图像分类中基于分类矢量量化的视觉词袋模型

特征表示是图像识别和分类的基础，视觉词袋是一种图像的特征表示方法。分析现有视觉词典构建方法的不足，提出一种新的视觉词典构建方法。首先利用梯度方差把特征矢量分为光滑类和边缘类，然后分别针对不同类别的特征矢量进行视觉词典的构建，最后根据两类视觉词典生成视觉词袋。图像分类实验表明，提出的新方法能提高分类准确率。     

STL模型特征信息的自适应分层的研究

模型的分层是3D打印前处理的一个重要环节，针对目前分层算法效率低、不能有效保留模型细微特征的问题，提出了一种新的基于STL模型特征信息的思想。首先提取模型的特征边，对特征边内实体表面采用区域增长算法进行表面分割，然后对分割后的表面判断其类型，最后对于不同的表面类型采用不同的分层求交处理算法。利用VC++6.0平台和OpenGL显示技术，依据模型表面几何特征自适应地变动层厚，减小台阶效应，加快分层效率，同样能很好地保留模型局部特征，且该算法可以根据实际模型的成型方向，实现分层方向的改变，保证成型零件的精度。     

Learning a Deep Predictive Coding Network for a Semi-Supervised 3D-Hand Pose Estimation

In this paper we present a CNN based approach for a real time 3D-hand pose estimation from the depth sequence. Prior discriminative approaches have achieved remarkable success but are facing two main challenges: Firstly, the methods are fully supervised hence require large numbers of annotated training data to extract the dynamic information from a hand representation. Secondly, unreliable hand detectors based on strong assumptions or a weak detector which often fail in several situations like complex environment and multiple hands. In contrast to these methods, this paper presents an approach that can be considered as semi-supervised by performing predictive coding of image sequences of hand poses in order to capture latent features underlying a given image without supervision. The hand is modelled using a novel latent tree dependency model (LDTM) which transforms internal joint location to an explicit representation. Then the modeled hand topology is integrated with the pose estimator using data dependent method to jointly learn latent variables of the posterior pose appearance and the pose configuration respectively. Finally, an unsupervised error term which is a part of the recurrent architecture ensures smooth estimations of the final pose. Experiments on three challenging public datasets, ICVL, MSRA, and NYU demonstrate the significant performance of the proposed method which is comparable or better than state-of-the-art approaches.      

Fabrication of poly(ε‐caprolactone) (PCL)/poly(propylene carbonate) (PPC)/ethylene‐α‐octene block copolymer (OBC) triple shape memory blends with cycling performance by constructing a co‐continuous phase morphology

In this paper, a triple shape memory material was prepared by ultra‐simple melt blending from poly(ε‐caprolactone) (PCL), poly(propylene carbonate) (PPC) and ethylene‐α‐octene block copolymer (OBC). The obtained material possessed a co‐continuous phase morphology and presented an excellent triple shape memory effect (triple‐SME). Theoretical prediction demonstrated that a special continuous phase morphology could be constructed by adjusting the proportions of the blend. Moreover, the results indicated that a close relationship existed between the phase morphology and the triple‐SME of PCL/PPC/OBC. The sample with 35 vol% PPC content contributed to the formation of a continuous phase morphology and exhibited the optimal triple‐SME. Additionally, the sample PCL/PPC/OBC (32.5/35/32.5) showed outstanding structure and performance stability during cycle loading–unloading tests, which evidenced the prominent cycling shape memory property (nearly 100% shape fixing and recovery of temporary shape). Overall, this work could provide an efficient, convenient and recyclable method to obtain high‐performance shape memory materials. © 2020 Society of Chemical Industry     

Two-dimensional temperature distribution estimation for a cross-flow planar solid oxide fuel cell stack

