Set Of Characteristic Parameters Research Articles

Constructing accurate and robust HMM/GMM models for an Arabic speech recognition system

Conventional Hidden Markov Model (HMM) based Automatic Speech Recognition (ASR) systems generally utilize cepstral features as acoustic observation and phonemes as basic linguistic units. Some of the most powerful features currently used in ASR systems are Mel-Frequency Cepstral Coefficients (MFCCs). Speech recognition is inherently complicated due to the variability in the speech signal which includes within- and across-speaker variability. This leads to several kinds of mismatch between acoustic features and acoustic models and hence degrades the system performance. The sensitivity of MFCCs to speech signal variability motivates many researchers to investigate the use of a new set of speech feature parameters in order to make the acoustic models more robust to this variability and thus improve the system performance. The combination of diverse acoustic feature sets has great potential to enhance the performance of ASR systems. This paper is a part of ongoing research efforts aspiring to build an accurate Arabic ASR system for teaching and learning purposes. It addresses the integration of complementary features into standard HMMs for the purpose to make them more robust and thus improve their recognition accuracies. The complementary features which have been investigated in this work are voiced formants and Pitch in combination with conventional MFCC features. A series of experimentations under various combination strategies were performed to determine which of these integrated features can significantly improve systems performance. The Cambridge HTK tools were used as a development environment of the system and experimental results showed that the error rate was successfully decreased, the achieved results seem very promising, even without using language models.

Multi-objective optimization of thermo-acoustic devices using teaching-learning-based optimization algorithm

The current article presents three case studies based on a multi-objective optimization approach to optimize the performance of thermo-acoustic devices by obtaining the best possible set of geometrical characteristic parameters. In case study 1, the performance of a thermo-acoustic refrigerator is measured in terms of three objectives namely, acoustic cooling load (ΦC), coefficient of performance, and acoustic power loss (W02). Each objective is assigned a weight to facilitate suitable user-defined significance. The case study 2 aims to optimize a thermoacoustic prime-mover. The influence of stack position and its length, resonator length, plate thickness, and plate spacing are considered as design variables. Two objectives namely, pressure amplitude (P) and frequency (f) are considered as objectives for multi-objective optimization of the thermo-acoustic prime-mover. In case study 3, the performance of a thermo-acoustic engine is measured in terms of five objectives namely, work output (W), viscous loss (Rv), conductive heat loss (Qcond), convective heat loss (Qconv), and radiative heat loss (Qrad). Since the multiple objectives are to be optimized simultaneously, each objective is assigned a weight to facilitate suitable user-defined significance. The multi-objective optimization is carried out by the teaching-learning-based optimization algorithm. The results of application of teaching-learning-based optimization algorithm are compared with the results of General Algebraic Modeling System, response surface methodology, and experimental results. The results of the teaching-learning-based optimization algorithm are found better compared to those given by the other approaches.

3D parametric human face modeling for personalized product design: Eyeglasses frame design case

Personalized design enhances the values added by a product or service by satisfying individual customer requirements. It has nowadays become a trend in consumer product development. This paper proposes a computational framework for personalized design of the eyeglasses frame based on parametric face modeling. A large amount of three-dimensional facial models is collected by non-contact scanning as training data. Applying principal component analysis reduces the data complexity while preserving sufficient data variance. The reduced models are modified using cross-parameterization so that they have the same mesh connectivity. Kriging characterizes the correlation between the mesh point coordinates of a face model and a set of feature parameters. The Kriging result synthesizes 3D facial geometry approximating to individual users with given parameter values. Rendering the synthesized geometry with facial images of real persons generates realistic face models. These models not only allow adjusting the frame design in real-time, but also evaluating whether or how the design style fits individual face characteristics. This study enhances the practical values of 3D anthropometric data by realizing the concept of human-centric design.

