Adaptive boundary control for unstable parabolic PDEs—Part II: Estimation-based designs

The certainty equivalence approach to adaptive control is commonly used with two types of identifiers: passivity-based identifiers and swapping identifiers. The “passive” (also known as “observer-based”) approach is the prevalent identification technique in existing results on adaptive control for PDEs but has so far not been used in boundary control problems. The swapping approach, prevalent in finite-dimensional adaptive control is employed here for the first time in adaptive control of PDEs. For a class of unstable parabolic PDEs we prove a separation principle result for both the passive and swapping identifiers combined with the backstepping boundary controllers. The result is applicable in any dimension. For physical reasons we restrict our attention to dimensions no higher than three. The results of the paper are illustrated by simulations.     

Characterization of ultrashort optical pulses with third-harmonic-generation based triple autocorrelation

We present a method to obtain complete information of femtosecond pulses. By measuring triple-optical autocorrelation directly with third-harmonic generation, without spectral information, a temporal pulse shape can be obtained by analytical calculation without direction-of-time ambiguity. Combining the resulting optical pulse shape with its corresponding optical spectrum, the exact phase and color variations in time can all be recovered with a Gerchberg-Saxton algorithm through an iterative calculation with an O(n) complexity.     

The geometric nature of plasticity laws

This paper concerns the plasticity constitutive laws in small strain. In the thermodynamic approach developed here, the key concept is that of internal variables. The differential nature of plasticity law has been pointed out for a long time. If we unite the invariance condition of these laws in a state variable transformation, this involves, ultimately, that the natural mathematic frame of plasticity theory is Differential Geometry. The system state is defined as a point of a differentiable manifold. The state variable are the local coordinates of this point in a chart. The internal stresses are the components of a covariant vector of the cotangent bundle to internal state manifold and the elastic domain is a convex part of cotangent vector space. The plastic yield criteria such as von Mises condition define a Riemannian structure over the manifold. The metric element is identified with the internal dissipation element. Constitutive laws link the covariant derivatives of the thermodynamic stress with the state variable. Hardening modulus splits up in two parts, kinematic hardening and metric hardening. This last is defined by Christoffel connection coefficients. Applied to von Mises isotropic yield condition, the metric hardening is identified with isotropic hardening. The Baltov-Sawczuk model is also analysed. The use of appropriate polar coordinates simplifies significantly the computations. Generalization to a significant category of non-differentiable yield criteria, such as Tresca condition, is considered by introducting a metric tensor family. The adaptation of Drucker's postulate to the proposed model requires the introduction of parallel transport of the internal stress covector. Generally, this transport is different over distinctive paths joining two points. This fact expresses internal state manifold curvature. The Riemann-Christoffel tensor is computed for von Mises, Baltov-Sawczuk and Tresca models.     

Interaction bounds in multivariable control systems

Time-domain limitations due to right half-plane zeros and poles in linear multivariable control systems are studied. Lower bounds on the interaction are derived. They show not only how the location of zeros and poles are critical in multivariable systems, but also how the zero and pole directions influence the performance. The results are illustrated on the quadruple-tank process, which is a new multivariable laboratory process.     

An Innovative Understanding of Metal–Insulator–Metal (MIM)-Capacitor Degradation Under Constant-Current Stress

This paper provides a new understanding of metal-insulator-metal-capacitor-degradation behavior under a wide range of constant-current-stress conditions. It was found that capacitance degrades with stress, but the behavior of the degradation strongly depends on the stress-current density. At high stress levels, the capacitance increases logarithmically as the injection charge increases until dielectric breakdown occurs. At lower stress conditions, the degradation rate is proportional to the stress current and reverses after a certain period of time. A metal-insulator interlayer is observed using cross-sectional transmission-electron-microscopy micrographs, which possibly explains this reversal phenomenon.     

Hardware and software architecture for power quality analysis

This paper presents a novel concept for power quality hardware and software architecture. Software tools related to detection, classification and characterization of power quality events can be integrated into a Distribution Control Center in which the interconnection between different subsystems is being carried out through the Internet allowing flexibility to the system. This system is implemented using wavelet analysis applied in all proposed algorithms. Different power quality events are taken as examples to illustrate the capabilities of the proposed method. The obtained results reveal that the proposed architecture is feasible to be implemented as an integral part of a control center system.The proposed system has the property of fast and accurate detection and classification of any power quality disturbance event and introduces a new PQ index determination that allows characterizing any type of disturbance including the non-periodic signals.     

Extensions of LDA by PCA mixture model and class-wise features

Linear discriminant analysis (LDA) is a data discrimination technique that seeks transformation to maximize the ratio of the between-class scatter and the within-class scatter. While it has been successfully applied to several applications, it has two limitations, both concerning the underfitting problem. First, it fails to discriminate data with complex distributions since all data in each class are assumed to be distributed in the Gaussian manner. Second, it can lose class-wise information, since it produces only one transformation over the entire range of classes. We propose three extensions of LDA to overcome the above problems. The first extension overcomes the first problem by modelling the within-class scatter using a PCA mixture model that can represent more complex distribution. The second extension overcomes the second problem by taking different transformation for each class in order to provide class-wise features. The third extension combines these two modifications by representing each class in terms of the PCA mixture model and taking different transformation for each mixture component. It is shown that all our proposed extensions of LDA outperform LDA concerning classification errors for synthetic data classification, hand-written digit recognition, and alphabet recognition.     

A tolerant approach to reconstruct topology from unorganized trimmed surfaces

Presented in this paper is a topology reconstruction algorithm from a set of unorganized trimmed surfaces. Error-prone small geometric elements are handled to give proper topological information. It gives complete topology to topologically complete models, and it is also tolerant to incomplete models. The proposed algorithm is vertex-based in that clues for topological information are searched from the set of vertices first, not from that of edges.     

A scale model for fitting object shapes from fixed location data

In this paper we present a scale model for fitting an ideal shape to an object. The measurements of the object are taken corresponding to a fixed coordinate system at a set of well-defined locations on the surface of the object. We propose an algorithm to estimate the model parameters and hypothesis tests to make statistical inferences about several possible special cases of the general model. The model is tested with an example that analyzes data on the feet of people in Hong Kong.