基于改进DenseNet的乳腺钼靶肿块分类方法

乳腺X线摄影技术是目前乳腺癌早期发现和诊断的重要手段。然而乳腺X线图像中肿块边缘模糊,分类相对困难,因此提升乳腺肿块的诊断精度从而及早预防和治疗仍是医学领域的一大挑战。针对乳腺肿块的特点,提出了一种结合密集卷积神经网络（DenseNet）和压缩激励（SE）模块的新网络（DSAMNet）,该网络融合了二者优势,既加强特征重用,又实现特征提取过程中的特征重标定。根据SE模块嵌入DenseNet的不同位置,提出了模型SE-DenseNet-A、SE-DenseNet-B和SE-DenseNet-C。对SE-DenseNet的池化函数进行改进,提出了模型DSAMNet-A、DSAMNet-B和DSAMNet-C。综合不同结构和不同深度的网络模型在公开数据集CBIS-DDSM上进行训练和测试。实验结果表明,DSAMNet-B有更加优异的性能,其准确率比DenseNet模型的准确率提高了10.8%,AUC达到了0.929。     

一种激光诱导荧光光谱特征提取新方法

利用主成分分析（Principal Component Analysis,PCA）和Fisher线性判别分析（Fisher Linear Discriminative Analysis,FLDA）方法相结合提取特征,提出了一种荧光光谱特征提取新方法——PCA_FLDA。实验证明,新方法提高了激光诱导自体荧光光谱对早期结肠癌的诊断精度。对预处理后的240条荧光光谱,利用PCA_FLDA算法提取了50个特征变量,利用支持向量机将其分为正常组织和癌变组织,分类敏感性、特异性和准确率可分别达到97.5%、95.12%和96.25%。     

肺结节检测中特征提取方法研究

计算机辅助诊断(Computer-Aided Diagnosis,CAD)系统为肺癌的早期检测和诊断提供了有力的支持.本文对孤立性肺结节特征提取问题进行研究.通过对肺结节和肺内各组织在序列CT图像上的医学征象分析和研究对比,结合专家提供的知识,提出了肺结节特征提取总体方案.该方案分别从肺部CT图像的灰度特征、肺结节形态、纹理、空间上下文特征等几个方面,对关键的医学征象进行图像分析,从而实现对ROI(Regions ofInterest)区域的特征提取和量化;提出特征提取的评价方案,实验结果表明,本文提取的特征提取方案是有效的.利用本文提取的特征,肺结节检测正确率达到93.05%,敏感率为94.53%.     

基于1D-CNN-LSTM注意力网络的抽油机井故障诊断

针对传统基于示功图的抽油机井故障诊断方法存在特征提取复杂、模型参数量大、诊断效率低的问题，提出一种基于1D-CNN-LSTM注意力网络的故障诊断方法。将示功图转化为载荷位移序列作为网络输入，使用一维卷积神经网络（1D-CNN）在提取序列局部特征的同时减小序列长度；考虑到序列的时序特性，进一步使用长短时记忆网络（LSTM）提取序列的时序特征；为突出关键特征影响，引入Attention机制，对故障类型相关的时序特征赋予更高的注意力权重；最后将加权特征输入全连接层，利用Softmax分类器实现故障诊断。实验结果表明，所提方法的平均准确率、精确率、召回率和F1值分别达到99.13%、99.35%、99.17%和99.25%，模型大小仅为98 kB。相比基于特征工程的方法具有更高的诊断精度和泛化能力，相比基于二维卷积神经网络（2D-CNN）模型的诊断方法，显著减少了模型参数量和训练时间，提高了故障诊断效率。     

