Correspondence analysis and establishment of evaluation model of classification performance indices for a turbo air classifier

The operational parameters of a turbo air classifier including feeding speed, rotor cage's rotary speed and air inlet velocity affect its classification performance directly, such as cut size, classification precision, classification efficiency, fine powder yield, particle fineness and degree of dispersion. Current methods of optimizing operational parameters and improving the classification performance of a turbo air classifier are almost single objective decision only for one of the classification performance indices. In this paper, the multi‐objective programming (MOP) model on classification performance for a turbo air classifier is established to evaluate these performance indices comprehensively and achieve optimal classification performance. To minimize the effect of repeatability within these classification performance indices, correspondence analysis is applied to determine the evaluation indices of this MOP model. According to correspondence analysis on the fine talc classification experimental data as well as the calcium carbonate classification experimental data, there is a very strong correlation between cut size and D90; there is also a very strong correlation between cut size and fine powder yield. Thus D90 and fine powder yield are filtered out and they aren't discussed in the evaluation model. The variation coefficient method is introduced to calculate weights of the evaluation function, and the dimensionless transformation method is used to eliminate the effects of different dimension. Thus, the optimal solution among the experimental data is obtained through solving the evaluation function. For the talc classification experiments, the optimal operational parameter combinations are: the feeding speed is 40 kg · h^–1, the air inlet velocity is 5 m · s^–1 and the rotor cage's rotary speed is 1200 ? min^–1. The classification performance indices are: cut size is 16.5 μm, classification precision index is 0.59, Newton classification efficiency is 57% and degree of dispersion is 2.13. For the calcium carbonate classification experiments, the optimal operational parameter combinations are: the feeding speed is 92 kg · h^–1, the air inlet velocity is 12 m · s^–1 and the rotor cage's rotary speed is 1200 ? min^–1. The classification performance indices are: cut size is 31.4 μm, classification precision index is 0.74, Newton classification efficiency is 74% and degree of dispersion is 1.27. This evaluation model avoids the limitation of evaluation for the single classification performance index and incomplete information got by the means of single factor experiment of operational parameters. It also provides the quantitative evaluating criteria for classification performance of a turbo air classifier, which offers a theoretical basis for effective production. This multi‐objective programming optimizing method and evaluation model on classification performance can be applied to other dynamic air classifiers as well.     

一种改进的基于分解的多目标进化算法

利用基于分解的多目标进化算法框架(MOEA/D),将混合策略的进化算法用于求解分解后的若干单目标优化子问题,提出了一种带局部搜索的基于分解的多目标混合策略进化算法(LMS-MOEA/D)。算法利用均匀设计产生子问题的聚合权重向量,混合交叉策略能够充分利用不同交叉算子的优势;同时算法针对演化过程收敛的特点,结合局部搜索策略,获得逼近Pareto前沿的最优解集。最后通过实验验证算法在多样性和收敛性方面的有效性。     

硫基复合肥生产技术的改进

介绍山东鲁中化工总厂建设4万吨/年硫基复合肥生产线的生产工艺.对浓硫酸与氧化钾转化法进行了改良,比间歇法所用设备少、自动化程度高;比连续转化法投资省、自动化程度低.改造后的工艺设备减少,能耗投资降低.     

Indoor productivity measured by common response patterns to physical and psychosocial stimuli

Productivity is the essential organizational outcome. It is vaguely understood and difficult to quantify, especially at the individual level in office companies. Our objective was to quantify and describe the part of productivity, which is systematically influenced by the indoor environment. We, therefore, introduce the concept 'indoor productivity,' which is defined by measuring operations. We used the multivariate method Marker Object Projection to transform questionnaire data into an 'indoor productivity index' (IPI), which reflects the relative impact of the variables that systematically influence indoor productivity. The reasoning behind IPI is that indoor productivity cannot be separated from experienced indoor environment and wellness at work. IPI is calculated for each respondent based on own, colleagues', and two fictive respondents' answers. Conservatively, IPI was calculated to constitute about 25% of what determines the productivity of an individual. By using IPI as dependent variable in multiple regression analyses, the relative contribution of all indoor variables was identified. Physical indoor variables were as important as psychosocial ones. During the cold seasons, 'dry air' was the most important factor reducing IPI.     

映像和元对象协议实现面向方面编程技术

文中介绍了AOP的基本思想，通过学生课程注册管理系统重点叙述映像和元程序设计是如何实现AOP，指出映像和元程序设计实现AOP的优点和不足。     

射频能量捕获传感网移动能量源的布置策略研究

射频能量捕获传感网（RF Energy Harvesting Wireless Sensor Network,RFEH-WSN）由专用射频能量源设备（Energy Transmitter,ET）和具有射频能量捕获功能的传感器节点（Energy Harvesting Recevier,简称EHR）组成.该网络解决了传感器网络中电池不易更换与节点能量容易耗尽的问题,使得RFEH-WSN应用前景更加广阔.RFEH-WSN应用中一个值得研究的问题是如何布置ET的充电位置,降低ET能耗且提高覆盖率.已有的工作主要考虑ET布置中单目标优化问题,如最小充电时间、最小功耗、最大覆盖率等.本文以时间最小和覆盖率最大为目标建立多目标优化模型,并提出利用粒子群算法（Particle Swarm Optimization,PSO）求解多目标函数（Multiple Object Program,MOP）的低复杂度近似算法,获得了最优Pareto解集.仿真结果表明,多目标优化可以满足不同情况的需求,提高充电效用.     

多目标车辆路径的遗传算法

传统的单目标遗传算法运行一次只能得到一个解,而多目标遗传算法运行一次可以得到一个解集,多个解可以为决策者提供更多的选择余地,作出更好的决策。本算法通过设计新的改进遗传算子,进一步提高了算法的性能,并设计采用擂台法则构建非支配集,降低了时间复杂度。通过实验验证表明,此算法能有效的解决车辆路径问题。     

环链种群结构的多目标教与学优化算法*

针对教与学算法采用贪婪进化机制,易造成种群多样性较差的问题,将环链拓扑结构引入到多目标教与学算法中,并改进了自我学习机制,提出了一种环链种群结构的多目标教与学优化算法。根据多种群进化方式,通过一种环链结构将种群划分为多个邻域,每个邻域代表一个小种群,且相邻种群之间存在重叠。在教与学进化过程中,在每个小种群中设置一名教师,由每一位教师引导各自的种群独立进化,且彼此之间存在进化信息交流。同时,提出一种改进的学习机制来提升局部寻优能力,由此平衡算法的全局搜索和局部寻优。该算法通过与五种对等算法在ZDT和DTLZ系列组成的12个多目标测试问题进行测试,实验结果表明了新算法在收敛性、多样性和稳定性等方面均优于或部分优于其他的对比算法。     

解决无线传感器网络能量洞问题的贪婪算法

无线传感器网络中的能量洞问题是影响网络寿命的关键因素之一。在基于环模型的多跳传感器网络中,通过优化所有环的传输距离可以有效地延长网络寿命。提出了一种近似的贪婪算法（ASGT）,该算法将最优传输距离序列问题转化为最优生成树问题,在降低搜索（算法）复杂度的同时得到与最优解近似的结果。模拟实验证明了采用ASGT算法的网络寿命逼近于理想最优序列下的网络生命时间,并且与已有的文献算法相比,ASGT可以延长网络寿命两倍以上。