An intelligent web-page classifier with fair feature-subset selection

The explosion of on-line information has given rise to many manually constructed topic hierarchies (such as Yahoo!!). But with the current growth rate in the amount of information, manual classification in topic hierarchies results in an immense information bottleneck. Therefore, developing an automatic classifier is an urgent need. However, classifiers suffer from enormous dimensionality, since the dimensionality is determined by the number of distinct keywords in a document corpus. More seriously, most classifiers are either working slowly or they are constructed subjectively without any learning ability. In this paper, we address these problems with a fair feature-subset selection (FFSS) algorithm and an adaptive fuzzy learning network (AFLN) for classification. The FFSS algorithm is used to reduce the enormous dimensionality. It not only gives fair treatment to each category but also has ability to identify useful features, including both positive and negative features. On the other hand, the AFLN provides extremely fast learning ability to model the uncertain behavior for classification so as to correct the fuzzy matrix automatically. Experimental results show that both FFSS algorithm and the AFLN lead to a significant improvement in document classification, compared to alternative approaches.     

Estimating and reducing the memory requirements of signal processing codes for embedded systems

Most embedded systems have limited amount of memory. In contrast, the memory requirements of the digital signal processing (DSP) and video processing codes (in nested loops, in particular) running on embedded systems is significant. This paper addresses the problem of estimating and reducing the amount of memory needed for transfers of data in embedded systems. First, the problem of estimating the region associated with a statement or the set of elements referenced by a statement during the execution of nested loops is analyzed. For a fixed execution ordering, a quantitative analysis of the number of elements referenced is presented; exact expressions for uniformly generated references and a close upper and lower bound for nonuniformly generated references are derived. Second, in addition to presenting an algorithm that computes the total memory required, this paper also discusses the effect of transformations (that change the execution ordering) on the lifetimes of array variables, i.e., the time between the first and last accesses to a given array location. The term maximum window size is introduced, and quantitative expressions are derived to compute the maximum window size. A detailed analysis of the effect of unimodular transformations on data locality, including the calculation of the maximum window size, is presented.     

Numerical Study of Finite Element Method Based Solutions for Propagation of Wetting Fronts in Unsaturated Soil

The accurate prediction of the propagation of a wetting front in an unsaturated soil subjected to surficial infiltration is of practical importance to many geotechnical and geoenvironmental problems. The finite element method is the most common solution technique as the hydraulic soil properties are highly nonlinear. Two important issues are often found to create difficulties in such analyses. First, numerical oscillations are usually observed in the calculated pore pressures at the wetting front. Second, when a reasonable mesh size and time step are used, the elevation of the wetting front may be seriously overpredicted. This paper is focused on the second issue. The under-relaxation (UR) technique used in the iterative process within each time step is found to have a serious impact on rate of convergence with refinement in mesh size and time step. Two different techniques are typically used; the first evaluates the hydraulic conductivity using an average of heads calculated from the preceding time node and the most recent iteration of the current time node (UR1), and the second evaluates the hydraulic conductivity using the average of heads calculated from the two most recent iterations of the current time nodes (UR2). The study shows that UR1, which is adopted in programs such as SEEP/W, ensures that the solution converges rapidly to a stable solution within a time step, but may converge to the wrong wetting front at a given elapsed time unless a sufficiently refined mesh is used. UR2 converges much more slowly within a time step, but the error in the wetting front is smaller than that generated by UR1.     

黄海地质构造与油气资源

针对以往黄海油气勘探在找油思路、勘探深度、勘探领域、勘探部署和勘探技术等方面存在的问题 ,概述了南、北黄海地质构造、盆地演化、油气地质特征及成藏条件 ,并在此基础上 ,提出了对黄海油气资源远景的几点认识     

网络管理中事件关联检测机制的研究

网络事件的关联检测是网络管理需要解决的一个关键问题,本文根据分布式网络管理的特点,首次明确定义了网络事件的基本关联关系,在此基础上提出了一种基于Petri Net的事件关联检测机制,实现了基于事件内容的细粒度关联检测,并且在事件检测中充分考虑了时间因素,有效地提高了事件关联检测的准确性。     

