语法和语义相结合的中文对话系统问题理解研究

针对中文口语问句的表达多样性对对话系统问题理解带来的挑战,该文采用“在语法结构之上获取语义知识”的设计理念,提出了一种语法和语义相结合的口语对话系统问题理解方法。首先人工编制了独立于领域和应用方向的语法知识库,进而通过句子压缩模块简化复杂句子,取得结构信息,再进行问题类型模式识别,得到唯一确定问题的语义组织方法、查询策略和应答方式的句型模式。另一方面,根据领域语义知识库,从源句子中提取相应的语义信息,并根据识别到的句型模式所对应的知识组织方法进行语义知识组织,完成对问句的理解。该文的方法被应用到开发的中文手机导购对话系统。测试结果表明,该方法能有效地完成对话流程中的用户问题理解。     

基于广东区域性高职院校创业教育的思考——记广州城建职业学院学生创业教育的探索

职业素质是劳动者对社会职业了解与适应能力的一种综合体现,主要包括职业修养和职业技能。在高职院校中开展创业教育是我国经济形势发展的需要,对于培养学生的实践能力、创新精神和解决学生的就业压力有着十分重要的意义。广州城建职业学院的创业教育可以从以下三方面着手：一是转变观念,营造氛围;二是传授本领,打好基础;三是开展活动,提高能力,逐步探索出一条适合广州城建职业学院特色高职学生创业教育的新路子。     

Research and Practice of Hybrid Teaching for Software Testing based on Interactive Learning

To address the problems of insufficient number of personalized exercises and cases and teachers' lack of grasp of students' weak knowledge points in the current software testing online courses, we study the strategy of establishing and updating intelligent exercise sets and case libraries and analyze the answers and dig out the weak points of knowledge through group intelligence reasoning and interactive machine learning methods. This will help teachers to make uniform and targeted explanations, reduce manual judgment, and achieve intelligent teaching quality reform, and implement the educational concepts of "keeping up with the times" and "teaching according to students' abilities".     

Parametric Stabilization of Quantized Interconnected Systems with Application to Coupled Inverted Pendulums

This paper studies the analysis of parametric stability and decentralized state feedback control of a kind of quantized interconnected systems. The output of each controller is quantized logarithmically before it is input to the subsystem, and the quantized density would affect the stability of the systems. First, a decentralized state feedback controller is designed for interconnected systems without quantization and the corresponding stable region is obtained. Second, for a given controller, the lower bound of the quantization density is evaluated from parameters of local controllers. Finally, the proposed method is applied to coupled inverted pendulums systems which can be viewed as quantized interconnected systems. The simulation results show that by using the proposed quantized controllers, the interconnected inverted pendulum systems are parametrically stabilized.     

Oscillating bubbles in teardrop cavities for microflow control

Microstreaming generated from oscillating microbubbles has great potential in microfluidic applications for localized flow control. In this study, we explore the use of teardrop-shaped cavities for trapping microbubbles. Upon acoustic actuation, these microbubbles confined in teardrop cavities can be utilized to generate a directional microstreaming flow. We further show that by altering the acoustic excitation frequency, a flow-switch for altering flow direction in microfluidic environments can be achieved using two oppositely arranged teardrop cavities with different sizes. In the end, we show that an array of such bubble-filled teardrop cavities can act as a fixated microfluidic transport system allowing for on-chip particle manipulation in complex flow patterns. This inexpensive method to create flows to switch and transport elements based on teardrop cavities can be widely employed for microfluidic applications such as drug delivery systems.     

我国变频器的现状及其未来的技术发展

分析了我国变频市场具有的特殊性,市场现状、中国企业特点、电力电子行业特点等,介绍了变频技术的现状及变频技术发展方向.最后分析了国产变频器的发展走向.     

Classifying mental tasks based on features of higher-order statistics from EEG signals in brain-computer interface

In order to characterize the non-Gaussian information contained within the EEG signals, a new feature extraction method based on bispectrum is proposed and applied to the classification of right and left motor imagery for developing EEG-based brain-computer interface systems. The experimental results on the Graz BCI data set have shown that based on the proposed features, a LDA classifier, SVM classifier and NN classifier outperform the winner of the BCI 2003 competition on the same data set in terms of either the mutual information, the competition criterion, or misclassification rate.     

Efficient embedded wavelet codec based on list structure

A new efficient image codec called embedded wavelet coding based on list structure (EWCBL) is proposed in this paper. By combining the techniques of morphological representation and quadtree partitioning, EWCBL effectively exploits both within-subband clustering and cross-subband similarity of wavelet coefficients. Based on the list structure, a fine fractional bit-plane coding is employed to achieve excellent rate-distortion performance. The patterned morphological dilation is introduced to greatly reduce the redundant test operations. Experimental results show that the proposed EWCBL outperforms state-of-the-art embedded codecs for both lossy and lossless compression. Moreover, the good performance and features of embeddedness and scalability are achieved with relatively low complexity.     

Extracting Takagi-Sugeno Fuzzy Rules with Interpretable Submodels via Regularization of Linguistic Modifiers

In this paper, a method for constructing Takagi-Sugeno (TS) fuzzy system from data is proposed with the objective of preserving TS submodel comprehensibility, in which linguistic modifiers are suggested to characterize the fuzzy sets. A good property held by the proposed linguistic modifiers is that they can broaden the cores of fuzzy sets while contracting the overlaps of adjoining membership functions (MFs) during identification of fuzzy systems from data. As a result, the TS submodels identified tend to dominate the system behaviors by automatically matching the global model (GM) in corresponding subareas, which leads to good TS model interpretability while producing distinguishable input space partitioning. However, the GM accuracy and model interpretability are two conflicting modeling objectives, improving interpretability of fuzzy models generally degrades the GM performance of fuzzy models, and vice versa. Hence, one challenging problem is how to construct a TS fuzzy model with not only good global performance but also good submodel interpretability. In order to achieve a good tradeoff between GM performance and submodel interpretability, a regularization learning algorithm is presented in which the GM objective function is combined with a local model objective function defined in terms of an extended index of fuzziness of identified MFs. Moreover, a parsimonious rule base is obtained by adopting a QR decomposition method to select the important fuzzy rules and reduce the redundant ones. Experimental studies have shown that the TS models identified by the suggested method possess good submodel interpretability and satisfactory GM performance with parsimonious rule bases.