Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Two-photon excitation provides a means of activating chemical or physical processes with high spatial resolution in three dimensions and has made possible the development of three-dimensional fluorescence imaging, optical data storage, and lithographic microfabrication. These applications take advantage of the fact that the two-photon absorption probability depends quadratically on intensity, so under tight-focusing conditions, the absorption is confined at the focus to a volume of order λ3 (where λ is the laser wavelength). Any subsequent process, such as fluorescence or a photoinduced chemical reaction, is also localized in this small volume. Although three-dimensional data storage and microfabrication have been illustrated using two-photon-initiated polymerization of resins incorporating conventional ultraviolet-absorbing initiators, such photopolymer systems exhibit low photosensitivity as the initiators have small two-photon absorption cross-sections (δ). Consequently, this approach requires high laser power, and its widespread use remains impractical. Here we report on a class of π;-conjugated compounds that exhibit large δ (as high as 1, 250 × 10−50 cm4 s per photon) and enhanced two-photon sensitivity relative to ultraviolet initiators. Two-photon excitable resins based on these new initiators have been developed and used to demonstrate a scheme for three-dimensional data storage which permits fluorescent and refractive read-out, and the fabrication of three-dimensional micro-optical and micromechanical structures, including photonic-bandgap-type structures.

Two-photon polymerization initiators for three-dimensional optical data storage and microfabrication

System Identification I

This paper reviews recent advances in chemical synthesis, self-assembly, and potential applications of monodisperse binary FePt nanoparticles. After a brief introduction to nanomagnetism and conventional processes of fabricating FePt nanoparticles, the paper focuses on recent developments in solution-phase syntheses of monodisperse FePt nanoparticles and their self-assembly into nanoparticle superlattices. The paper further outlines the surface, structural, and magnetic properties of the FePt nanoparticles and gives examples of three potential applications in data storage, permanent magnetic nanocomposites, and biomedicine.

Recent Advances in Chemical Synthesis, Self‐Assembly, and Applications of FePt Nanoparticles

https://iris.unipa.it/bitstream/10447/74239/1/Di%20Paola-J%20hzardous%20mater%202012-survey.pdf

A survey of photocatalytic materials for environmental remediation.

Recently, criterion functions based on information theoretic measures (entropy, mutual information, information divergence) have attracted attention and become an emerging area of study in signal processing and system identification domain. This book presents a systematic framework for system identification and information processing, investigating system identification from an information theory point of view. The book is divided into six chapters, which cover the information needed to understand the theory and application of system parameter identification. The authors' research provides a base for the book, but it incorporates the results from the latest international research publications. One of the first books to present system parameter identification with information theoretic criteria so readers can track the latest developmentsContains numerous illustrative examples to help the reader grasp basic methods

System Parameter Identification: Information Criteria and Algorithms

In this paper, a neural network learning adaptive robust controller (NNLARC) is synthesized for an industrial linear motor stage to achieve good tracking performance and excellent disturbance rejection ability. The NNLARC scheme contains parametric adaption part, robust feedback part, and radial basis function (RBF) neural network (NN) part in a parallel structure. The adaptive part and the robust part are designed based on the system dynamics to meet the challenge of parametric variations and uncertain random disturbances. It must be noted that in actual industrial machining situations, precision motion equipment is always disturbed by unknown factors, which usually cannot be described by mathematical models but affect the tracking accuracy significantly. Therefore, the RBF NN part is employed to further approximate and compensate the complicated disturbances with high reconstructing accuracy and fast training rate. The stability of the proposed NNLARC strategy is analyzed and proved through the Lyapunov theorem. Comparative experiments under various external disturbances such as completely unknown disturbance added by polyfoam are conducted on an industrial linear motor stage. The experimental results consistently validate that the proposed NNLARC control strategy can excellently meet the challenge of complicated disturbance in practical applications. The proposed scheme also provides a guidance for control strategy synthesis with both good tracking performance and disturbance rejection.