Improving the low Mach number steady state convergence of the cascaded lattice Boltzmann method by preconditioning

Cascaded lattice Boltzmann method (LBM) involves the use of central moments in a multiple relaxation time formulation in prescribing the collision step. When the goal is to simulate low Mach number stationary flows, the greater the disparity between the magnitude of fluid speed and the sound speed, the higher is the numerical stiffness, which results in relatively large number of time steps for convergence of the LBM. One way to improve the steady state convergence of the scheme is to precondition the cascaded LBM, which reduces the disparities between the characteristic speeds or equivalently those between the eigenvalues of the system. In this paper we present a new preconditioned, two-dimensional cascaded LBM, where a preconditioning parameter is introduced into the equilibrium moments as well as the forcing terms. Particular focus is given to preconditioning differently the moments due to forcing at first and second orders so as to avoid any spurious effects in the emergent macroscopic equations. A Chapman–Enskog analysis performed on this approach shows consistency to the preconditioned Navier–Stokes equations. This modified central moment based scheme is then validated for accuracy by comparison against prior analytical or numerical results for certain benchmark problems, including those involving spatially variable body forces. Finally, significant steady state convergence acceleration of the preconditioned cascaded LBM is demonstrated for a set of characteristic parameters.

Evaluating Characteristic Parameters for Carbon Dioxide in the Sanchez–Lacombe Equation of State

For many different pure substances, large numbers of competing characteristic parameter sets exist in the literature for the Sanchez–Lacombe equation of state. This is due in part to differing research requirements or differing procedures used for determining the parameters. The existing parameters for carbon dioxide are reviewed in order to determine whether a single set of parameters can describe the equation of state over large ranges of temperature and pressure. It is found that by consideration of a large collection of experiments, a good fit can be achieved over much larger temperature and pressure ranges than previously thought possible. Properties directly related to the equation of state, such as the thermal expansivity and isothermal compressibility, are also predicted well; however, as expected, properties that depend on the internal degrees of freedom of molecules, such as the specific heats, do not correlate well. Closely agreeing parameter sets are found in the literature that fit the equati...

Automatic Recognition Based on Spectral Preprocessing in Satellite Communication

Based on the spectral characteristic, automatic modulation recognition of satellite communication signal was studied in this paper. Traditional spectral analysis was based on the assumption that mean and variance of background noise spectrum did not vary with frequency. However, in actual satellite communication environment, noise spectrum did not meet the hypothesis. This paper used signal preprocessing by sliding widow least square to weaken the influence of the noise. By analyzing the power spectrum, square spectrum and the forth spectrum of common used modulated signals, the offset ratio was extracted to detect the value of single frequency and a set of characteristic parameters were extracted to recognize signals in the satellite communication without any prior knowledge. Simulation result showed that the method had high practicability, owing to being more resolvable and steady which was not sensitive to the roll-off factor and modulation index of the digital signals. The improved method could also achieve recognition accuracy in the adaptive channel and had certain practical significance.

Influence of the antioxidant on the long‐term ageing characteristics of oil–paper insulation and the deposition and migration of copper sulphide in oil‐immersed transformers

This paper investigated the influence of 2, 6-di-tert-butyl-p-cresol (DBPC) on the long-term ageing characteristics of oil–paper insulation and the deposition and migration of copper sulphide induced by the reaction of copper and dibenzyl disulphide (DBDS) in oil-immersed transformers. A thermal ageing experiment at 130°C were designed and conducted by adding different concentrations of DBPC to the naphthenic mineral oil and after the experiments a set of characteristic parameters were tested. The result indicates that the excessive concentration (≥0.6%) of DBPC will slightly increase the ageing of insulating oil and increase the carboxyl group in the mineral oil, affecting the reaction of DBDS and copper, and the insulating paper wrapped on the copper strip was also aged seriously accordingly. Meanwhile, the deposition of copper sulphide on the insulating paper is found to be slightly increased with the increase of the concentration of DBPC. When insulating oil contains DBPC and DBDS, due to migration of DBDS–Cu and DBPC–Cu in oil, the copper ions in the oil would be fluctuating at a certain period of time. Although the addition of a small concentration of DBPC can improve the antioxygenic property of the insulting oil to some extent, the transformers that contain excessive concentration of DBPC may be more harmful than beneficial.

