Crack Stability and Its Effect on Fracture Toughness of Hot-Pressed Silicon Nitride Beam Specimens

The effect of stable crack extension on fracture toughness test results was determined using single-edge precracked beam specimens. Crack growth stability was examined theoretically for bars loaded in three-point bending under displacement control. The calculations took into account the stiffness of both the specimen and the loading system. The results indicated that the stiffness of the testing system played a major role in crack growth stability. Accordingly, a test system and specimen dimensions were selected which would result in unstable or stable crack extension during the fracture toughness test, depending on the exact test conditions. Hot-pressed silicon nitride bend bars (NC132) were prepared with precracks of different lengths, resulting in specimens with different stiffnesses. The specimens with the shorter precracks and thus higher stiffness broke without stable crack extension, while those with longer cracks, and lower stiffness, broke after some stable crack extension. The fracture toughness values from the unstable tests were 10% higher than those from the stable tests. This difference, albeit small, is systematic and is not considered to be due to material or specimen-to-specimen variation. It is concluded that instability due to the stiffness of test system and specimen must be minimized to ensure some stable crack extension in a fracture toughness test of brittle materials in order to avoid inflated fracture toughness values.     

Fractionation of transgenic corn for recovery of recombinant enzymes

Two germ-separation methods, dry-milling and density separation by flotation, were evaluated for recovering recombinant β-glucuronidase (rGUS) that accumulated primarily in the germ of transgenic corn. The dry-milling process consisted of (i) seed tempering, (ii) degerming with a horizontal-drum degermer/dehuller, (iii) particle size fractionation with standard sieves, (iv) germ and endosperm separation by roller milling and sifting, and (v) removal of hulls by aspiration. Sieves nos. 5, 6, and 7 retained the majority of germ, and subfractions from these sieves were pooled as a germ-rich fraction. Mass balances showed that the germ-rich fraction, which constituted 17% of the total dry-milled corn weight, contained 49% of rGUS activity and 64% of the total recoverable oil. Germ fractionation by flotation was tested as a proof-of-concept method aimed at separating corn fractions based on their difference in specific gravity (sp gr). The process consisted of impact-grinding of corn kernels followed by density separation using 1.15 or 1.3 specific gravity sodium nitrate solution. The oil-containing germ fraction floated, whereas the heavier endosperm fraction sedimented. The flotation method was simpler and resulted in higher enzyme recovery, that is, the germ-rich fraction was 20% (w/w) of the initial corn weight, and accounted for 80% of rGUS activity and 77% of total oil. The sodium nitrate solution did not have an adverse effect on the enzyme activity.     

Color image segmentation using adaptive unsupervised clustering approach

This paper presents the Region Splitting and Merging-Fuzzy C-means Hybrid Algorithm (RFHA), an adaptive unsupervised clustering approach for color image segmentation, which is important in image analysis and in understanding pattern recognition and computer vision field. Histogram thresholding technique is applied in the formation of all possible cells, used to split the image into multiple homogeneous regions. The merging technique is applied to merge perceptually close homogeneous regions and obtain better initialization for the Fuzzy C-means clustering approach. Experimental results have demonstrated that the proposed scheme could obtain promising segmentation results, with 12% average improvement in clustering quality and 63% reduction in classification error compared with other existing segmentation approaches.     

Adaptive neuro-fuzzy inference system multi-objective optimization using the genetic algorithm/singular value decomposition method for modelling the discharge coefficient in rectangular sharp-crested side weirs

In the present article, the adaptive neuro-fuzzy inference system (ANFIS) is employed to model the discharge coefficient in rectangular sharp-crested side weirs. The genetic algorithm (GA) is used for the optimum selection of membership functions, while the singular value decomposition (SVD) method helps in computing the linear parameters of the ANFIS results section (GA/SVD-ANFIS). The effect of each dimensionless parameter on discharge coefficient prediction is examined in five different models to conduct sensitivity analysis by applying the above-mentioned dimensionless parameters. Two different sets of experimental data are utilized to examine the models and obtain the best model. The study results indicate that the model designed through GA/SVD-ANFIS predicts the discharge coefficient with a good level of accuracy (mean absolute percentage error?=?3.362 and root mean square error?=?0.027). Moreover, comparing this method with existing equations and the multi-layer perceptron–artificial neural network (MLP-ANN) indicates that the GA/SVD-ANFIS method has superior performance in simulating the discharge coefficient of side weirs.     

