改进的卷积神经网络单幅图像超分辨率重建

针对经典的基于卷积神经网络的单幅图像超分辨率重建方法网络较浅、提取的特征少、重建图像模糊等问题，提出了一种改进的卷积神经网络的单幅图像超分辨率重建方法，设计了由密集残差网络和反卷积网络组成的新型深度卷积神经网络结构。原始低分辨率图像输入网络，利用密集残差学习网络获取更丰富的有效特征并加快特征梯度流动，其次通过反卷积层将图像特征上采样到目标图像大小，再利用密集残差学习高维特征，最后融合不同卷积核提取的特征得到最终的重建图像。在Set5和Set14数据集上进行了实验，并和Bicubic、K-SVD、SelfEx、SRCNN等经典重建方法进行了对比，重建出的图像在整体清晰度和边缘锐度方面更好，另外峰值信噪比（PSNR）平均分别提高了2.69?dB、1.68?dB、0.74?dB和0.61?dB。实验结果表明，该方法能够获取更丰富的细节信息，得到更好的视觉效果，达到了图像超分辨率的增强任务。     

不同发射率下红外热图像的非稳态温度场测量研究

通过分析红外测温公式,研究物体发射率对红外热图像测温精度的影响,并找出影响物体发射率的因素。根据经典计算方法,发现了物体发射率在红外热图像测温应用中的局限。从红外热图像的角度对发射率的计算进行了建模,基于修正物体发射率得到提高红外热图像对非稳态温度场测量精度的模型。对铸铁、不锈钢进行了3组测温实验,红外热图像直接测量的最大误差分别为2.1℃、1.7℃、2.3℃,而所提出的方法测量的最大误差分别为1.0℃、0.7℃、1.6℃。通过对比分析3组测量结果数据验证了测量方法的可行性。     

A parametric investigation on seat/occupant contact forces and their relationship with initially perceived discomfort using a configurable seat

The present work investigates the contact forces between sitters and seat as well as their correlations with perceived discomfort. Twelve different economy class aeroplane seat configurations were simulated using a multi-adjustable experimental seat by varying seat pan and backrest angles, as well as seat pan compressed surface. Eighteen males and 18 females, selected by their body mass index and stature, tested these configurations for two sitting postures. Perceived discomfort was significantly affected by seat parameters and posture and correlated both with normal force distribution on the seat-pan surface and with normal forces at the lumbar and head supports. Lower discomfort ratings were obtained for more evenly distributed normal forces on the seat pan. Shear force at the seat pan surface was at its lowest when sitters were allowed to self-select their seat-pan angle, supporting that a shear force should be reduced but not zeroed to improve seating comfort.

Practitioner Summary: The effects of seat-pan and backrest angle, anthropometric dimensions and sitting posture on contact forces and perceived discomfort were investigated using a multi-adjustable experimental seat. In addition to preferred seat profile parameters, the present work provides quantitative guidelines on contact force requirement for improving seating comfort.     

In‐plane shear damage behaviours of 2D needled C/SiC composites

Understanding the in‐plane shear behaviour of composites is essential to establish the design basis for practical applications. This study aims to investigate the shear damage behaviours of 2D needled C/SiC composites by various characterization techniques. The effect of layer arrangement on shear modulus and strength was discussed via shear stress‐strain responses. The shear strain field evolution and uniformity variation were studied by digital image correlation. It shows that the uniformity of shear strain field changes with the shear load, and the shear strain field evolution consist of 5 stages. The electrical resistivity measurement results indicate that structural deformation and damage evolution caused the electrical resistivity change. Furthermore, the damage evolution has a double effect on the electrical resistivity variation. The acoustic emission monitoring shows that the shear damage evolution is a 3‐stage nonlinear process before failure. The shear damages were categorized via acoustic characteristics. Besides, the postfailure behaviours were also discussed in this study.     

