Kinematic modeling of Exechon parallel kinematic machine

The studies on PKMs have attracted a great attention to robotics community. By deploying a parallel kinematic structure, a parallel kinematic machine (PKM) is expected to possess the advantages of heavier working load, higher speed, and higher precision. Hundreds of new PKMs have been proposed. However, due to the considerable gaps between the desired and actual performances, the majorities of the developed PKMs were the prototypes in research laboratories and only a few of them have been practically applied for various applications; among the successful PKMs, the Exechon machine tool is recently developed. The Exechon adopts unique over-constrained structure, and it has been improved based on the success of the Tricept parallel kinematic machine. Note that the quantifiable theoretical studies have yet been conducted to validate its superior performances, and its kinematic model is not publically available. In this paper, the kinematic characteristics of this new machine tool is investigated, the concise models of forward and inverse kinematics have been developed. These models can be used to evaluate the performances of an existing Exechon machine tool and to optimize new structures of an Exechon machine to accomplish some specific tasks.

Fast automatic two-stage nonlinear model identification based on the extreme learning machine

It is convenient and effective to solve nonlinear problems with a model that has a linear-in-the-parameters (LITP) structure. However, the nonlinear parameters (e.g. the width of Gaussian function) of each model term needs to be pre-determined either from expert experience or through exhaustive search. An alternative approach is to optimize them by a gradient-based technique (e.g. Newton’s method). Unfortunately, all of these methods still need a lot of computations. Recently, the extreme learning machine (ELM) has shown its advantages in terms of fast learning from data, but the sparsity of the constructed model cannot be guaranteed. This paper proposes a novel algorithm for automatic construction of a nonlinear system model based on the extreme learning machine. This is achieved by effectively integrating the ELM and leave-one-out (LOO) cross validation with our two-stage stepwise construction procedure [1]. The main objective is to improve the compactness and generalization capability of the model constructed by the ELM method. Numerical analysis shows that the proposed algorithm only involves about half of the computation of orthogonal least squares (OLS) based method. Simulation examples are included to confirm the efficacy and superiority of the proposed technique.

Langerin(+) Dermal Dendritic Cells Are Critical for CD8(+) T Cell Activation and IgH gamma-1 Class Switching in Response to Gene Gun Vaccines

The C-type lectin langerin/CD207 was originally discovered as a specific marker for epidermal Langerhans cells (LC). Recently, additional and distinct subsets of langerin(+) dendritic cells (DC) have been identified in lymph nodes and peripheral tissues of mice. Although the role of LC for immune activation or modulation is now being discussed controversially, other langerin(+) DC appear crucial for protective immunity in a growing set of infection and vaccination models. In knock-in mice that express the human diphtheria toxin receptor under control of the langerin promoter, injection of diphtheria toxin ablates LC for several weeks whereas other langerin(+) DC subsets are replenished within just a few days. Thus, by careful timing of diphtheria toxin injections selective states of deficiency in either LC only or all langerin(+) cells can be established. Taking advantage of this system, we found that, unlike selective LC deficiency, ablation of all langerin(+) DC abrogated the activation of IFN-gamma producing and cytolytic CD8(+) T cells after gene gun vaccination. Moreover, we identified migratory langerin(+) dermal DC as the subset that directly activated CD8(+) T cells in lymph nodes. Langerin(+) DC were also critical for IgG1 but not IgG2a Ab induction, suggesting differential polarization of CD4(+) T helper cells by langerin(+) or langerin-negative DC, respectively. In contrast, protein vaccines administered with various adjuvants induced IgG1 independently of langerin(+) DC. Taken together, these findings reflect a highly specialized division of labor between different DC subsets both with respect to Ag encounter as well as downstream processes of immune activation. The Journal of Immunology, 2011, 186: 1377-1383.