Enhancement of power conversion efficiency of PTB7:PCBM-based solar cells by gate bias

We designed and fabricated poly[[4,8-bis[(2-ethylhexyl)oxy]benzo[1,2-b:4,5-b′]dithiophene-2,6-diyl] [3-fluoro-2-[(2-ethylhexyl)carbonyl] thieno[3,4-b]thiophenediyl]] (PTB7): [6,6]-phenyl-C70-butyric-acid-methyl-ester (PC₇₀BM)–based solar cells with gate electrodes, which can introduce an additional electric field within the devices just as in organic thin film transistors (OTFTs). Our proposed realize the simple and convenient modulation of electric field within the device, and power conversion efficiency (PCE) of 8.1% is reached at 2.0 V gate bias, significantly higher than the PCE of 6.8% at the case of no gate structure. By calculating the carrier mobility and the rate of exciton dissociation efficiency in detail, the role of electric field to the exciton dissociation and carrier transport was investigated, respectively. Meanwhile, the feasibility of the proposed device structure in practical application was discussed. The results suggest that such a gate structure has a great of prospects in achieving high efficiency polymer solar cells.     

On the equivalence between global recurrence and the existence of a smooth Lyapunov function for hybrid systems

We study a weak stability property called recurrence for a class of hybrid systems. An open set is recurrent if there are no finite escape times and every complete trajectory eventually reaches the set. Under sufficient regularity properties for the hybrid system we establish that the existence of a smooth, radially unbounded Lyapunov function that decreases along solutions outside an open, bounded set is a necessary and sufficient condition for recurrence of that set. Recurrence of open, bounded sets is robust to sufficiently small state dependent perturbations and this robustness property is crucial for establishing the existence of a Lyapunov function that is smooth. We also highlight some connections between recurrence and other well studied properties like asymptotic stability and ultimate boundedness.     

Improved classification with allocation method and multiple classifiers

Classification is the most used supervized machine learning method. As each of the many existing classification algorithms can perform poorly on some data, different attempts have arisen to improve the original algorithms by combining them. Some of the best know results are produced by ensemble methods, like bagging or boosting. We developed a new ensemble method called allocation. Allocation method uses the allocator, an algorithm that separates the data instances based on anomaly detection and allocates them to one of the micro classifiers, built with the existing classification algorithms on a subset of training data. The outputs of micro classifiers are then fused together into one final classification. Our goal was to improve the results of original classifiers with this new allocation method and to compare the classification results with existing ensemble methods. The allocation method was tested on 30 benchmark datasets and was used with six well known basic classification algorithms (J48, NaiveBayes, IBk, SMO, OneR and NBTree). The obtained results were compared to those of the basic classifiers as well as other ensemble methods (bagging, MultiBoost and AdaBoost). Results show that our allocation method is superior to basic classifiers and also to tested ensembles in classification accuracy and f-score. The conducted statistical analysis, when all of the used classification algorithms are considered, confirmed that our allocation method performs significantly better both in classification accuracy and f-score. Although the differences are not significant for each of the used basic classifier alone, the allocation method achieved the biggest improvements on all six basic classification algorithms. In this manner, allocation method proved to be a competitive ensemble method for classification that can be used with various classification algorithms and can possibly outperform other ensembles on different types of data.     

Evaluation of different initial solution algorithms to be used in the heuristics optimization to solve the energy resource scheduling in smart grids

Over the last years, an increasing number of distributed resources have been connected to the power system due to the ambitious environmental targets, which resulted into a more complex operation of the power system. In the future, an even larger number of resources is expected to be coupled which will turn the day-ahead optimal resource scheduling problem into an even more difficult optimization problem. Under these circumstances, metaheuristics can be used to address this optimization problem. An adequate algorithm for generating a good initial solution can improve the metaheuristic’s performance of finding a final solution near to the optimal than using a random initial solution. This paper proposes two initial solution algorithms to be used by a metaheuristic technique (simulated annealing). These algorithms are tested and evaluated with other published algorithms that obtain initial solution. The proposed algorithms have been developed as modules to be more flexible their use by other metaheuristics than just simulated annealing. The simulated annealing with different initial solution algorithms has been tested in a 37-bus distribution network with distributed resources, especially electric vehicles. The proposed algorithms proved to present results very close to the optimal with a small difference between 0.1%. A deterministic technique is used as comparison and it took around 26 h to obtain the optimal one. On the other hand, the simulated annealing was able of obtaining results around 1 min.     

