Robust tracking control design for uncertain robotic systems with persistent bounded disturbances

In this study, a robust nonlinear L_∞‐gain tracking control design for uncertain robotic systems is proposed under persistent bounded disturbances. The design objective is that the peak of the tracking error in time domain must be as small as possible under persistent bounded disturbances. Since the nonlinear L_∞ ‐gain optimal tracking control cannot be solved directly, the nonlinear L_∞ ‐gain optimal tracking problem is transformed into a nonlinear L_∞ ‐gain tracking problem by given a prescribed disturbance attenuation level for the L_∞ ‐gain tracking performance. To guarantee that the L_∞ ‐gain tracking performance can be achieved for the uncertain robotic systems, a sliding‐mode scheme is introduced to eliminate the effect of the parameter uncertainties. By virtue of the skew‐symmetric property of the robotic systems, sufficient conditions are developed for solving the robust L_∞ ‐gain tracking control problems in terms of an algebraic equation instead of a differential equation. The proposed method is simple and the algebraic equation can be solved analytically. Therefore, the proposed robust L_∞ ‐gain tracking control scheme is suitable for practical control design of uncertain robotic systems. Copyright © 2008 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Parameter determination of support vector machine and feature selection using simulated annealing approach

Support vector machine (SVM) is a novel pattern classification method that is valuable in many applications. Kernel parameter setting in the SVM training process, along with the feature selection, significantly affects classification accuracy. The objective of this study is to obtain the better parameter values while also finding a subset of features that does not degrade the SVM classification accuracy. This study develops a simulated annealing (SA) approach for parameter determination and feature selection in the SVM, termed SA-SVM.To measure the proposed SA-SVM approach, several datasets in UCI machine learning repository are adopted to calculate the classification accuracy rate. The proposed approach was compared with grid search which is a conventional method of performing parameter setting, and various other methods. Experimental results indicate that the classification accuracy rates of the proposed approach exceed those of grid search and other approaches. The SA-SVM is thus useful for parameter determination and feature selection in the SVM.     

Synthesis of bio-functionalized copolymer particles bearing carboxyl groups via a microfluidic device

Monodisperse copolymer particles carrying surface carboxyl groups in the range of 50–200 μm were prepared by in situ UV polymerization of ethyleneglycol dimethacrylate (EGDMA) with acrylic acid (AA) via a microfluidic flow-focusing device (MFFD). The design of the coaxial orifices in the MFFD enables the confinement of the comonomer liquid thread to the central axis of the microchannel, which can avoid the wetting problem of comonomer liquid with the microchannel and can successfully produce monodisperse copolymer microspheres with coefficient of variance below 5%. The effects of concentration of EGDMA and AA on droplet diameters and the distribution of carboxyl group on particle surfaces were examined. It has been found that, increasing the concentration of AA would decrease particle sizes, but increase the distribution of carboxyl group on particle surfaces. Bioconjugation of the carboxylated copolymer particles with the anti-rabbit IgG–Cy3 conjugates was successfully demonstrated. By increasing the concentration of AA accompanied with decreasing the particle sizes, high efficiency of bioconjugation on carboxylated copolymer particles was achieved. The rapid continuous synthesis of carboxylated copolymer particles via a microfluidic device provides a reliable control of particle sizes and composition for massive production in biotechnological applications.     

Large-scale protein identification of human urine proteome by multi-dimensional LC and MS/MS

Urine is a human specimen that is easily obtained non-invasively for clinical diagnosis. We attempted to enhance the resolution of current human urine proteomes and construct a comprehensive reference database for advanced studies, such as the discovery of biomarkers for renal diseases. Multi-dimensional LC-MS/MS was coupled with de novo sequencing and database matching. The proposed approach improved the identification of not only the proteins, but also the post-translational sites of urinary proteins. We identified 165, 200 and 259 unique gene products in the urine proteomes from males, females and pregnant women, respectively. When all of the results were combined and the redundancies removed, a total of 1095 distinct peptides were identified. Of these, 1016 peptides were associated with 334 unique gene products. In this study, over 100 gene products, including some disease-related proteins, were detected in urine for the first time by proteomic approaches. Various proteins with novel post-translational hydroxylation were identified using the MASCOT program and de novo sequencing. All proteins with peptide information were summarized into a comprehensive urine protein database. We believe that this comprehensive urine proteome database will assist in the identification of urinary proteins/polypeptides whose spectra are difficult to interpret in the discovery of urinary biomarkers.     