The uniform temperature distribution of a cross-flow planar solid oxide fuel cell (SOFC) stack plays an essential role in stack thermal safety and electrical property. However, because of the strict requirements in stack sealing struture, it is hard to acquire the temperature inside the stack using thermal detection devices within an acceptable cost. Therefore, accurately estimating the two-dimensional (2-D) temperature distribution of the cross-flow stack is crucial for its thermal management. In this paper, Firstly, a 2-D mechanism model of a cross-flow planar SOFC stack is established. The stack is divided into 5*5 nodes along the gas flow directions, which can reflect the stack states with moderate computational burden. Then, experimental test data is utilized to modify and validate the stack model, guaranteeing the model accuracy as well as the reliability of model-based state estimator design. Finally, easily-measured stack inputs and outputs are selected, and a temperature distribution estimator combined with unscented kalman filter (UFK) approach is developed to achieve accurate and fast temperature distribution estimation of a cross-flow SOFC stack. Simulation results demonstrate that the UKF-based temperature distribution estimator can precisely and quickly achieve the temperature distribution estimation of the cross-flow stack under both static state and dynamic state changes and is applicable to cross-flow stacks with different size or cell number as well, the maximum estimated absolute error is less than 0.15 K with an absolute error rate of 0.015%, which indicates the developed estimator has good estimation performances.     

Solidification of Be-free Ni-based dental alloy

A novel, Nb- and Si-rich and Be-free Ni-based alloy was cast by two methods of investment casting and continuous casting to study the microstructure evolution during solidification and its mechanical properties. The solidification of the alloy started with the primary crystallization of FCC-γ, followed by a binary eutectic reaction, with the formation of a heterogeneous constituent: FCC-γ+G-phase, which replaced the low-melting eutectic (FCC-γ+NiBe) in the Be-bearing alloys. AlNi₆Si₃ and γ′ formed during the terminal stages of solidification by investment casting, while the formation of AlNi₆Si₃ was suppressed by continuous casting. The Scheil solidification model agreed very well with the experimental results.     

王庄煤矿卡轨车系统应用设计

本文基于王庄煤矿运输大巷实现连续运输要求,对运输大巷卡轨车系统进行了设计,并对巷道条件及卡轨车运输效果等进行了分析。     

基于改进交叉熵算法的电力系统随机生产模拟

蒙特卡洛模拟的计算效率与系统的可靠性密切相关，在其用于高可靠性系统的随机模拟时存在计算效率偏低的问题。为此，提出了一种基于多层交叉熵与对偶变数抽样技术相结合的随机模拟算法。首先使用多层交叉熵构造零方差概率密度函数的近似函数，提高小概率失效事件的抽取概率；其次基于已构造的近似概率函数，采用对偶变数抽样法进行抽样，进一步提高抽样的收敛速度。以IEEE RTS修改系统为例进行了算例分析，算例结果验证了所提出的基于改进交叉熵的电力系统随机生产模拟算法的有效性。     

机翼极限环颤振的CFD/CSD全隐式紧耦合方法

针对轴承振动实时状态监控的需要，从符号动力学角度提出了符号聚合近似与Lempel-ziv复杂度（SAX-LZC）融合的振动监控参数。首先，以Logistic映射和Duffing方程为对象，从理论角度验证了SAX-LZC对动力学结构表征的准确性，并验证了该指标的抗噪能力和计算效率；其次，将SAX-LZC指标与信息熵、样本熵、多分段Lempel-ziv复杂度等动力学参数性能进行了综合对比；最后，从实验角度对轴承早期微弱异常进行了监测，并对轴承典型故障进行了特征提取。理论研究结果表明，SAX-LZC具有动力学结构表征准确、抗噪能力好、计算高效简洁等优点，克服了常规动力学参数工程应用能力弱的问题。实验研究结果表明，SAX-LZC对早期微弱异常有准确的监测，对不同种类故障具有较好的区分度，弥补了时域和频域对轴承微弱故障表征能力不足的缺陷，是一种轴承振动实时状态监控与故障特征提取的有效参数。     

自适应SA-PSO优化的威布尔混合分布参数估计方法及应用

针对采用传统参数估计方法得到的模型拟合误差较大的问题，建立多重威布尔混合分布参数估计的非线性最小二乘模型，并提出基于模拟退火（SA）思想的自适应粒子群（PSO）算法进行求解。在PSO算法优化过程中，采用自适应方法调整惯性权重和加速因子，加快其收敛速度；引入模拟退火机制，根据Metropolis准则确定最优粒子的取舍，改善其全局搜索能力。将该方法应用到某型柴油机喷油器失效分布的参数估计中，并与图解法、基于Levenberg-Marquardt的非线性最小二乘法、标准PSO算法、自适应PSO算法求解的结果进行比较，分析所提方法的优化性能及精度。结果表明，该方法能够有效提高多重威布尔混合分布模型参数估计的精度和效率。