• Composite Materials and Engineering
• /
• v.3 no.3
• /
• pp.201-220
• /
• 2021

In this study, potential of three machine learning techniques i.e., M5P, Support vector machines and Gaussian processes were evaluated to find the best algorithm for the prediction of flexural strength of concrete mix with steel fibre. The study comprises the comparison of results obtained from above-said techniques for given dataset. The dataset consists of 124 observations from past research studies and this dataset is randomly divided into two subsets namely training and testing datasets with (70-30)% proportion by weight. Cement, fine aggregates, coarse aggregates, water, super plasticizer/ high-range water reducer, steel fibre, fibre length and curing days were taken as input parameters whereas flexural strength of the concrete mix was taken as the output parameter. Performance of the techniques was checked by statistic evaluation parameters. Results show that the Gaussian process technique works better than other techniques with its minimum error bandwidth. Statistical analysis shows that the Gaussian process predicts better results with higher coefficient of correlation value (0.9138) and minimum mean absolute error (1.2954) and Root mean square error value (1.9672). Sensitivity analysis proves that steel fibre is the significant parameter among other parameters to predict the flexural strength of concrete mix. According to the shape of the fibre, the mixed type performs better for this data than the hooked shape of the steel fibre, which has a higher CC of 0.9649, which shows that the shape of fibers do effect the flexural strength of the concrete. However, the intricacy of the mixed fibres needs further investigations. For future mixes, the most favorable range for the increase in flexural strength of concrete mix found to be (1-3)%.

• ETRI Journal
• /
• v.26 no.5
• /
• pp.413-422
• /
• 2004

This paper investigates the impacts of array weight errors (AWE) in an antenna array (AA) on a parallel interference cancellation (PIC) receiver in uplink synchronous and asynchronous direct sequence code division multiple access (DS-CDMA) systems. The performance degradation due to an AWE, which is approximated by a Gaussian distributed random variable, is estimated as a function of the variance of the AWE. Theoretical analysis, confirmed by simulation, demonstrates the tradeoffs encountered between system parameters such as the number of antennas and the variance of the AWE in terms of the achievable average bit error rate and the user capacity. Numerical results show that the performance of the PIC with the AA in the DS-CDMA uplink is sensitive to the AWE. However, either a larger number of antennas or uplink synchronous transmissions have the potential of reducing the overall sensitivity, and thus improving its performance.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.12 no.1
• /
• pp.37-43
• /
• 2012

In this paper, we analysis wideband spectrum sensing with distance based weight combining for Cognitive Radio (CR) systems. CR systems is implemented the spectrum of the Primary User(PU) by using a energy detection method. Threshold is determined in accordance with the constant false alarm rate (CFAR) algorithm for energy detection. The signal of PU is BPSK signal and the wireless channel between a PU and CR systems is modeled as Gaussian channel. From the simulation results, the wideband sensing with distance based and Distance based weight Combing (DWC) methods shows higher spectrum sensing performance than single CR user spectrum sensing.

• Journal of applied mathematics & informatics
• /
• v.18 no.1_2
• /
• pp.205-228
• /
• 2005

Let I(f) be the integral defined by : $I(f) = \int\limits_{a}^{b} f(x)w(x)dx$ with f a given function, w a nonclassical weight function and [a, b] an interval of IR (of finite or infinite length). We propose to calculate the approximate value of I(f) by using a new scheme for deriving a non-linear system, satisfied by the three-term recurrence coefficients of semi-classical orthogonal polynomials. Finally we studies the Stability and complexity of this scheme.

• Journal of Biomedical Engineering Research
• /
• v.17 no.2
• /
• pp.189-200
• /
• 1996

In this paper, we compared the performance of the Kaiser window with those of others as a weight function of well known anodization technique for regression of side lobe in a field pattern resulted from focusing of transducer array. The Kaiser window is an window providing many types of curve with several variables. In order to compare performance of the Kaiser window as the weight function, anodization results of the previously used Hamming window function and the Matched Gaussian function are compared Result of computer simulation, the pertormance of Kaiser window with $\delta$=0.0025 in side lobe regression was better than that of Hamming window or Matched Gausian function.

