The application of DBF neural networks for object recognition

On the basis of DBF nets proposed by Wang Shoujue, the model and properties of DBF neural network were discussed in this paper. When applied in pattern recognition, the algorithm and implement on hardware were presented respectively. We did experiments on recognition of omnidirectionally oriented rigid objects on the same level, using direction basis function neural networks, which acts by the method of covering the high dimensional geometrical distribution of the sample set in the feature space. Many animal and vehicle models (even with rather similar shapes) were recognized omnidirectionally thousands of times. For total 8800 tests, the correct recognition rate is 98.75%, the error rate and the rejection rate are 0.5% and 1.25% respectively. (C) 2003 Elsevier Inc. All rights reserved.

Object-recognition with oblique observation directions based on biomimetic pattern recognition

In this paper, we propose a new scheme for omnidirectional object-recognition in free space. The proposed scheme divides above problem into several onmidirectional object-recognition with different depression angles. An onmidirectional object-recognition system with oblique observation directions based on a new recognition theory-Biomimetic Pattern Recognition (BPR) is discussed in detail. Based on it, we can get the size of training samples in the onmidirectional object-recognition system in free space. Omnidirection ally cognitive tests were done on various kinds of animal models of rather similar shapes. For the total 8400 tests, the correct recognition rate is 99.89%. The rejection rate is 0.11% and on the condition of zero error rates. Experimental results are presented to show that the proposed approach outperforms three types of SVMs with either a three degree polynomial kernel or a radial basis function kernel.

Biomimetic (topological) pattern recognition - A new model of pattern recognition theory and its application

A new theoretical model of Pattern Recognition principles was proposed, which is based on "matter cognition" instead of "matter classification" in traditional statistical Pattern Recognition. This new model is closer to the function of human being, rather than traditional statistical Pattern Recognition using "optimal separating" as its main principle. So the new model of Pattern Recognition is called the Biomimetic Pattern Recognition (BPR)(1). Its mathematical basis is placed on topological analysis of the sample set in the high dimensional feature space. Therefore, it is also called the Topological Pattern Recognition (TPR). The fundamental idea of this model is based on the fact of the continuity in the feature space of any one of the certain kinds of samples. We experimented with the Biomimetic Pattern Recognition (BPR) by using artificial neural networks, which act through covering the high dimensional geometrical distribution of the sample set in the feature space. Onmidirectionally cognitive tests were done on various kinds of animal and vehicle models of rather similar shapes. For the total 8800 tests, the correct recognition rate is 99.87%. The rejection rate is 0.13% and on the condition of zero error rates, the correct rate of BPR was much better than that of RBF-SVM.

On the Gas-Liquid Two-Phase Flow Regimes and their Recognition Methods

气液两相流体系是一个复杂的多变量随机过程体系,流型的定义、流型过渡准则和判别方法等方面的研究是多相流学科目前研究的重点内容。本文就与气液两相流流型及其判别有关的研究状况进行了回顾和评述,力图反映近年来气液两相流流型及其判别问题研究的状态和趋势。

Pattern Recognition With Weighted Complex Networks

In this paper we introduce a weighted complex networks model to investigate and recognize structures of patterns. The regular treating in pattern recognition models is to describe each pattern as a high-dimensional vector which however is insufficient to express the structural information. Thus, a number of methods are developed to extract the structural information, such as different feature extraction algorithms used in pre-processing steps, or the local receptive fields in convolutional networks. In our model, each pattern is attributed to a weighted complex network, whose topology represents the structure of that pattern. Based upon the training samples, we get several prototypal complex networks which could stand for the general structural characteristics of patterns in different categories. We use these prototypal networks to recognize the unknown patterns. It is an attempt to use complex networks in pattern recognition, and our result shows the potential for real-world pattern recognition. A spatial parameter is introduced to get the optimal recognition accuracy, and it remains constant insensitive to the amount of training samples. We have discussed the interesting properties of the prototypal networks. An approximate linear relation is found between the strength and color of vertexes, in which we could compare the structural difference between each category. We have visualized these prototypal networks to show that their topology indeed represents the common characteristics of patterns. We have also shown that the asymmetric strength distribution in these prototypal networks brings high robustness for recognition. Our study may cast a light on understanding the mechanism of the biologic neuronal systems in object recognition as well.

CALL混杂复合材料的弯曲试验研究

用云纹干涉法，研究了单向增强CALL混杂复合材料在纤维方向和垂直于纤维方向的弯曲特征。

An experimental-study of the bending behavior of call hybrid composites

The bending behavior and damage characteristics of CALL (Carbon fiber/epoxy/AL Laminate) hybrid composites have been studied by moire interferometry. The shear strain distribution along the cross-section and the forms of damage of bending beams are obtained. The results show that the magnitude of the shear strain in a carbon/epoxy layer is obviously larger than that in a corresponding aluminum layer and the shear strain distribution of a CFRP layer along the cross-section conforms basically to a parabolic distribution curve, as for the shear strain distribution in aluminum layers along the cross-section. Shear damage, either in the interfaces or in carbon-fiber/epoxy laminae, and tensile failure of CFRP laminae in the tension surface represent, respectively, the damage forms of the longitudinal and transverse bending specimen.

