航空三相静止变流器死区效应分析与补偿

为了解决航空三相静止变流器输出波形失真问题,详细分析了死区效应造成电压畸变的原因和由此带来的谐波成分.采用正弦脉宽调制(SPWM)控制技术,使变流器输出的波形更接近于正弦波,提出了设置死区与电压补偿相结合的控制方法,并通过SABER进行了仿真分析.同时,利用TMS320F2812数字芯片实现了相应的补偿策略.仿真和试验结果证明了该控制方法的有效性.     

基于障碍物群的k全局相异最优有序路径查询

提出障碍k全局相异最优有序路径的查询问题,利用可视图的思想给出近似查询算法,通过作用集与障碍角度点的引入有效地减少构造可视图障碍对象的数量,分析查询点和数据点构成的线段与可视图的顶点和弧的关系,减少内部障碍路径的计算次数,实现算法的全面优化。实验结果表明,该算法具有较好的性能。     

万维网链接结构的复杂性分析

对于凭借网页间链接结构所构成的超大规模复杂网络,万维网的链接结构并不符合传统的随机网络模型。为此,通过对万维网真实链接数据的提取及分析,计算并对比节点的度分布、平均路径长度及集群系数等相关网络指标。分析结果表明,万维网具有小世界效应,整个网络的平均路径长度约为15跳,具有相似主题的网页组成若干个集群,进而构成无标度网络。     

基于多主体的消费者行为模式仿真

为了更好地探究消费者保守行为模式,发掘市场中隐含的内在机制,使用了基于多主体的模型来模拟虚拟市场中的消费者行为。通过创建大量受到动机函数驱使,并且能够交互的主体,从而来模拟市场中人们的购买决策行为,了解人们的购买决策受到哪些因素的影响。同时在主体模型的基础上,构建了抽象社会关系网络。随着市场的发展,与之对应的底层网络结构也会随之演化。由此涌现出的一些宏观群体特性,也从不同的角度合理解释了诱饵效应和锁定效应这样的消费者行为模式。证实了不同阶层的消费者消费行为模式会有所不同。     

Single-machine scheduling jobs with exponential learning functions

In a manufacturing system workers are involved in doing the same job or activity repeatedly. Hence, the workers start learning more about the job or activity. Because of the learning, the time to complete the job or activity starts decreasing, which is known as “learning effect”. In this paper, an exponential sum-of-actual-processing-time based learning effect is introduced into single-machine scheduling. By the exponential sum-of-actual-processing-time based learning effect, we mean that the processing time of a job is defined by an exponential function of the sum-of-the-actual-processing-time of the already processed jobs. Under the proposed learning model, we show that under a sufficient condition, the makespan minimization problem, the sum of the θth (θ > 0) power of completion times minimization problem, and some special cases of the total weighted completion time minimization problem and the maximum lateness minimization problem remain polynomially solvable.     

A branch and bound algorithm for minimizing makespan on a single machine with unequal release times under learning effect and deteriorating jobs

We present a single-machine problem with the unequal release times under learning effect and deteriorating jobs when the objective is minimizing the makespan. In this study, we introduced a scheduling model with unequal release times in which both job deterioration and learning exist simultaneously. By the effects of learning and deterioration, we mean that the processing time of a job is defined by increasing function of its execution start time and position in the sequence. A branch-and-bound algorithm incorporating with several dominance properties and lower bounds is developed to derive the optimal solution. A heuristic algorithm is proposed to obtain a near-optimal solution. The computational experiments show that the branch-and-bound algorithm can solve instances up to 30 jobs, and the average error percentage of the proposed heuristic is less than 0.16%.     

An expertise reversal effect of segmentation in learning from animated worked-out examples

Many animations impose a high cognitive load due to the transience of information, which often hampers learning. Segmentation, that is presenting animations in pieces (i.e., segments), has been proposed as a means to reduce this high cognitive load. The expertise reversal effect shows, however, that design measures that have a positive effect on cognitive load and learning for students with lower levels of prior knowledge, might not be effective, or might even have a negative effect on cognitive load and learning for students with higher levels of prior knowledge. This experiment with animated worked-out examples showed an expertise reversal effect of segmentation: segmented animations were more efficient than continuous animations (i.e., equal test performance with lower investment of mental effort during learning) for students with lower levels of prior knowledge, but not for students with higher levels of prior knowledge.     

A historical perspective of algorithmic lateral inhibition and accumulative computation in computer vision

Certainly, one of the prominent ideas of Professor José Mira was that it is absolutely mandatory to specify the mechanisms and/or processes underlying each task and inference mentioned in an architecture in order to make operational that architecture. The conjecture of the last fifteen years of joint research has been that any bottom-up organization may be made operational using two biologically inspired methods called “algorithmic lateral inhibition”, a generalization of lateral inhibition anatomical circuits, and “accumulative computation”, a working memory related to the temporal evolution of the membrane potential. This paper is dedicated to the computational formulation of both methods. Finally, all of the works of our group related to this methodological approximation are mentioned and summarized, showing that all of them support the validity of this approximation.     

A high-density multi-point LAPS set-up using a VCSEL array and FPGA control

A new LAPS (light-addressable potentiometric sensor) set-up will be introduced, in which the light sources are miniaturised by the utilisation of a VCSEL (vertical-cavity surface-emitting laser) array to increase the measurement spot density. An FPGA (field-programmable gate array) is used to generate modulation signals for individual illumination of each measurement spot of the LAPS. The new set-up can operate a large number of measurement spots simultaneously by reading out the sum photocurrent and separate the signals of the individual measurement spots by an FFT analysis. The frequency, amplitude and offset of the modulation signal can be configured for each measurement spot by software. The new system can be combined with a positioning stage allowing the parallel read out of a single line of measurement spots and a scan perpendicular to that line in a similar manner, like for an optical scanner set-up. First measurements demonstrate the functionality of the new LAPS set-up as a chemical imaging system.     

Room temperature hydrogen gas sensor nanocomposite based on Pd-decorated multi-walled carbon nanotubes thin films

Multi-walled carbon nanotubes (MWCNTs) are successfully processed in the form of thin films (buckypapers), and their morphology and electrical behaviour are characterized. The MWCNTs are synthesized by the floating catalyst chemical vapour deposition process. The effects of a sequence of treatments applied for MWCNTs purification on the buckypapers electrical behaviour are also examined. Nanocomposite thin films constituted of pristine and purified MWCNTs and Pd nanoparticles are prepared in order to evaluate their viability as H₂ sensors at room temperature. For this purpose, the electrical resistance of the nanocomposite films in atmospheres with different H₂ concentrations, is determined. Scanning electron microscopy (SEM) images show that the buckypapers and the nanocomposite films are 2D structures constituted by randomly oriented MWCNTs. The buckypapers present a semiconductor-like electrical behaviour as determined by the standard four point method. Room temperature resistivity values of around 10⁻³ Ω m are assessed. Nanocomposite films show different electrical behaviour depending on the purification treatment applied to the MWCNTs employed. Furthermore, the electrical resistance of the nanocomposite films is found to increase when the measurements are performed in H₂ atmosphere. Values of H₂ sensitivity at room temperature of the nanocomposite films up to 2.15% are determined for H₂ average concentration higher than 350 ppm with short recovery time.