基于连续损伤的岩石渗流有限元分析

该文基于连续损伤力学,建立了含损伤的岩石渗流模型,研究岩石损伤破坏并分析其渗流特性。该文采用Weibull分布函数来模拟岩石弹性模量和强度的非均质特性,利用Biot本构关系建立含孔隙结构的弹性变形方程,结合Drucker-Prager强度准则、统计强度理论和连续损伤理论推导出岩石连续损伤演化控制方程及其定解条件,进而采用有限元法对其求解。该文给出的数值算例结果表明所提出的含损伤渗流模型适用于研究岩石的渗流特性,分析结果与实验数据吻合较好,且该求解方法可以对岩石体积压裂、岩层弱面进行可靠、有效的数值分析。     

基于梯度提升回归树的冷水机组能耗预测方法

冷水机组作为空调系统最主要的能耗设备,建立冷水机组能耗预测模型对于节能运行优化具有重要意义。本文针对冷水机组运行参数繁多,能耗预测模型超参数难以调优等特点,提出了基于梯度提升回归树的冷水机组能耗预测方法,并利用冷水机组实测数据对模型进行了训练与验证,同时对比了支持向量回归和决策树模型。结果表明:基于梯度提升回归树的能耗预测模型能够更准确的预测冷水机组能耗。对比其他两种模型,MAE和RMSE分别平均降低了24.5%和45.5%,相关系数达到0.999 7,并且模型对超参数不敏感,能够在较宽泛的范围内比较好地拟合数据,具有较高的实用价值。     

TiC/Ni/TiC材料的SHS合成研究

通过ＳＨＳ结合准等静压法得到了ＴｉＣ／Ｎｉ／ＴｉＣ材料,在ＳＨＳ过程中,Ｎｉ沿试样厚度方向向两层ＴｉＣ中对称渗流,在生层ＴｉＣ当中,Ｎｉ呈梯度分布,材料的硬度从中心向两边呈对称分布。理论上分析了Ｎｉ在两层ＴｉＣ中呈梯度分布所需的渗流动力。     

真武山隧道渗流场边界元有限元耦合分析

地下渗流场是隧道工程中的一个重要问题。本文由Ｄａｒｃｙ定律,利用有限元和边界地理论推导了地下三维渗流场的耦合分析模型,并采用本文计算模型对重庆真武山道有排水和排水被阻塞两种极端情况进行了渗流场分析,分析结果为真武山隧道防排水措施的选取提供了重要后理论依据。     

铝合金熔体在多孔介质中的二维微管渗流模型

在对填料颗粒堆积简化的基础上，建立了由渗流单元组成的二维微管模型．使用该模型对渗流过程进行模拟计算，渗流时间的计算值与实验值符合良好，并获得渗流过程的如下新规律：铝熔体在流过填料颗粒时，在填料颗粒的背压区封闭一定质量的气体，封闭的气体使附加孔隙率和通孔度增大．在渗流过程中静压力的变化分两个阶段：渗流开始时，静压力先缓慢增加，在渗流结束时瞬间，压力增加到最大值；后者对高孔隙率多孔铝的制备有重要意义．这些渗流规律为控制渗流过程和孔结构提供了依据．     

环境同位素示踪方法研究新安江右坝肩绕坝渗流

通过分析新安江右坝区环境水中的同位素2H，18O，3H以及温度与电导分布，研究新安江右坝存在的绕坝渗流场。环境同位素数据证实了右坝肩基岩中存在着绕坝渗漏，排水廊道中的排水既有边坡降雨入渗，又有库水的绕坝肩补给，排水廊道扇形孔中的渗水完全来自于绕坝肩的库水渗漏。3E1-1扬压力升高的原因是由于F0断层渗漏造成的结果，但6坝段和7坝段下部的F1断层基本上不存在渗漏,灌浆廊道G7-4排水孔中渗水中的3H高达19.54TU，证实该渗漏水来自坝后区的降雨补给。右坝肩观测孔R3和R6等孔中发现绕坝库水成分，环境同位素     

