新型节能玻璃钢门窗的研究

本文主要围绕节能主题，从能耗方式、性能指标、结构形式、制作安装等方面论述了玻璃钢门窗在不同气候地区、不同环境和不同领域中的开发和应用。     

VOID FRACTION PROFILES IN PLANE-POISEUILLE FLOW OF VISCOELASTIC FOAMS

This investigation focuses on slow, isothermal, two-phase flow of gas bubble suspensions in Separan solutions, prepared with the help of a sodium borohydride blowing agent at room temperature. The total residence time in these experiments is much smaller than characteristic times for growth or rise of bubbles. The variation of bubble volume fraction across a narrow gap between two planes, is recorded at two locations along the flow direction. This is done with a Cesium gamma-radiation source focused on a region of area.012 cm² in the flow plane, and a Sodium iodide detector across the channel yielding a resolution of.01 over the range of void fractions investigated from.02 to.08. This measurement allows us to identify conditions under which the two-phase flow may be described by a two fluid model with a uniform bubbly core and a bubble free wall layer. With this two phase flow structure, a relative viscosity equation for the suspension is used to compute an apparent viscosity. Such calculations indicate that the observed reduction in apparent viscosity for the two phase flow may be attributed to a bubbly core which is more shear thinning than the medium. The additional shear thinning factor for the suspension is related to the elasticity of the medium.     

遥感器像移与图像参数的分析建模研究

卫星平台运动会产生恶化像质的像移,像移是影响遥感图像质量的重要因素.针对这种情况,建立了像移和图像参数的关系模型,可通过卫星生成图像的内在特征定量研究卫星成像过程中产生的像移,而不必通过产生像移的卫星平台等因素建立物理模型来研究像移.首先模拟了遥感器在轨成像过程,通过人工制造出影响因素的干扰获得了仿真图像数据,然后定量分析了像移与图像参数之间的相关性,选出了相关性较强且适合建模的图像参数,最后通过对这些图像参数构成的向量的降维得到了反映图像质量的综合参数,并建立了综合参数与像移的关系模型,以通过图像综合参数来计算像移.实验结果表明,这种模型能够很好地反映图像综合指标与像移之间的关系.该研究通过图像参数定量研究像移,为在轨运行卫星的遥感器像移与外景条件下的遥感图像要素的相关建模研究提供了部分技术支持.     

霍尔传感器在气动健身器材中的应用

为了能更好地掌握运动者的运动力度,尽可能地减小运动中由于运动过剧造成的损伤,设计了一种气动健身器材,通过实际的气缸压力与霍尔传感器检测出气缸活塞的位移来计算器材使用者所承受的拉力,并结合记录气缸活塞的运动次数来计算运动者消耗的热量,更直观地显示出运动者的运动情况。经过测试,霍尔传感器反应灵敏,检测结果准确,系统稳定性良好,有较高的实用性。     

离子色谱法测定配煤、焦炭和焦油中的氯含量

研究了用带电导检测器的离子色谱对配煤、焦炭和焦油中的Cl- 进行测定的方法。在YSA型8126A-4# 阴离子分离柱上用3.5mmol/LNa2CO3、5.3mmol/LNaHCO3做淋洗液对Cl-进行测定,方法简便快速。Cl-的检出限为2×10-6。该法也适用于F-、NO2-、NO3-、SO42-等的测定。     

Efficient multilevel image segmentation through fuzzy entropy maximization and graph cut optimization

The fuzzy c-partition entropy has been widely adopted as a global optimization technique for finding the optimized thresholds for multilevel image segmentation. However, it involves expensive computation as the number of thresholds increases and often yields noisy segmentation results since spatial coherence is not enforced. In this paper, an iterative calculation scheme is presented for reducing redundant computations in entropy evaluation. The efficiency of threshold selection is further improved through utilizing the artificial bee colony algorithm as the optimization technique. Finally, instead of performing thresholding for each pixel independently, the presented algorithm oversegments the input image into small regions and uses the probabilities of fuzzy events to define the costs of different label assignments for each region. The final segmentation results is computed using graph cut, which produces smooth segmentation results. The experimental results demonstrate the presented iterative calculation scheme can greatly reduce the running time and keep it stable as the number of required thresholds increases. Quantitative evaluations over 20 classic images also show that the presented algorithm outperforms existing multilevel segmentation approaches.     

3D Shape Segmentation and Labeling via Extreme Learning Machine

We propose a fast method for 3D shape segmentation and labeling via Extreme Learning Machine (ELM). Given a set of example shapes with labeled segmentation, we train an ELM classifier and use it to produce initial segmentation for test shapes. Based on the initial segmentation, we compute the final smooth segmentation through a graph‐cut optimization constrained by the super‐face boundaries obtained by over‐segmentation and the active contours computed from ELM segmentation. Experimental results show that our method achieves comparable results against the state‐of‐the‐arts, but reduces the training time by approximately two orders of magnitude, both for face‐level and super‐face‐level, making it scale well for large datasets. Based on such notable improvement, we demonstrate the application of our method for fast online sequential learning for 3D shape segmentation at face level, as well as realtime sequential learning at super‐face level.     

Image super-resolution based on multi-space sparse representation

Sparse representation provides a new method of generating a super-resolution image from a single low resolution input image. An over-complete base for sparse representation is an essential part of such methods. However, discovering the over-complete base with efficient representation from a large amount of image patches is a difficult problem. In this paper, we propose a super-resolution construction based on multi-space sparse representation to efficiently solve the problem. In the representation, image patches are decomposed into a structure component and a texture component represented by the over-complete bases of their own spaces so that their high-level features can be captured by the bases. In the implementation, a prior knowledge about low resolution images generation is combined to the typical base construction for high construction quality. Experiment results demonstrate that the proposed method significantly improves the PSNR, SSIM and visual quality of reconstructed high-resolution image.     

Prediction-based realistic 3D model compression

The benefit of using the geometry image to represent an arbitrary 3D mesh is that the 3D mesh can be re-sampled as a completely regular structure and coded efficiently by common image compression methods. For geometry image-based 3D mesh compression, we need to code the normal-map images while coding geometry images to improve the subjective quality and realistic effects of the reconstructed model. In traditional methods, a geometry image and a normal-map image are coded independently. However a strong correlation exists between these two kinds of images, because both of them are generated from the same 3D mesh and share the same parameterization. In this paper we propose a predictive coding framework, in which the normal-map image is predicted based on the geometric correlation between them. Additionally we utilize the strong geometric correlation among three components of normal-map image to improve the predicting accuracy. The experimental results show the proposed coding framework improves the coding efficiency of normal-map image, meanwhile the realistic effect of a 3D mesh is significantly enhanced.