基于多尺度特征融合的改进臂丛神经分割方法

臂丛神经超声影像信噪比（SNR）低、边缘模糊且人工分割难度较大。现有的分割模型虽然取得了一些成果,但碍于臂丛神经结构目标区域小、形状不规则,分割效果欠佳。针对上述问题,设计基于多尺度特征融合的臂丛神经分割模型,即针对神经部位分割的特征金字塔网络（Ner-FPN）。在特征提取阶段,设计一种仿Xception的结构进行多尺度特征提取;在预测分割阶段,采用双向FPN结构进行特征融合预测。在Kaggle臂丛神经超声影像分割竞赛的BP数据集上的实验结果表明,Ner-FPN模型对臂丛神经分割的Dice相似系数（DSC）可达0.703,与主流的深度学习分割模型U-Net、SegNet相比,分别提高了10.7个百分点和14.5个百分点,对比相同数据集中的其他改进模型QU-Net和Efficient+U-Net,DSC分别提高了5.5个百分点和3.4个百分点,可见所提模型能够起到辅助诊断的效果。     

多尺度深度特征提取的肝脏肿瘤CT图像分类

目的 肝脏肿瘤是人体最具侵袭性的恶性肿瘤之一,传统的肿瘤诊断依靠观察患者的CT（computed tomography）图像,工作量大时易造成疲劳,难免会产生误诊,为此使用计算机辅助的方法进行诊断,但现有的深度学习方法中存在肿瘤分类准确率低、网络的特征表达能力和特征提取能力较弱等问题。对此,本文设计了一种多尺度深度特征提取的分类网络模型。方法 首先在原始CT图像中选取感兴趣区域,然后根据CT图像的头文件进行像素值转换,并进行数据增强来扩充构建数据集,最后将处理后的数据输入到本文提出的分类网络模型中输出分类结果。该网络通过多尺度特征提取模块来提取图像的多尺度特征并增加网络的感受野,使用深度特征提取模块降低背景噪声信息,并着重关注病灶区域有效特征,通过集成并行的空洞卷积使得尺度多元化,并将普通卷积用八度卷积替换来减少参数量,提升分类性能,最终实现了对肝脏肿瘤的精确分类。结果 本文模型达到了87.74%的最高准确率,比原始模型提升了9.92%;与现有主流分类网络进行比较,多项评价指标占优,达到了86.04%的召回率,87%的精准率,86.42%的F1分数;此外,通过消融实验进一步验证了所提方法的有效性。结论 本文方法可以较为准确地对肝脏肿瘤进行分类,将此方法结合到专业的医疗软件当中去,能够为医生早期的诊断和治疗提供可靠依据。     

基于多尺度特征融合的行人重识别方法

针对行人重识别任务在特征提取时缺乏对行人特征尺度变化的考虑,导致其易受环境影响而具有低行人重识别准确率的问题,提出了一种基于多尺度特征融合的行人重识别方法。首先,在网络浅层通过混合池化操作来提取多尺度的行人特征,从而帮助网络提升特征提取能力;然后,在残差块内添加条形池化操作以分别提取水平和竖直方向的远程上下文信息,从而避免无关区域的干扰;最后,在残差网络之后利用不同尺度的空洞卷积进一步保留多尺度的特征,从而帮助模型灵活有效地解析场景结构。实验结果表明,在Market-1501数据集上,所提方法的Rank1达到95.9%,平均精度均值（mAP）为88.5%;在DukeMTMC-reID数据集上,该方法的Rank1达到90.1%,mAP为80.3%。可见所提方法能够较好地保留行人特征信息,从而提高行人重识别任务准确率。     

改进Xception模型的乳腺钼靶图像识别研究

乳腺X线摄影技术是早期发现乳腺癌的主要方法,但其结果很大程度上受放射科医师临床诊断经验的限制;基于卷积神经网络对乳腺钼靶图像自动分类的研究可以为放射科医师临床诊断提供意见,然而乳腺癌肿块边缘模糊且良恶性肿块特征差异较小,分类任务面临重重挑战;为了提高乳腺钼靶图像分类的准确率,提出一种基于Xception模型的改进优化算法,改进模型中的残差连接模块,并嵌入Squeeze-and-excitation（SE）注意力机制对模型进行优化;采用优化后的Xception模型并结合迁移学习算法进行乳腺钼靶图像特征提取,并优化全连接层网络进行图像分类,使用公开的乳腺癌图像数据库CBIS-DDSM进行实验,将乳腺钼靶图像自动分为良性和恶性;实验结果表明该方法可以有效提高模型的分类效果,准确率和AUC分别达到了97.46%和99.12%。     

