Augmented Space Research Articles

Regional input-to-state stabilization of fuzzy state-delayed discrete-time systems with saturating actuators

We address the regional input-to-state stabilization of discrete-time Takagi-Sugeno fuzzy models under delayed state, saturating actuators, and external energy bounded disturbances. We propose a convex method to design state feedback-based non-parallel distributed compensation, ensuring the fuzzy closed-loop system’s regional stability. Moreover, we introduce a geometrical estimate of the region of attraction in an augmented space, allowing a more extensive set of safe initial sequences. Furthermore, the minimization of the ℓ2-gain from the external disturbance to the controlled output ensures closed-loop performance. Also, we provide optimization procedures allowing the maximization of the estimate of the region of attraction or the disturbance energy tolerance. By employing numerical examples, we compare our approach with similar ones found in the literature, showing our proposal’s efficacy, and its outperform concerning the compared methods.

3d-mapping as a synthesis of technology and art: genesis, evolution, and relevance in the modern world

The purpose of the article is to identify the features of the formation of projection mapping as a synthesis of technology and art; consider the specifics of development and trends in the use of 3D-mapping technology in historical retrospective. Methodology. To achieve this goal and solve the problems caused by it, an integrated approach to studying the genesis of video mapping, as well as methods of modern art history, in particular, the historical method (to study the features of using video projections in the space of social art in retrospect) was applied; evolutionary method (to consider the dynamics of the development of video mapping as a synthesis of art and technology); typological method (to identify the features of using 3D-mapping, in accordance with the specifics of modern multimedia technologies) and other. Scientific novelty. The formation and development of 3D Video Mapping, as a unique direction in audiovisual art, is investigated; The features of the use of media technologies in the second half of the XX - beginning of the XXI century are revealed and characterized; the evolution of video mapping at the present stage and the specifics of the use of projection mapping in innovative multimedia technologies of the present, in particular the augmented reality space, are examined. Conclusions. The history of world art shows that innovative artists have always relied on innovative methods, tools, and materials, in order to express their creative vision. At the present stage, 3D Video Mapping operates in accordance with the specifics of the modern space of social art and media landscape. However, digital culture and new media are only typical for the end of the XX - beginning of the XXI century. the background of this art form, which actually has a long cultural tradition. At the present stage, video art can be created and displayed on the screen by adding various interfaces and instruments as part of the production or installations. Video installations of the beginning of the XXI century form the relationship between space (external or internal walls of buildings, stage space, etc.) and materials that are video-louvered and displayed on a specific surface. In accordance with the specifics, space plays an active role in the process of demonstration and recognition of innovative art forms in public space.

O Guarda-Chuva:

Esse artigo apresenta o contexto de criação da instalação artística “Guarda-chuva” (2018), analisa sua relevância dentro do campo da Arte e Tecnologia e a relaciona com os conceitos de paisagem sonora (SCHAFER,1977), espaço aumentado (MANOVICH, 2002) e mensagem estética (ECO, 2013). A instalação consiste em um Guarda-chuva alterado eletronicamente para emitir sons de três paisagens sonoras de Belém, Pará, conforme o interator se desloca com ele pelo espaço expositivo. O projeto da instalação explora concepções diferentes de espaço e propõe uma reflexão sobre a relação da arte, da tecnologia, da natureza e do homem para repensar o espaço da cidade.

Synthesis and comparative analysis of three model-based observers for normal load and friction estimation in intelligent tyre concepts

Recently, the growing trends towards autonomous driving and full automation of terrestrial vehicles have intensified the need for intelligent and interconnected systems to collect and communicate reliable data in real-time. Since the tyres represent the primary sensing system between the vehicle and the ground, it comes natural to designate them to acquire information about their interaction with the road. Intelligent or smart tyre technologies can, indeed, be used to estimate both vehicle’s performance and environmental conditions, leading to handling, NVH and comfort improvements. Inspired by some encouraging results found in previous works, in this investigation we present three model-based estimators to detect the forces acting in the contact patch for an intelligent tyre. The underlying mathematical foundation is the Flexible Ring Tyre Model (FRM), which is able to describe the in-plane dynamics of intelligent tyre systems. More specifically, it represents the tyre treadband by means of a flexible ring restrained at its mean radius by a viscoelastic foundation. Due to its relatively simplicity, the FRTM allows to obtain a closed-form solution for the treadband displacements, accelerations and circumferential strain, whilst being accurate enough to capture all the relevant phenomena concerning the tyre dynamics. The governing Partial Differential Equations (PDEs) of the system are reduced to a set of coupled Ordinary Differential Equations (ODEs) by performing the Fourier series expansion. An optimal observer is then designed based on the Unscented Kalman Filter to account for the nonlinearities which arise when the vertical load acting on the tyre and the friction coefficient are included in the augmented state space representation. The three different technologies considered in this study are: strain, displacement, and acceleration-based intelligent tyres. All of them are shown to be capable of estimating the quantities of interest – the actual state of the system, the normal force and the adhesion coefficient – with high accuracy in very short times. Furthermore, the robustness of the proposed approach is validated by considering a step variation in both the vertical load and the available friction. The performances of the observers are finally compared by using the RSS and MSE indices.

