一种新型并联混合有源滤波器的研究

本文提出了一种新型并联混合型有源滤波器的拓扑，新型拓扑通并联谐 振抑制流过无源网络的基波无功电流，减小了有源滤波器承担的基波电流。同时在此拓扑基础上讨论了它的控制策略，在此控制策略下，有源滤波器不承担基波电矿井，且只承担很小的基波电流，因此有源滤波器的容量达到了最小，本文分析了并联谐振对无源网络阻抗的影响，并对此分析了控制策略在传统拓扑和新型拓扑下对有源滤波器容量的影响，最后通过仿真证明了结论的正确性。     

PHEV驱动系统结构分析与仿真研究

对插电式混合动力汽车(PHEV)的串、并联驱动系统结构进行分析.以中型SUV为例,确定了整车动力系统参数和控制策略;使用Advisor进行仿真.仿真结果表明:在控制策略和工况既定的前提下,当所用电池容量较小时,并联PHEV总体性能优于串联PHEV;电池容量增加后,串联PHEV表现出良好的适应性.     

基于支持向量机补偿的灰色模型网络流量预测

针对网络测量与控制技术中提高流量预测准确性的问题,提出基于支持向量机残差补偿的灰色模型网络流量预测模型。该模型采用灰色模型进行趋势预测,支持向量机进行残差序列预测,实现残差补偿。实验结果表明:该模型具有预测模型样本小,预测精度高等优点,适合于网络流量预测。     

A Low-Latency Multi-layer Prefix Grouping Technique for Parallel Huffman Decoding of Multimedia Standards

Huffman coding is a popular and important lossless compression scheme for various multimedia applications. This paper presents a low-latency parallel Huffman decoding technique with efficient memory usage for multimedia standards. First, the multi-layer prefix grouping technique is proposed for sub-group partition. It exploits the prefix characteristic in Huffman codewords to solve the problem of table size explosion. Second, a two-level table lookup approach is introduced which can promptly branch to the correct sub-group by level-1 table lookup and decode the symbols by level-2 table lookup. Third, two optimization approaches are developed; one is to reduce the branch cycles and the other is parallel processing between two-level table lookup and direct table lookup approaches to fully utilize the advantage of VLIW parallel processing. An AAC Huffman decoding example is realized on the Parallel Architecture Core DSP (PAC DSP) processor. The simulation results show that the proposed method can further improve about 89% of decoding cycles and 33% of table size comparing to the linear search method.

Cell Generator: A Self‐Sustaining Biohybrid System Based on Energy Harvesting from Engineered Cardiac Microtissues

Biohybrid soft robotic devices present unique advantages for designing biologically active machines that can dynamically sense and interact with complex bioelectrical signals. Here, a controllable cell‐based machine is developed that harvests energy from arrays of beating cardiomyocytes to generate electricity for biomedical microscale robotic applications. The “Cell Generator” device is based on an array of piezoelectric microcantilevers wrapped with 3D patterned cardiac cells. Spontaneous contraction of the engineered cardiac constructs provides the source of mechanical energy for electricity generation. It is demonstrated that a single “Cell Generator” unit with 40 cantilevers can output peak voltages of ≈70 mV, and a larger array of 540 cantilevers can directly generate a pulsed output as high as ≈1 V. When integrated with an electrical rectification and storage circuit, it is further shown that the “Cell Generator” can provide functional outputs and work as a self‐powered neural stimulator to evoke action potentials in cultured neuronal networks. This demonstration of “Cell Generator” technology provides an innovative perspective of exploiting live biological powering system on biomedical microscale robotic devices in the human body.     

多实例并行机动仿真模型开发

针对仿真系统中对机动模型的多实例并行需求,提出基于C++和Matlab混合编程的方式进行模型开发。就Matlab Function实现的功能函数在多实例运行过程中将出现的数据混用问题,在分析Matlab数据管理调用方式的基础上,提出基于Map的数据管理方法实现不同对象实例数据,并完成了模型开发。     

用于数据挖掘的多维数据可视化技术

用于数据挖掘的多维数据可视化技术,将多维数据通过平行坐标、圆形分段或多维缩放变换以二维图形显示.平行坐标用其坐标点显示变量,直线连接坐标点为折线.圆形分段以每个彩色像素点映射一个值,并将每一维的数据在不同区域显示.多维缩放则在尽可能保留数据点间距的同时,在低维空间内表示多维数据.     

Asymmetric Semi‐Supervised Boosting Scheme for Interactive Image Retrieval

Support vector machine (SVM) active learning plays a key role in the interactive content‐based image retrieval (CBIR) community. However, the regular SVM active learning is challenged by what we call “the small example problem” and “the asymmetric distribution problem.” This paper attempts to integrate the merits of semi‐supervised learning, ensemble learning, and active learning into the interactive CBIR. Concretely, unlabeled images are exploited to facilitate boosting by helping augment the diversity among base SVM classifiers, and then the learned ensemble model is used to identify the most informative images for active learning. In particular, a bias‐weighting mechanism is developed to guide the ensemble model to pay more attention on positive images than negative images. Experiments on 5000 Corel images show that the proposed method yields better retrieval performance by an amount of 0.16 in mean average precision compared to regular SVM active learning, which is more effective than some existing improved variants of SVM active learning.