Super-Resolution Reconstruction of Images Based on Microarray Camera

In the field of images and imaging, super-resolution (SR) reconstruction of images is a technique that converts one or more low-resolution (LR) images into a highresolution (HR) image. The classical two types of SR methods are mainly based on applying a single image or multiple images captured by a single camera. Microarray camera has the characteristics of small size, multi views, and the possibility of applying to portable devices. It has become a research hotspot in image processing. In this paper, we propose a SR reconstruction of images based on a microarray camera for sharpening and registration processing of array images. The array images are interpolated to obtain a HR image initially followed by a convolution neural network (CNN) procedure for enhancement. The convolution layers of our convolution neural network are 3×3 or 1×1 layers, of which the 1×1 layers are used to improve the network performance particularly. A bottleneck structure is applied to reduce the parameter numbers of the nonlinear mapping and to improve the nonlinear capability of the whole network. Finally, we use a 3×3 deconvolution layer to significantly reduce the number of parameters compared to the deconvolution layer of FSRCNN-s. The experiments show that the proposed method can not only ameliorate effectively the texture quality of the target image based on the array images information, but also further enhance the quality of the initial high resolution image by the improved CNN.     

多特征融合的兴趣点推荐算法

基于位置社交网络的兴趣点推荐越来越受到工业界和学术界的关注。由于用户签到数据集的稀疏性以及签到地理位置的聚集性，使得目前的推荐算法效率普遍不高，特别是当用户外出到新的地点时，推荐效果更是急剧下降。因此本文提出了一种基于用户-区域-内容主题的多特征联合推荐算法（UCRTM），以隐主题模型为基础，在统一的框架下利用隐含因子关联性融合了用户的偏好、兴趣点的内容以及兴趣点所属地理区域主题等信息来进行推荐，使得用户无论身处何地，都能获得理想的推荐服务。本文在两种真实的数据集上进行了实验，结果表明该方法不仅能够克服数据的稀疏性以及弱语义性等问题，而且与其他方法相比具有更高的推荐准确率。     

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.      

水射流表面改性对国产Zirlo锆合金包壳管加速腐蚀氧化膜形貌的影响

为进一步提高国产锆合金包壳管性能,提高反应堆安全性,对国产Zirlo锆合金进行水射流冲击强化,研究在高温高压水腐蚀条件下不同冲击压力对样品表面氧化膜的影响,并与未冲击样品进行对比。结果表明,样品表面在不同压力的冲击下耐蚀性差异较大,当冲击压力达到40 MPa时样品表面氧化膜状态较好,相比于未冲击样品氧化膜厚度减少4.3%,在10 MPa和20 MPa冲击下则出现腐蚀加速情况。     

东河煤矿2206工作面无煤柱自成巷关键技术研究及应用

针对传统沿空留巷采煤技术因增设巷道掘进作业造成煤炭资源浪费的问题,以东河煤矿2260工作面为工程背景,在分析该工作面地质、水文以及煤层情况的基础上,研究了无煤柱自成巷关键技术,并应用到2260工作面中,最终取得了较为理想的留巷效果,为类似综采工作面避免煤炭资源浪费和提升作业人员安全系数提供了有益借鉴.     

基于FAHP的机械振动WSN均衡拓扑构建方法

针对机械振动无线传感器网络因拓扑不均衡导致传输时延和网络传输能耗增加的问题,提出了一种基于模糊层次分析(fuzzy analytic hierarchy process,简称FAHP)的均衡拓扑构建方法,该方法由构建模糊判断矩阵和计算权重向量组成。首先,传感器节点进行簇内通信获取信标广播信息,将信标节点网络决策因子统一量纲化,利用网络决策因子构建模糊判断矩阵;其次,检验模糊判断矩阵一致性,采用行和归一化处理或拉格朗日最小二乘法计算权重向量;最后,传感器节点通过权重向量计算出各个信标节点综合权值,关联最优信标节点为父节点加入网络,将提出的模糊层次分析拓扑构建方法与基于链路质量单准则构建网络拓扑机制进行对比。实验结果表明,该方法能有效改善传输时延和机械振动无线传感器节点网络寿命。     

Automated classification of simulated wind field patterns from multiphysics ensemble forecasts

In this study, we have proposed an automated classification approach to identify meaningful patterns in wind field data. Utilizing an extensive simulated wind database, we have demonstrated that the proposed approach can identify low‐level jets, near‐uniform profiles, and other patterns in a reliable manner. We have studied the dependence of these wind profile patterns on locations (eg, offshore vs onshore), seasons, and diurnal cycles. Furthermore, we have found that the probability distributions of some of the patterns depend on the underlying planetary boundary layer schemes in a significant way. The future potential of the proposed approach in wind resource assessment and, more generally, in mesoscale model parameterization improvement is touched upon in this paper.     

Synthesis,thermal properties,conductivity and lifetime of proton conductors based on nanocrystalline cellulose surface-functionalized with triazole and imidazole

A new proton conductor based on 1H-1,2,3-triazole doped nanocrystalline cellulose (2.66 CNC-Tri) has been synthesized for possible use as an electrolyte in proton exchange membrane (PEM) cells. The physicochemical properties of 2.66 CNC-Tri were determined and compared with those of imidazole-doped nanocrystalline (1.17 CNC-Im) and pure nanocrystalline cellulose (CNC). The composites were obtained in the form of a film and their synthesis proceeded under vacuum. The maximum conductivity of 2.66 CNC-Tri was measured to be 0.1 × 10⁻⁴ S/m at 175 °C and that of 1.17 CNC-Im to be 1.6 × 10⁻² S/m at 155 °C, in the anhydrous state. The composite 2.66 CNC-Tri, compared to 1.17 CNC-Im, has much better thermal properties manifested as stability of the matrix and durability of the heterocyclic molecule. The lifetimes of 2.66 CNC-Tri fulfills the requirements of the U.S. Department of Energy for the minimum lifetimes of a PEM based fuel cell for cars.     

Precise Molecular Sieving Architectures with Janus Pathways for Both Polar and Nonpolar Molecules

Precise molecular sieving architectures with Janus superhighways are constructed via a molecularly engineered interfacial reaction between cyclodextrin (CD) and trimesoyl chloride (TMC). Interestingly, the CD/TMC nanofilms constructed with both hydrophobic inner cavities and hydrophilic channels exhibit exceptionally high permeances for both polar and nonpolar solvents. The precise molecular sieving functions are determined by the type of CD building blocks and the inner cavities of intrinsic 3D hollow bowls. Positron annihilation spectroscopy (PAS) confirms that a larger inner CD cavity tends to generate a larger free volume and higher microporosity. Based on the rejection ratio of various dyes, the estimated molecular weight cutoff of CD/TMC nanofilms follows the trend of α‐CD/TMC (320 Da) <β‐CD/TMC (400 Da) <γ‐CD/TMC (550 Da), which is in strict accordance with the orders of their free volumes measured by PAS and inner cavity sizes of α‐CD <β‐CD <γ‐CD. This kind of novel CD/TMC molecular sieving membrane with intrinsic microporosity containing tunable pore size and sharp pore‐size distribution can effectively discriminate molecules with different 3D sizes.