Deformation Behavior Of Fe-Based Bulk Metallic Glass During Nanoindentation

Fe-based bulk metallic glasses (BMGs) normally exhibit super high strength but significant brittleness at ambient temperature. Therefore, it is difficult to investigate the plastic deformation behavior and mechanism in these alloys through conventional tensile and compressive tests due to lack of distinct macroscopic plastic strain. In this work, the deformation behavior of Fe52Cr15Mo9Er3C15B6 BMG was investigated through instrumented nanoindentation and uniaxial compressive tests. The results show that serrated flow, the typical plastic deformation feature of BMGs, could not be found in as-cast and partially crystallized samples during nanoindentation. In addition, the deformation behavior and mechanical properties of the alloy are insensitive to the applied loading rate. The mechanism for the appearance of the peculiar deformation behavior in the Fe-based BMG is discussed in terms of the temporal and spatial characteristics of shear banding during nanoindentation.

Plastic deformation in Ce-based bulk metallic glasses during depth-sensing indentation

The deformation behavior and the effect of the loading rate on the plastic deformation features in (numbers indicate at.%) Ce60Al15Cu10Ni15, Ce65Al10Cu10Ni10Nb5, Ce68Al10Cu20Nb2, and Ce70Al10Cu20 bulk metallic glasses (BMGs) were investigated through nanoindentation. The load-displacement (P-h) curves of Ce65Al10Cu10Ni10Nb5, Ce68Al10Cu2, and Ce70Al10Cu20 BMGs exhibited a continuous plastic deformation at all studied loading rate. Whereas, the P-h curves of Ce60Al15Cu10Ni15 BMG showed a quite unique feature, i.e. homogeneous plastic deformation at low loading rates, and a distinct serrated flow at high strain rates. Moreover, a creep deformation during the load holding segment was observed for the four Ce-based BMGs at room temperature. The mechanism for the appearance of the "anomalous" plastic deformation behavior in the Ce-based BMGs was discussed. (c) 2006 Elsevier B.V. All rights reserved.

Donut-Shaped Fingerprint In Homologous Polypeptide Relationships-A Topological Feature Related To Pathogenic Structural Changes In Conformational Disease

Features of homologous relationship of proteins can provide us a general picture of protein universe, assist protein design and analysis, and further our comprehension of the evolution of organisms. Here we carried Out a Study of the evolution Of protein molecules by investigating homologous relationships among residue segments. The motive was to identify detailed topological features of homologous relationships for short residue segments in the whole protein universe. Based on the data of a large number of non-redundant Proteins, the universe of non-membrane polypeptide was analyzed by considering both residue mutations and structural conservation. By connecting homologous segments with edges, we obtained a homologous relationship network of the whole universe of short residue segments, which we named the graph of polypeptide relationships (GPR). Since the network is extremely complicated for topological transitions, to obtain an in-depth understanding, only subgraphs composed of vital nodes of the GPR were analyzed. Such analysis of vital subgraphs of the GPR revealed a donut-shaped fingerprint. Utilization of this topological feature revealed the switch sites (where the beginning of exposure Of previously hidden "hot spots" of fibril-forming happens, in consequence a further opportunity for protein aggregation is Provided; 188-202) of the conformational conversion of the normal alpha-helix-rich prion protein PrPC to the beta-sheet-rich PrPSc that is thought to be responsible for a group of fatal neurodegenerative diseases, transmissible spongiform encephalopathies. Efforts in analyzing other proteins related to various conformational diseases are also introduced. (C) 2009 Elsevier Ltd. All rights reserved.

