A Generalized Sylvester Identity and Fraction-free Random Gaussian Elimination

Sylvester’s identity is a well-known identity that can be used to prove that certain Gaussian elimination algorithms are fraction free. In this paper we will generalize Sylvester’s identity and use it to prove that certain random Gaussian elimination algorithms are fraction free. This can be used to yield fraction free algorithms for solving Ax = b(x ≥  0) and for the simplex method in linear programming.     

A hybrid classifier combining SMOTE with PSO to estimate 5-year survivability of breast cancer patients

In this study, we propose a set of new algorithms to enhance the effectiveness of classification for 5-year survivability of breast cancer patients from a massive data set with imbalanced property. The proposed classifier algorithms are a combination of synthetic minority oversampling technique (SMOTE) and particle swarm optimization (PSO), while integrating some well known classifiers, such as logistic regression, C5 decision tree (C5) model, and 1-nearest neighbor search. To justify the effectiveness for this new set of classifiers, the g-mean and accuracy indices are used as performance indexes; moreover, the proposed classifiers are compared with previous literatures. Experimental results show that the hybrid algorithm of SMOTE + PSO + C5 is the best one for 5-year survivability of breast cancer patient classification among all algorithm combinations. We conclude that, implementing SMOTE in appropriate searching algorithms such as PSO and classifiers such as C5 can significantly improve the effectiveness of classification for massive imbalanced data sets.     

Application of evolutionary optimization techniques for finding the optimal set of concentric circular antenna array

In this paper the maximum sidelobe level (SLL) reductions, optimal beam patterns and optimal beam widths of various designs of three-ring planar concentric circular antenna arrays (PCCAA) are examined using three different classes of evolutionary optimization techniques to finally determine the global optimal three-ring PCCAA design and then establish some sort of ranking among the techniques. Apart from physical construction of a PCCAA, one may broadly classify its design into two major categories: uniformly excited arrays and non-uniformly excited arrays. The present paper assumes non-uniform excitations and uniform spacing of excitation elements in each three-ring PCCAA design and a design goal of maximizing SLL reduction associated with optimal beam patterns and beam widths. The design problem is modeled as an optimization problem for each PCCAA design and solved using different evolutionary optimization techniques to determine an optimum set of normalized excitation weights for PCCAA elements, which, when incorporated, results in a radiation pattern with optimal (maximum) SLL reduction. Among the various PCCAA designs, one which yields the global minimum SLL with global minimum first null beamwidth is the global optimal design. In this work the three-ring PCCAA containing (N₁ = 4, N₂ = 6, N₃ = 8) elements proves to be such global optimal design. The optimization techniques employed are real coded GA (RGA), canonical PSO (CPSO), craziness based PSO (CRPSO), evolutionary programming (BEP), hybrid evolutionary programming (HEP). While ranking the techniques after 30 total runs for each design, HEP, CRPSO, RGA, CPSO, BGA hold the first five ranks in order of optimization capability. HEP yields global minimum SLL (?32.86 dB) and global minimum BWFN (77.0°) for the optimal design. BEP often changes the rank from second to fifth depending on the design set. Further, when compared to a uniformly excited PCCAA having equal number of elements and same radii a reduction of major lobe beamwidth is also observed in the optimal non-uniformly excited case.     

Prediction of flow pattern of gas–liquid flow through circular microchannel using probabilistic neural network

The present study attempts to develop a flow pattern indicator for gas–liquid flow in microchannel with the help of artificial neural network (ANN). Out of many neural networks present in literature, probabilistic neural network (PNN) has been chosen for the present study due to its speed in operation and accuracy in pattern recognition. The inbuilt code in MATLAB R2008a has been used to develop the PNN. During training, superficial velocity of gas and liquid phase, channel diameter, angle of inclination and fluid properties such as density, viscosity and surface tension have been considered as the governing parameters of the flow pattern. Data has been collected from the literature for air–water and nitrogen–water flow through different circular microchannel diameters (0.53, 0.25, 0.100 and 0.050 mm for nitrogen–water and 0.53, 0.22 mm for air–water). For the convenience of the study, the flow patterns available in literature have been classified into six categories namely; bubbly, slug, annular, churn, liquid ring and liquid lump flow. Single PNN model is unable to predict the flow pattern for the whole range (0.53 mm–0.050 mm) of microchannel diameter. That is why two separate PNN models has been developed to predict the flow patterns of gas–liquid flow through different channel diameter, one for diameter ranging from 0.53 mm to 0.22 mm and another for 0.100 mm–0.05 mm. The predicted map and their transition boundaries have been compared with the corresponding experimental data and have been found to be in good agreement. Whereas accuracy in prediction of transition boundary obtained from available analytical models used for conventional channel is less for all diameter of channel as compared to the present work. The percentage accuracy of PNN (～94% for 0.53 mm ID and ～73% for 0.100 mm ID channel) has also been found to be higher than the model based on Weber number (～86% for 0.53 mm ID and ～36% for 0.05 mm ID channel).     