Stereo object tracking with fusion of texture,color and disparity information

A novel method for visual object tracking in stereo videos is proposed, which fuses an appearance based representation of the object based on Local Steering Kernel features and 2D color–disparity histogram information. The algorithm employs Kalman filtering for object position prediction and a sampling technique for selecting the candidate object regions of interest in the left and right channels. Disparity information is exploited, for matching corresponding regions in the left and right video frames. As tracking evolves, any significant changes in object appearance due to scale, rotation, or deformation are identified and embodied in the object model. The object appearance changes are identified simultaneously in the left and right channel video frames, ensuring correct 3D representation of the resulting bounding box in a 3D display monitor. The proposed framework performs stereo object tracking and it is suitable for application in 3D movies, 3D TV content and 3D video content captured by consuming stereo cameras. Experimental results proved the effectiveness of the proposed method in tracking objects under geometrical transformations, zooming and partial occlusion, as well as in tracking slowly deforming articulated 3D objects in stereo video.     

Performance and fairness enhancement in IEEE 802.11 WLAN networks

The multi-rate transmission mechanism in IEEE 802.11 can improve its reliability and robustness. However, it causes a performance anomaly. After analyzing the reasons for the performance anomaly in multi-rate mechanism, we propose a new scheme to solve the performance anomaly. By adjusting packet size according to the transmission rate, this scheme guarantees that these nodes with different transmit rates can access wireless channel fairly. Theoretical analysis and performance evaluation show that the proposed scheme can well solve the performance anomaly problem.     

基于相似理论的大型构造物理模拟装置的设计与研究

相似理论是构造物理模拟实验的理论依据,而大型构造物理模拟装置又是一套机电一体化实验系统,在对主要地质构造现象与成因分析的基础上,从动力学和运动学的角度,总结了地质体形成的受力与变形规律,提出了实验装置动力加载单元应该具有的基本功能,并按照相似性理论,运用模块化设计方法,完成了系统的总体方案和各功能模块的结构设计.重点分析和解决了非均质体和分段变形地质体受力机理模拟、地质体走滑受力机理及物理模拟和地质体多点多方向应力加载等问题.在实验装置功能分析的基础上.采用"分散控制、集中管理"的结构,以整个系统为对象优化组合,开发了基于可编程序控制器的分布式运动控制系统.最后,利用该实验装置完成了不同构造样式的构造物理模拟标准实验,运行实践表明,研制的设备运行良好,达到了设计要求,为构造物理模拟定量分析提供了新的技术手段和实验平台.     

一款结合数字校正技术的流水线ADC设计

基于65 nm CMOS工艺、1.2 V供电电压,设计了一款结合偏移双通道技术的流水线模数转换器(analog-to-digital convertor,ADC)。芯片的测试结果表明,该校正方法有效地消除和补偿了电容失配、级间增益误差和放大器谐波失真对流水线ADC综合性能的制约。流水线ADC在125 MS/s采样率、3 MHz正弦波输入信号的情况下,信噪失真比(signal-and-noise distortionratio,SNDR)从校正前的28 dB提高到61 dB,无杂散动态范围(spurious-free dynamic range,SFDR)从校正前的37 dB提高到62 dB。ADC芯片的功耗为72 mW,面积为1.56 mm2。偏移双通道数字校正技术在计算机软件上实现,数字电路在65 nm CMOS工艺、125 MHz时钟下估计得出的功耗为12 mW,面积为0.21 mm2。     

一种新的基于双层PCNN的自适应图像分割算法

提出一种新的基于双层脉冲耦合神经网络（PCNN）的自适应图像分割算法。双层PCNN的前级以简化PCNN模型为基础,获得区域生长的种子;后级采用区域生长机制,征募区域内灰度相似像素,完成前级种子的生长。新算法PCNN的关键参数可自适应更新,避免了传统PCNN参数设置难的问题;区域生长机制强化了PCNN的区域特性。实验结果...     

融合局部熵二维熵的空中目标跟踪算法研究

提出了一种将局部熵算法和二维熵算法结合的空中目标跟踪算法。此算法通过一定的准则合理地融合局部熵和二维熵算法,然后进行目标定位跟踪。既能实现空中小目标的准确跟踪,也能实现空中面目标的准确跟踪;同时针对大小不变的带宽窗口可能导致目标跟踪丢失的问题,采用了自动调节带宽窗口的方法,让跟踪结果稳定可靠。并且算法实现运用了局部熵和二维熵的快速算法,大大减小了系统检测模块所消耗的时间,使跟踪具有实时性和鲁棒性,此外也能将融合算法有效地移植到DSP上,实现工程应用。此算法可以应用于导弹跟踪、空中监控、海空预警等许多领域。