Neural Network Learning Adaptive Robust Control of an Industrial Linear Motor-Driven Stage With Disturbance Rejection Ability

Magnetically levitated planar motor is a new-generation motion device in modern precision industry, while its advanced motion controller design is still of main concern. In this paper, a learning adaptive robust control (LARC) motion controller is proposed for a magnetically levitated planar motor developed in our laboratory to achieve good tracking performance. The planar motor consists of a Halbach permanent magnetic array as the stator, and a levitated platen containing four groups of three-phase windings as the mover. Based on the Lorentz force law, the mover placed in the magnetic field is subjected to vertical force for levitation and horizonal force for planar motion through dynamics decoupling and current allocation. An LARC control scheme containing adaptive robust control (ARC) term and iterative learning control (ILC) term in a serial structure is then proposed for the magnetically levitated planar motor to achieve high-performance tracking even there exist parametric variations and uncertain disturbances. Comparative experiments between traditional lead, ARC, ILC, and the proposed LARC are carried out on the planar motor to track sinusoidal, point-to-point, and planar circular motions, respectively. The experimental results consistently validate that the proposed LARC control strategy outperforms other controllers much, and possesses not only good transient/steady-state tracking performance but also parametric adaptation ability and uncertain disturbance robustness. The proposed scheme actually provides a practically effective technique for motion control of magnetically levitated planar motors in industrial applications.

Performance-Oriented Precision LARC Tracking Motion Control of a Magnetically Levitated Planar Motor With Comparative Experiments

Linear flat switched flux permanent magnet (SFPM) brushless machines have significant potential for many applications including transportation, automobile and aerospace. Novel E-core and C-core linear SFPM machines are proposed and optimized by individual parameter optimization, when the copper loss and volume is fixed, in this paper by finite element (FE) analysis. Then, the results of the individual parameter optimizations are examined by a global optimization with genetic algorithm. Furthermore, the magnet thickness of all the machines and the middle teeth of the E-core machines are analyzed individually. Finally, the electromagnetic performance of the novel machines are analyzed and compared with the conventional machine. It shows that both E-core and C-core machines require significantly smaller magnet volume since only about half the number of permanent magnets is required. Furthermore, the C-core machines have the largest back-EMF and force density due to the largest slot area, and the E-core machines are suitable for fault-tolerant operations while the 6/11 mover/stator pole E-core SFPM machine has the smallest cogging force.

Optimization and Comparison of Novel E-Core and C-Core Linear Switched Flux PM Machines

This paper develops a data-driven decoupling feedforward control scheme with iterative tuning to meet the challenge of the crosstalk problem in multiple-input multiple-output (MIMO) motion control systems. In contrast to model-based approaches, iterative tuning fully utilizes the available data to address the practical difficulty in obtaining an accurate dynamic model. The MIMO feedforward signal is iteratively updated by minimizing the developed crosstalk criterion. Specifically, to make the optimal problem convex, the MIMO feedforward controller is structuralized with a finite impulse response (FIR) filter and is parameterized by corresponding coefficients. A data-driven unbiased gradient approximation based on the Toeplitz matrix is then developed for updating the parameter vector. Furthermore, to deal with the Hessian inverse problem encountered in the numerical calculation of the update law, a stable inversion method combined with singular value decomposition is employed. The basic characteristics of the proposed scheme, including convergence accuracy and convergence rate, are illustrated through simulation. Finally, the proposed data-driven decoupling control scheme is applied to a developed ultraprecision motion stage, and the results show that the approach can significantly attenuate the servo error caused by the crosstalk problem. This simplicity and accuracy oriented control method without need of dynamic modeling is definitely suitable for industrial applications.

Yu Zhu

Papers

System Parameter Identification: Information Criteria and Algorithms

Neural Network Learning Adaptive Robust Control of an Industrial Linear Motor-Driven Stage With Disturbance Rejection Ability

Performance-Oriented Precision LARC Tracking Motion Control of a Magnetically Levitated Planar Motor With Comparative Experiments

Optimization and Comparison of Novel E-Core and C-Core Linear Switched Flux PM Machines

A Data-Driven Iterative Decoupling Feedforward Control Strategy With Application to an Ultraprecision Motion Stage