Heidelberg-mCT-Analyzer: a novel method for standardized microcomputed-tomography-guided evaluation of scaffold properties in bone and tissue research.

Bone tissue engineering and bone scaffold development represent two challenging fields in orthopaedic research. Micro-computed tomography (mCT) allows non-invasive measurement of these scaffolds’ properties in vivo. However, the lack of standardized mCT analysis protocols and, therefore, the protocols’ user-dependency make interpretation of the reported results difficult. To overcome these issues in scaffold research, we introduce the Heidelberg-mCT-Analyzer. For evaluation of our technique, we built 10 bone-inducing scaffolds, which underwent mCT acquisition before ectopic implantation (T0) in mice, and at explantation eight weeks thereafter (T1). The scaffolds’ three-dimensional reconstructions were automatically segmented using fuzzy clustering with fully automatic level-setting. The scaffold itself and its pores were then evaluated for T0 and T1. Analysing the scaffolds’ characteristic parameter set with our quantification method showed bone formation over time. We were able to demonstrate that our algorithm obtained the same results for basic scaffold parameters (e.g. scaffold volume, pore number and pore volume) as other established analysis methods. Furthermore, our algorithm was able to analyse more complex parameters, such as pore size range, tissue mineral density and scaffold surface. Our imaging and post-processing strategy enables standardized and user-independent analysis of scaffold properties, and therefore is able to improve the quantitative evaluations of scaffold-associated bone tissue-engineering projects.

Non-destructive measurement of chlorophyll in tomato leaves using spectral transmittance

In this study, spectral transmittances were measured in the wavelength range from 300 nm to 1100 nm of tomato leaves with different chlorophyll contents and compositions, and the correlations between the spectral transmittances and the contents of chlorophyll a, chlorophyll b and total chlorophyll were analyzed. With the characteristic wavelengths of 560 nm, 650 nm, 720 nm and the reference wavelength of 940 nm, nine sets of characteristic spectral parameters were obtained. According to the results of correlation analysis and regression model exploration, characteristic spectral parameters of T940/T560, T940/T650 and log (T940/T560) among the nine sets of parameters were highly correlated to the estimated contents of chlorophyll a, chlorophyll b and total chlorophyll of tomato leaves. The relative errors of total chlorophyll and chlorophyll a/b ratio were (5.1±3.7)% and (4.9±4.3)%, respectively. Therefore, the above three characteristic spectral parameters could be applied in the rapid non-destructive estimation of the contents of chlorophyll a, chlorophyll b and total chlorophyll as well as chlorophyll a/b ratio of tomato leaves. Keywords: chlorophyll, characteristic wavelength, characteristic spectral parameter, spectral transmittance DOI: 10.3965/j.ijabe.20150805.1931 Citation: Wang J F, He D X, Song J X, Dou H J, Du W F. Non-destructive measurement of chlorophyll in tomato leaves using spectral transmittance. Int J Agric & Biol Eng, 2015; 8(5): 73－78.

Life grade recognition of rotating machinery based on Supervised Orthogonal Linear Local Tangent Space Alignment and Optimal Supervised Fuzzy C-Means Clustering

A novel life grade recognition method based on Supervised Orthogonal Linear Local Tangent Space Alignment (SOLLTSA) and Optimal Supervised Fuzzy C-Means Clustering (OSFCM) is proposed for rotating machinery in this paper. Firstly, the time–frequency feature parameter sets are constructed to completely extract the features of different life grades. Then, SOLLTSA is proposed to compress the time–frequency feature parameter sets of testing and training samples into low-dimensional eigenvectors with clearer clustering. Finally, the low-dimensional eigenvectors of testing and training samples are put into OSFCM to realize life grade recognition. SOLLTSA not only combines the local geometry with class information for manifold decoupling, but also solves the optimal low dimensional embedding subspace by spectral regression and subspace orthonormalization approach, thus improving the life grade feature extraction power of LLTSA. Meanwhile, OSFCM defines an optimized objective function that adopts the average distance measure between training and testing samples to lead the clustering process, and further applies training samples to partition matrix initialization for raising pattern recognition efficiency and avoiding local minimum. This allows OSFCM to have a higher life grade recognition accuracy than Fuzzy C-Means Clustering (FCM) does. A life grade recognition example for deep groove ball bearings demonstrates that the proposed method is effective in life grade recognition.