(Pr-Nd-La-Ce)_2Fe_(14)B基混合稀土永磁合金磁性能的机器学习预测

目的:找到内禀矫顽力(H_(cj))、剩磁(B_r)、最大磁能积((BH)_(max))和配方成分、工艺参数之间的内在关联性。方法:我们从相关论文收集了140余条数据,使用配方成分、工艺参数作为数据特征,使用内禀矫顽力、剩磁、最大磁能积作为目标属性,采用随机森林算法和梯度提升树的机器学习算法构建预测模型,最后通过我们实验室的数据来测试模型的预测能力。结果:3个模型的平均绝对误差(MAE)分别为91.8 kA/m、0.047 T和6.583 kJ/m³。H_(cj)的预测值与实验室测得数据的MAE为78.0 kA/m。预测数据与实际数据基本一致。结论:使用梯度提升树构建的预测模型在配方成分和工艺参数对磁性能的映射上有良好的预测性,可以为寻找低成本高性能的磁体提供指导,有利于从根本上提高混合稀土永磁合金的研究速度。     

浅海海底地形对吊放声呐探测距离的影响

吊放声呐的探测距离不仅与装备的自身性能和海洋水声环境特性有关,还受海底地形影响.利用射线声学模型,仿真分析了正声速梯度和带有跃变层声速梯度下,不同高度和深度的海底山体和海底盆地(简称:海盆)两种典型地形下的声传播特性.通过声传播特性,得出了不同高度和深度的海底山体和海盆两种典型地形对吊放声呐作用距离的影响.根据声传播特...     

多孔金属通孔度的控制

依据多孔金属的渗流制备原理,建立了控制通孔度的理论计算模型,研究了压差△Ｐ,球状填料半径Ｒ、金属液的表面张力σ以及金属液／颗粒间澜有θ对通孔度Ｉ的影响规律,实验证实该模型与实验结果良好吻合,可用于渗流法制备多孔金属时通孔度的定量控制。     

MWNTs/PVDF复合材料导电和介电性能的研究

用熔融共混和热压工艺制备了多壁碳纳米管(MWNTs)和羧基化多壁碳纳米管(MWNTs-COOH)填充聚偏氟乙烯(PVDF)复合材料.研究了多壁碳纳米管体积含量、测试频率和温度对复合体系的电性能的影响.实验发现,MWNTs-COOH/PVDF复合体系的渗流阈值和渗流阈值附近的介电常数明显大于MWNTs/PVDF体系.利用渗流理论、Maxwell-Wagner界面极化效应和微电容模型解释了实验现象.     

多波束声呐海底底质半监督学习分类方法

多波束测深声呐的反向散射数据中包含海底表层的声学信息,可以用来进行海底表层底质分类。但实际中通过物理采样获得大范围的底质类型的标签信息所需成本过高,制约了传统监督分类算法的性能。针对实际应用中只拥有大量无标签数据和少量有标签数据的情况,文章提出了基于自动编码器预训练以及伪标签自训练的半监督学习底质分类算法。利用2018年和2019年两次同一海域实验采集的多波束测深声呐反向散射数据,对所提算法进行了验证。数据处理结果表明,相比仅利用有标签数据的监督分类算法,提出的半监督学习分类算法保证分类准确率的同时所需的有标签数据更少。自动编码器预训练的半监督学习分类方法在有标签样本数量极少时的准确率仍高于75%。     

基于卷积神经网络的印刷套准识别方法

目的 针对目前印刷套准识别方法依赖于经验人工设计特征提取的问题,提出一种不需要人工提取图像特征的卷积神经网络模型,实现印刷套准状态的识别.方法 采用图像增强技术实现不均衡训练集的均衡化,增加训练集图像的数量,提高模型的识别准确率.设计基于AlexNet网络结构的印刷套准识别模型的结构参数,分析批处理样本数量和基础学习率对模型性能的影响规律.结果 文中方法获得的总印刷套准识别准确率为0.9860,召回率为1.0000,分类准确率几何平均数为0.9869.结论 文中方法能自动提取图像特征,不依赖于人工设计的特征提取方法.在构造的数据集上,文中方法的分类性能优于实验中的支持向量机方法.     

