抽水引起岩溶塌陷的机理及非线性预测研究

Karst collapse is one of the most important engineering geology hazards in Karst district, which seriously endangers the living of humankind and the environment around us, as well as the natural resources. Generally speaking, there exist three processes of overburden karst collapse:the formation of soil cavity, the expansion of soil cavity and the fall of the cavity roof. During these processes, groundwater is always the most active factor and plays a key role. Pumping will bring into the great change of groundwater in flow state, flowrate, frequency of fluctuation as well as hydraulic gradient and will speed the fall. Statistics shows that most of the man-made karst collapse are induced by pumping, so studying the mechanism of Karst collapse induced by pumping will provide theoretical base for the prediction and precaution of collapse. By theoretically studying the initial condition for the forming and expanding of a soil cavity, Spalling step by step the essential mechanism of Karst collapse induced by pumping is put forward. The catastrophe model for the collapse induced by pumping is set up to predict the fall probability of a cavity roof, and the criterion for the collapse is determined. Simultaneously, Karst collapse induced by pumping is predicted with manmade neural network theory. Finally, the appropriate precaution measurements for the collapse induced by pumping are provided. The creative opinions of the paper is following: The initial condition of forming a soil cavity is put forwarded as formula (4-1-5), (4-1-24),(4-1-25) and (4-1-27); which provide theoretical base for foreclosing the formation of a soil cavity and defending collapse. Spaliing step by step as the essential mechanism of Karst collapse induced by pumping is put forward. The spaliing force is defined as formula (4-2-15). The condition for the expanding of a soil cavity is that spaliing force is greater than tensile strength of soil. The stability of a soil cavity is first studied with catastrophe theory. It is concluded that the process of development up to ground collapse of a small cavity is continuous, however, the process of a big cavity is catastrophic. It is feasibility that the Karst collapse be predicted with manmade neural network theory as a new way.

同事关系质量及其作用机制

Although the research into coworker relationship quality has been recognized one of key factors related to organization performance, and has been thought a new trend in organization behavior research with the flatting of organization structure and complication of task assignment, there is relatively little empirical research on the mechanism between coworkers’ interaction, contraring to the fruitful results on member exchange research based on social network theory, say nothing of the influence of cultural differences such as GUANXI. This research developed the scale for the assessment of Coworker Relationship Quality by literature review, deep interview, and questionnaires, compared the predictable ability of Coworker Relationship Quality (CRQ) scale and Coworker Exchange (CWX) scale on employees’ work attitudes and behaviors. Finally, the mediating effect of Coworker Relationship Quality between employees’ similarities on personality and their work attitudes and behaviors was investigated. Following are main results. Firstly, we found that the interpersonal communication, trust, and mutual support are the key factors of coworker relationship quality, which is similar to the result getting from western samples. But Chinese people are more GUANXI ORIENTATION, means they want to build longtime relationship with others, not only when they are coworkers, but also when one of them left the organization. Secondly, though the core meaning of CRQ and CWX are same, their predictable ability on organization outcomes is different. CRQ is more powerful than CWX, especially on turnover intention. The result showed that after controlling the effect of demographic variables and CRQ, CWX cannot predict turnover intention significantly, but CRQ can still predict turnover intention significantly after controlling demographic variables and CWX. Thirdly, the partial mediating effect of CRQ between positive affectivity similarity and organizational citizenship behavior, coworker satisfaction, organizational commitment and turnover intention are validated, but we did not find the mediating effect of CRQ between demographic variable similarity and workers’ attitudes and behaviors. The Similarity Attraction Paradigm, Social Identity Theory, and Self Category Theory were supported.

Correcting the systematic error of the density functional theory calculation: the alternate combination approach of genetic algorithm and neural network

The alternate combinational approach of genetic algorithm and neural network (AGANN) has been presented to correct the systematic error of the density functional theory (DFT) calculation. It treats the DFT as a black box and models the error through external statistical information. As a demonstration, the AGANN method has been applied in the correction of the lattice energies from the DFT calculation for 72 metal halides and hydrides. Through the AGANN correction, the mean absolute value of the relative errors of the calculated lattice energies to the experimental values decreases from 4.93% to 1.20% in the testing set. For comparison, the neural network approach reduces the mean value to 2.56%. And for the common combinational approach of genetic algorithm and neural network, the value drops to 2.15%. The multiple linear regression method almost has no correction effect here.

