Effect of the Geometric Parameters of a Layered Powder Composite on Its Magnetic Properties

The effect of thickness of magnetic conductor and number of electrically insulating layers on the magnetic properties in an alternating field of a layered powder-metallurgy soft-magnetic material based on iron was investigated. It was determined that increasing the thickness of the magnetic conductor and frequency of the alternating current deteriorated the magnetic properties of the material. Creation of a layered structure in which ferromagnetic layers alternate with electrically insulating layers decreased specific magnetic losses.     

SOME NEW ASPECT OF LYAPUNOV TYPE THEOREMS FOR STOCHASTIC DIFFERENCE EQUATIONS WITH CONTINUOUS TIME

Some new Lyapunov type theorems for stochastic difference equations with continuous time are proven. It is shown that these theorems simplify an application of Lyapunov functionals construction method.     

IR spectroscopic study of Pt/Kl zeolites using adsorption of CO as a molecular probe

FTIR spectra of CO adsorbed on Pt/KL catalysts show that the relative band intensities and the total dispersion markedly depend on the catalyst preparation method (ion exchange, incipient wetness impregnation or co-impregnation with KCl). The CO stretching frequency of the dominant band for linear CO is shifted to higher wavenumbers, parallel with the proton concentration in the reduced catalyst, which is derived independently from the intensity of the IR bands of the OH groups. The results are in accordance with the model that electron-deficient platinum particles are platinum-proton adducts.On leave from: N.D. Zelinsky Institute of Organic Chemistry, Academy of Sciences, Moscow, USSR.     

On stability of second-order quasi-polynomials with a single delay

In this note the stability of a second-order quasi-polynomial with a single delay is studied. Although there is a vast literature on this problem, most available solutions are limited to some particular cases. Moreover, some published results on this subject appear to contain imprecise, or even wrong, conditions. The purpose of this note is to show that by accurate application of known theories, a complete explicit characterization of stability regions can be derived in a most general case. As a byproduct of the proposed analysis, we show that in the high-order case the quasi-polynomial is delay-independent unstable whenever its delay-free version has an odd number of unstable roots (or, equivalently, a negative static gain).     

Human attributes from 3D pose tracking

It is well known that biological motion conveys a wealth of socially meaningful information. From even a brief exposure, biological motion cues enable the recognition of familiar people, and the inference of attributes such as gender, age, mental state, actions and intentions. In this paper we show that from the output of a video-based 3D human tracking algorithm we can infer physical attributes (e.g., gender and weight) and aspects of mental state (e.g., happiness or sadness). In particular, with 3D articulated tracking we avoid the need for view-based models, specific camera viewpoints, and constrained domains. The task is useful for man–machine communication, and it provides a natural benchmark for evaluating the performance of 3D pose tracking methods (vs. conventional Euclidean joint error metrics). We show results on a large corpus of motion capture data and on the output of a simple 3D pose tracker applied to videos of people walking.     

Domain Adaptation for Structured Regression

Discriminative regression models have proved effective for many vision applications (here we focus on 3D full-body and head pose estimation from image and depth data). However, dataset bias is common and is able to significantly degrade the performance of a trained model on target test sets. As we show, covariate shift, a form of unsupervised domain adaptation (USDA), can be used to address certain biases in this setting, but is unable to deal with more severe structural biases in the data. We propose an effective and efficient semi-supervised domain adaptation (SSDA) approach for addressing such more severe biases in the data. Proposed SSDA is a generalization of USDA, that is able to effectively leverage labeled data in the target domain when available. Our method amounts to projecting input features into a higher dimensional space (by construction well suited for domain adaptation) and estimating weights for the training samples based on the ratio of test and train marginals in that space. The resulting augmented weighted samples can then be used to learn a model of choice, alleviating the problems of bias in the data; as an example, we introduce SSDA twin Gaussian process regression (SSDA-TGP) model. With this model we also address the issue of data sharing, where we are able to leverage samples from certain activities (e.g., walking, jogging) to improve predictive performance on very different activities (e.g., boxing). In addition, we analyze the relationship between domain similarity and effectiveness of proposed USDA versus SSDA methods. Moreover, we propose a computationally efficient alternative to TGP (Bo and Sminchisescu 2010), and it’s variants, called the direct TGP. We show that our model outperforms a number of baselines, on two public datasets: HumanEva and ETH Face Pose Range Image Dataset. We can also achieve 8–15 times speedup in computation time, over the traditional formulation of TGP, using the proposed direct formulation, with little to no loss in performance.     

Shaping stable periodic motions of inertia wheel pendulum: theory and experiment

We consider an underactuated two‐link robot called the inertia wheel pendulum. The system consists of a free planar rotational pendulum and a symmetric disk attached to its end, which is directly controlled by a DC‐motor. The goal is to create stable oscillations of the pendulum, which is not directly actuated. We exploit a recently proposed feedback‐control design strategy based on motion planning via virtual holonomic constraints. This strategy is shown to be useful for design of regulators for achieving orbitally exponentially stable oscillatory motions. The main contribution is a step‐by‐step procedure on how to achieve oscillations with pre‐specified amplitude from a given range and an arbitrary independently chosen period. The theoretical results are verified via experiments with a real hardware setup. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Formalizing process-based risk with Value-Focused Process Engineering

Following calls to advance the integration of risk and business process modeling paradigms, this paper formalizes the process of incorporating risk into business process models through the principles of Value-Focused Process Engineering (VFPE). In doing so, the paper aims to extend the existing VFPE modeling notation to reflect a set of necessary constructs required to adequately represent risk in goal-oriented business-process models. The extended set of constructs is proposed to support a formal systems view of process-based risk. Process-based risk is formalized on the one hand, as a product of complex interactions between activity-based elements, and on the other hand, as a natural component of the value creation mechanism of an elementary function or a complex process. The proposed risk-aware VFPE formalism also formulates rules for decomposing risk in process models according to the organizational values, thereby enabling better risk visibility, reducing process complexity, and ensuring continuity of business processes.     

SDP-based approximation of stabilising solutions for periodic matrix Riccati differential equations

Numerically finding stabilising feedback control laws for linear systems of periodic differential equations is a nontrivial task with no known reliable solutions. The most successful method requires solving matrix differential Riccati equations with periodic coefficients. All previously proposed techniques for solving such equations involve numerical integration of unstable differential equations and consequently fail whenever the period is too large or the coefficients vary too much. Here, a new method for numerical computation of stabilising solutions for matrix differential Riccati equations with periodic coefficients is proposed. Our approach does not involve numerical solution of any differential equations. The approximation for a stabilising solution is found in the form of a trigonometric polynomial, matrix coefficients of which are found solving a specially constructed finite-dimensional semidefinite programming (SDP) problem. This problem is obtained using maximality property of the stabilising solution of the Riccati equation for the associated Riccati inequality and sampling technique. Our previously published numerical comparisons with other methods shows that for a class of problems only this technique provides a working solution. Asymptotic convergence of the computed approximations to the stabilising solution is proved below under the assumption that certain combinations of the key parameters are sufficiently large. Although the rate of convergence is not analysed, it appeared to be exponential in our numerical studies.