Visual pattern recognition network: Its training algorithm and its optoelectronic architecture

A visual pattern recognition network and its training algorithm are proposed. The network constructed of a one-layer morphology network and a two-layer modified Hamming net. This visual network can implement invariant pattern recognition with respect to image translation and size projection. After supervised learning takes place, the visual network extracts image features and classifies patterns much the same as living beings do. Moreover we set up its optoelectronic architecture for real-time pattern recognition. (C) 1996 Optical Society of America

Ultrafast temporal pattern generation and recognition

Ultrafast temporal pattern generation and recognition with femtosecond laser technology is presented, analyzed, and experimentally implemented. Ultrafast temporal pattern generation and recognition are realized by taking advantage of two well-known techniques: the space-time conversion technique and the ultrafast pulse measurement technique. Here the temporal pattern for the designed multiple pulses, optimized with a preassumed Gaussian spectral distribution of an ultrashort pulse, is described. With the simulation of a Gaussian spectral distribution, we realize that the uniformity of the generated multiple ultrafast temporal pulses is relevant to the repeated number of modulation periods in the mask in the spectral plane. Moreover, the change of Gaussian spectral phases with the wavelengths in the modulated phase plate is considered. Experiments of ultrafast temporal pattern recognition by the frequency-resolved optical gating (FROG) characterization technique are also given. (C) 2004 Society of Photo-Optical Instrumentation Engineers.

Direct detection of Yersinia pestis from the infected animal specimens by a fiber optic biosensor

The FOB-3, anew type fiber optic biosensor, is designed to rapidly detect a variety of biological agents or analytes with better stability, sensitivity and specificity. In order to detect Y. Pestis, a sandwich immunoassay was developed by using the purified antibody against antigen FI immobilized on polystyrene probes as the capture antibody and the monoclonal antibody-Cy5 conjugate as the detector. After a series of optimization for the stability, sensitivity and specificity of the FOB-3, 50-1000 ng/ml of antigen FI and 6 x 10(1)-6 x 10(7) CFU/ml Y. pestis could be detected constantly in about 20 min, and Y pestis could be detected specifically from Y. pseudotuberculosis, Y. enterocolitica, B. anthracis and E. coli. Then, 39 blind samples, including 27 tissues of mice infected with Y pestis and 12 tissues of healthy mice as negative control, were detected with the FOB-3. 92.6% infected tissues were identified from the tissues of healthy mice and the tissues containing more than 100 CFU/ml bacteria could be detected by the biosensor. The results demonstrated the feasibility of the FOB-3 as an effective method to detect Y. pestis rapidly and directly from the infected animal specimens with the advantage of portability, simple-operation as well as high sensitivity and specificity. (c) 2006 Elsevier B.V. All rights reserved.

Effects of morphine and its withdrawal on Y-maze spatial recognition memory in mice

Effects of morphine on acquisition and retrieval of memory have been proven in the avoidance paradigms. In present study, we used a two-trial recognition Y-maze to test the effects of acute morphine and morphine withdrawal on spatial recognition memory. T

Genetic diversity and conservation of endangered animal species

The loss of biodiversity resulting from extinctions is receiving increasing attention. Over several thousands of animal species have been evaluated and recognized as endangered species. Inbreeding depression has been demonstrated in many wild animal speci

Effect of Co-administration of Morphine and Cholinergic Antagonists on Y-maze Spatial Recognition Memory Retrieval and Locomotor Activity in Mice

吗啡和胆碱能系统的相互作用已在多项研究中提到,本实验想查明吗啡是否能和胆碱能拮抗剂、东莨菪碱以及阿托品共同作用对小鼠的Y迷宫空间识别记忆提取产生影响.采用测试前腹腔给药的方法,选用3种剂量的吗啡(5、1.5、0.5mg/kg),两种剂量的东莨菪碱(1、0.1mg/kg),以及两种剂量的阿托品(0.5、0.1mg/kg),剂量由高到低相配对作为联合给药的手段.其结果表明:1)0.5mg/kg低剂量吗啡与0.1 mg/kg低剂量的东莨菪碱,或与0.1 mg/kg低剂最的阿托品联合给药的小鼠,在记忆提取测试中, 空间探查行为(各臂停留时间百分比)对新异臂没有偏好,而新奇探索行为(各臂访问次数百分比)仍保持了对新异臂的偏好,而相应剂最药物单独给药的小鼠记忆提取均没有被损害;2)吗啡能和东莨菪碱相互作用使小鼠的活动性显著增强.暗示吗啡和胆碱能拮抗剂对小鼠空间记忆提取的破坏存在一定程度的相互作用.

Improved spatial recognition memory in mice lacking adenosine A(2A) receptors

Adenosine receptors play an important role in learning and memory as their antagonists have been found to facilitate learning and memory in various tasks in rodents. However, few studies have examined the effect of adenosine A(2A) receptor deficiency on c