化学工程学的发展趋势和在科学发展中的地位

化学工程学不仅是一个经典的工程技术学科 (四大工程技术学科 :建筑工程、机械工程、电子工程和化学工程之一 ) ,而且是进一步探索生命科学 (植物生理学和动物生理学 )的基础 ;在强调绿色经济 (即 :平衡发展 )的今天 ,化学工程学将会发挥出越来越大的作用。     

基于间歇测量的状态估计稳定性和精度研究*

针对分布式控制系统状态估计过程出现的测量值丢失问题,本文量化分析测量值丢失对估计稳定性和精度的影响。首先,根据随机测量模型重新推导带测量值丢失的卡尔曼滤波器,得到估计误差协方差迭代式。然后,将随机误差协方差迭代式建模为修正的黎卡提微分方程,提出了估计稳定性和精度分析方法。最后,列举不同的系统实例,证明临界包到达率的存在性,当测量值到达率大于临界值时,平均误差协方差从发散过渡到有界,进而得到估计精度与包达率之间的函数关系。     

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。     

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.