Developing and applying novel spectral feature parameters for classifying soil salt types in arid land

Soil salinization is a major desertification process that threatens especially the stability of arid ecosystems. There is an urgent need for intensive monitoring and quick assessment of salinization through remote sensing as a tool for combating desertification in such ecosystems. Recent researches have revealed that in order to retrieve soil salt contents accurately from hyperspectral reflectance, a pre-knowledge of salt types is required, which greatly outlines the spectral features of saline soil reflectance. In this study, a set of feature parameters have been developed after a thorough investigation of spectral responses to different soil salt types and salt contents for quick and accurate classification of soil salt types. The application has been validated using three independent datasets composed from: laboratory experiments (dataset I), in-situ field measurements (dataset II), and satellite-borne Hyperion image (dataset III). For comparison, four other common classification algorithms have been validated using the same datasets. The results showed that the new approach proposed in this study performed well with not only single-type but also multiple-type salts for which the four common algorithms performed rather fairly. Furthermore, validating using datasets II and III showed that the newly proposed approach had a stable performance while the other four failed, indicating the advantage of the new approach. The feature parameters developed in this study hence provide a novel and efficient approach for salt type classification from reflectance spectra, and we foresee its potential applications on large-scale soil salt type mapping towards better understanding soil salinity characterization from remote sensing data.

Dynamic behavior of adjacent buildings connected by linear viscous/viscoelastic dampers

This paper analyzes the properties of the dynamic behavior of two adjacent buildings of different height connected by viscous/viscoelastic dampers located at the top of the shortest building. The adjacent structures are described through a continuum approach as two interconnected uniform shear beams. This permits to identify the set of characteristic parameters describing the building and damper properties that completely control the dynamic behavior of the system. An analytical solution is derived for solving the eigenvalue problem via complex modal analysis, and a parametric study is carried out to evaluate the influence of these characteristic parameters on the properties of the dynamic system. A reduced-order model of the system is also developed, and simplified formulas are obtained for estimating the damping ratios of the first two modes of the coupled system. Two case studies consisting of realistic shear-type adjacent buildings coupled by viscous dampers are finally analyzed. The estimates of the modal properties obtained by using the proposed analytical approach and a finite element formulation are compared with each other. It is shown that the proposed approach provides information useful for the preliminary design of the damper properties ensuring the optimal control against seismic loadings. Copyright © 2015 John Wiley & Sons, Ltd.

Probabilistic seismic response assessment of linear systems equipped with nonlinear viscous dampers

SUMMARYThe paper aims at evaluating the influence of damper properties on the probabilistic seismic response of structural systems equipped with nonlinear viscous dampers.For this purpose, a linear single‐degree‐of‐freedom system with an added linear or nonlinear viscous damper is considered, and the response statistics are evaluated for a set of natural records describing the ground motion uncertainty. A dimensional analysis of the seismic problem is carried out first to identify the minimum set of characteristic parameters describing the system and controlling the seismic response. An extensive parametric study is then performed to estimate the influence of the damper properties on the statistics of the main response quantities of interest (i.e. maximum displacements, accelerations and damper forces), for a wide range of values of the characteristic parameters. Finally, a set of case studies is investigated to show some interesting issues concerning the influence of the damper nonlinear behaviour on the evaluation of the system reliability and to highlight some limitations of current deterministic approaches neglecting the probabilistic properties of the response. Copyright © 2014 John Wiley & Sons, Ltd.

Polarization imaging and classification of Jurkat T and Ramos B cells using a flow cytometer.

