基于投机执行的两级退休机制

针对超标量处理器中指令长时间占用重排序缓存引起指令退休缓慢的问题,提出了一种基于投机执行的两级退休机制.该方案根据指令有无异常和预测错误风险将指令分为有风险指令和无风险指令,对重排序缓存进行轻量化改进,只有存在异常和预测风险的指令才允许进重排序缓存,在确认风险消除后将指令快速退休.重命名寄存器从重排序缓存分离,负责寄存器重命名和结果乱序回写.实验结果表明,在硬件资源相同的情况下,基于该方案的处理器比传统的按序退休处理器的性能平均提高28.8％以上.     

基于竞争窗口递减因子自适应调整退避算法

包括IEEE802.11、802.15.4标准在内的许多无线网络协议都采用二进制指数退避机制管理数据的重发。在动态分布式的网络环境中,二进制指数退避算法固定的竞争窗口递减方式难以适应动态变化的网络规模。针对这一问题,提出了一种改进的回退机制,该机制通过引入竞争窗口递减因子,自适应地调整无线节点的等待时间,以实现网络吞吐量的最大化。同时,在算法实现上提出一种启发式算法以跟踪网络中竞争节点数量的改变。在IEEE 802.11DCF协议中以相同的物理层参数进行仿真,结果表明改进算法提高了网络吞吐量,降低了分组平均接入时延。     

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.