采用磁流变阻尼器的人体座椅悬架系统模糊ANFIS-PID复合控制技术研究

对座椅悬架用单出杆式磁流变阻尼器进行阻尼特性试验,并借助MATLAB多项式拟合工具箱对改进多项式模型中未知参数进行辨识。分析人体振动特性,建立五自由度人体座椅悬架模型。综合模糊控制器与自适应模糊神经推理系统(Adaptive Neuro-Fuzzy Inference System,ANFIS)整定PID控制器的优点,提出一种模糊ANFIS-PID复合控制策略。采用正弦信号作为外界激励,分别对被动悬架、传统模糊控制、ANFIS-PID控制及模糊ANFIS-PID复合控制人体座椅悬架系统进行仿真分析。结果表明,辨识得出的参数和建立的改进多项式模型均可满足后续仿真要求;采用模糊ANFIS-PID复合控制策略的隔振效果明显优于传统模糊控制及ANFIS-PID控制,能有效改善人体座椅悬架系统的行驶平顺性及驾乘人员乘坐舒适性。     

一种广义不可分支持向量机算法

针对标准的C-SVM(C-support vector machine)算法在处理很多实际分类问题时，对识别错误代价损失差异很大的极端情况表现出的局限性，提出一种通用的 广义支持向量机算法。根据识别错误后所付出的代价，可以把最优分类面向代价损失低的一方进行推移，留给代价损失高的一方更大的空间，提高其识别率，从而减小识别错误后带来的代价损失。该方法进一步提高了标准C SVM的适用性以及样本的正确识别率，将新算法应用到高分辨雷达距离像的识别中，实验证明,广义C-SVM能取得比传统C-SVM更好的识别效果。     

Rapidly finding CAD features using database optimization

Automatic feature recognition aids downstream processes such as engineering analysis and manufacturing planning. Not all features can be defined in advance; a declarative approach allows engineers to specify new features without having to design algorithms to find them. Naive translation of declarations leads to executable algorithms with high time complexity. Database queries are also expressed declaratively; there is a large literature on optimizing query plans for efficient execution of database queries. Our earlier work investigated applying such technology to feature recognition, using a testbed interfacing a database system (SQLite) to a CAD modeler (CADfix). Feature declarations were translated into SQL queries which are then executed.The current paper extends this approach, using the PostgreSQL database, and provides several new insights: (i) query optimization works quite differently in these two databases, (ii) with care, an approach to query translation can be devised that works well for both databases, and (iii) when finding various simple common features, linear time performance can be achieved with respect to model size, with acceptable times for real industrial models. Further results also show how (i) lazy evaluation can be used to reduce the work performed by the CAD modeler, and (ii) estimating the time taken to compute various geometric operations can further improve the query plan. Experimental results are presented to validate our main conclusions.     

Human action recognition in H.264/AVC compressed domain using meta-cognitive radial basis function network

In this paper, we propose a H.264/AVC compressed domain human action recognition system with projection based metacognitive learning classifier (PBL-McRBFN). The features are extracted from the quantization parameters and the motion vectors of the compressed video stream for a time window and used as input to the classifier. Since compressed domain analysis is done with noisy, sparse compression parameters, it is a huge challenge to achieve performance comparable to pixel domain analysis. On the positive side, compressed domain allows rapid analysis of videos compared to pixel level analysis. The classification results are analyzed for different values of Group of Pictures (GOP) parameter, time window including full videos. The functional relationship between the features and action labels are established using PBL-McRBFN with a cognitive and meta-cognitive component. The cognitive component is a radial basis function, while the meta-cognitive component employs self-regulation to achieve better performance in subject independent action recognition task. The proposed approach is faster and shows comparable performance with respect to the state-of-the-art pixel domain counterparts. It employs partial decoding, which rules out the complexity of full decoding, and minimizes computational load and memory usage. This results in reduced hardware utilization and increased speed of classification. The results are compared with two benchmark datasets and show more than 90% accuracy using the PBL-McRBFN. The performance for various GOP parameters and group of frames are obtained with twenty random trials and compared with other well-known classifiers in machine learning literature.     