Label-free and rapid classification of cells can have awide range of applications in biology. We report a robust method of polarization diffraction imaging flow cytometry (p-DIFC) for achieving this goal. Coherently scattered light signals are acquired from single cells excited by a polarized laser beam in the form of two cross-polarized diffraction images. Image texture and intensity parameters are extracted with a gray level co-occurrence matrix (GLCM) algorithm to obtain an optimized set of feature parameters as the morphological "fingerprints" for automated cell classification. We selected the Jurkat T cells and Ramos B cells to test the p-DIFC method's capacity for cell classification. After detailed statistical analysis, we found that the optimized feature vectors yield accuracies of classification between the Jurkat and Ramos ranging from 97.8% to 100% among different cell data sets. Confocal imaging and three-dimensional reconstruction were applied to gain insights on the ability of p-DIFC method for classifying the two cell lines of highly similar morphology. Based on these results we conclude that the p-DIFC method has the capacity to discriminate cells of high similarity in their morphology with "fingerprints" features extracted from the diffraction images, which may be attributed to subtle but statistically significant differences in the nucleus-to-cell volume ratio in the case of Jurkat and Ramos cells.

A Frame work of Automatic Analysis System of Electrocardiogram Signals

The aim of this paper is twofold. First, we define an ECG feature parameter set (32 features) which could represent ECG signal as adequately as possible for diagnosing requirements. Second, we design an automatic classification framework. After benchmark point detection, feature parameter will be extracted. And then the classifier methods and its comparison based on SVM and QNN are presented. The long-term objective is to design a thorough system to realize the recognition of real-time ECG signal and enhance medical treatment.

Charactering Light and Temperature Effects on Rice Grain Filling Using Extended Richards' Equation

Richards equation has been widely used to simulate rice grain filling rates, but the lack of capability to relate environmental factors in its simulation impedes the application of Richards' equation in evaluating the impact of adverse climate on grain filling process. Therefore, a mathematical modeling method, together with two-year filed-seeding experiment(2012–2013) data, was used in this paper to examine the feasibility of an extended Richards' equation on this issue. The two-year experiment was carried out in Nanjing with two indica two-line hybrid rice cultivars, Lingliangyou 268 and Liangyoupeijiu. On the basis of the observations, Richards equation was first used to estimate rice grain filling parameters and calculate mean grain filling rate in each effective grain filling period. Subsequently, a radiation and temperature correction function was introduced into the Richards' equation to form an extended equation by linking the mean grain filling rates to daily radiation and mean temperature observations. A set of radiation and temperature characteristic parameters were included in the correction function, which theoretically reflect the response of mean grain filling rates to the change of temperature and radiation conditions and form response curves for the two tested varieties. To estimate these parameters, we applied the Shuffled Complex Evolution(SCE-UA) global optimization algorithm in this paper, since the extended equation was highly nonlinear. Statistical methods were also applied to analyze the relations between retrieved rice grain filling parameters and climatic factors. The results showed that insufficient sun radiation had an indirect and negative effect on mean grain filling rate by imposing an adverse influence on CO2 assimilation. Daily mean temperature mainly showed an evident effect on mean grain filling rate which could be accelerated under high temperatures or slow down at adverse low temperatures, but it also had a great influence on CO2 assimilation as well as the accumulation of assimilation products in grains. The estimated critical radiation and critical temperatures for Lingliangyou 268 were 18.94 MJ m–2 d–1, 6.81℃, 33.29℃, 30.28℃, respectively, and 21.71 MJ m–2 d–1, 6.10℃, 33.74℃, and 24.16℃ for Liangyoupeijiu, which demonstrate an evident difference between the two varieties. Quantitative analyses based on the temperature and radiation response curves showed that the correction coefficients for rice seeding dates in May in 2012 for both rice varieties were generally higher than that of other seeding dates, which implies that the time of seeding is critical to grain filling and yield formation. In addition, Lingliangyou 268 exhibited stronger temperature sensitivity in grain filling period, which means that the mean grain filling rate is more sensitive to the temperature change than to the radiation variation. In comparison, Liangyoupeijiu represented greater radiation sensitivity in grain filling, which indicates that this cultivar can be greatly influenced by insufficient radiation during the grain filling stage. On the whole, the mathematical model presented successfully retrieved the critical parameters representing the effects of daily mean temperature and radiation on mean grain filling rates from field observations. Although more validations are expected to examine the reliability and effectiveness of the proposed method, it can be an important reference for developing agro-meteorological indexes for monitoring and evaluating the effects of meteorological factors on rice grain filling as well as on yield formation.

