An improved ν-twin support vector machine

Twin support vector machine (TSVM) is regarded as a milestone in the development of the powerful SVM. It finds two nonparallel planes by resolving a pair of smaller-sized quadratic programming problems rather than a single large one, which makes the learning speed of TSVM approximately four times faster than that of the standard SVM. However, the empirical risk minimization principle is implemented in the TSVM, so it easily leads to the over-fitting problem and reduces the prediction accuracy of the classifier. ν-TSVM, as a variant of TSVM, also implements the empirical risk minimization principle. To enhance the generalization ability of the classifier, we propose an improved ν-TSVM by introducing a regularization term into the objective function, so there are two parts in the objective function, one of which is to maximize the margin between the two parallel hyper-planes, and the other one is to minimize the training errors of two classes of samples. Therefore the structural risk minimization principle is implemented in our improved ν-TSVM. Numerical experiments on one artificial dataset and nine benchmark datasets show that our improved ν-TSVM yields better generalization performance than SVM, ν-SVM, and ν-TSVM. Moreover, numerical experiments with different proportions of outliers demonstrate that our improved ν-TSVM is robust and stable. Finally, we apply our improved ν-TSVM to two BCI competition datasets, and also obtain better prediction accuracy.     

Backstepping control for output-constrained nonlinear systems based on nonlinear mapping

In this paper, nonlinear mapping (NM)-based backstepping control design is presented for a class of strict-feedback nonlinear systems with output constraint. By mapping output value set onto the set of all real numbers, the constrained system is transformed into a new strict-feedback unconstrained system to employ the traditional backstepping control while simultaneously preventing the constraint from being violated. It is proved that the original system has the similar convergence and bounded properties with the new one. Besides the nominal case where full knowledge of the plant is available, we also tackle scenarios wherein parametric uncertainties are present. Furthermore, the comparison with barrier Lyapunov function-based algorithm reveals the advantages of NM algorithm. The closed-loop system is guaranteed to be stable in the sense that all signals involved remain bounded, and the tracking error converges to zero asymptotically. Simulation studies illustrate the performance of the proposed control.     

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.     

碳沉积法制富氮用炭分子筛的研究

本文提出的碳沉积法是用煤焦油馏分油的有机溶液浸渍煤炭化物,然后热解以进行“孔调变”。确定了最佳工艺条件,制得富氮性能良好的炭分子筛。并用D-A方程求取了各种样品的微孔结构参数。     

Lithium-ion battery remaining useful life estimation based on fusion nonlinear degradation AR model and RPF algorithm

The lithium-ion battery cycle life prediction with particle filter (PF) depends on the physical or empirical model. However, in observation equation based on model, the adaptability and accuracy for individual battery under different operating conditions are not fully considered. Therefore, a novel fusion prognostic framework is proposed, in which the data-driven time series prediction model is adopted as observation equation, and combined to PF algorithm for lithium-ion battery cycle life prediction. Firstly, the nonlinear degradation feature of the lithium-ion battery capacity degradation is analyzed, and then, the nonlinear accelerated degradation factor is extracted to improve prediction ability of linear AR model. So an optimized nonlinear degradation autoregressive (ND–AR) time series model for remaining useful life (RUL) estimation of lithium-ion batteries is introduced. Then, the ND–AR model is used to realize multi-step prediction of the battery capacity degradation states. Finally, to improve the uncertainty representation ability of the standard PF algorithm, the regularized particle filter is applied to design a fusion RUL estimation framework of lithium-ion battery. Experimental results with the lithium-ion battery test data from NASA and CALCE (The Center for Advanced Life Cycle Engineering, the University of Maryland) show that the proposed fusion prognostic approach can effectively predict the battery RUL with more accurate forecasting result and uncertainty representation of probability density distribution (pdf).     

富勒型篦冷机的性能特点和技术改造

富勒型篦冷机具有急冷熟料、热回收效率高和冷却效率高等性能。自1985年引进富勒篦冷机设计和制造技术以来，我国的篦冷机技术已完成了第一代、第二代、茅三代的技术更新和发展历程。针对第二篦冷机一些固有的缺陷，巢湖铁道水泥厂采用第三代篦冷机技术，对原609S-819S／809S-1025S型第二代篦冷机进行了改造并获成功。改造4年来，篦冷机运行稳定，维修较少，经济效益明显。     

改性低毒脲醛树脂的合成及性能研究

采用六亚甲基四胺做催化剂，加入聚乙烯醇（PVA）、三聚氰胺和硫脲作为改性剂，通过降低甲醛与尿素物质的量比、采用尿素分批加入的合成方法，制备出改性低毒的脲醛树脂（UF），使游离甲醛质量分数低于0．30％；采用捏合和模压成型方法，将改性和普通UF树脂分别与剑麻纤维（SF）制备UF／SF共混复合材料，研究了复合材料的力学性能和吸水性能。结果表明，甲醛和尿素物质的量比为1．1时，游离甲醛质量分数降低到0．16％；甲醛和尿素物质的量比为1．2时，三聚氰胺、PVA及硫脲加入量分别为尿素总量的2．0％，1．5％，1．0％（质量分数）时，游离甲醛质量分数降低到了0．10％，粘度达到0．41Pa·S，贮存期大于80d；改性UF／SF复合材料的性能比普通UF／SF复合材料的冲击性能和耐磨性能分别提高了114％和261％。     

腐植酸与普洱茶茶色素

对云南普洱茶特有品质与化学组成(多酚类、儿茶素、黄酮类、茶黄素、茶红素、茶褐素)的关系进行简要地阐述。茶色素被证实是一类具有多种生理活性的物质。依据腐植酸的形成及化学性质、结构特征,提出了普洱茶中茶色素与腐植酸,尤其是茶褐素和黄腐酸,在化学性质方面(结构和官能团)存在一定的相似性。由此提出了腐植酸(黄腐酸)在生理活性及治疗功能方面与茶色素应具有相似性。为腐植酸(黄腐酸)在医药方面的应用研究提出了新思路。     

多酚氧化酶催化单宁的氧化聚合反应研究

研究了多酚氧化酶催化单宁的氧化聚合反应情况 ,探索了反应条件及 7种化合物对酶活性的抑制效果 ,对产物进行了红外光谱和紫外光谱测试 ,相对粘度法测定了产物的分子量。结果表明 ,反应的最佳条件是pH为 5 .2 9,温度为 3 0℃ ,在所用抑制剂中 ,硫酸铜的抑制作用最强 ,与单宁酸的IR图相比 ,产物的 -OH吸收带变窄 ,在 12 60cm-1处吸收峰增强 ,而紫外吸收无变化。产物平均分子量为 3 5 41,说明多酚氧化酶能催化单宁的氧化聚合