Unsupervised feature selection using swarm intelligence and consensus clustering for automatic fault detection and diagnosis in Heating Ventilation and Air Conditioning systems

Various sensory and control signals in a Heating Ventilation and Air Conditioning (HVAC) system are closely interrelated which give rise to severe redundancies between original signals. These redundancies may cripple the generalization capability of an automatic fault detection and diagnosis (AFDD) algorithm. This paper proposes an unsupervised feature selection approach and its application to AFDD in a HVAC system. Using Ensemble Rapid Centroid Estimation (ERCE), the important features are automatically selected from original measurements based on the relative entropy between the low- and high-frequency features. The materials used is the experimental HVAC fault data from the ASHRAE-1312-RP datasets containing a total of 49 days of various types of faults and corresponding severity. The features selected using ERCE (Median normalized mutual information (NMI) = 0.019) achieved the least redundancies compared to those selected using manual selection (Median NMI = 0.0199) Complete Linkage (Median NMI = 0.1305), Evidence Accumulation K-means (Median NMI = 0.04) and Weighted Evidence Accumulation K-means (Median NMI = 0.048). The effectiveness of the feature selection method is further investigated using two well-established time-sequence classification algorithms: (a) Nonlinear Auto-Regressive Neural Network with eXogenous inputs and distributed time delays (NARX-TDNN); and (b) Hidden Markov Models (HMM); where weighted average sensitivity and specificity of: (a) higher than 99% and 96% for NARX-TDNN; and (b) higher than 98% and 86% for HMM is observed. The proposed feature selection algorithm could potentially be applied to other model-based systems to improve the fault detection performance.     

Automatic identification of discourse markers in dialogues: An in-depth study of like and well

The lexical items like and well can serve as discourse markers (DMs), but can also play numerous other roles, such as verb or adverb. Identifying the occurrences that function as DMs is an important step for language understanding by computers. In this study, automatic classifiers using lexical, prosodic/positional and sociolinguistic features are trained over transcribed dialogues, manually annotated with DM information. The resulting classifiers improve state-of-the-art performance of DM identification, at about 90% recall and 79% precision for like (84.5% accuracy, κ = 0.69), and 99% recall and 98% precision for well (97.5% accuracy, κ = 0.88). Automatic feature analysis shows that lexical collocations are the most reliable indicators, followed by prosodic/positional features, while sociolinguistic features are marginally useful for the identification of DM like and not useful for well. The differentiated processing of each type of DM improves classification accuracy, suggesting that these types should be treated individually.     

Speech authentication system using digital watermarking and pattern recovery

The objective of this paper is to detect speech forgery using digital audio watermarking and pattern recovery techniques. A digital watermark pattern has been attached with the speech signal to detect three kinds of alterations or forgeries such as substitution, insertion, and removal. The watermark pattern will be modified if some changes have been made to the speech contents. Modification and forgery can be measured and detected by pattern recovery. The proposed method uses the cyclic pattern embedding to overcome synchronizing problems of previous detection techniques. In addition, pattern recovery enhances the robustness to compression. This method has been tested and verified using six recording devices, which was used for collecting verbal data. The speech signals were sampled at the rate of 8 kHz and digitized at 16 bits resolution. Randomly chosen regions were substituted, removed, and compressed in MP3 at the rate of 16 kbps as well as in CELP at the rate of 11.5 kbps. The experiment shows the perfect detection for three kinds of forgeries and it proved the validity of the proposed method.     

Photonic crystal slabs with hexagonal air holes fabricated by selective area metal organic vapor phase epitaxy

It is shown that the photonic crystal slab (PCS) with hexagonal air holes has band gaps in the guided mode spectrum, which can be compared to that of the PCS with circular air holes, thus it is also a good candidate to be used for the PC devices. The PC with hexagonal air holes and a = 0.5 μm and r = 0.15 μm was fabricated successfully by selective area metal organic vapor phase epitaxy (SA-MOVPE). The vertical and smooth sidewalls are formed and the uniformity is very good. The same process was also used to fabricate a hexagonal air hole array with the width of 0.1 μm successfully. The air-bridge PCS with hexagonal air holes and a = 0.3 μm and r = 0.09 μm was also fabricated successfully by SA-MOVPE. Further optimization of the growth conditions for the sacrificial layer and the selective etching of the GaAs cap layer is also needed. Our experimental results indicate that SA-MOVPE is a promising method for fabricating PC devices and photonic nanostructures.     