Double synaptic weight neuron theory and its application

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.

Iris recognition algorithm based on point covering of high-dimensional space and neural network

In this paper, we constructed a Iris recognition algorithm based on point covering of high-dimensional space and Multi-weighted neuron of point covering of high-dimensional space, and proposed a new method for iris recognition based on point covering theory of high-dimensional space. In this method, irises are trained as "cognition" one class by one class, and it doesn't influence the original recognition knowledge for samples of the new added class. The results of experiments show the rejection rate is 98.9%, the correct cognition rate and the error rate are 95.71% and 3.5% respectively. The experimental results demonstrate that the rejection rate of test samples excluded in the training samples class is very high. It proves the proposed method for iris recognition is effective.

Discussion on the basic mathematical models of neurons - Double synaptic weight neuron in general-purpose neurocomputer, theory and application

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.

Ground target detection, classification and sensor fusion in distributed fiber seismic sensor network - art. no. 683015

This paper describes the ground target detection, classification and sensor fusion problems in distributed fiber seismic sensor network. Compared with conventional piezoelectric seismic sensor used in UGS, fiber optic sensor has advantages of high sensitivity and resistance to electromagnetic disturbance. We have developed a fiber seismic sensor network for target detection and classification. However, ground target recognition based on seismic sensor is a very challenging problem because of the non-stationary characteristic of seismic signal and complicated real life application environment. To solve these difficulties, we study robust feature extraction and classification algorithms adapted to fiber sensor network. An united multi-feature (UMF) method is used. An adaptive threshold detection algorithm is proposed to minimize the false alarm rate. Three kinds of targets comprise personnel, wheeled vehicle and tracked vehicle are concerned in the system. The classification simulation result shows that the SVM classifier outperforms the GMM and BPNN. The sensor fusion method based on D-S evidence theory is discussed to fully utilize information of fiber sensor array and improve overall performance of the system. A field experiment is organized to test the performance of fiber sensor network and gather real signal of targets for classification testing.

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.

Promotion of cooperation in the form C0C1D classified by 'degree grads' in a scale-free network

In this paper, we revisit the issue of the public goods game (PGG) on a heterogeneous graph. By introducing a new effective topology parameter, 'degree grads' phi, we clearly classify the agents into three kinds, namely, C-0, C-1, and D. The mechanism for the heterogeneous topology promoting cooperation is discussed in detail from the perspective of C0C1D, which reflects the fact that the unreasoning imitation behaviour of C-1 agents, who are 'cheated' by the well-paid C-0 agents inhabiting special positions, stabilizes the formation of the cooperation community. The analytical and simulation results for certain parameters are found to coincide well with each other. The C0C1D case provides a picture of the actual behaviours in real society and thus is potentially of interest.

A spatial sampling optimization package using MSN theory

The density and distribution of spatial samples heavily affect the precision and reliability of estimated population attributes. An optimization method based on Mean of Surface with Nonhomogeneity (MSN) theory has been developed into a computer package with the purpose of improving accuracy in the global estimation of some spatial properties, given a spatial sample distributed over a heterogeneous surface; and in return, for a given variance of estimation, the program can export both the optimal number of sample units needed and their appropriate distribution within a specified research area. (C) 2010 Elsevier Ltd. All rights reserved.

Potential energy landscape and robustness of a gene regulatory network: Toggle switch

Finding a multidimensional potential landscape is the key for addressing important global issues, such as the robustness of cellular networks. We have uncovered the underlying potential energy landscape of a simple gene regulatory network: a toggle switch. This was realized by explicitly constructing the steady state probability of the gene switch in the protein concentration space in the presence of the intrinsic statistical fluctuations due to the small number of proteins in the cell. We explored the global phase space for the system. We found that the protein synthesis rate and the unbinding rate of proteins to the gene were small relative to the protein degradation rate; the gene switch is monostable with only one stable basin of attraction. When both the protein synthesis rate and the unbinding rate of proteins to the gene are large compared with the protein degradation rate, two global basins of attraction emerge for a toggle switch. These basins correspond to the biologically stable functional states. The potential energy barrier between the two basins determines the time scale of conversion from one to the other. We found as the protein synthesis rate and protein unbinding rate to the gene relative to the protein degradation rate became larger, the potential energy barrier became larger. This also corresponded to systems with less noise or the fluctuations on the protein numbers.