聚酯酰胺热熔胶的合成及其性能研究

研究了以聚酯胶和聚酰胺胶为原料在催化剂作用下加热共熔,通过大分子链交换反应制备织物用聚酯酰胺热熔胶的方法。用聚酯胶的溶剂对所得产品进行溶取试验并且通过红外光谱分析都证明了链交换反应的存在。用该法所得产品粘接强度大,既耐水洗又耐干洗,适用于粘合衬生产。     

α,á-二氨基-二(脂肪)羧酸合成方法的研究进展

本文主要阐述了重要中间体α,á-二氨基-二(脂肪)二氨基-二脂肪羧酸类化合物的研究进展及制备方法.总结了通过生物、化学、电化学等途径制备该类化合物的方法,较全面地给出了该类化合物的制备方法.     

不饱和聚酯树脂光固化工艺的研究-光引发剂浓度

本文选用TPO、651两种光引发剂，分别配制引发剂浓度为0．25、0．5、1、2wt％的不饱和聚酯体系。通过测定光固化试样的显微硬度分析光引发剂浓度对固化程度的影响。结果表明，提高引发剂浓度能抑制氧阻聚的作用，但对于较厚的试样，引发剂浓度过高会导致固化不完全。     

大丝束聚丙烯腈基预氧丝炭化研究

采用正交实验法研究了炭化温度、炭化时间、升温速率等因素对两种聚丙烯腈基预氧丝炭化产物结构和性能的影响.结果表明,进口东邦48 K大丝束聚丙烯腈基预氧丝环化程度优于国产吉林6 K小丝束聚丙烯腈基预氧丝,在炭化温度950℃,升温速率4℃/min,保温时间10 min条件下,炭化可取得最佳性能,炭化产物纤维拉伸强度达2.96 GPa,而国产吉林6 K小丝束在炭化温度1 100℃,升温速率4℃/min,保温时间5 min条件下炭化产物性能最佳,产物炭纤维拉伸强度近3.80 GPa.伴随两种预氧丝炭化过程,纤维颜色加深,表面缺陷减少,拉伸断裂模式均由假塑性向脆性过渡,为复合材料低成本开发中预氧丝坯体制备与炭化工艺提供了参考依据.     

Android平台i-Jetty服务器在智能家居中的应用研究

针对智能家居系统架构复杂、开发难度较大等问题,提出了一种基于Android平台嵌入式Web服务器i-Jetty的智能家居系统方案。在ARM硬件平台及Android软件平台上,采用i-Jetty服务器实现了家居环境信息网络远程监控功能。系统以Mini6410开发板为中央控制器、服务器和室内监控平台,采用蓝牙与被控设备进行通信,通过Linux层驱动加载、Android本地服务和Web服务的联合编程,实现对传感器数据的获取、家电控制和信息发布。实验结果证明了方案的可行性和实用性。     

Simulating the Evolution of Market Shares: The Effects of Customer Learning and Local Network Externalities

This paper develops a simulation model to investigate competitive technology diffusion, and focuses on examining the influence of customer learning on the evolution of market share when both technologies exhibit local network externalities. Results show that: (1) the equilibrium market share of the new technology is determined by two key factors: the characteristics of customer learning behavior and the strength of local network externalities; (2) moderate network externalities can be beneficial for new technology to dominate the entire market when customers adopt belief-based learning rule; (3) moderate learning rate would facilitate the diffusion of new technology when customers make their decisions based on reinforcement learning; (4) the decay of customer learning and the proportions of imitators in market would help the old technology establish advantage by maintaining demand inertia of customer. The joint effects of psychology of customer behavior and local interactions of customers offer a new mechanism to explain the diffusion of technology in a competitive market with network externalities.     

Incorporating mutation scheme into krill herd algorithm for global numerical optimization

Recently, Gandomi and Alavi proposed a robust meta-heuristic optimization algorithm, called Krill Herd (KH), for global optimization. To improve the performance of the KH algorithm, harmony search (HS) is applied to mutate between krill during the process of krill updating instead of physical diffusion used in KH. A novel hybrid meta-heuristic optimization approach HS/KH is proposed to solve global numerical optimization problem. HS/KH combines the exploration of harmony search (HS) with the exploitation of KH effectively, and hence, it can generate the promising candidate solutions. The detailed implementation procedure for this improved meta-heuristic method is also described. Fourteen standard benchmark functions are applied to verify the effects of these improvements, and it is demonstrated that, in most cases, the performance of this hybrid meta-heuristic method (HS/KH) is superior to, or at least highly competitive with, the standard KH and other population-based optimization methods, such as ACO, BBO, DE, ES, GA, HS, KH, PSO, and SGA. The effect of the HS/FA parameters is also analyzed.     

3D image retrieval based on differential geometry and co-occurrence matrix

3D image retrieval approach is a challenging problem in the research of content-based image retrieval. In this paper, a novel retrieval approach combined differential geometry and co-occurrence matrix is presented. Firstly, Gaussian curvature and mean curvature are utilized to represent the inherent characteristic of spatial surface, and then we use co-occurrence matrix to store the shape information of 3D images. Secondly, normalization process is applied to the co-occurrence matrix and the invariants independence of the translation, scaling, and rotation transforms are proved. In comparison with the recent methods, experiments indicate a lower computation complexity and a better retrieval rate to 3D images with slight different shape characteristic.     

Memristor-based chaotic neural networks for associative memory

In chaotic neural networks, the rich dynamic behaviors are generated from the contributions of spatio-temporal summation, continuous output function, and refractoriness. However, a large number of spatio-temporal summations in turn make the physical implementation of a chaotic neural network impractical. This paper proposes and investigates a memristor-based chaotic neural network model, which adequately utilizes the memristor with unique memory ability to realize the spatio-temporal summations in a simple way. Furthermore, the associative memory capabilities of the proposed memristor-based chaotic neural network have been demonstrated by conventional methods, including separation of superimposed pattern, many-to-many associations, and successive learning. Thanks to the nanometer scale size and automatic memory ability of the memristors, the proposed scheme is expected to greatly simplify the structure of chaotic neural network and promote the hardware implementation of chaotic neural networks.