High speed detection of potato and clod using an acoustic based intelligent system

Discriminating between potato tubers and clods is the first step in developing an automatic separation system on potato harvesters. In this study, an acoustic-based intelligent system was developed for high speed discriminating between potato tubers and soil clods. About 500 kg mixture of potato tubers and clods were loaded on a belt conveyer and were impacted against a steel plate at four different velocities. The resulting acoustic signals were recorded, processed and potential features were extracted from the analysis of sound signals in both time and frequency domains. A multilayer perceptron neural network with a back propagation algorithm was used for pattern recognition. Altogether, 17 potential discriminating features were selected and fed as input vectors to the artificial neural network models. Optimal network was selected based on mean square error, correct detection rate and correlation coefficient. At the belt velocity of 1 m s^?1, detection accuracy of the presented system was about 97.3% and 97.6% for potatoes and clods, respectively. Increasing the belt velocity resulted in the reduction of detection accuracy and increase in the number of miss classified samples. By using this system, it is expected that a potato harvester may operate at a capacity of 20 ton hr^?1 with the accuracy of about 97%.     

Wavelet based shock wave and muzzle blast classification for different supersonic projectiles

The article presents a pattern recognition approach to acoustic shock wave and muzzle blast detection. Gunshot signatures are divided into multiple classes, given by combination of 3 types of supersonic weapons of different caliber: 7.62 mm, 5.56 mm and 9 mm and 3 types of acoustic events: shock wave, muzzle blast and reflections. The classification is performed on wavelet compressed 100 μs time frames. The experiment shows that the choice of a fitting wavelet base is crucial for the quality of recognition.     

Application of artificial neural network (ANN)–self-organizing map (SOM) for the categorization of water,soil and sediment quality in petrochemical regions

The utilization of mathematical and computational tools for pollutant assessment frameworks has become increasingly valuable due to the capability to interpret integrated variable measurements. Artificial neural networks (ANNs) are considered as dependable and inexpensive techniques for data interpretation and prediction. The self-organizing map (SOM) is an unsupervised ANN used for data training to classify and effectively recognize patterns embedded in the input data space. Application of SOM–ANN is useful for recognizing spatial patterns in contaminated zones by integrating chemical, physical, ecotoxicological and toxicokinetic variables in the identification of pollution sources and similarities in the quality of the samples. Water (n = 11), soil (n = 38) and sediment (n = 54) samples from four areas in the Niger Delta (Nigeria) were classified based on their chemical, toxicological and physical variables applying the SOM. The results obtained in this study provided valuable assessment using the SOM visualization capabilities and highlighted zones of priority that might require additional investigations and also provide productive pathway for effective decision making and remedial actions.     

Consensus scoring model for the molecular docking study of mTOR kinase inhibitor

The discovery of mammalian target of rapamycin (mTOR) kinase inhibitors has always been a research hotspot of antitumor drugs. Consensus scoring used in the docking study of mTOR kinase inhibitors usually improves hit rate of virtual screening. Herein, we attempt to build a series of consensus scoring models based on a set of the common scoring functions. In this paper, twenty-five kinds of mTOR inhibitors (16 clinical candidate compounds and 9 promising preclinical compounds) are carefully collected, and selected for the molecular docking study used by the Glide docking programs within the standard precise (SP) mode. The predicted poses of these ligands are saved, and revaluated by twenty-six available scoring functions, respectively. Subsequently, consensus scoring models are trained based on the obtained rescoring results by the partial least squares (PLS) method, and validated by Leave-one-out (LOO) method. In addition, three kinds of ligand efficiency indices (BEI, SEI, and LLE) instead of pIC₅₀ as the activity could greatly improve the statistical quality of build models. Two best calculated models 10 and 22 using the same BEI indice have following statistical parameters, respectively: for model 10, training set R² = 0.767, Q² = 0.647, RMSE = 0.024, and for test set R² = 0.932, RMSE = 0.026; for model 22, raining set R² = 0.790, Q² = 0.627, RMSE = 0.023, and for test set R² = 0.955, RMSE = 0.020. These two consensus scoring model would be used for the docking virtual screening of novel mTOR inhibitors.     

