• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.1
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• pp.80-93
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• 1997

This paper proposes a new technique of optimal codebook design in multistage gain-shape vector quantization (MS-GS VQ) for wireless image communication. An original image is divided into a smany blocks as possible in order to get strong robustness to channel transmission errors: the original image is decomposed into a number of subband images, each of which contains a sperate spatial frequency information and is obtained by the biorthogonal wavlet transform; each subband is separated into several consecutive VQ stages, where each stage has a residual information of the previous stage; one vector in each stage is divided into two components-gain and shape. But, this decomposition genrates too many blocks and it thus makes the determination of optimal codebooks difficult. We overcome this difficulty by evolving each block's codebook independently with different genetic algorithm that uses each stage's individual training vectors. Th eimpact of th eproposed VQ technique on the channel transmission errors is compared with that of other VQ techniques. Simulation results show that the proposed VQ technique (MS-GS VQ) with the optimal codebook designe dy genetic algorithms is very robust to channel transmission errors even under the bursty and high BER conditions.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.56 no.1
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• pp.35-43
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• 2011

Thirty five isolates of Trichoderma species collected from seven different locations of Bangladesh were studied for morphological characters and molecular variation. Mycelial diameters of the isolates varied from 8.28 cm to 9.00 cm. Based on colony colour, isolates were grouped into five such as dark green, green, light green, yellowish green and whitish green. Maximum isolates were green and light green. On the basis of growth habit and colony consistency, the isolates were categorized into three groups, in which most species had fast growth and were compact in appearance. PCR-based Random Amplified Polymorphic DNA (RAPD) technique employing 3 decamer primers produced 36 scorable bands of which all (100%) were polymorphic. The co-efficient of gene differentiation (Gst) was 1.0000 reflecting the existence of high level of genetic diversity among the isolates. The Unweighted Pair Group Method of Arithmetic Means (UPGMA) dendrogram constructed from Nei's (1972) genetic distance produced 2 main clusters (13 isolates in cluster 1 and 22 isolates in cluster 2). The result indicating their genetic diversity has opened new possibility of using the most efficient and more isolates of Trichoderma in the preparation of biopesticide and decomposition of municipality waste.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.6
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• pp.661-667
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• 2005

In this paper, we propose a new hybrid genetic algorithm for sequential ordering problem (SOP). In the proposed genetic algorithm, the Voronoi quantized crossover (VQX) is used as a crossover operator and the path-preserving 3-Opt is used as a local search heuristic. VQX is a crossotver operator that uses the epistasis information of given problem instance. Since it is a crossover proposed originally for the traveling salesman problem (TSP), its application to SOP requires considerable modification. In this study, we appropriately modify VQX for SOP, and develop three algorithms, required in the modified VQX, named Feasible solution Generation Algorithm, Precedence Cycle Decomposition Algorithm, and Genic Distance Assignment Method. The results of the tests on SOP instances obtained from TSPLIB and ZIB-MP-Testdata show that the proposed genetic algorithm outperforms other genetic algorithms in stability and solution quality.

• 한국전산유체공학회:학술대회논문집
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• 2009.04a
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• pp.57-65
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• 2009

An efficient and high-fidelity design approach for wing-body shape optimization is presented. Depending on the size of design space and the number of design of variable, aerodynamic shape optimization process is carried out via different optimization strategies at each design stage. In the first stage, global optimization techniques are applied to planform design with a few geometric design variables. In the second stage, local optimization techniques are used for wing surface design with a lot of design variables to maintain a sufficient design space with a high DOF (Degree of Freedom) geometric change. For global optimization, Kriging method in conjunction with Genetic Algorithm (GA) is used. Asearching algorithm of EI (Expected Improvement) points is introduced to enhance the quality of global optimization for the wing-planform design. For local optimization, a discrete adjoint method is adopted. By the successive combination of global and local optimization techniques, drag minimization is performed for a multi-body aircraft configuration while maintaining the baseline lift and the wing weight at the same time. Through the design process, performances of the test models are remarkably improved in comparison with the single stage design approach. The performance of the proposed design framework including wing planform design variables can be efficiently evaluated by the drag decomposition method, which can examine the improvement of various drag components, such as induced drag, wave drag, viscous drag and profile drag.

• Journal of Educational Research in Mathematics
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• v.25 no.4
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• pp.499-524
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• 2015

In the present study, we propose four participating $8^{th}$ grade students' genetic decomposition of congruent transformation and investigate the role of their dragging activities while understanding the concept of congruent transformation in GSP(Geometer's Sketchpad). The students began to use two major schema, 'single-point movement' and 'identification of transformation' simultaneously in their transformation activities, but they were inclined to rely on the single-point movement schema when dealing with relatively difficult tasks. Through dragging activities, they could expand the domain and range of transformation to every point on a plane, not confined to relevant geometric figures. Dragging activities also helped the students recognize the role of a vector, a center of rotation, and an axis of symmetry.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.10a
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• pp.365-373
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• 1999

Recently, numerous studies have demonstrated that artificial intelligence such as neural networks can be an alternative methodology for classification problems to which traditional statistical methods have long been applied. In building neural network model, the selection of independent and dependent variables should be approached with great care and should be treated as a model construction process. Irrespective of the efficiency of a learning procedure in terms of convergence, generalization and stability, the ultimate performance of the estimator will depend on the relevance of the selected input variables and the quality of the data used. Approaches developed in statistical methods such as correlation analysis and stepwise selection method are often very useful. These methods, however, may not be the optimal ones for the development of neural network models. In this paper, we propose a genetic algorithms approach to find an optimal or near optimal input variables for neural network modeling. The proposed approach is demonstrated by applications to bankruptcy prediction modeling. Our experimental results show that this approach increases overall classification accuracy rate significantly.

