厚膜熔断器的电气特性模拟

利用ANSYS软件建立了高性能厚膜熔断器的有限元模型,并对其在正常工作状态下的瞬态温度场变化过程进行了数值模拟,得到熔断器引脚温升为65.30℃,电阻为0.05719Ω,发热功率为0.91504W等参数,并进行了实验验证。结果表明:实验和数值模拟的误差率为3.00%~4.00%,为熔断器的设计制造提供了方法和理论依据。     

基于BP神经网络的模塑封电子器件优化设计

针对塑封SOT(小外形晶体菅)器件的使用失效案例,从芯片设计角度出发,提出一种优化设计方法,该方法利用误差反向传播神经网络(BPNN),结合主成分分析(PCA)、遗传算法(GAs)及均匀设计的针对非线性系统的优化设计,设计了该塑封SOT器件的尺寸参数。结合实验和有限元模拟分析,验证该优化设计结果的有效性。结果表明,优化设计的器件各关键界面点的最大应力约减少了70~180MPa,器件的界面层裂现象得到消除,提高了器件的可靠性。     

Cu@Ag包覆粉体的SPS烧结及其致密机理研究

采用化学镀法制备Cu@Ag包覆粉体,并利用放电等离子烧结技术(SPS)对其进行烧结,利用扫描电子显微镜、透射电子显微镜研究包覆粉体、烧结样品的微观结构,对烧结样品的物相、致密度及其致密化机理进行表征与分析.结果表明,化学镀法制备的Cu@Ag粉体表面存在高纯的包覆层.在较低的烧结温度下可以得到致密度高的Cu-Ag烧结块体...     

锂离子电池简化电化学模型理论误差分析研究

准确的电池模型是电池状态估计和能量管理的基础。锂离子电池电化学模型描述电池内部机理,具有物理意义明确和准确度高的优点。目前,常用的电化学模型为P2D伪二维电化学模型、SPM单粒子模型和SPMe考虑电解质动态的单粒子模型。其中,P2D模型的精度最高而计算量最大,而简化模型SPM或SPMe的计算效率高,但存在模型简化产生的误差。为探究误差的产生机理,首先比较SPM、SPMe和P2D模型的电极电流源、固相浓度和液相浓度,然后从平衡电势、过电势、液相浓差极化电势、液相欧姆压降和端电压对简化模型进行了误差分析,并指出误差传递路径。结果表明,电极电流源是模型端电压误差产生的根源,液相正负极欧姆压降和负极平衡电势误差是端电压误差的主要来源。上述结果可为SPM和SPMe模型的误差补偿方法设计提供理论依据。最后还提出了模型端电压误差补偿建议。     

基于高倍过采样与加窗插值FFT的电力谐波分析

为提高谐波分析精度,分析了信号加窗引起的信噪比损失以及AD转换产生的量化误差,阐述了过采样技术提高信噪比的原理。在此基础上,提出了基于高倍过采样和加窗插值快速傅里叶变换(fast Fourier transform, FFT)的谐波分析方法。该方法充分利用AD转换器的潜力,以尽量高的采样速率进行AD采样,同时通过均值滤波避免高倍过采样引起的采样数据量激增问题。详细研究了所提谐波分析方法对信号中谐波分量幅值和相位的影响,并给出了简洁实用的谐波幅值和相位校正方法。仿真表明,所提方法可在不增加系统成本的前提下改善加窗插值FFT的抗噪声能力,提高谐波分析精度。     

A Bayesian network incorporating observation error to predict phosphorus and chlorophyll a in Saginaw Bay

Empirical relationships between lake chlorophyll a and total phosphorus concentrations are widely used to develop predictive models. These models are often estimated using sample averages as implicit surrogates for unknown lake-wide means, a practice than can result in biased parameter estimation and inaccurate predictive uncertainty. We develop a Bayesian network model based on empirical chlorophyll-phosphorus relationships for Saginaw Bay, an embayment on Lake Huron. The model treats the means as unknown parameters, and includes structure to accommodate the observation error associated with estimating those means. Compared with results from an analogous simple model using sample averages, the observation error model has a lower predictive uncertainty and predicts lower chlorophyll and phosphorus concentrations under contemporary lake conditions. These models will be useful to guide pending decision-making pursuant to the 2012 Great Lakes Water Quality Agreement.     

MIMO SAR OFDM chirp waveform design and GMTI with RPCA based method

Multiple-input multiple-output synthetic aperture radar (MIMO SAR) has drawn wide attention for its increased degrees of freedom (DOFs) compared to the traditional multi-channel SAR (MC-SAR) system. The MIMO SAR system, which makes each antenna illuminate one subswath, can widen the range swath without decreasing PRF. One of the foremost tasks involved in the MIMO SAR system is the waveform design at the transmitter. Moreover, at the receiver, how to accurately separate the auto-correlation signal from the cross-correlation interferences is the other important issue. In this paper, an orthogonal frequency division multiplexing (OFDM) chirp signal designing method is firstly proposed, which is based on low cross-correlation interferences and good peak-to-sidelobe ratio (PSLR) rules. Using the designed OFDM chirp signals, the focused signal of MIMO SAR system matched the robust principal component analysis (RPCA) basic model. The moving target (sparse signal), clutter (low-rank signal) and noise (noise signal) can be separated directly. Hence, RPCA based method can be employed for ground moving target indication (GMTI) with no need for extracting the matched signal or suppressing the unmatched interferences. Extensive simulations demonstrate the effectiveness of the waveform designing method and GMTI with RPCA based method for MIMO SAR system.     

Data‐driven bounded‐error fault detection

In this paper, a new data‐driven fault‐detection method is proposed. This method is based on a new nonparametric system identification approach, which constitutes the principal contribution to this work. The fault‐detection method is a parametric model‐free approach that can be applied to nonlinear systems that work at various operating points. Not only can the fault‐detection process be applied to the steady state of each operating point, but it can also be applied to the transient state resulting from a change in the operating point. In order to detect faults, the proposed method uses an interval predictor based on bounded‐error techniques. The utilization of techniques based on bounded error enables system uncertainties to be included in an explicit way. This in turn leads to the possibility of obtaining interval predictions of the behaviour of the system, which include information on the reliability of the prediction itself. In order to show the effectiveness of the fault‐detection method, two examples are presented: in the form of a simulated process (counter‐flow shell‐and‐tube heat‐exchanger system) and an example of a real application (two‐tanks system). A comparison with two fault‐detection methods has also been included. Copyright © 2013 John Wiley & Sons, Ltd.     

Mapping average equivalized income using robust small area methods

Measures of economic well‐being are often needed for geographically small areas, as economic indicators may be distributed unevenly among the subsets of relatively small regions. We consider small area estimation of average equivalized income. Disposable household income data are usually available only for a sample of households, typically too small to provide reliable estimates for small regions. We consider a small area estimation technique that is robust to outliers, produces results consistent with design weighted estimates obtained for larger areas and yield maps with approximately no shrinkage. The proposed methodology is applied to the Local Labour Systems in Tuscany (Italy).