基于机器视觉的机车齿轮毂全自动双轴拧紧机的设计和应用

设计了基于机器视觉的机车齿轮毂全自动双轴拧紧机,根据拧紧工艺和扭矩控制原理实现对螺栓拧紧力矩的控制;通过丝杠变径机构,实现不同规格齿轮毂螺栓拧紧;利用机器视觉实现螺栓位置的识别以及初始位置的确定;讨论了自动拧紧机控制系统的硬件组成和软件系统配置,并介绍了防错、防漏功能的设计流程。螺栓自动拧紧机可提高装配精度和操作效率,降低工人的劳动强度。     

基于液压活塞式惯容器的车辆悬架性能研究

阐述了液压活塞式惯容器的基本结构、工作原理与承载力大的特点，建立了考虑摩擦力、寄生阻尼力和油液弹性效应的惯容器非线性力学模型。在数控液压伺服激振台上对液压活塞式惯容器进行了力学性能试验，基于试验结果，利用Matlab参数辨识工具箱对非线性力学模型中的参数进行辨识。在此基础上建立了包含液压活塞式惯容器非线性因素的车辆惯容器 弹簧 阻尼器（inerter-spring-damper，简称ISD）悬架动力学模型并进行了仿真分析。结果表明，当考虑液压活塞式惯容器非线性因素时，车辆ISD悬架系统的车身加速度均方根值增加5%，而轮胎动载荷与悬架动行程均方根值均有所减小,降低了车辆的行驶平顺性。     

HPLC-ESI-MS/MS分析黄芪蜜炙前后活性成分差异

建立了HPLC-ESI-MS/MS联用技术分析生黄芪与炙黄芪的成分，并比较两者成分差异。采用Agilent ZORBAX SB-C18色谱柱（4.6 mm×250 mm×5 μm），以0.1%甲酸乙腈溶液-0.1%甲酸水溶液为流动相进行梯度洗脱，流速1 mL/min，检测波长203 nm，柱温25 ℃；使用ESI离子源，分别在正、负离子模式下进行全扫描和二级扫描，质量扫描范围m/z 100~2000。采用该方法鉴定了生黄芪与炙黄芪中的62种成分，并对黄芪蜜炙前后的成分进行了比较，实验结果可为黄芪炮制方法的选择提供参考依据。     

GA-based learning for rule identification in fuzzy neural networks

Employing an effective learning process is a critical topic in designing a fuzzy neural network, especially when expert knowledge is not available. This paper presents a genetic algorithm (GA) based learning approach for a specific type of fuzzy neural network. The proposed learning approach consists of three stages. In the first stage the membership functions of both input and output variables are initialized by determining their centers and widths using a self-organizing algorithm. The second stage employs the proposed GA based learning algorithm to identify the fuzzy rules while the final stage tunes the derived structure and parameters using a back-propagation learning algorithm. The capabilities of the proposed GA-based learning approach are evaluated using a well-examined benchmark example and its effectiveness is analyzed by means of a comparative study with other approaches. The usefulness of the proposed GA-based learning approach is also illustrated in a practical case study where it is used to predict the performance of road traffic control actions. Results from the benchmarking exercise and case study effectively demonstrate the ability of the proposed three stages learning approach to identify relevant fuzzy rules from a training data set with a higher prediction accuracy than alternative approaches.     

Molecular Identification of Dendrobium Species (Orchidaceae) Based on the DNA Barcode ITS2 Region and Its Application for Phylogenetic Study

The over-collection and habitat destruction of natural Dendrobium populations for their commercial medicinal value has led to these plants being under severe threat of extinction. In addition, many Dendrobium plants are similarly shaped and easily confused during the absence of flowering stages. In the present study, we examined the application of the ITS2 region in barcoding and phylogenetic analyses of Dendrobium species (Orchidaceae). For barcoding, ITS2 regions of 43 samples in Dendrobium were amplified. In combination with sequences from GenBank, the sequences were aligned using Clustal W and genetic distances were computed using MEGA V5.1. The success rate of PCR amplification and sequencing was 100%. There was a significant divergence between the inter- and intra-specific genetic distances of ITS2 regions, while the presence of a barcoding gap was obvious. Based on the BLAST1, nearest distance and TaxonGAP methods, our results showed that the ITS2 regions could successfully identify the species of most Dendrobium samples examined; Second, we used ITS2 as a DNA marker to infer phylogenetic relationships of 64 Dendrobium species. The results showed that cluster analysis using the ITS2 region mainly supported the relationship between the species of Dendrobium established by traditional morphological methods and many previous molecular analyses. To sum up, the ITS2 region can not only be used as an efficient barcode to identify Dendrobium species, but also has the potential to contribute to the phylogenetic analysis of the genus Dendrobium.     