• Journal of the Korean Mathematical Society
• /
• v.56 no.2
• /
• pp.523-537
• /
• 2019

We estimate the essential norms of Volterra-type integral operators $V_g$ and $I_g$, and multiplication operators $M_g$ with holomorphic symbols g on a large class of generalized Fock spaces on the complex plane ${\mathbb{C}}$. The weights defining these spaces are radial and subjected to a mild smoothness conditions. In addition, we assume that the weights decay faster than the classical Gaussian weight. Our main result estimates the essential norms of $V_g$ in terms of an asymptotic upper bound of a quantity involving the inducing symbol g and the weight function, while the essential norms of $M_g$ and $I_g$ are shown to be comparable to their operator norms. As a means to prove our main results, we first characterized the compact composition operators acting on the spaces which is interest of its own.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.54 no.1
• /
• pp.88-95
• /
• 2017

In this paper, we present a saliency detection method using entropy weight and Weber contrast in the wavelet transform domain. Our method is based on the commonly exploited conventional algorithms that are composed of the local bottom-up approach and global top-down approach. First, we perform the multi-level wavelet transform for the CIE Lab color images, and obtain global saliency by adding the local Weber contrasts to the corresponding low-frequency wavelet coefficients. Next, the local saliency is obtained by applying Gaussian filter that is weighted by entropy of wavelet high-frequency subband. The final saliency map is detected by non-lineally combining the local and global saliencies. To evaluate the proposed saliency detection method, we perform computer simulations for two image databases. Simulations results show the proposed method represents superior performance to the conventional algorithms.

• The Journal of the Acoustical Society of Korea
• /
• v.14 no.1
• /
• pp.22-30
• /
• 1995

In this Paper we study the convergence behavior of the least mean fourth (LMF) algorithm where the error raised to the power of four is minimized for a multiple sinusoidal input and Gaussian measurement noise. Here we newly obtain the convergence equation for the sum of the mean of the squared weight errors, which indicates that the transient behavior can differ depending on the relative sizes of the Gaussian noise and the convergence constant. It should be noted that no similar results can be expected from the previous analysis by Walach and Widrow.

• International Journal of Contents
• /
• v.8 no.1
• /
• pp.23-29
• /
• 2012

Multiple expression levels of genes obtained using time series microarray experiments have been exploited effectively to enhance understanding of a wide range of biological phenomena. However, the unique nature of microarray data is usually in the form of large matrices of expression genes with high dimensions. Among the huge number of genes presented in microarrays, only a small number of genes are expected to be effective for performing a certain task. Hence, discounting the majority of unaffected genes is the crucial goal of gene selection to improve accuracy for disease diagnosis. In this paper, a non-Gaussian weight matrix obtained from an incremental model is proposed to extract useful features of multivariate time series microarrays. The proposed method can automatically identify a small number of significant features via discovering hidden variables from a huge number of features. An unsupervised hierarchical clustering representative is then taken to evaluate the effectiveness of the proposed methodology. The proposed method achieves promising results based on predictive accuracy of clustering compared to existing methods of analysis. Furthermore, the proposed method offers a robust approach with low memory and computation costs.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2017.05a
• /
• pp.793-796
• /
• 2017

Image signal can be damaged by a variety of noises during the signal processing, and multiple studies have been conducted to restore these signals. The representative noises to be added in the image are salt and pepper noise, additive white Gaussian noise(AWGN), and the composite noise which two noises are combined. Therefore, the algorithms were proposed to process with quadratic spline interpolation and median filter in case of salt and pepper noise with the central pixel of the local mask, and to process with weight filter by pixel changes in case of AWGN, upon noise determination to restore the damaged image in the composite noise environment, in this article.