Transfer Learning-based Computer-aided Diagnosis System for Predicting Grades of Diabetic Retinopathy

Diabetic retinopathy (DR) diagnosis through digital fundus images requires clinical experts to recognize the presence and importance of many intricate features. This task is very difficult for ophthalmologists and time-consuming. Therefore, many computer-aided diagnosis (CAD) systems were developed to automate this screening process of DR. In this paper, a CAD-DR system is proposed based on preprocessing and a pre-train transfer learning-based convolutional neural network (PCNN) to recognize the five stages of DR through retinal fundus images. To develop this CAD-DR system, a preprocessing step is performed in a perceptual-oriented color space to enhance the DR-related lesions and then a standard pre-train PCNN model is improved to get high classification results. The architecture of the PCNN model is based on three main phases. Firstly, the training process of the proposed PCNN is accomplished by using the expected gradient length (EGL) to decrease the image labeling efforts during the training of the CNN model. Secondly, the most informative patches and images were automatically selected using a few pieces of training labeled samples. Thirdly, the PCNN method generated useful masks for prognostication and identified regions of interest. Fourthly, the DR-related lesions involved in the classification task such as micro-aneurysms, hemorrhages, and exudates were detected and then used for recognition of DR. The PCNN model is pre-trained using a high-end graphical processor unit (GPU) on the publicly available Kaggle benchmark. The obtained results demonstrate that the CAD-DR system outperforms compared to other state-of-the-art in terms of sensitivity (SE), specificity (SP), and accuracy (ACC). On the test set of 30,000 images, the CAD-DR system achieved an average SE of 93.20%, SP of 96.10%, and ACC of 98%. This result indicates that the proposed CAD-DR system is appropriate for the screening of the severity-level of DR.     

A transfer learning framework for traffic video using neuro-fuzzy approach

One of the main challenges in the Traffic Anomaly Detection (TAD) system is the ability to deal with unknown target scenes. As a result, the TAD system performs less in detecting anomalies. This paper introduces a novelty in the form of Adaptive Neuro-Fuzzy Inference System-Lossy-Count-based Topic Extraction (ANFIS-LCTE) for classification of anomalies in source and target traffic scenes. The process of transforming the input variables, learning the semantic rules in source scene and transferring the model to target scene achieves the transfer learning property. The proposed ANFIS-LCTE transfer learning model consists of four steps. (1) Low level visual items are extracted only for motion regions using optical flow technique. (2) Temporal transactions are created using aggregation of visual items for each set of frames. (3) An LCTE is applied for each set of temporal transaction to extract latent sequential topics. (4) ANFIS training is done with the back-propagation gradient descent method. The proposed ANFIS model framework is tested on standard dataset and performance is evaluated in terms of training performance and classification accuracies. Experimental results confirm that the proposed ANFIS-LCTE approach performs well in both source and target datasets.     

半导体精密控温技术在航天器包装容器的应用

目的 对某型号航天器包装容器进行温控系统设计,以达到航天器高精度控温的要求.方法 对总体保温布局进行设计,优化被动保温结构,采用半导体控温方式,利用Ansys Workbench进行稳态和瞬态热力学分析.通过试验测试,验证保温结构设计和热力学分析结果的合理性.结果 在外部施加热源温度36℃和0℃情况下,随着热源区域的远离,包装容器内部的温度也趋于平稳,内部装载产品区域温度基本能维持在20.99～22.662℃.实际试验结果显示箱内温度变化不大于±1℃,比传统的空调控温精度高出70％左右.结论 通过优化箱体的被动保温结构,采用半导体精密控温,可以满足未来航天器小型化、高精度运输要求.     

