STM32F207和DM9161A的以太网实现方案

电力抄表系统常通过网络采集和传输电网中的谐波等信息。本文提出了一种适合电力系统的网络设计方案。在STM32F207和DM9161A为核心的硬件平台上,完成了LwIP协议栈的移植,实现了远程终端和上位机通信。使电力系统更具实时性与交互性,并保证了通信的可靠性。     

数字化校园综合查询服务平台的设计与研究

本文在对数字化校园中心共享数据库及公共数据交换平台的研究基础之上设计了一种综合查询服务平台架构及查询服务功能,同时分析了查询实现过程。     

基于微电压调节的太阳能充放电系统设计

基于微电压调节的太阳能充放电系统,是以微控制器LPC1114为核心的太阳能电池板充放电电路,是实现高效率的智能充放电设计方案。微电压调节理论整合了经典电压回授法和多种太阳能最大功率点追踪思想,利用简单合理的DC／DC平衡技术,充分考虑系统功耗等问题,以简单的电路设计和创新的软件思想实现更高效率的太阳能系统。随着能源危机的加剧,高效率、低成本的太阳能系统备受社会关注,其在民用、交通等领域的运用也更加重要。     

基于Nios Ⅱ的CCD采集系统的设计

设计了一种在NiosⅡ处理器上的CCD数据采集系统。电荷耦合器件（Charge—Coupled Device,CCD）采集到的信号经过前端的差分运放处理后再进行A／D转换,转换后的数据存储于外部SDRAM中,被读取后显示在LCD上。本文重点介绍了各器件的电路设计和驱动程序的编写。实践表明,该设计成本低,实时性较高。     

基于FPGA的高速多路视频数据采集系统

针对同时处理高速多路视频数据的需求,以NiosII软核CPU为核心,通过在FPGA上构建可编程片上系统（Sys—ternOnProgrammableChip,SOPC）,利用SOPC系统自定义外设接口,配合DMA技术,完成对A／D转换后的多路视频数据的同时解码采集。视频解码模块采用滑动窗法快速检测定时基准信号。FPGA可重构的特性可以使系统根据实际应用需要在原方案基础上扩展、裁减功能模块,并根据资源情况重构系统,达到资源与效率的最优匹配。     

Influence of base resistance on extracting thermal resistance for SiGe HBTs

In this article, the influence of base resistance on extracting thermal resistance for SiGe heterojunction bipolar transistors is studied and an improved approach for determining the junction temperature and thermal resistance is presented. The proposed method for extracting thermal resistance is based on the temperature sensitivity of the base–emitter (B–E) voltage when the device is biased with a fixed emitter current density. This approach not only takes into account the self‐heating during the different ambient temperature measurement but also revises the empirical equation of B–E voltage due to the influence of base resistance during the power dissipation increment measurement. Results are obtained for devices with different emitter lengths and fingers. Compared with the conventional method, the thermal resistance is about up to 15% improvement for the device with 0.3 × 1.9 μm² emitter area and 13.8% for the device with 0.3 × 13.9 μm² emitter area. The accurate thermal resistance implemented in HICUM model has resulted in better fit for transistor output characteristics. © 2012 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2012.     

Evaluating a color-based active basis model for object recognition

Wu and coworkers introduced an active basis model (ABM) for object recognition in 2010, in which the learning algorithm tends to sketch edges in textures. A grey-value local power spectrum was used to find a common template and deformable templates from a set of training images and to detect an object in new images by template matching. In this paper, we propose a color-based active basis model (color-based ABM for short), which incorporates color information. We adopt the framework of Wu et al. in the learning, detection, and classification of the color-based ABM. However, in order to improve the performance in object recognition, we modify the framework of Wu et al. by using different color-based features in both the learning and template matching algorithms. In this color-based ABM approach, two types of learning (i.e., supervised learning and unsupervised learning) are also explored. Moreover, the usefulness of the color-based ABM for practical object recognition in computer vision applications is demonstrated and its significant improvement in recognizing objects is reported.     

A new time invariant fuzzy time series forecasting method based on particle swarm optimization

In the analysis of time invariant fuzzy time series, fuzzy logic group relationships tables have been generally preferred for determination of fuzzy logic relationships. The reason of this is that it is not need to perform complex matrix operations when these tables are used. On the other hand, when fuzzy logic group relationships tables are exploited, membership values of fuzzy sets are ignored. Thus, in defiance of fuzzy set theory, fuzzy sets’ elements with the highest membership value are only considered. This situation causes information loss and decrease in the explanation power of the model. To deal with these problems, a novel time invariant fuzzy time series forecasting approach is proposed in this study. In the proposed method, membership values in the fuzzy relationship matrix are computed by using particle swarm optimization technique. The method suggested in this study is the first method proposed in the literature in which particle swarm optimization algorithm is used to determine fuzzy relations. In addition, in order to increase forecasting accuracy and make the proposed approach more systematic, the fuzzy c-means clustering method is used for fuzzification of time series in the proposed method. The proposed method is applied to well-known time series to show the forecasting performance of the method. These time series are also analyzed by using some other forecasting methods available in the literature. Then, the results obtained from the proposed method are compared to those produced by the other methods. It is observed that the proposed method gives the most accurate forecasts.     

On the sizing of a solar thermal electricity plant for multiple objectives using evolutionary optimization

Design, implementation and operation of solar thermal electricity plants are no more an academic task, rather they have become a necessity. In this paper, we work with power industries to formulate a multi-objective optimization model and attempt to solve the resulting problem using classical as well as evolutionary optimization techniques. On a set of four objectives having complex trade-offs, our proposed procedure first finds a set of trade-off solutions showing the entire range of optimal solutions. Thereafter, the evolutionary optimization procedure is combined with a multiple criterion decision making (MCDM) approach to focus on preferred regions of the trade-off frontier. Obtained solutions are compared with a classical generating method. Eventually, a decision-maker is involved in the process and a single preferred solution is obtained in a systematic manner. Starting with generating a wide spectrum of trade-off solutions to have a global understanding of feasible solutions, then concentrating on specific preferred regions for having a more detailed understanding of preferred solutions, and then zeroing on a single preferred solution with the help of a decision-maker demonstrates the use of multi-objective optimization and decision making methodologies in practice. As a by-product, useful properties among decision variables that are common to the obtained solutions are gathered as vital knowledge for the problem. The procedures used in this paper are ready to be used to other similar real-world problem solving tasks.     

自适应变步长最大风能捕获算法

风能具有随机性、不稳定性的特点,为了提高风力发电系统中风能的利用效率,在比较各种最大风能捕获算法的基础上,分析了爬山搜索法和叶尖速比法的不足,提出了自适应变步长搜索算法来捕获最大风能.通过改进爬山搜索法的变步长策略,明显加快了搜索速度,通过引入初始估计叶尖速比值,大大缩小了搜索范围.该算法不需要实时检测准确风速,不依赖风力机最佳功率曲线,有效地降低了成本,提高风力发电的效率.文中重点分析了算法的自适应性和变步长策略,仿真结果表明,该算法能够使风力机更快速到达最大功率点,动态响应快,收敛性好.