Noise analysis of robot manipulator using neural networks

Due to a lot of robot manipulators application in industry, low noise degree is very important criteria for robot manipulator's joints. In this paper, joint noise problem of a robot manipulator with five joints is investigated both theoretically and experimentally. The investigation is consisted of two steps. First step is to analyze the noise of joints using a hardware and software. The hardware is a part of noise sensors. The second step; according to experimental results, some neural networks are employed for finding robust neural noise analyzer. Five types of neural networks are used to compare each other. From the results, it is noted that the proposed RBFNN gives the best results for analyzing joint noise of the robot manipulator.     

Approximate periodic solutions for the Helmholtz-Duffing equation

Approximate periodic solutions for the Helmholtz-Duffing oscillator are obtained in this paper. He’s Energy Balance Method (HEBM) and He’s Frequency Amplitude Formulation (HFAF) are adopted as the solution methods. Oscillation natural frequencies are analytically analyzed. Error analysis is carried out and accuracy of the solution methods is evaluated.     

Extreme learning machine assessment for estimating sediment transport in open channels

The minimum velocity required to prevent sediment deposition in open channels is examined in this study. The parameters affecting transport are first determined and then categorized into different dimensionless groups, including “movement,” “transport,” “sediment,” “transport mode,” and “flow resistance.” Six different models are presented to identify the effect of each of these parameters. The feed-forward neural network (FFNN) is used to predict the densimetric Froude number (Fr) and the extreme learning machine (ELM) algorithm is utilized to train it. The results of this algorithm are compared with back propagation (BP), genetic programming (GP) and existing sediment transport equations. The results indicate that FFNN-ELM produced better results than FNN-BP, GP and existing sediment transport methods in both training (RMSE = 0.26 and MARE = 0.052) and testing (RMSE = 0.121 and MARE = 0.023). Moreover, the performance of FFNN-ELM is examined for different pipe diameters.     

An enhanced Support Vector Machine classification framework by using Euclidean distance function for text document categorization

This paper presents the implementation of a new text document classification framework that uses the Support Vector Machine (SVM) approach in the training phase and the Euclidean distance function in the classification phase, coined as Euclidean-SVM. The SVM constructs a classifier by generating a decision surface, namely the optimal separating hyper-plane, to partition different categories of data points in the vector space. The concept of the optimal separating hyper-plane can be generalized for the non-linearly separable cases by introducing kernel functions to map the data points from the input space into a high dimensional feature space so that they could be separated by a linear hyper-plane. This characteristic causes the implementation of different kernel functions to have a high impact on the classification accuracy of the SVM. Other than the kernel functions, the value of soft margin parameter, C is another critical component in determining the performance of the SVM classifier. Hence, one of the critical problems of the conventional SVM classification framework is the necessity of determining the appropriate kernel function and the appropriate value of parameter C for different datasets of varying characteristics, in order to guarantee high accuracy of the classifier. In this paper, we introduce a distance measurement technique, using the Euclidean distance function to replace the optimal separating hyper-plane as the classification decision making function in the SVM. In our approach, the support vectors for each category are identified from the training data points during training phase using the SVM. In the classification phase, when a new data point is mapped into the original vector space, the average distances between the new data point and the support vectors from different categories are measured using the Euclidean distance function. The classification decision is made based on the category of support vectors which has the lowest average distance with the new data point, and this makes the classification decision irrespective of the efficacy of hyper-plane formed by applying the particular kernel function and soft margin parameter. We tested our proposed framework using several text datasets. The experimental results show that this approach makes the accuracy of the Euclidean-SVM text classifier to have a low impact on the implementation of kernel functions and soft margin parameter C.     

Dielectric properties of sol-gel derived Ta2O5 thin films

The dielectric constant and the dielectric loss of tantalum pentoxide (Ta₂O₅) thin films, produced by sol-gel spin-coated process on Corning glass substrates, have been investigated in the frequency range of 20-10⁵ Hz and the temperature range of 183-403 K, using ohmic Al electrodes. The frequency and temperature dependence of relaxation time has also been determined. The capacitance and loss factor were found to decrease with increasing frequency and increase with increasing temperature. The activation energy values were evaluated and a good agreement between the activation energy values obtained from capacitance and dielectric loss factor measurements were observed.