基于分布式认知理论的扩展资源模型

分布式认知理论通过协调人机对话，结合人和计算机各自的优势解决问题，在人机交互研究中扮演了指导者的角色．尽管分布式认知理论支持的资源模型在分析人机交互时取得了成功，但模型存在不能提供复杂用户任务支持、缺乏对模型中元素的准确定义等问题，在一定程度上导致了表现形式上的混乱．使用分布式认知理论构造了扩展资源模型，建立人机交互活动中的动作和表征之间的联系，从而指导界面的设计和实现．扩展资源模型从静态结构和交互策略两个方面对界面交互动作提供支持，在交互中减少人的认知负担．该研究对设计符合人的认知特点的界面具有一定的指导作用．

coconceptmap: a system for collaborative concept mapping

Concept mapping is a technique for visualizing the relationships between different concepts, and collaborative concept mapping is used to model knowledge and transfer expert knowledge. Because of lacking some features,existing systems can’t support collaborative concept mapping effectively. In this paper, we analysis the collaborative concept mapping process according to the theory of distributed cognition, and argue the functions effective systems ought to include. A collaborative concept mapping system should have the following features: visualization of concept map, flexible collaboration style,supporting natural interaction, knowledge management and history management. Furthermore, we describe every feature in details. Finally,a prototype system has been built to fully explore the above technologies.

A general theory on media with randomly distributed inclusions .1. The average field behaviors

In this paper, a theory is developed to calculate the average strain field in the materials with randomly distributed inclusions. Many previous researches investigating the average field behaviors were based upon Mori and Tanaka's idea. Since they were restricted to studying those materials with uniform distributions of inclusions they did not need detailed statistical information of random microstructures, and could use the volume average to replace the ensemble average. To study more general materials with randomly distributed inclusions, the number density function is introduced in formulating the average field equation in this research. Both uniform and nonuniform distributions of inclusions are taken into account in detail.

Biomimetic pattern recognition theory and its applications

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.

Cognitive models in biomimetic pattern cognition

We describe a new model which is based on the concept of cognizing theory. The method identifies subsets of the data which are embedded in arbitrary oriented lower dimensional space. We definite k-mean covering, and study its property. Covering subsets of points are repeatedly sampled to construct trial geometry space of various dimensions. The sampling corresponding to the feature space having the best cognition ability between a mode near zero and the rest is selected and the data points are partitioned on the basis of the best cognition ability. The repeated sampling then continues recursively on each block of the data. We propose this algorithm based on cognition models. The experimental results for face recognition demonstrate that the correct rejection rate of the test samples excluded in the classes of training samples is very high and effective.

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.

Design of surface emitting distributed feedback quantum cascade laser with single-lobe far-field pattern and high outcoupling efficiency

A 7.8-mu m surface emitting second-order distributed feedback quantum cascade laser (DFB QCL) structure with metallized surface grating is studied. The modal property of this structure is described by utilizing coupled-mode theory where the coupling coefficients are derived from exact Floquet-Bloch solutions of infinite periodic structure. Based on this theory, the influence of waveguide structure and grating topography as well as device length on the laser performance is numerically investigated. The optimized surface emitting second-order DFB QCL structure design exhibits a high surface outcoupling efficiency of 22% and a low threshold gain of 10 cm(-1). Using a pi phase-shift in the centre of the grating, a high-quality single-lobe far-field radiation pattern is obtained.

Stable single-mode distributed feedback quantum cascade laser with surface metal grating

A design of single-mode distributed feedback quantum cascade lasers (DFB-QCLs) with surface metal grating is described. A rigorous modal expansion theory is adopted to analyse the interaction between the waveguide mode and the surface plasmon wave for different grating parameters. A stable single-mode operation can be obtained in a wide range of grating depths and duty cycles. The single-mode operation of surface metal grating DFB-QCLs at room temperature for lambda = 8.5 mu m is demonstrated. The device shows a side-mode suppression ratio of above 20 dB. A linear tuning of wavelength with temperature indicates the stable single-mode operation without mode hopping.

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.

Surface Emitting Distributed Feedback Quantum Cascade Laser around 8.3 mu m

We demonstrate surface emitting distributed feedback quantum cascade lasers emitting at wavelengths from 8.1 mu m at 90 K to 8.4 mu m at 210 K. The second-order metalized grating is carefully designed using a modified coupled-mode theory and fabricated by contact lithography. The devices show single mode behavior with a side mode suppression ratio above 18 dB at all working temperatures. At 90 K, the device emits an optical power of 101 mW from the surface and 199 mW from the edge. In addition, a double-lobe far-field pattern with a separation of 2.2 degrees is obtained in the direction along the waveguide.

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.