硅微机械梳齿静电谐振器的建模与分析

基于参数化的计算机辅助设计（CAD）软件，对硅微机械梳齿静电谐振器进行了实体建模，以有限元分析软件为工具，进行了谐振器的模态分析，静态分析和谐响应分析，初步揭示了谐振器的静、动态特性，有助于改善设计效率和质量，展示了计算机辅助工程（CAE）技术在微机电系统（MEMS）研究中的重要作用。     

A Neural Network Approach for Signal Detection in Digital Communications

In this paper, we propose a new approach for signal detection in wireless digital communications based on the neural network with transient chaos and time-varying gain (NNTCTG), and give a concrete model of the signal detector after appropriate transformations and mappings. It is well known that the problem of the maximum likelihood signal detection can be described as a complex optimization problem that has so many local optima that conventional Hopfield-type neural networks fail to solve. By refraining from the serious local optima problem of Hopfield-type neural networks, the NNTCTG makes use of the time-varying parameters of the recurrent neural network to control the evolving behavior of the network so that the network undergoes the transition from chaotic behavior to gradient convergence. It has richer and more flexible dynamics rather than conventional neural networks only with point attractors, so that it can be expected to have much ability to search for globally optimal or near-optimal solutions. After going through a transiently inverse-bifurcation process, the NNTCTG can approach the global optimum or the neighborhood of global optimum of our problem. Simulation experiments have been performed to show the effectiveness and validation of the proposed neural network based method for the signal detection in digital communications.     

Activity-dependent changes in voltage-dependent calcium currents and transmitter release

The use of capillary electrophoresis (CE) for clinically relevant assays is attractive since it often presents many advantages over contemporary methods. The small-diameter tubing that holds the separation medium has led to the development of multicapillary instruments, and simultaneous sample analysis. Furthermore, CE is compatible with a wide range of detectors, including UV-Vis, fluorescence, laser-induced fluorescence, electrochemistry, mass spectrometry, radiometric, and more recently nuclear magnetic resonance, and laser-induced circular dichroism systems. Selection of an appropriate detector can yield highly specific analyte detection with good mass sensitivity. Another attractive feature of CE is the low consumption of sample and reagents. However, it is paradoxical that this advantage also leads to severe limitation, namely poor concentration sensitivity. Often high analyte concentrations are required in order to have injection of sufficient material for detection. In this regard, a series of devices that are broadly termed 'analyte concentrators' have been developed for analyte preconcentration on-line with the CE capillary. These devices have been used primarily for non-specific analyte preconcentration using packing material of the C18 type. Alternatively, the use of very specific antibody-containing cartridges and enzyme-immobilized microreactors have been demonstrated. In the current report, we review the likely impact of the technology of capillary electrophoresis and the role of the CE analyte concentrator-microreactor on the analysis of biomolecules, present on complex matrices, in a clinical laboratory. Specific examples of the direct analysis of physiologically-derived fluids and microdialysates are presented, and a personal view of the future of CE in the clinical environment is given.     

一种多晶硅高温压力传感器

制作压力传感器时，在二氧化硅层上淀积多晶硅膜，既可利用优良的机械特性，又可保证压敏电阻与衬底间具有良好的绝缘性，由此可大大提高器件的温度特性。介绍了一种多晶硅压力传感器的原理和设计。实验结果表明，这类传感器具有灵敏度好，精度高等特点，电路工作范围为０－２５０℃，且具有良好的温度稳定性。     

Learning concepts in parallel based upon the strategy of versionspace

Applies the technique of parallel processing to concept learning. A parallel version-space learning algorithm based upon the principle of divide-and-conquer is proposed. Its time complexity is analyzed to be O(k log₂n) with n processors, where n is the number of given training instances and k is a coefficient depending on the application domains. For a bounded number of processors in real situations, a modified parallel learning algorithm is then proposed. Experimental results are then performed on a real learning problem, showing that our parallel learning algorithm works, and being quite consistent with the results of theoretical analysis. We conclude that when the number of training instances is large, it is worth learning in parallel because of its faster execution