Tuning and hybrid parallelization of a genetic-based multi-point statistics simulation code

One of the main difficulties using multi-point statistical (MPS) simulation based on annealing techniques or genetic algorithms concerns the excessive amount of time and memory that must be spent in order to achieve convergence. In this work we propose code optimizations and parallelization schemes over a genetic-based MPS code with the aim of speeding up the execution time. The code optimizations involve the reduction of cache misses in the array accesses, avoid branching instructions and increase the locality of the accessed data. The hybrid parallelization scheme involves a fine-grain parallelization of loops using a shared-memory programming model (OpenMP) and a coarse-grain distribution of load among several computational nodes using a distributed-memory programming model (MPI). Convergence, execution time and speed-up results are presented using 2D training images of sizes 100 × 100 × 1 and 1000 × 1000 × 1 on a distributed-shared memory supercomputing facility.     

Fabrication and chromatic characteristics of the greenish LCD colour-filter layer with nano-particle ink using inkjet printing technique

Inkjet-printed greenish color filter layer for the application of active-matrix TFT-LCD had been significantly fabricated by the modified inkjet printing technology. The greenish ink with nano-particle pigments was injected in the stripe-type pattern with 90 μm in width and 250 μm in length by pre-designed inkjet head and driving system, and only green color was tested. The height of transparent rib wall, prepared by lithographic processes, on black matrix is 5.0 μm. The chromatic coordinates of color filter with greenish subpixel patterns in CIE 1931 standard diagram can achieve in the region of x=0.3095±0.04, y=0.5912±0.04, brightness of Y=58.887 for 50 droplets and x=0.3103±0.04, y=0.5784±0.04, brightness of Y=60.328 for 41 droplets. The structural surface morphologies of the greenish subpixel patterns remain homogenous, smooth and flatten.     

Damaged corporate reputation: Can celebrity Tweets repair it?

These days, many corporations engage in Twitter activities as a part of their communication strategy. Corporations can use this medium to share information with stakeholders, to answer customer questions, or to build on their image. In this study we examined the extent to which celebrity Tweet messages can be used to repair a damaged corporate reputation, and how this message should be designed and what celebrity should be ‘used’.In two experiments, a 2 × 2 (attractive celebrity versus intelligent celebrity) × (personal message versus general message) design was used. In total, 163 respondents first expressed their feelings regarding the two organisations in a baseline reputation measurement (M = 4.72 on 7 point Likert scale). After that a news items was presented communicating a big fraud and mismanagement, resulting in a decreased reputation score (M = 4.10). In the final stage one of the four experimental Tweets was presented, aimed at repairing the damaged reputation, which succeeded (M = 4.43). For both organisations, the crisis prime significantly decreased reputation scores, and the Tweet significantly increased reputation score again. The analysis of variance shows a main effect for type of celebrity. In our experiment the intelligent celebrity’s Tweet was best to use.The study reveals that celebrities’ Tweets can restore a positive public opinion about corporations. This study shows that when it comes to serious matters, an intelligent celebrity, who has the best fit with the topic, is of best impact. Consequences for corporate communication and future research are discussed.     

Bivariate quality control using two-stage intelligent monitoring scheme

In manufacturing industries, it is well known that process variation is a major source of poor quality products. As such, monitoring and diagnosis of variation is essential towards continuous quality improvement. This becomes more challenging when involving two correlated variables (bivariate), whereby selection of statistical process control (SPC) scheme becomes more critical. Nevertheless, the existing traditional SPC schemes for bivariate quality control (BQC) were mainly designed for rapid detection of unnatural variation with limited capability in avoiding false alarm, that is, imbalanced monitoring performance. Another issue is the difficulty in identifying the source of unnatural variation, that is, lack of diagnosis, especially when dealing with small shifts. In this research, a scheme to address balanced monitoring and accurate diagnosis was investigated. Design consideration involved extensive simulation experiments to select input representation based on raw data and statistical features, artificial neural network recognizer design based on synergistic model, and monitoring–diagnosis approach based on two-stage technique. The study focused on bivariate process for cross correlation function, ρ = 0.1–0.9 and mean shifts, μ = ±0.75–3.00 standard deviations. The proposed two-stage intelligent monitoring scheme (2S-IMS) gave superior performance, namely, average run length, ARL₁ = 3.18–16.75 (for out-of-control process), ARL₀ = 335.01–543.93 (for in-control process) and recognition accuracy, RA = 89.5–98.5%. This scheme was validated in manufacturing of audio video device component. This research has provided a new perspective in realizing balanced monitoring and accurate diagnosis in BQC.     