• Smart Structures and Systems
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• v.16 no.1
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• pp.1-26
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• 2015

Minimizing construction cost and reducing seismic damage are two conflicting objectives in the design of any new structure. In the present work, we try to develop a framework in order to solve the optimum performance-based design problem considering the construction cost and the seismic damage of steel moment-frame structures. The Park-Ang damage index is selected as the seismic damage measure because it is one of the most realistic measures of structural damage. The non-dominated sorting genetic algorithm (NSGA-II) is employed as the optimization algorithm to search the Pareto optimal solutions. To improve the time efficiency of the proposed framework, three simplifying strategies are adopted: first, simplified nonlinear modeling investigating minimum level of structural modeling sophistication; second, fitness approximation decreasing the number of fitness function evaluations; third, wavelet decomposition of earthquake record decreasing the number of acceleration points involved in time-history loading. The constraints of the optimization problem are considered in accordance with Federal Emergency Management Agency's (FEMA) recommended seismic design specifications. The results from numerical application of the proposed framework demonstrate the efficiency of the framework in solving the present multi-objective optimization problem.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.145-152
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• 2021

In the Recent times, various technological enhancements in the field of artificial intelligence and big data has been noticed. This advancement coupled with the evolution of the 5G communication and Internet of Things technologies, has helped in the development in the domain of smart mine construction. The development of unmanned vehicles with enhanced and smart scheduling system for open-pit mine transportation is one such much needed application. Traditional open-pit mining systems, which often cause vehicle delays and congestion, are controlled by human authority. The number of sensors has been used to operate unmanned cars in an open-pit mine. The sensors haves been used to prove the real-time data in large quantity. Using this data, we analyses and create an improved transportation scheduling mechanism so as to optimize the paths for the vehicles. Considering the huge amount the data received and aggregated through various sensors or sources like, the GPS data of the unmanned vehicle, the equipment information, an intelligent, and multi-target, open-pit mine unmanned vehicle schedules model was developed. It is also matched with real open-pit mine product to reduce transport costs, overall unmanned vehicle wait times and fluctuation in ore quality. To resolve the issue of scheduling the transportation, we prefer to use algorithms based on artificial intelligence. To improve the convergence, distribution, and diversity of the classic, rapidly non-dominated genetic trial algorithm, to solve limited high-dimensional multi-objective problems, we propose a decomposition-based restricted genetic algorithm for dominance (DBCDP-NSGA-II).

• Toxicological Research
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• v.24 no.4
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• pp.321-328
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• 2008

Smoke flavors based on the thermal decomposition of wood have been applied to a variety of food products as an alternative for traditional smoking. Despite its increasing use, the available genotoxicity data on wood smoke flavors (WSF) are still controversial. Thus, potential genotoxic effects of WSF in four short-term in vitro genotoxicity assays were investigated, which included the Ames assay, chromosomal aberration assay, micronucleus test and the alkaline comet assay. WSF did not cause any mutation in the Ames assay using five tester strains at six concentrations of 0.16, 0.31, 0.63, 1.25, 2.5 and 5 ${\mu}l/plate$. To assess clastogenic effect, the in vitro chromosomal aberration assay was performed using Chinese hamster lung cells. No statistically significant increase in the number of metaphases with structural aberrations was observed at the concentrations of 1.25, 2.5, and 5 ${\mu}l/ml$. The in vitro comet assay and micronucleus test results obtained on L5178Y cells also revealed that WSF has no genotoxicity potential, although there was a marginal increase in micronuclei frequencies and DNA damage in the respective micronucleus and comet assays. Taken together, based on the results obtained from these four in vitro studies, it is concluded that WSF is not a mutagenic agent in bacterial cells and causes no chromosomal and DNA damage in mammalian cells in vitro.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.33-36
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• 2008

This paper presents soil moisture retrieval from measured polarimetric backscattering coefficients of a vegetated surface. Based on the analysis of the quite complicate first-order radiative transfer scattering model for vegetated surfaces, a simplified scattering model is proposed for an inversion algorithm. Extraction of the surface-scatter component from the total scattering of a vegetation canopy is addressed using the simplified model, and also using the three-component decomposition technique. The backscattering coefficients are measured with a polarimetric L-band scatterometer during two months. At the same time, the biomasses, leaf moisture contents, and soil moisture contents are also measured. Then the measurement data are used to estimate the model parameters for vv-, hh-, and vh-polarizations. The scattering model for tall-grass-covered surfaces is inverted to retrieve the soil moisture content from the measurements using a genetic algorithm. The retrieved soil moisture contents agree quite well with the in-situ measured soil moisture data.