双分支多维注意特征融合的高光谱图像分类

为改善高光谱图像小样本类别的分类性能,提高模型特征表达的稳健性,提出了双分支多维注意力特征融合的神经网络分类模型（DBMD）。DBMD采用两个分支分别进行光谱特征提取和混合特征提取。光谱分支通过密集连接的扩张卷积逐级提取特征,然后融合低、中、高级语义信息作为特征输出。混合分支采用3D-2D网络架构,并通过改进的Inception块提取空间尺度特征。此外,注意力机制分别应用于光谱、空间和空谱特征,进行特征细化,增强重要区域的特征响应。最后,将不同维度的细化特征联合输入至分类器进行分类。在Indian Pines和Salinas Valley数据集上利用5%和1%的样本进行实验,分别取得了98.40%和99.78%的总体精度,与其他六种网络架构相比,该模型在准确性和稳定性上都具有更优的表现。     

基于视觉显著性的图像特征提取算法

针对现有特征提取算法中存在运算速度慢、误匹配率高、特征点数量冗杂等问题,提出了一种基于视觉显著性特性的图像特征提取算法。首先,分解彩色图像为HSV矩阵,并改进二维局部熵方法,根据人眼视觉特性,计算出图像的对比度、饱和度、相对亮度和轮廓的信息密度;其次,对归一化后的对比度、饱和度、亮度和轮廓矩阵进行变异系数线性组合,构成图像显著性矩阵;然后,通过非极大值抑制、图像指标均匀化筛选以及剔除小面积区域来进一步分离背景,优化感兴趣区域（ROI）;最后,对图像ROI进行分割,再利用改进的尺度不变特征变换（SIFT）算法提取特征。实验结果表明：在显著性区域提取方面,相较于基于定向快速旋转（ORB）和最小凸包的ROI检测算法,所提算法的SP值减小了约15%;在特征提取方面,相较于改进Harris-SIFT算子的快速图像配准算法和信息熵加权的方向梯度直方图（HOG）特征提取算法,所提算法用时分别减少了24%和44%,特征点正确匹配率分别提高了4.8%和3.1%,提取出的特征点数量分别增加了195%和93%。     

Only connect: teaching, technology and telesis

Abstract This paper describes an approach to the design of interactive multimedia materials being developed in a European Community project. The developmental process is seen as a dialogue between technologists and teachers. This dialogue is often problematic because of the differences in training, experience and culture between them. Conditions needed for fruitful dialogue are described and the generic model for learning design used in the project is explained.     

European Community policy and the market

Abstract This paper starts with some reflections on the policy considerations and priorities which are shaping European Commission (EC) research programmes. Then it attempts to position the current projects which seek to capitalise on information and communications technologies for learning in relation to these priorities and the apparent realities of the marketplace. It concludes that while there are grounds to be optimistic about the contribution EC programmes can make to the efficiency and standard of education and training, they are still too technology driven.     

一种自适应子融合集成多分类器方法

融合集成方法已经广泛应用在模式识别领域,然而一些基分类器实时性能稳定性较差,导致多分类器融合性能差,针对上述问题本文提出了一种新的基于多分类器的子融合集成分类器系统。该方法考虑在度量层融合层次之上通过对各类基多分类器进行动态选择,票数最多的类别作为融合系统中对特征向量识别的类别,构成一种新的自适应子融合集成分类器方法。实验表明,该方法比传统的分类器以及分类融合方法识别准确率明显更高,具有更好的鲁棒性。     

Avoiding semantic and temporal gaps in developing software intensive systems

Development of software intensive systems (systems) in practice involves a series of self-contained phases for the lifecycle of a system. Semantic and temporal gaps, which occur among phases and among developer disciplines within and across phases, hinder the ongoing development of a system because of the interdependencies among phases and among disciplines. Such gaps are magnified among systems that are developed at different times by different development teams, which may limit reuse of artifacts of systems development and interoperability among the systems. This article discusses such gaps and a systems development process for avoiding them.     