Automatic microstructural characterization and classification using artificial intelligence techniques on ultrasound signals

Secondary phases such as Laves and carbides are formed during the final solidification stages of nickel based superalloy coatings deposited during the gas tungsten arc welding cold wire process. However, when aged at high temperatures, other phases can precipitate in the microstructure, like the γ″ and δ phases. This work presents a new application and evaluation of artificial intelligent techniques to classify (the background echo and backscattered) ultrasound signals in order to characterize the microstructure of a Ni-based alloy thermally aged at 650 and 950 °C for 10, 100 and 200 h. The background echo and backscattered ultrasound signals were acquired using transducers with frequencies of 4 and 5 MHz. Thus with the use of features extraction techniques, i.e., detrended fluctuation analysis and the Hurst method, the accuracy and speed in the classification of the secondary phases from ultrasound signals could be studied. The classifiers under study were the recent optimum-path forest (OPF) and the more traditional support vector machines and Bayesian. The experimental results revealed that the OPF classifier was the fastest and most reliable. In addition, the OPF classifier revealed to be a valid and adequate tool for microstructure characterization through ultrasound signals classification due to its speed, sensitivity, accuracy and reliability.     

Empirical evaluation of the effects of mixed project data on learning defect predictors

ContextDefect prediction research mostly focus on optimizing the performance of models that are constructed for isolated projects (i.e. within project (WP)) through retrospective analyses. On the other hand, recent studies try to utilize data across projects (i.e. cross project (CP)) for building defect prediction models for new projects. There are no cases where the combination of within and cross (i.e. mixed) project data are used together.ObjectiveOur goal is to investigate the merits of using mixed project data for binary defect prediction. Specifically, we want to check whether it is feasible, in terms of defect detection performance, to use data from other projects for the cases (i) when there is an existing within project history and (ii) when there are limited within project data.MethodWe use data from 73 versions of 41 projects that are publicly available. We simulate the two above-mentioned cases, and compare the performances of naive Bayes classifiers by using within project data vs. mixed project data.ResultsFor the first case, we find that the performance of mixed project predictors significantly improves over full within project predictors (p-value < 0.001), however the effect size is small (Hedges′ g = 0.25). For the second case, we found that mixed project predictors are comparable to full within project predictors, using only 10% of available within project data (p-value = 0.002, g = 0.17).ConclusionWe conclude that the extra effort associated with collecting data from other projects is not feasible in terms of practical performance improvement when there is already an established within project defect predictor using full project history. However, when there is limited project history, e.g. early phases of development, mixed project predictions are justifiable as they perform as good as full within project models.     

Small molecules with pyridine backbone modified with carbazole,fluorine and bromine for white light-emitting diode applications

Seven compounds with pyridine as the backbone modified by carbazole moiety, bromine atom and fluorine atom were synthesized. Compounds 1, 2, 3 with bromo substitution at the 2-position and carbazole modification at the 5-position of pyridine emit not only a sharp blue singlet fluorescence but also a wide banded excimer-based orange emission. The two colors coming from a single molecule can be used to fabricate a simplified white light emitting device. The electroluminescence based on 1 and 2 exhibits white-light emission with CIE coordinates of x = 0.25 and y = 0.30 for 1 and x = 0.33 and y = 0.37 for 2 at high current densities, very close to pure white emission. In addition, the role of bromo-substitution at pyridine is concluded to be essential to generate molecular interaction thus an excimer emission.     

Speech emotion recognition: Features and classification models

To solve the speaker independent emotion recognition problem, a three-level speech emotion recognition model is proposed to classify six speech emotions, including sadness, anger, surprise, fear, happiness and disgust from coarse to fine. For each level, appropriate features are selected from 288 candidates by using Fisher rate which is also regarded as input parameter for Support Vector Machine (SVM). In order to evaluate the proposed system, principal component analysis (PCA) for dimension reduction and artificial neural network (ANN) for classification are adopted to design four comparative experiments, including Fisher + SVM, PCA + SVM, Fisher + ANN, PCA + ANN. The experimental results proved that Fisher is better than PCA for dimension reduction, and SVM is more expansible than ANN for speaker independent speech emotion recognition. The average recognition rates for each level are 86.5%, 68.5% and 50.2% respectively.