三峡大坝坝基粘弹性应力场与渗流场耦合分析

根据三峡大坝基岩的具体特点,本文提出了坝基岩体粘弹性应力场与渗流场的耦合分析模型,并对坝基开挖过程进行了模拟计算分析。该模型采用伯格斯模型模拟基岩的粘弹性流变特性,并假设其渗透系数与应力的关系为指数函数关系,考虑了应力场与渗流场的耦合效应。结果表明,渗流场的作用是明显的。     

Incremental Learning Framework for Mining Big Data Stream

At this current time, data stream classification plays a key role in big data analytics due to its enormous growth. Most of the existing classification methods used ensemble learning, which is trustworthy but these methods are not effective to face the issues of learning from imbalanced big data, it also supposes that all data are pre-classified. Another weakness of current methods is that it takes a long evaluation time when the target data stream contains a high number of features. The main objective of this research is to develop a new method for incremental learning based on the proposed ant lion fuzzy-generative adversarial network model. The proposed model is implemented in spark architecture. For each data stream, the class output is computed at slave nodes by training a generative adversarial network with the back propagation error based on fuzzy bound computation. This method overcomes the limitations of existing methods as it can classify data streams that are slightly or completely unlabeled data and providing high scalability and efficiency. The results show that the proposed model outperforms state-of-the-art performance in terms of accuracy (0.861) precision (0.9328) and minimal MSE (0.0416).     

基于随机森林的统计控制图模式识别研究

引入随机森林方法进行统计控制图模式识别的研究。提取了控制图的统计特征和形状特征,设计了5种不同的特征组合方法,利用蒙特卡洛仿真方法产生训练数据集和测试数据集,选取了常用的3种模式识别方法（支持向量机方法、人工神经网络方法、决策树方法）进行对比。实验结果表明,随机森林方法相比其他3种分类器方法,在分类准确率和消耗时间两个维度上都有明显优势,可以应用于统计过程控制图模式识别。     

Wavelet packet correlation methods in biometrics

We introduce wavelet packet correlation filter classifiers. Correlation filters are traditionally designed in the image domain by minimization of some criterion function of the image training set. Instead, we perform classification in wavelet spaces that have training set representations that provide better solutions to the optimization problem in the filter design. We propose a pruning algorithm to find these wavelet spaces by using a correlation energy cost function, and we describe a match score fusion algorithm for applying the filters trained across the packet tree. The proposed classification algorithm is suitable for any object-recognition task. We present results by implementing a biometric recognition system that uses the NIST 24 fingerprint database, and show that applying correlation filters in the wavelet domain results in considerable improvement of the standard correlation filter algorithm.     

Leveraging the nugget parameter for efficient Gaussian process modeling

Gaussian process (GP) metamodels have been widely used as surrogates for computer simulations or physical experiments. The heart of GP modeling lies in optimizing the log‐likelihood function with respect to the hyperparameters to fit the model to a set of observations. The complexity of the log‐likelihood function, computational expense, and numerical instabilities challenge this process. These issues limit the applicability of GP models more when the size of the training data set and/or problem dimensionality increase. To address these issues, we develop a novel approach for fitting GP models that significantly improves computational expense and prediction accuracy. Our approach leverages the smoothing effect of the nugget parameter on the log‐likelihood profile to track the evolution of the optimal hyperparameter estimates as the nugget parameter is adaptively varied. The new approach is implemented in the R package GPM and compared to a popular GP modeling R package ( GPfit) for a set of benchmark problems. The effectiveness of the approach is also demonstrated using an engineering problem to learn the constitutive law of a hyperelastic composite where the required level of accuracy in estimating the response gradient necessitates a large training data set.