岩体移动参数反分析的模糊数学方法

在采矿和岩土工程问题中,岩土移动变形及工程参数分析是该领域的重要工程研究课题.本文针对地下工程开挖而导致的岩体移动变形分析问题,根据模糊数学理论建立了模型,对工程参数确定给出了反分析方法.分析结果表明,理论结果与现场实测资料吻合较好,表明本文所建立的模糊数学模型适用于分析实际工程问题.     

药剂不同添加方式对胶磷矿浮选的影响研究

对云南某风化质磷矿进行了在不同加药点添加调整剂YP1和YP11的浮选对比试验,结果表明,YP1和YP11加入到磨机中的试验结果优于加入到浮选槽中的结果.     

云南省磷矿资源开发面临的问题及建议

云南省乡镇和个体小矿泛滥 ,资源浪费大 ,环境破坏严重 ,磷矿石市场混乱。建议走可持续发展之路 ,建立健全综合管理体系 ,加大探矿找矿力度 ,加强中低品位磷矿石的选矿研究 ,全面推进矿山化工。     

Sphere mass center determination by velocity modulation

This paper presents a new technique, called velocity modulation, for measuring the mass center location of a sphere, improving upon the accuracy of previous methods by at least a factor of 4. The sphere is rolled down a set of parallel rails. The offset of the sphere’s mass center with respect to its geometric center modulates the sphere’s velocity at the rolling frequency, which is detected by a series of differential optical shadow sensors. The measured times that the sphere crosses the sensors is compared with a simplified mechanics model in order to recover the sphere’s mass center location. The precision of the technique is demonstrated by the repeatability of independent measurements in one plane of a 50.8 mm (2 in.) stainless steel gage sphere. The best standard deviation achieved is 145 nm (95% confidence interval = 227 nm).     

Applications of mass spectrometry to the study of siRNA

RNA interference (RNAi) has quickly become a well‐established laboratory tool for regulating gene expression and is currently being explored for its therapeutic potential. The design and use of double‐stranded RNA oligonucleotides as therapeutics to trigger the RNAi mechanism and a greater effort to understand the RNAi pathway itself is driving the development of analytical techniques that can characterize these oligonucleotides. Electrospray (ESI) and MALDI have been used routinely to analyze oligonucleotides and their ability to provide mass and sequence information has made them ideal for this application. Reviewed here is the work done to date on the use of ESI and MALDI for the study of RNAi oligonucleotides as well as the strategies and issues associated with siRNA analysis by mass spectrometry. While there is not a large body of literature on the specific application of mass spectrometry to RNAi, the work done in this area is a good demonstration of the range of experiments that can be conducted and the value that ESI and MALDI can provide to the RNAi field. © 2010 Wiley Periodicals, Inc., Mass Spec Rev 30:979–998, 2011     

Consideration of the BDNF gene in relation to two phenotypes: Hoarding and obesity.

The gene coding for the brain derived neurotrophic factor (BDNF) has emerged as an interesting candidate for multiple brain and brain disorder-related phenomena. The primary aim of the present investigation was to consider the relationship between the BDNF Val66Met variant and two phenotypes: compulsive hoarding as a symptom dimension of obsessive–compulsive disorder (OCD), and body mass index (BMI). We examined the BDNF gene in a large (N = 301) clinical sample of probands with OCD. Participants were classified as hoarding or nonhoarding using a strict, multimeasure grouping approach. Results revealed that the Val/Val genotype was linked with hoarding classification and more severe hoarding behaviors, as well as greater BMI levels. Hoarding status was also associated with greater BMI scores, with individuals in the hoarding group being far more likely to be classified as obese compared with the nonhoarding group. Our findings may provide a distinct avenue through which hoarding and BMI could be linked. These findings are suggestive of a complex gene, body weight, and psychopathology relationship wherein a primitive, survival “thrifty gene” strategy may be conserved and represented in a subgroup of humans manifesting severe hoarding symptoms. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Presentation attack detection based on two-stream vision transformers with self-attention fusion

Aiming at the performance degradation of the existing presentation attack detection methods due to the illumination variation, a two-stream vision transformers framework (TSViT) based on transfer learning in two complementary spaces is proposed in this paper. The face images of RGB color space and multi-scale retinex with color restoration (MSRCR) space are fed to TSViT to learn the distinguishing features of presentation attack detection. To effectively fuse features from two sources (RGB color space images and MSRCR images), a feature fusion method based on self-attention is built, which can effectively capture the complementarity of two features. Experiments and analysis on Oulu-NPU, CASIA-MFSD, and Replay-Attack databases show that it outperforms most existing methods in intra-database testing and achieves good generalization performance in cross-database testing.     

