Development of flexible displays using back‐channel‐etched In–Sn–Zn–O thin‐film transistors and air‐stable inverted organic light‐emitting diodes

We developed flexible displays using back‐channel‐etched In–Sn–Zn–O (ITZO) thin‐film transistors (TFTs) and air‐stable inverted organic light‐emitting diodes (iOLEDs). The TFTs fabricated on a polyimide film exhibited high mobility (32.9 cm²/Vs) and stability by utilization of a solution‐processed organic passivation layer. ITZO was also used as an electron injection layer (EIL) in the iOLEDs instead of conventional air‐sensitive materials. The iOLED with ITZO as an EIL exhibited higher efficiency and a lower driving voltage than that of conventional iOLEDs. Our approach of the simultaneous formation of ITZO film as both of a channel layer in TFTs and of an EIL in iOLEDs offers simple fabrication process.     

Chaotification of Switching Control Systems via Dwell Time Approach

In this paper, a class of switching control systems is investigated, and a novel switching control method is developed, which can be applied to generating chaos dynamics from switching control systems through dwell time restriction. A numerical example is given to illustrate the generated chaotic dynamic behavior of the systems, and the corresponding bifurcation diagram is sketched. Then, the validity of the main results is verified together with other phase portraits.     

基于ACC-RBF的水布垭面板堆石坝参数反演分析

蚁群聚类径向基函数(ACC-RBF)神经网络是将蚁群聚类算法和径向基函数神经网络组合运用的一种新型神经网络模型,把该网络用于水布垭高面板坝堆石体的多参数反演问题,在室内试验参数的基础上用有限元计算获得学习样本,采用该网络对坝体堆石料的邓肯E-B模型参数进行反演分析,用反演所得参数结合三维非线性有限元计算坝体应力变形,并与实测资料比较,得出很接近的结论.     

一种针对特定密度检测的快速FBP算法

提出了一种针对特定密度检测的快速FBP算法。首先进行不完全数据投影重建,然后依据对密度的先验知识对初步重建图像进行聚类分割,最后找出感兴趣区域来精确重建。文末对该方法进行了实验,并将实验结果和传统FBP算法、几何参数表法做出主观、客观上的比较评价。结果表明,该算法不但能在很大程度上提高传统FBP算法的速度,而且对于感兴趣区域的重建质量上没有影响。     

Fuzzy-neural approaches with example post-classification for estimating job cycle time in a wafer fab

Estimating the cycle time of a job in a wafer fabrication plant (wafer fab) is a critical task to the wafer fab. Many recent studies have shown that pre-classifying a job before estimating the cycle time was beneficial to the forecasting accuracy. However, most pre-classification approaches applied in this field could not absolutely classify jobs. Besides, whether the pre-classification approach combined with the subsequent forecasting approach was suitable for the data was questionable. For tackling these problems, two hybrid approaches with example post-classification, the equally-divided method and the proportional-to-error method, are proposed in this study in which a job is post-classified by a back propagation network (BPN) instead after the forecasting error is generated. In this novel way, only jobs whose cycle time forecasts are the same accurate will be clustered into the same category, and the classification algorithm becomes tailored to the forecasting approach. For evaluating the effectiveness of the proposed methodology and to make comparison with some existing approaches, production simulation (PS) is applied in this study to generate test data. According to experimental results, the forecasting accuracy (measured with root mean squared error, RMSE) of the proportional-to-error method was significantly better than those of the other approaches in most cases by achieving a 26–56% (and an average of 41%) reduction in RMSE over the comparison basis – multiple-factor linear combination (MFLC). The effect of post-classification was also statistically significant.     

改进 IGABP 模型补偿倾角传感器温度漂移研究

倾角传感器很容易受到环境温度变化的影响,产生测量误差,即温度漂移现象。针对此问题,设计了一种基于改进的遗传算法(IGA)优化反向传播神经网络(BPNN)的温度漂移补偿模型。其中遗传算法使用了新的选择策略和交叉变异因子,增加了跳出局部最优解机制。实验结果显示,IGABP补偿模型的均方误差(MSE)为0.003 28,经过补偿模型修正后的平均温度漂移为0.039°,远优于未修正时的平均温度漂移0.190°。研究结果表明,IGABP补偿模型与传统的神经网络模型相比,具有更快的收敛速度和更高的补偿精度,能够有效的补偿因温度导致的测量误差,提高倾角传感器的稳定性和精度。     