A scheme for multiple sequences alignment optimization-an improvement based on family representative mechanics features

As a basic tool of modern biology, sequence alignment can provide us useful information in fold, function, and active site of protein. For many cases, the increased quality of sequence alignment means a better performance. The motivation of present work is to increase ability of the existing scoring scheme/algorithm by considering residue–residue correlations better. Based on a coarse-grained approach, the hydrophobic force between each pair of residues is written out from protein sequence. It results in the construction of an intramolecular hydrophobic force network that describes the whole residue–residue interactions of each protein molecule, and characterizes protein's biological properties in the hydrophobic aspect. A former work has suggested that such network can characterize the top weighted feature regarding hydrophobicity. Moreover, for each homologous protein of a family, the corresponding network shares some common and representative family characters that eventually govern the conservation of biological properties during protein evolution. In present work, we score such family representative characters of a protein by the deviation of its intramolecular hydrophobic force network from that of background. Such score can assist the existing scoring schemes/algorithms, and boost up the ability of multiple sequences alignment, e.g. achieving a prominent increase (50%) in searching the structurally alike residue segments at a low identity level. As the theoretical basis is different, the present scheme can assist most existing algorithms, and improve their efficiency remarkably.

A reliable phase unwrapping algorithm based on the local fitting plane and quality map

A fast and reliable phase unwrapping (PhU) algorithm, based on the local quality-guided fitting plane, is presented. Its framework depends on the basic plane-approximated assumption for phase values of local pixels and on the phase derivative variance (PDV) quality map. Compared with other existing popular unwrapping algorithms, the proposed algorithm demonstrated improved robustness and immunity to strong noise and high phase variations, given that the plane assumption for local phase is reasonably satisfied. Its effectiveness is demonstrated by computer-simulated and experimental results.

A high-speed image sensing technique with adjustable frame rate based on an ordinary CCD

In the sinusoidal phase modulating interferometer technique, the high-speed CCD is necessary to detect the interference signals. The reason of ordinary CCD's low frame rate was analyzed, and a novel high-speed image sensing technique with adjustable frame rate based on ail ordinary CCD was proposed. And the principle of the image sensor was analyzed. When the maximum frequency and channel bandwidth were constant, a custom high-speed sensor was designed by using the ordinary CCD under the control of the special driving circuit. The frame rate of the ordinary CCD has been enhanced by controlling the number of pixels of every frame; therefore, the ordinary of CCD can be used as the high frame rate image sensor with small amount of pixels. The multi-output high-speed image sensor has the deficiencies of low accuracy, and high cost, while the high-speed image senor with small number of pixels by using this technique can overcome theses faults. The light intensity varying with time was measured by using the image sensor. The frame rate was LIP to 1600 frame per second (f/s), and the size of every frame and the frame rate were adjustable. The correlation coefficient between the measurement result and the standard values were higher than 0.98026, and the relative error was lower than 0.53%. The experimental results show that this sensor is fit to the measurements of sinusoidal phase modulating interferometer technique. (c) 2007 Elsevier GmbH. All rights reserved.

Study on Closed-set Speaker Identification Based on Biomimetic Pattern Recognition

In this paper, a new classifier of speaker identification has been proposed, which is based on Biomimetic pattern recognition (BPR). Distinguished from traditional speaker recognition methods, such as DWT, HMM, GMM, SVM and so on, the proposed classifier is constructed by some finite sub-space which is reasonable covering of the points in high dimensional space according to distributing characteristic of speech feature points. It has been used in the system of speaker identification. Experiment results show that better effect could be obtained especially with lesser samples. Furthermore, the proposed classifier employs a much simpler modeling structure as compared to the GMM. In addition, the basic idea "cognition" of Biomimetic pattern recognition (BPR) results in no requirement of retraining the old system for enrolling new speakers.

Experimental demonstration of distributed feedback semiconductor lasers based on reconstruction-equivalent-chirp technology

In this paper we report, to the best of our knowledge, the first experimental realization of distributed feedback (DFB) semiconductor lasers based on reconstruction-equivalent-chirp (REC) technology. Lasers with different lasing wavelengths are achieved simultaneously on one chip, which shows a potential for the REC technology in combination with the photonic integrated circuits (PIC) technology to be a possible method for monolithic integration, in that its fabrication is as powerful as electron beam technology and the cost and time-consuming are almost the same as standard holographic technology. (C) 2009 Optical Society of America

Digits speech recognition based on geometrical learning

We investigate the use of independent component analysis (ICA) for speech feature extraction in digits speech recognition systems.We observe that this may be true for a recognition tasks based on geometrical learning with little training data. In contrast to image processing, phase information is not essential for digits speech recognition. We therefore propose a new scheme that shows how the phase sensitivity can be removed by using an analytical description of the ICA-adapted basis functions via the Hilbert transform. Furthermore, since the basis functions are not shift invariant, we extend the method to include a frequency-based ICA stage that removes redundant time shift information. The digits speech recognition results show promising accuracy, Experiments show method based on ICA and geometrical learning outperforms HMM in different number of train samples.

