Evaluation of Linguistic Features for Word Sense Disambiguation with Self-Organized Document Maps

Word sense disambiguation automatically determines the appropriate senses of a word in context. We have previously shown that self-organized document maps have properties similar to a large-scale semantic structure that is useful for word sense disambiguation. This work evaluates the impact of different linguistic features on self-organized document maps for word sense disambiguation. The features evaluated are various qualitative features, e.g. part-of-speech and syntactic labels, and quantitative features, e.g. cut-off levels for word frequency. It is shown that linguistic features help make contextual information explicit. If the training corpus is large even contextually weak features, such as base forms, will act in concert to produce sense distinctions in a statistically significant way. However, the most important features are syntactic dependency relations and base forms annotated with part of speech or syntactic labels. We achieve 62.9% ± 0.73% correct results on the fine grained lexical task of the English SENSEVAL-2 data. On the 96.7% of the test cases which need no back-off to the most frequent sense we achieve 65.7% correct results.     

Unsupervised image segmentation using a simple MRF model with a new implementation scheme

A simple Markov random field model with a new implementation scheme is proposed for unsupervised image segmentation based on image features. The traditional two-component MRF model for segmentation requires training data to estimate necessary model parameters and is thus unsuitable for unsupervised segmentation. The new implementation scheme solves this problem by introducing a function-based weighting parameter between the two components. Using this method, the simple MRF model is able to automatically estimate model parameters and produce accurate unsupervised segmentation results. Experiments demonstrate that the proposed algorithm is able to segment various types of images (gray scale, color, texture) and achieves an improvement over the traditional method.     

Optimal Ordered Problem Solver

We introduce a general and in a certain sense time-optimal way of solving one problem after another, efficiently searching the space of programs that compute solution candidates, including those programs that organize and manage and adapt and reuse earlier acquired knowledge. The Optimal Ordered Problem Solver (OOPS) draws inspiration from Levin's Universal Search designed for single problems and universal Turing machines. It spends part of the total search time for a new problem on testing programs that exploit previous solution-computing programs in computable ways. If the new problem can be solved faster by copy-editing/invoking previous code than by solving the new problem from scratch, then OOPS will find this out. If not, then at least the previous solutions will not cause much harm. We introduce an efficient, recursive, backtracking-based way of implementing OOPS on realistic computers with limited storage. Experiments illustrate how OOPS can greatly profit from metalearning or metasearching, that is, searching for faster search procedures.     

Linear color segmentation and its implementation

A framework for color image segmentation is presented, which combines color histogram analysis and region merging approach. Its main goal is to segment an image at material boundaries (i.e., discontinuities of reflectance properties) while ignoring spatial color inhomogeneities of uniformly pigmented (colored) objects, caused by accidents of illumination and viewing geometry. Theoretical examination of light spectrum transformations upon light reflection from material surfaces and upon interaction with a sensor system shows that in a wide variety of viewed scenes (even containing interreflections and highlight areas) uniformly pigmented objects are projected to the color space of the sensor as planar, linear, or point-like clusters, depending on lighting and viewing conditions and object geometry. To detect such clusters in the color space, three methods are suggested: Generalized Hough Transform method, gradient descent method, and eigenvectors method. A framework algorithm of color segmentation based on region merging approach is developed, which can use any of these methods. Testing this algorithm with both artificially generated and real images shows quite reliable results.     

Mappings of Figure Spaces and Systems of These Spaces

The formation of an informal theory of figure spaces is considered. In particular, interesting and natural generalizations of such fundamental mathematical concepts as continuous and uniformly continuous mappings are proposed.     

采用矢量细分的异步电动机直接转矩控制系统

介绍了一种采用矢量细分和SVPWM调制的直接转矩控制方法,实现对电机转矩的控制。将该控制方法应用到异步电动机调速系统,通过系统仿真实验验证,该控制方法的输出转矩脉动小、电流谐波低、开关频率固定,调速系统有着良好的动态性能和调速精度。     

DSP空间矢量控制三电平逆变器的研究

介绍了三电平PWM逆变器的研究前景和三电平空间矢量控制的原理，提出了一种基于检测直流侧电容电压和中点电流方向的新型平衡中点电位的空间矢量调制方法，讨论了这种方法的平衡中点电位的原理、脉冲序列的安排和该方法中小脉冲的处理和死区处理等。给出了用DSPLF2407A实现这种矢量控制方法的流程图，讨论了DSP实现中的一些问题。最后建立了三电平逆变器实验模型，实验结果验证了中点电位平衡方法的有效性和用DSP实现SVPWM的方便性。     

基于相空间重构理论和优化递归神经网络结合的短期负荷预测方法

根据电力负荷序列的混沌特性,提出以相空间重构理论和优化递归神经网络结合的电力系统短期负荷预测方法,以相空间重构理论确定递归神经网络输入维数;训练样本集由对应预测相点的最近邻相点集构成,并按预测相点步进动态相轨迹生成;优化递归神经网络是以双重遗传算法来确定递归神经网络的隐层结构和权值,总体寻优性可抑制伪近邻点的影响,保证提高预测精度及其稳定性。对两类不同负荷系统日、周预测仿真测试,证实其比传统神经网络预测模型能有效地提高预测精度0.8%。因此,所研究的预测模型和方法在实际预测领域有较高的实用价值。     

有源滤波器的电压空间矢量双滞环电流控制新方法

该文提出了一种有源滤波器的电压空间矢量双滞环电流控制新方法。该方法直接在复平面上实施控制，内环选择的开关状态减少高次谐波分量，外环选择的开关状态电流响应速度快，有效限制误差电流，改善滤波器性能：利用逆变器输出电压及其引起的误差变化来判断有源滤波器参考电压矢量所在的区域，简单可靠。电压空间矢量的引入可以提高直流电压利用率，降低开关频率。仿真结果证明了其可行性。     

基于半实物的Ｓ频段卫星测控仿真系统平台设计

该仿真系统是为了满足Ｓ频段卫星测控演练的需要而提出的。根据航天测控系统和Ｓ频段测控设备的特点，明确了仿真系统的组成及功能，并对主要部分进行了详细的描述；确定了仿真系统运行工作模式、主要技术指标、接口之间关系；结合实际应用情况，通过与真星进行比对，证明通过建立半物理仿真数学模型，达到了在数据层上与真星具有等价性，并展望了未来发展方向。