Fracture Mechanisms And Size Effects Of Brittle Metallic Foams: In Situ Compression Tests Inside Sem

In situ compressive tests on specially designed small samples made from brittle metallic foams were accomplished in a loading device equipped in the scanning electron microscopy (SEM). Each of the small samples comprises only several cells in the effective test zone (ETZ), with one major cell in the middle. In such a system one can not only obtain sequential collapse-process images of a single cell and its cell walls with high resolution, but also correlate the detailed failure behaviour of the cell walls with the stress-strain response, therefore reveal the mechanisms of energy absorption in the mesoscopic scale. Meanwhile, the stress-strain behaviour is quite different from that of bulk foams in dimensions of enough large, indicating a strong size effect. According to the in situ observations, four failure modes in the cell-wall level were summarized, and these modes account for the mesoscopic mechanisms of energy absorption. Paralleled compression tests on bulk samples were also carried out, and it is found that both fracturing of a single cell and developing of fracture bands are defect-directed or weakness-directed processes. The mechanical properties of the brittle aluminum foams obtained from the present tests agree well with the size effect model for ductile cellular solids proposed by Onck et al. (C) 2008 Elsevier Ltd. All rights reserved.

Tensile Tests Of Micro Anchors Anodically Bonded Between Pyrex Glass And Aluminum Thin Film Coated On Silicon Wafer

Micro anchor is a kind of typical structures in micro/nano electromechanical systems (MEMS/NEMS), and it can be made by anodic bonding process, with thin films of metal or alloy as an intermediate layer. At the relative low temperature and voltage, specimens with actually sized micro anchor structures were anodically bonded using Pyrex 7740 glass and patterned crystalline silicon chips coated with aluminum thin film with a thickness comprised between 50 nm and 230 nm. To evaluate the bonding quality, tensile pulling tests have been finished with newly designed flexible fixtures for these specimens. The experimental results exhibit that the bonding tensile strength increases with the bonding temperature and voltage, but it decreases with the increase of the thickness of Al intermediate layer. This kind of thickness effect of the intermediate layer was not mentioned in the literature on anodic bonding. (C) 2008 Elsevier Ltd. All rights reserved.

Factors Resulting In Micron Indentation Hardness Descending In Indentation Tests

Some factors that affect the experimental results in nanoindentation tests such as the contact depth, contact area, load and loading duration are analyzed in this article. Combining with the results of finite element numerical simulation, we find that the creep property of the tested material is one of the important factors causing the micron indentation hardness descending with the increase of indentation depth. The analysis of experimental results with different indentation depths demonstrates that the hardness decrease can be bated if the continuous stiffness measurement technique is not adopted; this indicates that the test method itself may also be one of the factors causing the hardness being descended.

Attention deficit/hyperactivity disorder children with a 7-repeat allele of the dopamine receptor D4 gene have extreme behavior but normal performance on critical neuropsychological tests of attention

An association of the dopamine receptor D4 (DRD4) gene located on chromosome 11p15.5 and attention deficit/hyperactivity disorder (ADHD) has been demonstrated and replicated by multiple investigators. A specific allele [the 7-repeat of a 48-bp variable number of tandem repeats (VNTR) in exon 3] has been proposed as an etiological factor in attentional deficits manifested in some children diagnosed with this disorder. In the current study, we evaluated ADHD subgroups defined by the presence or absence of the 7-repeat allele of the DRD4 gene, using neuropsychological tests with reaction time measures designed to probe attentional networks with neuroanatomical foci in D4-rich brain regions. Despite the same severity of symptoms on parent and teacher ratings for the ADHD subgroups, the average reaction times of the 7-present subgroup showed normal speed and variability of response whereas the average reaction times of the 7-absent subgroup showed the expected abnormalities (slow and variable responses). This was opposite the primary prediction of the study. The 7-present subgroup seemed to be free of some of the neuropsychological abnormalities thought to characterize ADHD.

Neutrality tests using DNA polymorphism from multiple samples

The polymorphism of a gene or a locus is studied with increasing frequency by multiple laboratories or the same group at different times. Such practice results in polymorphism being revealed by different samples at different regions of the locus. Tests of neutrality have been widely conducted for polymorphism data but commonly used statistical tests cannot be applied directly to such data. This article provides a procedure to conduct a neutrality test and details are given for two commonly used tests. Applying the two new tests to the chemokine-receptor gene (CCR5) in humans, we found that the hypothesis that all mutations are selectively neutral cannot explain the observed pattern of DNA polymorphism.

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.

Information geometry theory of high-dimension space and application for speaker independent continuous digit speech recognition

In the light of descriptive geometry and notions in set theory, this paper re-defines the basic elements in space such as curve and surface and so on, presents some fundamental notions with respect to the point cover based on the High-dimension space (HDS) point covering theory, finally takes points from mapping part of speech signals to HDS, so as to analyze distribution information of these speech points in HDS, and various geometric covering objects for speech points and their relationship. Besides, this paper also proposes a new algorithm for speaker independent continuous digit speech recognition based on the HDS point dynamic searching theory without end-points detection and segmentation. First from the different digit syllables in real continuous digit speech, we establish the covering area in feature space for continuous speech. During recognition, we make use of the point covering dynamic searching theory in HDS to do recognition, and then get the satisfying recognized results. At last, compared to HMM (Hidden Markov models)-based method, from the development trend of the comparing results, as sample amount increasing, the difference of recognition rate between two methods will decrease slowly, while sample amount approaching to be very large, two recognition rates all close to 100% little by little. As seen from the results, the recognition rate of HDS point covering method is higher than that of in HMM (Hidden Markov models) based method, because, the point covering describes the morphological distribution for speech in HDS, whereas HMM-based method is only a probability distribution, whose accuracy is certainly inferior to point covering.

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.

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.

A speaker-independent continuous speech recognition system using biomimetic pattern recognition

In speaker-independent speech recognition, the disadvantage of the most diffused technology (HMMs, or Hidden Markov models) is not only the need of many more training samples, but also long train time requirement. This paper describes the use of Biomimetic pattern recognition (BPR) in recognizing some mandarin continuous speech in a speaker-independent manner. A speech database was developed for the course of study. The vocabulary of the database consists of 15 Chinese dish's names, the length of each name is 4 Chinese words. Neural networks (NNs) based on Multi-weight neuron (MWN) model are used to train and recognize the speech sounds. The number of MWN was investigated to achieve the optimal performance of the NNs-based BPR. This system, which is based on BPR and can carry out real time recognition reaches a recognition rate of 98.14% for the first option and 99.81% for the first two options to the persons from different provinces of China speaking common Chinese speech. Experiments were also carried on to evaluate Continuous density hidden Markov models (CDHMM), Dynamic time warping (DTW) and BPR for speech recognition. The Experiment results show that BPR outperforms CDHMM and DTW especially in the cases of samples of a finite size.

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.

Continuous speech research based on HyperSausage Neuron

In this paper, we presents HyperSausage Neuron based on the High-Dimension Space(HDS), and proposes a new algorithm for speaker independent continuous digit speech recognition. At last, compared to HMM-based method, the recognition rate of HyperSausage Neuron method is higher than that of in HMM-based method.