Method for improving classification performance of neural network based on fuzzy input and network inversion

In order to effectively improve the classification performance of neural network, first architecture of fuzzy neural network with fuzzy input was proposed. Next a cost function of fuzzy outputs and non-fuzzy targets was defined. Then a learning algorithm from the cost function for adjusting weights was derived. And then the fuzzy neural network was inversed and fuzzified inversion algorithm was proposed. Finally, computer simulations on real-world pattern classification problems examine the effectives of the proposed approach. The experiment results show that the proposed approach has the merits of high learning efficiency, high classification accuracy and high generalization capability.

Mandarin speech emotion recognition based on high dimensional geometry theory

In this paper, a novel approach for mandarin speech emotion recognition, that is mandarin speech emotion recognition based on high dimensional geometry theory, is proposed. The human emotions are classified into 6 archetypal classes: fear, anger, happiness, sadness, surprise and disgust. According to the characteristics of these emotional speech signals, the amplitude, pitch frequency and formant are used as the feature parameters for speech emotion recognition. The new method called high dimensional geometry theory is applied for recognition. Compared with traditional GSVM model, the new method has some advantages. It is noted that this method has significant values for researches and applications henceforth.

Viruses' complete genomes classification based on geometrical learning

In this paper, we proposed a method of classification for viruses' complete genomes based on graph geometrical theory in order to viruses classification. Firstly, a model of triangular geometrical graph was put forward, and then constructed feature-space-samples-graphs for classes of viruses' complete genomes in feature space after feature extraction and normalization. Finally, we studied an algorithm for classification of viruses' complete genomes based on feature-space-samples-graphs. Compared with the BLAST algorithm, experiments prove its efficiency.

Research on continuous speech recognition with small vocabulary based on biomimetic pattern recognition

Based on biomimetic pattern recognition theory, we proposed a novel speaker-independent continuous speech keyword-spotting algorithm. Without endpoint detection and division, we can get the minimum distance curve between continuous speech samples and every keyword-training net through the dynamic searching to the feature-extracted continuous speech. Then we can count the number of the keywords by investigating the vale-value and the numbers of the vales in the curve. Experiments of small vocabulary continuous speech with various speaking rate have got good recognition results and proved the validity of the algorithm.

Cancer subtypes recognition and its gene expression profiles analysis based on geometrical learning

The accurate recognition of cancer subtypes is very significant in clinic. Especially, the DNA microarray gene expression technology is applied to diagnosing and recognizing cancer types. This paper proposed a method of that recognized cancer subtypes based on geometrical learning. Firstly, the cancer genes expression profiles data was pretreated and selected feature genes by conventional method; then the expression data of feature genes in the training samples was construed each convex hull in the high-dimensional space using training algorithm of geometrical learning, while the independent test set was tested by the recognition algorithm of geometrical learning. The method was applied to the human acute leukemia gene expression data. The accuracy rate reached to 100%. The experiments have proved its efficiency and feasibility.

Continuous speech recognition based on ICA and geometrical learning

We investigate the use of independent component analysis (ICA) for speech feature extraction in digits speech recognition systems. We observe that this may be true for recognition tasks based on Geometrical Learning with little training data. In contrast to image processing, phase information is not essential for digits speech recognition. We therefore propose a new scheme that shows how the phase sensitivity can be removed by using an analytical description of the ICA-adapted basis functions. Furthermore, since the basis functions are not shift invariant, we extend the method to include a frequency-based ICA stage that removes redundant time shift information. The digits speech recognition results show promising accuracy. Experiments show that the method based on ICA and Geometrical Learning outperforms HMM in a different number of training samples.