Hyperspectral image super-resolution combining with deep learning and spectral unmixing

In recent years, hyperspectral image super-resolution has attracted the attention of many researchers and has become a hot topic in the field of computer vision. However, it is difficult to obtain high-resolution images due to imaging hardware devices. At present, many existing hyperspectral image super-resolution methods have not achieved good results. In this paper, we propose a hyperspectral image super-resolution method combining with deep residual convolutional neural network (DRCNN) and spectral unmixing. Firstly, the spatial resolution of the image is enhanced by learning a priori knowledge of natural images. The DRCNN reconstructs high spatial resolution hyperspectral images by concatenating multiple residual blocks, each containing two convolutional layers. Secondly, the spectral features of low-resolution and high-resolution hyperspectral images are linked by spectral unmixing. This approach aims to obtain the endmember matrix and the abundance matrix. The final reconstruction result is obtained by multiplying the endmember matrix and the abundance matrix. In addition, in order to improve the visual effect of the reconstructed image, the total variation regularity is used to impose constraints on the abundance matrix to enhance the relationship between the pixels. The experimental results of remote sensing data based on ground facts show that the proposed method has good performance and preserves spatial information and spectral information without the need for auxiliary images.     

A survey on analysis and implementation of state-of-the-art haze removal techniques

Haze is a poor-quality state described by the opalescent appearance of the atmosphere which reduces the visibility. It is caused by high concentrations of atmospheric air pollutants, such as dust, smoke and other particles that scatter and absorb sunlight. The poor visibility can result in the failure of multiple computer vision applications such as smart transport systems, image processing, object detection, surveillance etc. One of the major issues in the field of image processing is the restoration of images that are corrupted due to different degradations. Typically, the images or videos captured in the outside environment have low contrast, colour fade and restricted visibility due to suspended particles of the atmosphere that directly influence the image quality. This can cause difficulty in identifying the objects in the captured hazy images or frames. To address this problem, several image dehazing techniques have been developed in the literature, each of which has its own advantages and limitations, but effective image restoration remains a challenging task. In recent times, various learning (Machine learning & Deep learning) based methods greatly condensed the drawbacks of manual design of haze related features and reduces the difficulty in efficient restoration of images with less computational time and cost. The current state-of-the-art methods for haze free images, mainly from the last decade, are thoroughly examined in this survey. Moreover, this paper systematically summarizes the hardware implementations of various haze removal methods in real time. It is with the hope that this current survey acts as a reference for researchers in this scientific area and to provide a direction for future improvements based on current achievements.     

基于“数据驱动＋智能学习”的合成孔径雷达学习成像

对感兴趣目标的数量、位置、型号等参数信息的精确获取一直是合成孔径雷达(SAR)技术中最为重要的研究内容之一。现阶段的SAR信息处理主要分为成像和解译两大部分,两者的研究相对独立。SAR成像和解译各自开发了大量算法,复杂度越来越高,但SAR解译并未因成像分辨率提升而变得简单,特别是对重点目标识别率低的问题并未从本质上得以解决。针对上述问题,该文从SAR成像解译一体化角度出发,尝试利用“数据驱动+智能学习”的方法提升机载SAR的信息处理能力。首先分析了基于“数据驱动+智能学习”方法的SAR成像解译一体化的可行性及现阶段存在的主要问题;在此基础上,提出一种“数据驱动+智能学习”的SAR学习成像方法,给出了学习成像框架、网络参数选取方法、网络训练方法和初步的仿真结果,并分析了需要解决的关键性技术问题。