Multiplexed noise shaping structures for delta-sigma modulators

A new structure for realising a multiplexed noise-shaping A/D convertor is proposed. It improves the performance of first- or second-order Delta Sigma modulators by employing additional feedback paths for suppressing the quantiser error. The structure, its theoretical analysis, and simulations confirming the improved resolution are presented.<>     

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.     

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.     

Numerical modelling of the carrier gas phase in a laboratory-scale coal classifier model

The pneumatic transport of fine ideally combustible coal dust to the burner furnace is an important process in coal fired power plants. The strongly swirling air phase responsible for the particle separation and transport in a coal pulverising mill was characterised experimentally and numerically. Measurements of the swirl velocity component were taken in a scaled laboratory model of the device and compared to CFD model. In particular, an evaluation of the turbulence models used to describe the flow was performed. The modified isotropic k-epsilon turbulence models (RNG k-ε and Realizable k-ε) were compared to the anisotropic Reynolds stress model (RSM) and their ability to predict the bulk flow structure present in the classifier was assessed. The experiments showed that the swirling flow structure, responsible for coarse-fine particle classification, has several flow regimes which are governed by the areas it is bounded by. The numerical model predictions generally corroborate the results. However, a distinction in performance between the three models can be made based on accuracy, solution generation time and numerical stability. The RSM model predicted both the trends and magnitude the most accurately when compared to the isotropic models. However, the Realizable k-epsilon model, with its relatively low solution generation time, shows potential when using CFD as a classifier design optimization tool. The investigation has given some insight on single phase classifier flow and suggests a design improvement based on the results.     

Fabrication of novel hybrid composite La2‐xCoxCuO4 electrode for high performance supercapacitors

Hybrid composites La_2‐xCo_xCuO₄ (x = 0, 0.1, 0.2, and 0.3) are prepared using one‐step simple hydrothermal route as electrodes for supercapacitors. The effect of varying cobalt content on morphological, structural, and electrochemical properties has been explored using X‐ray diffraction, scanning electron microscopy, and cyclic voltammetry, respectively. The structural parameters obtained by X‐ray diffraction showed tetragonal phase of hybrid composite without any evident impurity phases. The analysis of morphological properties suggested a strong correlation with electrochemical properties, for instance, a relationship between fabric porous structures and electrochemically active sites for redox reactions and intercalation/de‐intercalation processes. The hybrid composite electrodes demonstrated high specific capacitance of the order of 1304 F/g at 10 mV/s scan rate and exhibited decreasing trend on increasing scan rate. Hybrid composites were also tested for their ability as an electrode of high performance supercapacitors in different aqueous electrolytes, i. e, KOH, H₂SO₄, and Na₂SO₄ to optimize the best compatible electrolyte. The composite electrode material showed excellent cyclic stability and 98% capacitance retention for 1 A/g after 2000 cycles. The remarkable performance of hybrid composite electrode entails its potential for commercial applications of supercapacitors.     

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.     

On-line analysis of out-of-control signals in multivariate manufacturing processes using a hybrid learning-based model

Advanced automatic data acquisition is now widely adopted in manufacturing industries and it is common to monitor several correlated quality variables simultaneously. Most of multivariate quality control charts are effective in detecting out-of-control signals based upon an overall statistics in multivariate manufacturing processes. The main problem of such charts is that they can detect an out-of-control event but do not directly determine which variable or group of variables has caused the out-of-control signal and what is the magnitude of out of control. This study presents a hybrid learning-based model for on-line analysis of out-of-control signals in multivariate manufacturing processes. This model consists of two modules. In the first module using a support vector machine-classifier, type of unnatural pattern can be recognized. Then by using three neural networks for shift mean, trend and cycle it can be recognized magnitude of mean shift, slope of trend and cycle amplitude for each variable simultaneously in the second module. The performance of the proposed approach has been evaluated using two examples. The output generated by trained hybrid model is strongly correlated with the corresponding actual target value for each quality characteristic. The main contributions of this work are recognizing the type of unnatural pattern and classification major parameters for shift, trend and cycle and for each variable simultaneously by proposed hybrid model.