Hydrogen trapping by defects in semiconductors studied by anelastic spectroscopy

We review some results obtained by anelastic spectroscopy on H-related defects in III–V semiconductors. Anelastic measurements on InP lead to the formulation of a model explaining the conversion to the semi-insulating (SI) state. Moreover, in GaAs:Zn an extraordinarily fast relaxation rate has been measured and a possible explanation has been suggested. This results are reviewed and discussed in the light of new experiments on InP:Zn, whose spectrum shows a relaxation process similar to the one in GaAs:Zn.     

用scanf()函数输入浮点数出现问题的解决办法

本文讨论了用scanf()函数输入浮点数出现的问题,并分析了该问题产生的原因,最后文章重点给出了针对这类问题的4种解决方案。     

SPOT5超模式数据处理技术研究

超模式技术是为提高法国SPOT5卫星影像分辨率而采用的一项创新性技术,尽管已经有相关的文献对其进行了介绍,但在SPOT5超模式影像数据处理方面的研究却很少报道。主要对SPOT5超模式影像数据的处理方法进行了研究,在简要介绍SPOT5超模式技术的基础上,根据超模式影像数据交错采样的特点进行了基于梅花采样的影像内插,并对内插影像的去噪与解卷积过程进行了大量的研究,分析了影响SPOT5成像系统点扩散函数的主要因素。最后用SPOT5超模式影像数据进行了试验,取得了比较理想的效果。     

An output-based adaptive iterative learning controller for high relative degree uncertain linear systems

In this paper, we derive an output tracking error model based on signals filtered from plant input and output, and then present a new output-based adaptive iterative learning controller for repeatable linear systems with unknown parameters, high relative degree, initial resetting error, input disturbance and output noise. The proposed controller solves the important robustness issues without assuming the bounds of uncertainties to be sufficiently small and can be applied to high relative degree plants without using output differentiation. Control parameters are updated between successive iterations so as to compensate for unknown system parameters and uncertainties. It is shown that the internal signals inside closed-loop learning system remain bounded and the output tracking error will asymptotically converge to a profile tunable by some design parameters. Furthermore, the learning speed is easily improved if the learning gain is increased.     

Iterative learning control design based on composite energy function with input saturation

In this work, an iterative learning control scheme is designed for a class of nonlinear uncertain systems with input saturation. The analysis of convergence in the iteration domain is based on composite energy function, which consists of both input and state information along the time and iteration axes. Through rigorous analysis, the learning convergence in the iteration domain can be guaranteed under the input saturation, provided the desired trajectory is realizable within the saturation bound.     

西门子双控制系统在数控车床改造中的应用

介绍西门子840D控制系统的通道功能在数控车床改造中的应用,同时介绍使用840D和802D两套数控系统控制同一台机床在信息交换上的处理方法,重点介绍控制要点及解决方案。     

基于多场景风险分析的含风电互联电网机组组合策略研究

为保证电力系统可靠运行,日前机组组合应考虑电网中不确定性因素所带来的风险。大规模风电并网给电力系统运行引入了更多的不确定性,电网互联一定程度上可削弱风电不确定性对电网运行的影响,但同时也增大了日前机组组合问题的复杂度。为了在有限的计算时间内获取计及风险的可行的机组组合方案,需要筛选典型场景来衡量电网运行风险。在风电、负荷预测误差的基础上设置了互联电网机组、联络线强迫停运场景集,构建了互联电网弃风电量期望（expected wind power curtailed,EWPC）和电量不足期望（expected energy not supplied,EENS）风险量化指标,并将其以罚函数的形式引入目标函数,建立了计及多场景运行风险的多区域互联电网安全约束机组组合模型,经两区域12节点系统验证了机组组合策略的正确性和有效性。     

售电侧放开环境下含需求响应虚拟电厂的电力市场竞价策略

售电侧市场放开是我国新一轮电力体制改革的重点任务之一。文章考虑售电侧放开对需求侧资源参与电力市场的影响,提出了售电侧放开环境下计及需求响应的虚拟电厂（virtual power plant, VPP）竞价策略,建立了含虚拟电厂的电力市场竞价模型,在对虚拟电厂内部进行优化调度的基础上采用自适应学习方法对竞价模型进行求解,并以社会效益最大化为目标进行市场出清,最后选取算例对竞价策略的有效性进行验证。结论表明,售电侧放开环境下发电商与售电商的经济性有所提高且市场整体的用电需求得到调整,同时售电侧放开有利于需求侧资源参与市场竞争。     

基于典型场景集的虚拟电厂与配电网协同定价策略

虚拟电厂（virtual power plant,VPP）技术能够有效地提升配电网运行灵活性和可再生能源消纳水平,为描述电力市场环境下配电系统中多种资源的不确定性,采用拉丁超立方抽样和多尺度NJW谱聚类算法获取了包含电价、负荷、可再生能源出力等多种随机因素的典型场景集;根据典型场景集,考虑配电网与虚拟电厂的网络结构、潮流安全、机组出力等约束条件,建立了基于虚拟电厂与配电网各自利益最大化的优化模型,并提出一种基于虚拟电厂与配电网交替优化的协同定价策略。算例仿真结果表明,所提方法充分地考虑了多种随机因素,能够合理地制定虚拟电厂与配电网的交易电价,维持系统功率平衡。     

基于ADuC824的模糊石油闪点测定仪

主要论述了利用美国AD公司的最新微芯片ADuC824和模糊控制技术设计的单片机模糊炉温控制系统，并将其应用于石油产品的闪点自动测试过程中。实践证明，该系统比传统的微芯片和PID调节控制系统具有实时性强，精度高和误差小等明显优点。