Group-Contribution Method with Proximity Effect for PC-SAFT Molecular Parameters. 2. Application to Association Parameters: Primary Alcohols and Amines

A new perturbative group-contribution method has been recently developed (J. Vijande, M. M. Piñeiro, J. L. Legido, D. Bessières, Ind. Eng. Chem. Res. 2010, 49, 9394) that is intended to obtain the PC-SAFT molecular equation of state characteristic parameters of nonassociating compounds on a functional group contribution scheme basis. The method takes into account the proximity effect among functional groups within the molecule in order to describe the deviations of shorter chain molecules’ characteristic parameters from the regular trend observed for the longer chain ones. The molecular parameters are considered as a linear combination of reference functional-group parameters and their mutual perturbations, which are responsible of the deviations from the reference values, and they were optimized to the available molecular parameters of nonassociating compounds published in literature. Now, this method has been extended to associating compounds as primary alcohols and amines in order to check the ability of the proposed method to describe the complete set of characteristic parameters of PC-SAFT EoS. The results are quantitatively convincing and show the purely predictive applicability of this calculation method for homologous molecular series.

Satellite Oil Spill Detection Using Artificial Neural Networks

Oil spills represent a major threat to ocean ecosystems and their health. Illicit pollution requires continuous monitoring and satellite remote sensing technology represents an attractive option for operational oil spill detection. Previous studies have shown that active microwave satellite sensors, particularly Synthetic Aperture Radar (SAR) can be effectively used for the detection and classification of oil spills. Oil spills appear as dark spots in SAR images. However, similar dark spots may arise from a range of unrelated meteorological and oceanographic phenomena, resulting in misidentification. A major focus of research in this area is the development of algorithms to distinguish oil spills from `look-alikes'. This paper describes the development of a new approach to SAR oil spill detection employing two different Artificial Neural Networks (ANN), used in sequence. The first ANN segments a SAR image to identify pixels belonging to candidate oil spill features. A set of statistical feature parameters are then extracted and used to drive a second ANN which classifies objects into oil spills or look-alikes. The proposed algorithm was trained using 97 ERS-2 SAR and ENVSAT ASAR images of individual verified oil spills or/and look-alikes. The algorithm was validated using a large dataset comprising full-swath images and correctly identified 91.6% of reported oil spills and 98.3% of look-alike phenomena. The segmentation stage of the new technique outperformed the established edge detection and adaptive thresholding approaches. An analysis of feature descriptors highlighted the importance of image gradient information in the classification stage.

Research on modeling and fuzzy control of magneto-rheological intelligent buffer system for impact load

So far the magneto-rheological (MR) effect mechanism of MR damper has not been known completely, especially in the impact load, and the problem becomes more complicated and difficult for analyzing. A set of characteristic tests and parameters’ identification are made to the MR damper by the experimental platform. The dynamical model of the damper is constructed based on the Bingham plastic model, and the buffer control strategy of aircraft undercarriage based on MR technology is established. Finally, the fuzzy control algorithm is applied to the process of automatic control for landing buffer of aircraft undercarriage. The simulation results show that the proposed MR damper pulley buffer can effectively recognize the impact energy. The research has a better application in the engineering.

Comparison between Huynen and Cameron monostatic parameters

For coherent targets, Huynen then Cameron proposed a set of characteristic parameters deduced from monostatic full polarimetric scattering matrices. Among these parameters, the two Authors have considered two parameters connected to the same physical properties: orientation and symmetry degree. As the Huynen definition seems to be more intuitive, we will try to compare these parameters by simulations with the aim to test the real connection of the Huynen parameters to physical properties. This study will show significant differences. However, it is shown that the Huynen parameters correctly represent the orientation and the symmetry degree for nearly symmetric targets. In some cases (real eigenvalues in particular), the two orientation angles can be equal to an ambiguity of 45°, even for asymmetric targets.