基于启发式BP算法的软件缺陷预测模型

针对软件缺陷预测时缺陷数据集中存在的类别分布不平衡问题，结合上采样算法SMOTE与Edited Nearest Neighbor (ENN) 数据清洗策略，提出了一种基于启发式BP神经网络算法的软件缺陷预测模型。模型中采用上采样算法SMOTE增加少数类样本以改善项目中的数据不平衡状况，并针对采样后数据噪声问题进行ENN数据清洗，结合基于启发式学习的模拟退火算法改进四层BP神经网络后建立分类预测模型，在AEEEM数据库上使用交叉验证对提出的方案进行性能评估，结果表明所提出的算法能够有效提高模型在预测类不平衡数据时的分类准确度。     

The day of the week when you were born in 700 ms: Calendar computation in an autistic savant.

Some individuals are able to determine the weekday of a given date in a few seconds (finding for instance that June 12, 1900, was a Tuesday). This ability has fascinated scientists for many years because it is predominantly observed in people with limited intelligence and may appear very early in life. Exceptional visual memory, exceptional concentration abilities, or privileged access to lower levels of information not normally available through introspection have been advanced to explain such phenomena. In the present article, the authors show that a simple cognitive model can explain all aspects of the performance of Donny, a young autistic savant who is possibly the fastest and most accurate calendar prodigy ever described. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Cued recall from image and sentence memory: A shift from episodic to identical elements representation.

The applicability of the identical elements (IE) model of arithmetic fact retrieval (T. C. Rickard, A. F. Healy, & L. E. Bourne, 1994) to cued recall from episodic (image and sentence) memory was explored in 3 transfer experiments. In agreement with results from arithmetic, speedup following even minimal practice recalling a missing word from an episodically bound word triplet did not transfer positively to other cued recall items involving the same triplet. The shape of the learning curve further supported a shift from episode-based to IE-based recall, extending some models of skill learning to cued recall practice. In contrast with previous findings, these results indicate that a form of representation that is independent of the original episodic memory underlies cued-recall performance following minimal practice. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

基于交会图和多元统计法的神经网络技术在火山岩识别中的应用

针对火山岩储层的特殊性(复杂性、离散性和随机性),应用BP神经网络技术对火山岩测井解释中岩性识别问题进行了研究。该方法的技术关键是样本集和初始权重的建立,以及模型的优选。本文提出了一种基于交会图和多元统计法的学习样本生成方法,即根据取心岩样的地球化学和岩石学研究,用交会图技术建立样本集,采用聚类分析和距离判别法确定初始权重。将研究方法应用在松辽盆地杏山地区火山岩岩性识别问题中,取得了很好的效果,岩性解释符合率高于90%。文中通过四种岩性识别处理模式的对比研究,表明赋权重处理模式为最优处理模型。在神经网络模型预测过程中,需充分利用已有的地质经验和测井曲线信息建立典型可靠的样本文件,同时考虑神经网络方法中各种因素的影响,优选模型和计算参数才能使预测结果符合实际情况。     

应用改进BP网络对潼关高程的预测

作者在分析了影响汛前和汛后潼关高程变化值的因素的基础上，利用改进BP网络模型对非特征汛期和汛期潼关高程的变化值进行了计算。计算中不采用潼关的绝对高程，而用高程的变化量作为网络的较出物正量，从而减少计算误差，同时又能客观地考虑各影响因子的作用。改进后的BP模型学习时间短，精度满足要求。用该模型计算的潼关高程变化趋势与实际水库调度运行中的高程基本吻合。     

Implementation of RNS-Based Distributed Arithmetic Discrete Wavelet Transform Architectures Using Field-Programmable Logic

Currently there are design barriers inhibiting the implementation of high-precision digital signal processing (DSP) objects with field programmable logic (FPL) devices. This paper explores overcoming these barriers by fusing together the popular distributed arithmetic (DA) method with the residue number system (RNS) for use in FPL-centric designs. The new design paradigm is studied in the context of a high-performance filter bank and a discrete wavelet transform (DWT). The proposed design paradigm is facilitated by a new RNS accumulator structure based on a carry save adder (CSA). The reported methodology also introduces a polyphase filter structure that results in a reduced look-up table (LUT) budget. The 2C-DA and RNS-DA are compared, in the context of a FPL implementation strategy, using a discrete wavelet transform (DWT) filter bank as a common design theme. The results show that the RNS-DA, compared to a traditional 2C-DA design, enjoys a performance advantage that increases with precision (wordlength).     

火焰图像特征在火灾数字图像识别中的运用

火焰尖角数目和圆形度是早期火灾火焰的主要特征,本文主要论述了如何在数学形态学基础上,利用火灾火焰的两个特点判断和识别早期火情。实验中,对火焰的二值图像进行开运算,即先腐蚀,再膨胀,目的是为了对火焰图像进行形态学滤波,去噪。从实验结果可以看出,该算法可以很好的识别火焰和干扰源。     

The effects of recent practice on task switching.

Four experiments investigated the effect of recent selective practice on the cost of switching between 2 tasks afforded by letter-digit pairs: alphabet arithmetic and shape comparison. Experiments 1 and 2 found a greater cost associated with switching to the more recently practiced task: evidence that task-set inertia contributes to switching costs. Experiment 3 found this effect to be limited to trials on which a recently trained stimulus followed another such stimulus: a result problematic for all current theories of task-set priming. Experiment 4 showed that the effect of recent practice was eliminated by active preparation for a task switch: It appears that endogenous task-set preparation reduces the effects of task-set inertia. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

32位MIPS微处理器中乘法器的设计和实现

在Booth算法的基础上,结合MIPS 4KC微处理器中的流水线结构和乘法器的工作过程,提出了一种改进的Booth乘法器的设计方法,并采用全制定方法实现,用这种方法实现的乘法器单元具有面积小、单元电路可重复性好、版图设计工作量小、功耗低等特点.     

神经网络分类器的快速构造算法

使用ＢＰ算法训练多层网络的速度很慢而且事先难于确定隐节点和隐层的适当数目。本文提出一个有效的算法，先构造决策树，然后将构造的决策树转换为神经网。文中使用一个全局准则函数控制决策树的增长，它较好地匹配了树的复杂性和训练样本量及错分率界。实验结果，本文的算法比用ＢＰ算法训练多层网络要快，而其分类精度不低于用ＢＰ算法训练的多层神经网。