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。     

基于CaO引导的甲烷蒸汽重整化学链燃烧制氢系统

为在重整气中得到高纯H_2和降低尾气CO_2分离成本,建立了基于CaO引导的甲烷蒸汽重整化学链燃烧制氢系统,该系统在重整反应器中加入CaO吸收剂,用以吸收重整器内的CO_2,提高重整气中H_2浓度,形成的CaCO_3固体在煅烧器中受热分解重新生成CaO。利用Aspen Plus进行了过程模拟及热力学分析,并研究主要参数对系统性能的影响,得到优化的操作条件为:CaO循环量/CH_4比为0.5,CH_4(燃料)/CH_4比为0.35,NiO循环量/CH_4比为1.4。CaO循环量/CH_4比从0变化到0.5时,重整气中H_2浓度从0.60增长到0.99;CH_4(燃料)/CH_4比在0.25~0.45区间变化时,重整气中H_2浓度从0.86提高到0.99,产气量增加;NiO循环量/CH_4比在1~1.6区间变化时,重整气中H_2浓度从0.88增长到0.99,系统有效能效率变化较小。     

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。     

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.     

Discriminative multi-task objects tracking with active feature selection and drift correction

In this paper, we propose a discriminative multi-task objects tracking method with active feature selection and drift correction. The developed method formulates object tracking in a particle filter framework as multi-Task discriminative tracking. As opposed to generative methods that handle particles separately, the proposed method learns the representation of all the particles jointly and the corresponding coefficients are similar. The tracking algorithm starts from the active feature selection scheme, which adaptively chooses suitable number of discriminative features from the tracked target and background in the dynamic environment. Based on the selected feature space, the discriminative dictionary is constructed and updated dynamically. Only a few of them are used to represent all the particles at each frame. In other words, all the particles share the same dictionary templates and their representations are obtained jointly by discriminative multi-task learning. The particle that has the highest similarity with the dictionary templates is selected as the next tracked target state. This jointly sparsity and discriminative learning can exploit the relationship between particles and improve tracking performance. To alleviate the visual drift problem encountered in object tracking, a two-stage particle filtering algorithm is proposed to complete drift correction and exploit both the ground truth information of the first frame and observations obtained online from the current frame. Experimental evaluations on challenging sequences demonstrate the effectiveness, accuracy and robustness of the proposed tracker in comparison with state-of-the-art algorithms.     

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.     

支持多种手机客户端的校园信息发布系统

针对数字化校园建设中存在的信息孤岛问题,设计开发了基于客户服务架构的能支持多种手机客户端的校园信息发布系统。分析了系统功能,描述了系统拓扑结构和软件架构,对数据交换接口、网页和手机客户端信息提醒等关键技术进行了探讨。试用结果表明系统运行良好,达到了设计目的。     

联合 LTP 和 LPQ 的人脸识别方法

提出一种联合局部三值模式（LTP）和局部相位量化（LPQ）的人脸识别方法,该方法首先对预处理后的人脸分别采用LTP算子、LPQ算子提取各自的直方图,然后,通过特征选择将两者联合为LTP＿LPQ直方图,最后通过最近邻分类器进行分类识别。在ORL和YALE标准人脸数据库上的实验表明,该方法能有效地提高人脸的识别率。     

基于偏序堆的Prim算法设计与实现

本文介绍了借助基于邻接表的偏序堆设计和实现Prim算法的具体方法,文中给出了程序类图、重要数据结构以及关Prim()算法的具体代码,并对算法的执行效率进行分析。