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.     

一个新的基于权限的委托模型CPPBDM

基于权限的委托模型PBDM实现了权限的委托,其实现方法是将角色进行划分,使组织的角色继承关系变得复杂,模型的实现和管理变得烦琐.为此,提出对PBDM进行改进的基于权限划分的新模型CPPBDM,模型引入新的角色间关系:委托继承、激活指派;新模型能够实现基于权限的委托与角色代理,简化了模型的实现与管理.     

基于规则引擎的智能决策支持系统开发

提出了解决基于规则专家系统的规则冲突的方法。构建了使用Eclipse和Drools的集成开发环境。     

基于概率A*的智能车路径规划算法

为保证智能车辆能够按照规划的路径安全行驶,且满足车辆动力学特性,针对混合A*算法中没有引导性策略所产生较多不必要的搜索问题,提出概率A*算法先得到搜索粗路径,提高在随后搜索过程中搜索效率。然后利用概率A*算法得到的路径点引导节点走向,避免节点向障碍物等搜寻。最后对节点的代价函数进行优化。仿真实验结果表明,与混合A*算法相比,本文所提出的算法平均减少搜索时间10.8%,且得到的路径相对规整平滑。该算法可以在较短时间内为智能车辆规划一条安全可行平滑的路径。     

Simultaneous CO2 and H2O Activation via Integrated Cu Single Atom and N Vacancy Dual-Site for Enhanced CO Photo-Production

Photocatalytic conversion of CO₂ into fuels using pure water as the proton source is of immense potential in simultaneously addressing the climate-change crisis and realizing a carbon-neutral economy. Single-atom photocatalysts with tunable local atomic configurations and unique electronic properties have exhibited outstanding catalytic performance in the past decade. However, given their single-site features they are usually only amenable to activations involving single molecules. For CO₂ photoreduction entailing complex activation and dissociation process, designing multiple active sites on a photocatalyst for both CO₂ reduction and H₂O dissociation simultaneously is still a daunting challenge. Herein, it is precisely construct Cu single-atom centers and two-coordinated N vacancies as dual active sites on CN (Cu₁/N_2CV-CN). Experimental and theoretical results show that Cu single-atom centers promote CO₂ chemisorption and activation via accumulating photogenerated electrons, and the N_2CV sites enhance the dissociation of H₂O, thereby facilitating the conversion from COO* to COOH*. Benefiting from the dual-functional sites, the Cu₁/N_2CV-CN exhibits a high selectivity (98.50%) and decent CO production rate of 11.12 µmol g⁻¹ h⁻¹. An ingenious atomic-level design provides a platform for precisely integrating the modified catalyst with the deterministic identification of the electronic property during CO₂ photoreduction process.     

NBR/PVC橡塑合金辐射防护材料的制备及性能研究

为了满足辐射防护的要求以及发展结构功能一体化柔性防护材料的需求,研制了一种新型的橡塑合金复合防护材料。通过力学性能和屏蔽性能测试发现,该复合材料具有较高的拉伸强度与断裂伸长率,且材料的中子与伽马屏蔽性能良好,随着功能填料含量的不断增加,中子和伽马屏蔽性能也不断提升,但材料的拉伸强度和断裂伸长率不断减小。辐照实验显示,电子束硫化后的样品比硫磺硫化后的样品拥有更出色的力学性能。综合考虑该材料良好的中子与伽马屏蔽性能、拉伸性能与柔韧性能等特性,其在形状较为复杂的核动力设施以及辐射防护服等领域具有较大的应用潜力。     

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.