高强度大桥缆索用盘条生产现状分析

最近5年，大桥缆索用镀锌钢丝抗拉强度有了大幅提高，从1 770 MPa级、1 860 MPa级、1 960 MPa级到目前的2 000 MPa及以上级别，扭转指标有了颠覆性的改变，从之前的几转到目前的十几转、二十几转。论述了国内外钢铁企业生产大桥缆索镀锌钢丝用盘条的不同现状。从成分设计、生产工艺流程、盘条韧化处理方式和组织特点等方面进行阐述，讨论了国内外不同生产工艺特点及发展趋势，为中国大桥缆索镀锌钢丝用盘条的生产提供理论支持。     

Study on estimating electric potential in space between parallel electrodes

We have been studying on estimating distribution of permittivity between measurement electrodes using capacitance and electric potential. Two arc electrodes were separated by long distance and there electrodes were surrounded by additional electrodes respectively. In past research work, we carried out numerical electric analysis for calculating the capacitance and electric potential using Finite Element Method (FEM) and compared with experimental and numerical results. The capacitance values were almost agreed with experimental and numerical results. However, the electric potential values were different between experimental and numerical results in conventional studies. In this paper, we proposed an equivalent circuit including the stray capacity and measurement method for capacitance, the electric potential in space between long distance electrodes was estimated.     

An improved machine learning technique based on downsized KPCA for Alzheimer's disease classification

Alzheimer's disease (AD), a neurodegenerative disorder, is a very serious illness that cannot be cured, but the early diagnosis allows precautionary measures to be taken. The current used methods to detect Alzheimer's disease are based on tests of cognitive impairment, which does not provide an exact diagnosis before the patient passes a moderate stage of AD. In this article, a novel classifier of brain magnetic resonance images (MRI) based on the new downsized kernel principal component analysis (DKPCA) and multiclass support vector machine (SVM) is proposed. The suggested scheme classifies AD MRIs. First, a multiobjective optimization technique is used to determine the optimal parameter of the kernel function in order to ensure good classification results and to minimize the number of retained principle components simultaneously. The optimal parameter is used to build the optimized DKPCA model. Second, DKPCA is applied to normalized features. Downsized features are then fed to the classifier to output the prediction. To validate the effectiveness of the proposed method, DKPCA was tested using synthetic data to demonstrate its efficiency on dimensionality reduction, then the DKPCA based technique was tested on the OASIS MRI database and the results were satisfactory compared to conventional approaches.     

基于深度学习的彩色以及近红外图像去马赛克

单传感器捕获的彩色-近红外（RGB-NIR）图像存在光谱干扰，从而导致重建出的标准彩色图像（RGB）图像与近红外（NIR）图像存在色彩失真以及细节信息模糊。针对这个问题提出一种基于深度学习的去马赛克方法，通过引入跳远连接与稠密连接解决了梯度消失和梯度弥散问题，使得网络更容易训练，并且提升了网络的拟合能力。首先，用浅层特征提取层提取了马赛克图像的像素相关性以及通道相关性等低级特征；然后，将得到的浅层特征图输入到连续多个的残差稠密块以提取专门针对去马赛克的高级语义特征；其次，为充分利用低级特征与高级特征，将多个残差稠密块提取到的特征进行组合；最后，通过全局跳远连接恢复最终的RGB-NIR图像。在深度学习框架Tensorflow上使用公共的图像与视觉表示组（IVRG）数据集、有植被的户外多光谱图像（OMSIV）数据集和森林（Forest）三个公开数据集进行实验。实验结果表明，所提方法优于基于多级自适应残差插值、基于卷积卷积和神经神经网络以及基于深度残差U型网络的主流的RGB-NIR图像去马赛克方法。     

基于改进粗糙集理论的SAR图像目标增强

图像增强在提高SAR图像舰船目标检测精度方面具有十分重要的意义。由于传统算法不能很好地对SAR图像进行目标增强，提出了基于改进粗糙集理论和引力场强度的目标增强算法。通过借鉴引力场相关理论知识，将粗糙集条件属性集中的梯度属性改进为引力场强度属性，从而实现对原图像的目标增强。与其他算法进行了实验比较，结果表明提出的改进算法相比于其他算法更适用于SAR图像特性，能更好地对舰船目标像素进行针对性增强，具备一定的工程应用价值。