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。     

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。     

n型单晶硅片的电阻率和扩散方阻对IBC太阳电池电性能的影响

叉指背接触式(IBC)太阳电池因正面没有金属栅线遮挡,具有较高的短路电流,且组件外观更加美观。但由于IBC太阳电池正负电极在背面交叉式分布,在制备过程中需要采用光刻掩模技术进行隔离,难以实现大规模生产。采用Quokka软件仿真模拟了电阻率和扩散方阻对n型IBC太阳电池效率的影响,并对不同电阻率和扩散方阻的电池片进行了实验验证,从n型单晶硅片电阻率的选择和扩散工艺优化方面为IBC太阳电池的规模化生产提供了理论基础。实验结果表明,电阻率为3~5Ω·cm、扩散方阻为70Ω/时,小批量生产的IBC太阳电池平均光电转换效率可达23.73%,开路电压为693 mV,短路电流密度为42.44 mA/cm²,填充因子为80.69%。     

Investigation of machining characteristics in rotary ultrasonic machining of alumina ceramic

Alumina ceramic is well documented as a much-demanded advanced ceramic in the present competitive structure of manufacturing and industrial applications owing to its excellent and superior properties. The current article aimed to experimentally investigate the influence of several process variables, namely: spindle speed, feed rate, coolant pressure, and ultrasonic power, on considered machining characteristics of interest, i.e., chipping size and material removal rate in the rotary ultrasonic machining of alumina ceramic. Response surface methodology has been employed in the form of a central composite rotatable design to design the experiments. Variance analysis testing has also been performed with a view to observing the consequence of the considered parameters. The microstructure of machined rod samples was evaluated and analyzed using a scanning electron microscope. This analysis has revealed and confirmed the presence of plastic deformation that caused removal of material along with brittle fractures in rotary ultrasonic machining of alumina ceramic. The validity and competence of the developed mathematical model have been verified with test results. The multi-response optimization of machining responses (material removal rate and chipping size) has also been attempted by employing a desirability approach, and at an optimized parametric setting the obtained experimental values for material removal rate and chipping size were 0.4166?mm³/s and 0.5134?mm, respectively, with a combined desirability index value of 0.849.     

特殊构造砖烟囱定向爆破拆除

介绍了定向控制爆破技术拆除一座烟囱、水塔的结合体,拟爆结构物地坪以上27 m处设置钢筋混凝土结构水箱,致使重心高于一般烟囱,其外壁采用ф12 mm钢筋网箍和60 mm厚混凝土进行加固。周边环境复杂,距高压配电房仅1.4 m,为避免地坪处+0.5 m烟道口对定向的影响,将爆破切口位置提高到+3.0 m处;为减小触地振动、防止倾倒过程中出现后座,采用梯形切口,爆破切口长度取其周长62%,爆破切口高度取1.4 m。为克服烟囱外壁加固层和圆拱墙体钻凿深度不准确的影响,同排相邻炮孔采用一深、一浅的布置方式,其炮眼深度差值2~3 cm,装药结构应用正、反向起爆综合技术。采用开挖减震沟、设置缓冲带控制爆破振动等措施,爆破效果理想,保证了邻近建筑物及设施安全。     

Automatic text classification using BPLion-neural network and semantic word processing

Text mining has become a major research topic in which text classification is the important task for finding the relevant information from the new document. Accordingly, this paper presents a semantic word processing technique for text categorization that utilizes semantic keywords, instead of using independent features of the keywords in the documents. Hence, the dimensionality of the search space can be reduced. Here, the Back Propagation Lion algorithm (BP Lion algorithm) is also proposed to overcome the problem in updating the neuron weight. The proposed text classification methodology is experimented over two data sets, namely, 20 Newsgroup and Reuter. The performance of the proposed BPLion is analysed, in terms of sensitivity, specificity, and accuracy, and compared with the performance of the existing works. The result shows that the proposed BPLion algorithm and semantic processing methodology classifies the documents with less training time and more classification accuracy of 90.9%.     

GZO厚度对非晶硅电池中复合背电极的影响

在非晶硅太阳能电池中加入复合背电极是提高非晶硅太阳能电池光电转换效率和稳定性的有效手段.本文利用磁控溅射技术在非晶硅薄膜太阳能电池上制备了ZnO :Ga(GZO)/Al复合背电极,研究了GZO厚度对GZO薄膜光电性质及非晶硅电池中GZO/Al复合背电极性能的影响.研究表明：随着GZO层厚度的增加,GZO薄膜的光电性质均表现出较高水平,适合制备GZO/Al复合背电极;相较于单层Al背电极的非晶硅太阳能电池,具有GZO/Al复合背电极的太阳能电池性能大幅提高.当GZO层厚度为100 nm时,太阳能电池的短路电流(I_SC)、开路电压(V_OC)和填充因子(FF)分别达到8.66 mA,1.62 V和54.7%.