• Animal cells and systems
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• v.13 no.3
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• pp.331-337
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• 2009

Twenty-eight Anopheles species has been so-far identified in Iran, while only 8 species was proved as malaria vector. In this study, we principally examined the cuticular hydrocarbon (CHC) potency in identification of Iranian vectors of malaria and then differentiation of vector and non-vector species of Anopheles. Seven species of malaria vectors and the non-vector species, Anopheles claviger were collected throughout Iran. Female extracts were made out of every five conspecific specimens by surface immersion in pure n-hexane. Each sample was injected into a FID-GC instrument along with the known concentrations of standards. CHC profiles of the eight Anopheles species indicated no qualitative difference. The average mass of each eluted CHC were compared using Repeated ANOVA and Mann-Whitney tests. Results confirmed a significant difference in mass of each single CHC at a specific retention time (RT). Statistical comparison of CHC mass in An. sacharovi, An. stephensi, An. culicifacies and An. fluviatilis at RT 39.6 indicated significant differences (P<0.05) among these species. Analysis of CHC mass of An. dthali, An. superpictus & An. sacharovi at RT 28.5, An. stephensi & An. sacharovi at RT 30.7 and An. sacharovi & An. claviger at RT 30.6 similarly indicated significant differences (P<0.05). An. sacharovi could be distinguished from other species, which showed only trace, by integratable peaks at retention times of 29.7, 31 and 32.6. Similarly, An. claviger could be distinguished from the other species with a trace peak at RT 30.6. In order to separate An. stephensi from the five other species, the integratable peak at RT 30.7 was used. An. dthali could be identified at RT 26.2 by an integratable peak v.s. the trace peaks of other species. An. superpictus had indicator peaks at RTs 27.4 & 28.5 v.s. trace peaks of other species. An. maculipennis with its trace peak at RT 39.6 could be easily differentiated from An. fluviatilis & An. culicifacies. This study proved that all of the examined species of Anopheles could be well identified based on their quantitative differences in CHCs, except for An. fluviatilis & An. culicifacies for which no CHC indicator peak was detected.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.5
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• pp.494-503
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• 2002

A motion vector re-estimation algorithm for image downscaling in discrete consine transform domain is presented. Kernel functions are difined using SAD (Aum of Absolute Difference) and edge information of a macroblock. The proposed method uses these kernel functions to re-estimate a new motion vector of the downscaled image. The motion vectors from the incoming bitstream of transcoder are reused to reduce computation burden of the block-matching motion estimation, and we also reuse the given motion vectors. Several experiments in this paper show that the computation efficiency and the PSNR (Peak Signal to Noise Ratio) and better than the previous methods.

• Journal of Biomedical Engineering Research
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• v.5 no.2
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• pp.121-126
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• 1984

A new approach to texture classification for quantitative ultrasound liver diagnosis using run difference matrix was developed. The run difference matrix comprised the gray level difference along with a distances. From this run difference matrix, we defined several vectors and parameters such as DOD, DGD, DAD vector, SHP, SMO, SMG, LDE, LDEL etc.Each parameter values calculated in fatty, cirrhotic, normal and chronic hepatitic liver images were plotted in a plane and we found that RDM method was more sensitive to small structural changes than the conventional run length method and showed improved classification ability between the diseases.

• Journal of computational fluids engineering
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• v.4 no.2
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• pp.57-66
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• 1999

Numerical Analysis has been done for the supersonic off-design jet flow due to the pressure difference between the jet and the ambient fluid. The difference of pressure generates an oblique shock or an expansion wave at the nozzle exit. The waves reflect repeatedly on the center axis and the sonic surface in the shear layer. The pressure difference is resolved across these reflected waves. In this paper, the axi-symmetric Navier-Stokes equation has been used with the κ-ε turbulence model. The second order TVD scheme with flux limiters, based on the flux vector split with the smooth eigenvalue split, has been used to capture internal shocks and other discontinuities. Numerical calculations have been done to analyze the off-design jet flow due to the pressure difference. The variation of pressure along the flow axis is compared with an experimental result and other numerical result. The characteristics of the interaction between the shock cell and the turbulence mixing layer have been analyzed.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.3
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• pp.535-544
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• 2023

In this paper, we study the effect of gradient vector calculation method(analytical method, central finite difference method) on adaptive beamforming to control weight distribution during iterated calculation when LMS algorithm (repeating method) is used to realize desired beam pattern. To this end, a quasi-ideal beam having an arbitrarily set beam width, a rotating beam, and a multi-beam were reviewed as examples. Numerical experiments applied the step parameters of the appropriate values to the adaptive beamforming system through trial and error equally to the two calculations, and compared the convergence characteristics of objective functions that evaluate adaptability and error using two methods for calculating gradient vectors.

• Proceedings of the KIEE Conference
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• 1996.07a
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• pp.12-14
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• 1996

In this paper, the simulation of the effects of variation of rotor resistance of induction motor for the performance of vector control is presented. Especially, this paper considered the effects as a difference variation of the rotor resistance between slip calculator and induction motor.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1993.04a
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• pp.75-79
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• 1993

본 연구에서는 원통형 셀을 Donnell-Mushitari의 Thin Shell로 모델링하고, 유체의 거동은 Hankel 함수를 배제하고 유한차분법(Finite Difference Method)으로 모델링하여, 상태 벡터(State Vector)해석법, 전달 행렬 및 푸리 에 변환(Fourier Transform)을 사용, 무한 원통형 몰수체의 강제 진동을 해 석하였다.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.403-404
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• 2006

In this paper, we proposed muli-dimensional feature vector matching method combined with disparity smoothness constraint. The smoothness constraint was calculated using the difference between disparity of center pixel and those of 4-neighbor pixels. By applying proposed algorithm to IKONOS satellite stereo imagery, we obtained robust stereo matching result in urban areas.

• Structural Engineering and Mechanics
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• v.67 no.6
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• pp.603-616
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• 2018

In this study, the acceleration vector in each time step is assumed to be a mth order time polynomial. By using the initial conditions, satisfying the equation of motion at both ends of the time step and minimizing the square of the residual vector, the m+3 unknown coefficients are determined. The order of accuracy for this approach is m+1, and it has a very low dispersion error. Moreover, the period error of the new technique is almost zero, and it is considerably smaller than the members of the Newmark method. The proposed scheme has an appropriate domain of stability, which is greater than that of the central difference and linear acceleration techniques. The numerical tests highlight the improved performance of the new algorithm over the fourth-order Runge-Kutta, central difference, linear and average acceleration methods.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.459-461
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• 1992

This paper presents the new method to recognize the 2D patterns dynamically by rotating the input patterns according to the difference vector. Generally neural network with many patterns leads to various recognition ratio. The dynamic management of input patterns means that we can move pixels to desired locations controlled by the difference vector. We divide dual neural network model into two parts at learning phase, respectively. And then we combine them to construct the total network. Our model has some good results such that it has less number of patterns and reduced learning time. At present, we only discuss the four way movement of input patterns. The research for the complex movement will be fulfilled later.