Structural health monitoring system based on multi-agent coordination and fusion for large structure

In practical applications of structural health monitoring technology, a large number of distributed sensors are usually adopted to monitor the big dimension structures and different kinds of damage. The monitored structures are usually divided into different sub-structures and monitored by different sensor sets. Under this situation, how to manage the distributed sensor set and fuse different methods to obtain a fast and accurate evaluation result is an important problem to be addressed deeply. In the paper, a multi-agent fusion and coordination system is presented to deal with the damage identification for the strain distribution and joint failure in the large structure. Firstly, the monitoring system is adopted to distributedly monitor two kinds of damages, and it self-judges whether the static load happens in the monitored sub-region, and focuses on the static load on the sub-region boundary to obtain the sensor network information with blackboard model. Then, the improved contract net protocol is used to dynamically distribute the damage evaluation module for monitoring two kinds of damage uninterruptedly. Lastly, a reliable assessment for the whole structure is given by combing various heterogeneous classifiers strengths with voting-based fusion. The proposed multi-agent system is illustrated through a large aerospace aluminum plate structure experiment. The result shows that the method can significantly improve the monitoring performance for the large-scale structure.     

Designing e-Coordinator for improved teams collaboration in graduation projects

The work presented in this paper describes the design for Graduation Project eCoordination System in our department. The proposed eCoordinator provides effective team–team, supervisor–team and coordinator–supervisors–teams collaboration. eCoordinator is an attribute based framework that serves evolving an object oriented software system into agent oriented software system. Nowadays, agent-oriented software development technologies have evolved rapidly; it is emerging as a new paradigm for constructing intelligent more autonomous software systems. Therefore, several methodologies are available and it is difficult to determine the most appropriate methodology for specific project within different domains. This is especially occurs when re-engineering current object oriented software system. The case study is going to be rebuilding a virtual graduation project coordinator in Information Technology Department at King Saud University.     

Nonlinear robust dual-loop control for electro-hydraulic load simulator

This paper investigates on the high performance torque control of electro-hydraulic load simulator (EHLS). In order to suppress actuator׳s motion disturbance, a nonlinear robust dual-loop control scheme is developed, which consists of a open-loop nonlinear velocity feed-forward compensator and a closed-loop nonlinear deterministic robust torque controller. The main function of the open-loop compensator is to decouple actuator׳s active motion disturbance, whereas the torque loop controller aims at guaranteeing the dynamics performance of tracking torque reference. Besides actuator׳s motion disturbance, both the nonlinearity characteristics and friction problem of the EHLS system are taken into consideration in this paper. The effectiveness of the developed method are verified through comparative co-simulations and experiments.     

Nonlinear adaptive filtering using kernel‐based algorithms with dictionary adaptation

Nonlinear adaptive filtering has been extensively studied in the literature, using, for example, Volterra filters or neural networks. Recently, kernel methods have been offering an interesting alternative because they provide a simple extension of linear algorithms to the nonlinear case. The main drawback of online system identification with kernel methods is that the filter complexity increases with time, a limitation resulting from the representer theorem, which states that all past input vectors are required. To overcome this drawback, a particular subset of these input vectors (called dictionary) must be selected to ensure complexity control and good performance. Up to now, all authors considered that, after being introduced into the dictionary, elements stay unchanged even if, because of nonstationarity, they become useless to predict the system output. The objective of this paper is to present an adaptation scheme of dictionary elements, which are considered here as adjustable model parameters, by deriving a gradient‐based method under collinearity constraints. The main interest is to ensure a better tracking performance. To evaluate our approach, dictionary adaptation is introduced into three well‐known kernel‐based adaptive algorithms: kernel recursive least squares, kernel normalized least mean squares, and kernel affine projection. The performance is evaluated on nonlinear adaptive filtering of simulated and real data sets. As confirmed by experiments, our dictionary adaptation scheme allows either complexity reduction or a decrease of the instantaneous quadratic error, or both simultaneously. Copyright © 2015 John Wiley & Sons, Ltd.