Designing economic np control charts: A programmed simulation approach

This paper presents control charts models and the necessary simulation software for the location of economic values of the control parameters. The simulation program is written in FORTRAN, requires only 10K of main storage, and can run on most mini and micro computers. Two models are presented - one describes the process when it is operating at full capacity and the other when the process is operating under capacity. The models allow the product quality to deteriorate to a further level before an existing out-of-control state is detected, and they can also be used in situations where no prior knowledge exists of the out-of-control causes and the resulting proportion defectives.     

The development of robot art

Going through a few examples of robot artists who are recognized worldwide, we try to analyze the deepest meaning of what is called “robot art” and the related art field definition. We also try to highlight its well-marked borders, such as kinetic sculptures, kinetic art, cyber art, and cyberpunk. A brief excursion into the importance of the context, the message, and its semiotics is also provided, case by case, together with a few hints on the history of this discipline in the light of an artistic perspective. Therefore, the aim of this article is to try to summarize the main characteristics that might classify robot art as a unique and innovative discipline, and to track down some of the principles by which a robotic artifact can or cannot be considered an art piece in terms of social, cultural, and strictly artistic interest. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Explanation and prediction: an architecture for default and abductive reasoning

Although there are many arguments that logic is an appropriate tool for artificial intelligence, there has been a perceived problem with the monotonicity of classical logic. This paper elaborates on the idea that reasoning should be viewed as theory formation where logic tells us the consequences of our assumptions. The two activities of predicting what is expected to be true and explaining observations are considered in a simple theory formation framework. Properties of each activity are discussed, along with a number of proposals as to what should be predicted or accepted as reasonable explanations. An architecture is proposed to combine explanation and prediction into one coherent framework. Algorithms used to implement the system as well as examples from a running implementation are given.     

Three Process Perspectives: Organizations, Teams, and People

This paper provides the author's personal views and perspectives on software process improvement. Starting with his first work on technology assessment in IBM over 20 years ago, Watts Humphrey describes the process improvement work he has been directly involved in. This includes the development of the early process assessment methods, the original design of the CMM, and the introduction of the Personal Software Process (PSP)^SM and Team Software Process (TSP){^SM}. In addition to describing the original motivation for this work, the author also reviews many of the problems he and his associates encountered and why they solved them the way they did. He also comments on the outstanding issues and likely directions for future work. Finally, this work has built on the experiences and contributions of many people. Mr. Humphrey only describes work that he was personally involved in and he names many of the key contributors. However, so many people have been involved in this work that a full list of the important participants would be impractical.     

基于复小波噪声方差显著修正的SAR图像去噪

提出了一种基于复小波域统计建模与噪声方差估计显著性修正相结合的合成孔径雷达(Synthetic Aperture Radar,SAR)图像斑点噪声滤波方法。该方法首先通过对数变换将乘性噪声模型转化为加性噪声模型,然后对变换后的图像进行双树复小波变换(Dualtree Complex Wavelet Transform,DCWT),并对复数小波系数的统计分布进行建模。在此先验分布的基础上,通过运用贝叶斯估计方法从含噪系数中恢复原始系数,达到滤除噪声的目的。实验结果表明该方法在去除噪声的同时保留了图像的细节信息,取得了很好的降噪效果。     

How do children do mathematics with LOGO?

Abstract  This paper considers some results of a study designed to investigate the kinds of mathematical activity undertaken by children (aged between 8 and 11) as they learned to program in LOGO. A model of learning modes is proposed, which attempts to describe the ways in which children used and acquired understanding of the programming/mathematical concepts involved. The remainder of the paper is concerned with discussing the validity and limitations of the model, and its implications for further research and curriculum development.