An online fault tolerant actor-critic neuro-control for a class of nonlinear systems using neural network HJB approach

In this paper, we propose an actor-critic neuro-control for a class of continuous-time nonlinear systems under nonlinear abrupt faults, which is combined with an adaptive fault diagnosis observer (AFDO). Together with its estimation laws, an AFDO scheme, which estimates the faults in real time, is designed based on Lyapunov analysis. Then, based on the designed AFDO, a fault tolerant actor- critic control scheme is proposed where the critic neural network (NN) is used to approximate the value function and the actor NN updates the fault tolerant policy based on the approximated value function in the critic NN. The weight update laws for critic NN and actor NN are designed using the gradient descent method. By Lyapunov analysis, we prove the uniform ultimately boundedness (UUB) of all the states, their estimation errors, and NN weights of the fault tolerant system under the unpredictable faults. Finally, we verify the effectiveness of the proposed method through numerical simulations.     

A novel part-based approach to mean-shift algorithm for visual tracking

Visual tracking is one of the most important problems considered in computer vision. To improve the performance of the visual tracking, a part-based approach will be a good solution. In this paper, a novel method of visual tracking algorithm named part-based mean-shift (PBMS) algorithm is presented. In the proposed PBMS, unlike the standard mean-shift (MS), the target object is divided into multiple parts and the target is tracked by tracking each individual part and combining the results. For the part-based visual tracking, the objective function in the MS is modified such that the target object is represented as a combination of the parts and iterative optimization solution is presented. Further, the proposed PBMS provides a systematic and analytic way to determine the scale of the bounding box for the target from the perspective of the objective function optimization. Simulation is conducted with several benchmark problems and the result shows that the proposed PBMS outperforms the standard MS.

     

Note: High speed optical profiler based on a phase-shifting technique using frequency-scanning lasers

We present a high speed optical profiler (HSOP) using frequency-scanning lasers for three-dimensional profile measurements of microscopic structures. To improve upon previous techniques for implementing the HSOP, we developed frequency-scanning lasers and a compact microscopic interferometer. The controller of the HSOP was also modified to generate proper phase-shifting steps. For measurements of step height specimens, the HSOP showed results comparable with a commercial optical profiler, even with much higher measurement speeds (up to 30 Hz). The typical repeatability of step height measurement was less than 1 nm. We also present measurements of microscopic structures to verify the HSOP's ability to perform high speed inline inspection for the semiconductor and flat-panel display industries.     

Ultrastructural investigation of intact orbital implant surfaces using atomic force microscopy

This study examined the surface nanostructures of three orbital implants: nonporous poly(methyl methacrylate) (PMMA), porous aluminum oxide and porous polyethylene. The morphological characteristics of the orbital implants surfaces were observed by atomic force microscopy (AFM). The AFM topography, phase shift and deflection images of the intact implant samples were obtained. The surface of the nonporous PMMA implant showed severe scratches and debris. The surface of the aluminum oxide implant showed a porous structure with varying densities and sizes. The PMMA implant showed nodule nanostructures, 215.56 ± 52.34 nm in size, and the aluminum oxide implant showed crystal structures, 730.22 ± 341.02 nm in size. The nonporous PMMA implant showed the lowest roughness compared with other implant biomaterials, followed by the porous aluminum oxide implant. The porous polyethylene implant showed the highest roughness and severe surface irregularities. Overall, the surface roughness of orbital implants might be associated with the rate of complications and cell adhesion. SCANNING 33: 211–221, 2011. © 2011 Wiley Periodicals, Inc.     

A survey on enhanced subspace clustering

Subspace clustering finds sets of objects that are homogeneous in subspaces of high-dimensional datasets, and has been successfully applied in many domains. In recent years, a new breed of subspace clustering algorithms, which we denote as enhanced subspace clustering algorithms, have been proposed to (1) handle the increasing abundance and complexity of data and to (2) improve the clustering results. In this survey, we present these enhanced approaches to subspace clustering by discussing the problems they are solving, their cluster definitions and algorithms. Besides enhanced subspace clustering, we also present the basic subspace clustering and the related works in high-dimensional clustering.     

Optimal scheduling of a communication channel for the centralized control of a platoon of vehicles

This paper proposes an off-line optimal channel scheduling algorithm for an interconnected vehicle control system. The optimal sequence obtained through the scheduling algorithm provides a switching controller with the best switching order if the controller can access only one plant at each time slot over the shared communication medium. Interconnected systems require the string stability as well as the dynamic stability of each unit. This paper shows that integrating the simple string stable control law with the approximately optimal linear-quadratic (LQ) tracker gives the optimal channel scheduling algorithm.     

Harmony search-based hidden Markov model optimization for online classification of single trial eegs during motor imagery tasks

This paper presents an improved method based on single trial EEG data for the online classification of motor imagery tasks for brain-computer interface (BCI) applications. The ultimate goal of this research is the development of a novel classification method that can be used to control an interactive robot agent platform via a BCI system. The proposed classification process is an adaptive learning method based on an optimization process of the hidden Markov model (HMM), which is, in turn, based on meta-heuristic algorithms. We utilize an optimized strategy for the HMM in the training phase of time-series EEG data during motor imagery-related mental tasks. However, this process raises important issues of model interpretation and complexity control. With these issues in mind, we explore the possibility of using a harmony search algorithm that is flexible and thus allows the elimination of tedious parameter assignment efforts to optimize the HMM parameter configuration. In this paper, we illustrate a sequential data analysis simulation, and we evaluate the optimized HMM. The performance results of the proposed BCI experiment show that the optimized HMM classifier is more capable of classifying EEG datasets than ordinary HMM during motor imagery tasks.     

牛奶加工过程中的压力监测

雀巢公司位于瑞士柯诺芬根的研发中心开发奶制品。并设计制造工艺。而监测换热器的压力,对于这家研发中心来说具有特殊的挑战性。     

Hermite interpolation with simple planar PH curves by speed reparametrization

We introduce a new method of solving C¹ Hermite interpolation problems, which makes it possible to use a wider range of PH curves with potentially better shapes. By characterizing PH curves by roots of their hodographs in the complex representation, we introduce PH curves of type K(t−c)²ⁿ⁺¹+d. Next, we introduce a speed reparametrization. Finally, we show that, for C¹ Hermite data, we can use PH curves of type K(t−c)²ⁿ⁺¹+d or strongly regular PH quintics satisfying the G¹ reduction of C¹ data, and use these curves to solve the original C¹ Hermite interpolation problem.     

Inventive Tool Use to Comprehend Big Code

Software developers often need to understand a large body of unfamiliar code with little or no documentation, no experts to consult, and little time to do it. A post appeared in January 2008 on Slashdot, a technology news Web site, asking for tools and techniques that could help. This article analyzes 301 often passionate and sometimes articulate responses to this query, including the themes and the associated tool recommendations. The most common suggestions were to use a code navigation tool, use a design recovery tool, use a debugger to step through the code, create a runtime trace, use problem-based learning, ask people for help, study the code from top down, and print out all the code. This analysis presents an intriguing snapshot of how software developers in industry go about comprehending big code.