A new reverse reduce-error ensemble pruning algorithm

Although greedy algorithms possess high efficiency, they often receive suboptimal solutions of the ensemble pruning problem, since their exploration areas are limited in large extent. And another marked defect of almost all the currently existing ensemble pruning algorithms, including greedy ones, consists in: they simply abandon all of the classifiers which fail in the competition of ensemble selection, causing a considerable waste of useful resources and information. Inspired by these observations, an interesting greedy Reverse Reduce-Error (RRE) pruning algorithm incorporated with the operation of subtraction is proposed in this work. The RRE algorithm makes the best of the defeated candidate networks in a way that, the Worst Single Model (WSM) is chosen, and then, its votes are subtracted from the votes made by those selected components within the pruned ensemble. The reason is because, for most cases, the WSM might make mistakes in its estimation for the test samples. And, different from the classical RE, the near-optimal solution is produced based on the pruned error of all the available sequential subensembles. Besides, the backfitting step of RE algorithm is replaced with the selection step of a WSM in RRE. Moreover, the problem of ties might be solved more naturally with RRE. Finally, soft voting approach is employed in the testing to RRE algorithm. The performances of RE and RRE algorithms, and two baseline methods, i.e., the method which selects the Best Single Model (BSM) in the initial ensemble, and the method which retains all member networks of the initial ensemble (ALL), are evaluated on seven benchmark classification tasks under different initial ensemble setups. The results of the empirical investigation show the superiority of RRE over the other three ensemble pruning algorithms.     

Fundamental analysis of recombinant human epidermal growth factor in solution with biophysical methods

Correlation of thermodynamic and secondary structural stability of proteins at various buffer pHs was investigated using differential scanning calorimetry (DSC), dynamic light scattering (DLS) and attenuated total reflection Fourier-transform infrared spectroscopy (ATR FT-IR). Recombinant human epithelial growth factor (rhEGF) was selected as a model protein at various pHs and in different buffers, including phosphate, histidine, citrate, HEPES and Tris. Particle size and zeta potential of rhEGF at each selected pH of buffer were observed by DLS. Four factors were used to characterize the biophysical stability of rhEGF in solution: temperature at maximum heat flux (T_m), intermolecular β-sheet contents, zeta size and zeta potential. It was possible to predict the apparent isoelectric point (pI) of rhEGF as 4.43 by plotting pH against zeta potential. When the pH of the rhEGF solution increased or decreased from pI, the absolute zeta potential increased indicating a reduced possibility of protein aggregation, since T_m increased and β-sheet contents decreased. The contents of induced intermolecular β-sheet in Tris and HEPES buffers were the lowest. Thermodynamic stability of rhEGF markedly increased when pH is higher than 6.2 in histidine buffer where T_m of first transition was all above 70?°C. Moreover, rhEGF in Tris buffer was more thermodynamically stable than in HEPES with higher zeta potential. Tris buffer at pH 7.2 was concluded to be the most favorable.     

The Role of hOGG1 C1245G Polymorphism in the Susceptibility to Lupus Nephritis and Modulation of the Plasma 8-OHdG in Patients with Systemic Lupus Erythematosus

We investigated whether the C1245G polymorphism of human 8-oxoguanine glycosylase 1 (hOGG1) gene confers the susceptibility to systemic lupus erythematosus (SLE) occurrence of lupus nephritis and affects the plasma level of 8-hydroxy-2''-deoxyguanosine (8-OHdG) in patients with SLE. A total of 45 healthy controls and 85 SLE patients were recruited. The C1245G polymorphism of the hOGG1 gene was determined by direct sequencing. The frequency of occurrence of the hOGG1 1245 GG genotype in SLE patients was 31.8% (27/85), which is lower than that of healthy controls of 53.3% (24/45). Thirty-three (33/85, 38.8%) SLE patients developed lupus nephritis. Significantly, SLE patients harboring the hOGG1 1245 GG genotype had a higher incidence to develop lupus nephritis than did those harboring the hOGG1 1245 CC or CG genotype (15/27, 55.6% vs.18/58, 31.0%, p = 0.031). Divided into subgroups, SLE patients harboring the hOGG1 1245 GG genotype had the highest plasma levels of 8-OHdG among patients with all genotypes, with regard to the coexistence of lupus nephritis (p = 0.020, ANOVA), including those with nephritis harboring the hOGG1 1245 CC or CG genotypes (p = 0.037), those without nephritis harboring the hOGG1 1245 GG genotype (p = 0.050), and those without nephritis harboring the hOGG1 1245 CC or CG genotype (p = 0.054). We conclude that the C1245G polymorphism of hOGG1 may be one of the factors that confer the susceptibility to lupus nephritis and modulate the plasma level of 8-OHdG in patients with SLE.     

Unreliable numbers: error and harm induced by bad design can be reduced by better design

Number entry is a ubiquitous activity and is often performed in safety- and mission-critical procedures, such as healthcare, science, finance, aviation and in many other areas. We show that Monte Carlo methods can quickly and easily compare the reliability of different number entry systems. A surprising finding is that many common, widely used systems are defective, and induce unnecessary human error. We show that Monte Carlo methods enable designers to explore the implications of normal and unexpected operator behaviour, and to design systems to be more resilient to use error. We demonstrate novel designs with improved resilience, implying that the common problems identified and the errors they induce are avoidable.