expense of 3% hardware overhead.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.2
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• pp.53-60
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• 2012

Nearest-neighbor classification predicts the class of an input data with the most frequent class among the near training data of the input data. Even though nearest-neighbor classification doesn't have a training stage, all of the training data are necessary in a predictive stage and the generalization performance depends on the quality of training data. Therefore, as the training data size increase, a nearest-neighbor classification requires the large amount of memory and the large computation time in prediction. In this paper, we propose a prototype selection algorithm that predicts the class of test data with the new set of prototypes which are near-boundary training data. Based on Tomek links and distance metric, the proposed algorithm selects boundary data and decides whether the selected data is added to the set of prototypes by considering classes and distance relationships. In the experiments, the number of prototypes is much smaller than the size of original training data and we takes advantages of storage reduction and fast prediction in a nearest-neighbor classification.