21 resultados para Social theory and historiography

em Chinese Academy of Sciences Institutional Repositories Grid Portal


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Based on the theory of the pumping well test, the transient injection well test was suggested in this paper. The design method and the scope of application are discussed in detail. The mathematical models are developed for the short-time and long-time transient injection test respectively. A double logarithm type curve matching method was introduced for analyzing the field transient injection test data. A set of methods for the transient injection test design, experiment performance and data analysis were established. Some field tests were analyzed, and the results show that the test model and method are suitable for the transient injection test and can be used to deal with the real engineering problems.

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A new strain gradient theory which is based on energy nonlocal model is proposed in this paper, and the theory is applied to investigate the size effects in thin metallic wire torsion, ultra-thin beam bending and micro-indentation of polycrystalline copper. First, an energy nonlocal model is suggested. Second, based on the model, a new strain gradient theory is derived. Third, the new theory is applied to analyze three representative experiments.

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We studied social organization, behavior, and range use of the Yunnan snub-nosed monkey (Rhinopithecus bieti) at Wuyapiya (99 degrees 12'E, 28 degrees 30'N, the People's Republic of China) over 12 months between May 1992 and June 1994. The Wuyapiya band contained greater than or equal to 175 members and had two levels of social organization. At one level, the monkeys formed multifemale, one-male units (OMUs) similar to those of many other colobines. At another level, 15 to 18 OMUs traveled together in a cohesive band. Unlike the bands of other species of Rhinopithecus, the Wuyapiya band of R. bieti did not show seasonal fission-fusion, although some social behavior, such as male-male aggression, was seasonal. With regard to range use, the Wuyapiya band had a large home range and long daily travel distances compared with other colobines. Minimum range size in 1 year at Wuyapiya is 16.25 km(2), although there is no asymptote for range size as a function of observation time. Range size for the Wuyapiya band is 25.25 km(2) over the 2-year study and appeared to cover 100 km(2) between 1985 and 1994. The primary food of R. bieti at Wuyapiya is lichens, which are ubiquitous in fir frees. The multitiered social organization of R. bieti appears to result from the interaction of food resource characters with the forces of mate competition, with band sizes based on female responses to the spatial and temporal characteristics of lichens and subdivisions within bands based on male competition for mates.

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A fiber laser hydrophone with enhanced sensitivity is demonstrated. Two diaphragms with a hard core fixed at each center are used as the sensing element. Theoretical analysis shows that the Young's modulus of the diaphragm and the radius of the hard core have significant effect on the acoustic sensitivity. Experiments are carried out to test this effect and the performance of the hydrophone. The experimental result agrees well with the theoretical result, and a sensitivity of 7 nm/MPa has been achieved.

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Biomimetic pattern recogntion (BPR), which is based on "cognition" instead of "classification", is much closer to the function of human being. The basis of BPR is the Principle of homology-continuity (PHC), which means the difference between two samples of the same class must be gradually changed. The aim of BPR is to find an optimal covering in the feature space, which emphasizes the "similarity" among homologous group members, rather than "division" in traditional pattern recognition. Some applications of BPR are surveyed, in which the results of BPR are much better than the results of Support Vector Machine. A novel neuron model, Hyper sausage neuron (HSN), is shown as a kind of covering units in BPR. The mathematical description of HSN is given and the 2-dimensional discriminant boundary of HSN is shown. In two special cases, in which samples are distributed in a line segment and a circle, both the HSN networks and RBF networks are used for covering. The results show that HSN networks act better than RBF networks in generalization, especially for small sample set, which are consonant with the results of the applications of BPR. And a brief explanation of the HSN networks' advantages in covering general distributed samples is also given.

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In this paper, a novel mathematical model of neuron-Double Synaptic Weight Neuron (DSWN)(l) is presented. The DSWN can simulate many kinds of neuron architectures, including Radial-Basis-Function (RBF), Hyper Sausage and Hyper Ellipsoid models, etc. Moreover, this new model has been implemented in the new CASSANN-II neurocomputer that can be used to form various types of neural networks with multiple mathematical models of neurons. The flexibility of the DSWN has also been described in constructing neural networks. Based on the theory of Biomimetic Pattern Recognition (BPR) and high-dimensional space covering, a recognition system of omni directionally oriented rigid objects on the horizontal surface and a face recognition system had been implemented on CASSANN-II neurocomputer. In these two special cases, the result showed DSWN neural network had great potential in pattern recognition.

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Mechanical principles of fibre-optic disc accelerometers (FODA) different from those assumed in previous calculation methods are presented. An FODA with a high sensitivity of 82 rad/ g and a resonance frequency of 360 Hz is designed and tested. In this system, the minimum measurable demodulation phase of the phase-generated carrier (PGC) is 10(-5) rad, and the minimum acceleration reaches 120 ng theoretically. This kind of FODA, with its high responsivity, all-optic-fibre configuration, small size, light weight and stiff shell housing, ensures effective performance in practice.

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Based on the introduction of the traditional mathematical models of neurons in general-purpose neurocomputer, a novel all-purpose mathematical model-Double synaptic weight neuron (DSWN) is presented, which can simulate all kinds of neuron architectures, including Radial-Basis-Function (RBF) and Back-propagation (BP) models, etc. At the same time, this new model is realized using hardware and implemented in the new CASSANN-II neurocomputer that can be used to form various types of neural networks with multiple mathematical models of neurons. In this paper, the flexibility of the new model has also been described in constructing neural networks and based on the theory of Biomimetic pattern recognition (BPR) and high-dimensional space covering, a recognition system of omni directionally oriented rigid objects on the horizontal surface and a face recognition system had been implemented on CASSANN-H neurocomputer. The result showed DSWN neural network has great potential in pattern recognition.

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Fourth-order spatial interference of entangled photon pairs generated in the process of spontaneous parametric down-conversion pumped by a femtosecond pulse laser has been performed for the first time. In theory, it takes into account the transverse correlation between the two photons and is used to calculate the dependence of the visibility of the interference pattern obtained in Young's double-slit experiment. In this experiment, a short focal length tens and two narrow band interference filters were adopted to eliminate the effects of the broadband pump laser and improve the visibility of the interference pattern under the condition of nearly collinear light and degenerate phase matching.

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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.