Noise of He-Cd laser and its suppression

Linear relations have been observed between the reciprocal of the noise ratio and the reciprocal of the output power of the He-Cd laser as well as between the noise ratio and the net gain within the laser cavity. By a feedback method with an acoustooptic modulator in the laser cavity at the Brewster angle for low optical loss, a stable laser with high power output and low noise has been obtained. The noise ratio and the output power are 0.8% and 33 mW, respectively, at 4416 A     

Study on shop floor control system in semiconductor fabrication by self-organizing map-based intelligent multi-controller

To confirm semiconductor wafer fabrication (FAB) operating characteristics, the scheduling decisions of shop floor control systems (SFCS) must develop a multiple scheduling rules (MSRs) approach in FABs. However, if a classical machine learning approach is used, an SFCS in FABs knowledge base (KB) can be developed by using the appropriate MSR strategy (this method is called an intelligent multi-controller in this study) as obtained from training examples. A classical machine learning approach main disadvantage is that the classes (scheduling decision variables) to which training examples are assigned must be pre-defined. This process becomes an intolerably time-consuming task. In addition, although the best decision rule can be determined for each scheduling decision variable, the combination of all the decision rules may not simultaneously satisfy the global objective function. To address these issues, this study proposes an intelligent multi-controller that incorporates three main mechanisms: (1) a simulation-based training example generation mechanism, (2) a data preprocessing mechanism, and (3) a self-organizing map (SOM)-based MSRs selection mechanism. These mechanisms can overcome the long training time problem of the classical machine learning approach in the training examples generation phase. Under various production performance criteria over a long period, the proposed intelligent multi-controller approach yields better system performance than fixed decision scheduling rules for each of the decision variables at the start of each production interval.     

Deformation Behavior of Duplex Zircaloy-4-Oxygen Alloys

The mechanical properties of two-phase Zircaloy-4-oxygen alloys at thermal equilibrium have been determined. The strength of these alloys depends to a large extent on their microstructure. The strengthening behavior for alloys having isolateda grains in the softer β matrix is similar to the dispersed particle strengthening. The yield strength of these alloys is found to obey the Petch relationship, (MPa), whereλ _β is the mean free path of β phase inμm. As the volume fraction ofa phase increases, its aspect ratio also increases. This allows more effective load transfer from the matrix to the hardera phase. It has been shown that the strength of these alloys obeys a modified rule of mixtures. The alloys having equiaxeda grains in the β matrix show large strain rate sensitivities at low strain rate. The deformation behavior is interpreted in terms of dislocation slip in the β matrix and diffusion assisted climb near the interphase boundary.     

Polymeric carbon nanocomposites from multiwalled carbon nanotubes functionalized with segmented polyurethane

Multiwalled carbon nanotubes (MWNT) were functionalized with segmented polyurethanes (PU) by the “grafting to” approach. Raman and X‐ray photoelectron spectroscopy (XPS) spectra show that the sidewalls of MWNTs have been functionalized with acid treatment, and the amount of COOH increases with increasing acid treatment time. FTIR and X‐ray diffraction (XRD) spectra confirm that PU is covalently attached to the sidewalls of MWNTs by esterification reaction. Similar to the parent PU, the functionalized carbon nanotube samples are soluble in highly polar solvents, such as dimethyl sulfoxide (DMSO) and N,N‐dimethylformamide (DMF). The functionalized acid amount and the grafted PU amount were determined by thermogravimetric analyses (TGA). Comparative studies, based on SEM images between the PU‐functionalized and chemically defunctionalized MWNT samples, also reveal the covalent coating character. Dynamic mechanical analysis (DMA) of nanocomposite films prepared from PU and PU‐functionalized MWNTs show enhanced mechanical properties and increased soft segment T_g. Tensile properties indicate that PU‐functionalized MWNTs are effective reinforcing fillers for the polyurethane matrix. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci 2007     

Lane Detection With Moving Vehicles in the Traffic Scenes

A lane-detection method aimed at handling moving vehicles in the traffic scenes is proposed in this brief. First, lane marks are extracted based on color information. The extraction of lane-mark colors is designed in a way that is not affected by illumination changes and the proportion of space that vehicles on the road occupy. Next, for vehicles that have the same colors as the lane marks, we utilize size, shape, and motion information to distinguish them from the real lane marks. The mechanism effectively eliminates the influence of passing vehicles when performing lane detection. Finally, pixels in the extracted lane-mark mask are accumulated to find the boundary lines of the lane. The proposed algorithm is able to robustly find the left and right boundary lines of the lane and is not affected by the passing traffic. Experimental results show that the proposed method works well on marked roads in various lighting conditions