支持向量机在交通量预测中的应用

交通量预测对于区域交通规划有重要意义。提出一种基于支持向量机理论的交通量预测方法。该方法以统计学习理论为基础，通过和BP神经网络进行比较的实验，证明其在交通量预测中的有效性。     

Threshold‐Based Camera Motion Characterization of MPEG Video

We propose an efficient scheme for camera motion characterization in MPEG‐compressed video. The proposed scheme detects six types of basic camera motions through threshold‐based qualitative interpretation, in which fixed thresholds are applied to motion model parameters estimated from MPEG motion vectors (MVs). The efficiency and robustness of the scheme are validated by the experiment with real compressed video sequences.     

Numerical modelling for scattering of waves in three-dimensional cracked material

In this paper, a mathematical model is proposed for the problem of the scattering of plane waves in a three-dimensional cracked materials. Instead of obtaining closed-form solutions as in conventional theoretical analysis methods, this approach, called the Equivalent Nodal Force (ENF) method formulates the mechanical effects of cracks as an equivalent nodal force in a numerical procedure, and physically translates cracked material into an equivalent continuous one. Several mechanical relations between waves and cracks are evident from this method. Also the results of several numerical calculations are presented and these are compared with those obtained by the conventional methods.     

Implementation of a vector quantization codebook design technique based on a competitive learning artificial neural network

We describe an implementation of a vector quantization codebook design algorithm based on the frequencysensitive competitive learning artificial neural network. The implementation, designed for use on high-performance computers, employs both multitasking and vectorization techniques. A C version of the algorithm tested on a CRAY Y-MP8/864 is discussed. We show how the implementation can be used to perform vector quantization, and demonstrate its use in compressing digital video image data. Two images are used, with various size codebooks, to test the performance of the implementation. The results show that the supercomputer techniques employed have significantly decreased the total execution time without affecting vector quantization performance.This work was supported by a Cray University Research Award and by NASA Lewis research grant number NAG3-1164.     

RLL码的计算机辅助设计

本文着重讨论计算机辅助设计RLL码的具体实现问题.该过程首先根据RLL码两个参数d,k确定一个编码率p/q,然后构造出RLL码的高阶状态转换-输出符号矩阵并求相应的近似特征向量.最后通过状态分裂和状态归并过程,即可得到一个满足d,k约束的编码输出表.其中状态转换-输出符号矩阵为实现计算机辅助设计RLL码提供了有效的数据结构,而且要优化RLL码编码器,就必须有选择地进行状态分裂.     

制作矢量轮廓字库

本文介绍了一种如何从常见的点阵字库制作矢量轮廓字库的算法。算法采用了预处理、轮廓跟踪和轮廓逼近这三个主要步骤来制作每一个矢量文字，然后采用一种２字节的索引结构加以存储。     

用线性状态观测器重构交流矢量控制系统中的负载扰动

在异步电动机磁场定向数学模型的基础上,给出了恒磁条件下异步电动机的解耦模型及传动系统的线性状态方程,并提出了重构负载扰动转矩的线性状态观测器。利用重构的负载转矩在矢量控制系统中进行扰动前馈,明显地提高了系统的抗负载扰动能力,从而为进一步提高矢量控制系统的动态性能开辟了一条有效的途径。     

一种新型微处理器功能验证

文中介绍一种新型微处理器的验证方法，并对相关的一些验证策略进行深入的讨论。     

Achievement of specificational information usage with true and false feedback in learning a visual relative-mass discrimination task.

Participants' usage of informational variables in learning visual relative-mass discrimination in collisions was tracked by means of PROBIT correlations. Four groups received feedback that was true or accorded with either of three nonspecificational cue variables. A majority in each group adopted the feedback, but several participants defied the false feedback. Unlike in previous research, the fit to data of the relative-mass invariant could not be bettered by post hoc linear combinations of the cues. Discriminability was lower in the use of the invariant. Analytic complexity was rejected as an explanation for discriminability differences. A "smart mechanism" for pickup of the relative-mass invariant was developed as an extension of G. Johansson's (1950/1994) vector model. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Two-tier architecture for unconstrained handwritten character recognition

In this paper, we propose an approach that combines the unsupervised and supervised learning techniques for unconstrained handwritten numeral recognition. This approach uses the Kohonen self-organizing neural network for data classification in the first stage and the learning vector quantization (LVQ) model in the second stage to improve classification accuracy. The combined architecture performs better than the Kohonen self-organizing map alone. In the proposed approach, the collection of centroids at different phases of training plays a vital role in the performance of the recognition system. Four experiments have been conducted and experimental results show that the collection of centroids in the middle of the training gives high performance in terms of speed and accuracy. The systems developed also resolve the confusion between handwritten numerals.