基于虚拟电感的PMSM无位置传感器混合控制策略

为实现永磁同步电机全速域无位置传感器控制,常采用低速I-F开环控制和高速反电势模型法相结合的混合控制策略。但这两种方法建立在不同的坐标系,存在着控制结构上的差异,在方案切换时易引起系统振荡。针对此问题,提出了一种基于虚拟电感的平滑切换策略。首先,在反电势模型中引入虚拟电感来人为改变转子位置估计值;再以I-F方案的坐标系为参考模型,反电动势模型为可调模型,运用Popov超稳定理论设计出恰当的虚拟电感使可调模型收敛于参考模型,从而确保两种方案在切换前建立在同一坐标系上,以实现高低速方案平滑、稳定地切换。最后通过仿真和实验验证了所提方案的可行性。     

混合遗传蚁群算法优化BP神经网络预测空气质量

为了进一步提高空气质量指数预测精度,提出一种混合遗传蚁群算法优化BP神经网络的方式对空气质量指数进行预测.首先初始化蚁群算法的信息素分布,对不满足适应度条件的进行遗传算法的交叉、变异操作,进而计算蚁群的状态转移概率和信息素浓度,当适应度值满足条件要求时,将寻优结果作为BP神经网络的最优权值和阈值,来改善单一BP神经网络的不足.最后通过运用西安市的空气质量指数日历史数据进行验证,实验表明,本文所提模型的各个评价指标相对其他对比模型误差更小,在预测精度方面具有更高的说服力,因此能够有效地预测空气质量指数.     

BP神经网络结合变量选择方法在牛奶蛋白质含量检测中的应用北大核心CSCD

牛奶中的蛋白质含量会影响牛奶的品质,利用高光谱图像的光谱特征信息研究对牛奶蛋白质含量预测的可行性。本文提出一种基于竞争性自适应重加权算法(competitive adaptive reweighted sampling, CARS)和连续投影算法(successive projections algorithm, SPA)结合多层前馈神经网络(back propagation, BP)的预测建模方法,实验以含有不同浓度蛋白质的牛奶为对象,利用可见光/近红外高光谱成像系统共采集到5种牛奶共计250组高光谱数据,通过实验对比选择采用标准化方法对获取到的吸收光谱预处理,然后采用CARS结合SPA筛选特征波长,得到18个特征波长,建立CARS-SPA-BP模型,经过试验,CARS-SPA-BP模型的训练集决定系数和测试集决定系数R;和R;分别达到0.971和0.968,训练集均方根误差(root mean square error of calibration,RMSEC)和测试集均方根误差(root mean square error of prediction,RMSEP)达到了0.033和0.034。研究发现,采用CARS结合SPA筛选的牛奶特征波长建立的多层前馈神经网络模型,其模型预测结果与全波长建模相比并没有明显降低,因此将CARS结合SPA用于波长筛选并且结合BP神经网络基本可以完成对牛奶蛋白质含量的预测。为验证CARS-SPA-BP模型的预测能力,在相同数据环境下,使用较为传统的偏最小二乘回归(partial least squares regression, PLSR)进行建模,实验结果表明,CARS-SPA-BP相较于PLSR,R;和RMSEP均有明显提升。研究表明,CARS-SPA-BP可充分利用牛奶光谱特征信息实现较高精度的牛奶蛋白质含量检测。     

基于BAS-BP分类器模型的电压暂降源识别

为提高不同电压暂降扰动源的识别正确率,对电压暂降进行有效治理,提出一种利用天牛须搜索(BAS)算法和反向传播(BP)神经网络构建BAS-BP分类器模型的电压暂降源识别方法。文中应用改进S变换提取16个特征指标,组成电压暂降源识别指标体系,为消除冗余信息对分类结果的影响,利用组合赋权法筛选出9个指标作为分类器的输入量。通过BAS算法对BP神经网络的初始权值和阈值寻优,构建BAS-BP分类器模型,实现对配电网不同类型电压暂降源的识别。仿真结果表明,该分类器模型具有一定的抗噪能力与适用性,并且与常规分类器模型相比,具有更好的分类效果。