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".     

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.     

Green Synthesis of Hierarchically Porous Carbon Nanotubes as Advanced Materials for High‐Efficient Energy Storage

Hierarchically porous carbon nanomaterials with well‐defined architecture can afford a promising platform for effectively addressing energy and environmental concerns. Herein, a totally green and straightforward synthesis strategy for the fabrication of hierarchically porous carbon nanotubes (HPCNTs) by a simple carbonization treatment without any assistance of soft/hard templates and activation procedures is demonstrated. A high specific surface area of 1419 m² g^?1 and hierarchical micro‐/meso‐/macroporosity can be achieved for the HPCNTs. The unique porous architecture enables the HPCNTs serving as excellent electrode/host materials for high‐performance supercapacitors and Li–sulfur batteries. The design strategy may pave a new avenue for the rational synthesis of hierarchically porous carbon nanostructures for high‐efficient energy storage applications.     

宽翼T形梁桥动力学理论与特性分析

考虑了剪滞翘曲应力自平衡条件、剪切变形和剪力滞后效应等因素的影响,本文提出了一种对宽翼薄壁T形梁动力学特性的分析方法.分析中为了准确反应T形梁翼板的动位移变化,三个广义动位移被引入,且以能量变分原理为基础建立了T形梁动力反应的控制微分方程和自然边界条件,据此对T形梁的动力反应特性进行了分析,揭示了T形梁桥动力反应的规律.算例中,对比了考虑和不考虑剪滞翘曲应力自平衡条件对T形梁动力反应的影响,结果显示考虑剪滞翘曲应力自平衡条件的计算方法与有限元数值解吻合更好.     

A novel approach to annotating web service based on interface concept mapping and semantic expansion

With the rapid development of web service technology in these years, traditional standards have been matured during the process of service registry and discovery. However, it is difficult for service requesters to discover satisfactory web services. The reason for this phenomenon is that the traditional service organization mode lacks semantic understanding ability for service function interface. This paper proposes a novel approach to annotating web services. We first adopt domain ontology as a semantic context, and give our general framework of service semantic annotation. Then, interface concept mapping algorithm and service interface expansion algorithm are respectively presented in detail. Finally, the generation process of semantic web service repository is presented based on preceding algorithms. Simulation experiment results demonstrate that annotated web services by the proposed method can more satisfy requirements for service requesters than traditional ones by service matchmaking engine. It can get better service discovery effectiveness.