A framework for adaptive MCMC targeting multimodal distributions

We propose a new Monte Carlo method for sampling from multimodal distributions. The idea of this technique is based on splitting the task into two: finding the modes of a target distribution $\pi$ and sampling, given the knowledge of the locations of the modes. The sampling algorithm relies on steps of two types: local ones, preserving the mode; and jumps to regions associated with different modes. Besides, the method learns the optimal parameters of the algorithm, while it runs, without requiring user intervention. Our technique should be considered as a flexible framework, in which the design of moves can follow various strategies known from the broad MCMC literature. In order to design an adaptive scheme that facilitates both local and jump moves, we introduce an auxiliary variable representing each mode, and we define a new target distribution $\tilde{\pi}$ on an augmented state space $\mathcal{X}\times\mathcal{I}$, where $\mathcal{X}$ is the original state space of $\pi$ and $\mathcal{I}$ is the set of the modes. As the algorithm runs and updates its parameters, the target distribution $\tilde{\pi}$ also keeps being modified. This motivates a new class of algorithms, Auxiliary Variable Adaptive MCMC. We prove general ergodic results for the whole class before specialising to the case of our algorithm.

Artificial protein sequences enable recognition of vicinal and distant protein functional relationships.

High divergence in protein sequences makes the detection of distant protein relationships through homology-based approaches challenging. Grouping protein sequences into families, through similarities in either sequence or 3-D structure, facilitates in the improved recognition of protein relationships. In addition, strategically designed protein-like sequences have been shown to bridge distant structural domain families by serving as artificial linkers. In this study, we have augmented a search database of known protein domain families with such designed sequences, with the intention of providing functional clues to domain families of unknown structure. When assessed using representative query sequences from each family, we obtain a success rate of 94% in protein domain families of known structure. Further, we demonstrate that the augmented search space enabled fold recognition for 582 families with no structural information available a priori. Additionally, we were able to provide reliable functional relationships for 610 orphan families. We discuss the application of our method in predicting functional roles through select examples for DUF4922, DUF5131, and DUF5085. Our approach also detects new associations between families that were previously not known to be related, as demonstrated through new sub-groups of the RNA polymerase domain among three distinct RNA viruses. Taken together, designed sequences-augmented search databases direct the detection of meaningful relationships between distant protein families. In turn, they enable fold recognition and offer reliable pointers to potential functional sites that may be probed further through direct mutagenesis studies.

An efficient stacking model with label selection for multi-label classification

Binary relevance (BR) is one of the most popular frameworks in multi-label learning. It constructs a group of binary classifiers, one for each label. BR is a simple and intuitive way to deal with multi-label problem, but fails to utilize label correlations. To deal with this problem, dependent binary relevance (DBR) and other works employ stacking learning paradigm for BR, in which all labels are viewed as additional features. Those works may be suboptimal as each label has its own most related label subset. In this paper, a novel two-layer stacking based approach, which is named a Stacking Model with Label Selection (SMLS), is induced to exploit proper label correlations for improving the performance of DBR. At the first layer, we construct several binary classifiers in the way of BR. At the second layer, we find the specific label subset through label selection for each labels , and expand them into feature space. The final binary classifiers are constructed based on their corresponding augmented feature space. Comprehensive experiments are conducted on a collection of benchmark data sets. Comparison results with the state-of-the-art approaches validate the competitive performance of our proposed approach. Comparison results with DBR shows that our approach is not only more time efficient but also more robust.

Time variant reliability estimation of randomly excited uncertain dynamical systems by combined Markov chain splitting and Girsanov’s transformation

The problem of estimating the time variant reliability of randomly parametered dynamical systems subjected to random process excitations is considered. Two methods, based on Monte Carlo simulations, are proposed to tackle this problem. In both the methods, the target probability of failure is determined based on a two-step approach. In the first step, the failure probability conditional on the random variable vector modelling the system parameter uncertainties is considered. The unconditional probability of failure is determined in the second step, by computing the expectation of the conditional probability with respect to the random system parameters. In the first of the proposed methods, the conditional probability of failure is determined analytically, based on an approximation to the average rate of level crossing of the dynamic response across a specified safe threshold. An augmented space of random variables is subsequently introduced, and the unconditional probability of failure is estimated by using variance-reduced Monte Carlo simulations based on the Markov chain splitting methods. A further improvement is developed in the second method, in which, the conditional failure probability is estimated by using Girsanov’s transformation-based importance sampling, instead of the analytical approximation. Numerical studies on white noise-driven single degree of freedom linear/nonlinear oscillators and a benchmark multi-degree of freedom linear system under non-stationary filtered white noise excitation are presented. The probability of failure estimates obtained using the proposed methods shows reasonable agreement with the estimates from existing Monte Carlo simulation strategies.

On-the-fly ab initio semiclassical evaluation of vibronic spectra at finite temperature.

To compute and analyze vibrationally resolved electronic spectra at zero temperature, we have recently implemented the on-the-fly ab initio extended thawed Gaussian approximation [A. Patoz et al., J. Phys. Chem. Lett. 9, 2367 (2018)], which accounts for anharmonicity, mode-mode coupling, and Herzberg-Teller effects. Here, we generalize this method in order to evaluate spectra at non-zero temperature. In line with thermo-field dynamics, we transform the von Neumann evolution of the coherence component of the density matrix to the Schrödinger evolution of a wavefunction in an augmented space with twice as many degrees of freedom. Due to the efficiency of the extended thawed Gaussian approximation, this increase in the number of coordinates results in nearly no additional computational cost. More specifically, compared to the original, zero-temperature approach, the finite-temperature method requires no additional ab initio electronic structure calculations. At the same time, the new approach allows for a clear distinction among finite-temperature, anharmonicity, and Herzberg-Teller effects on spectra. We show, on a model Morse system, the advantages of the finite-temperature thawed Gaussian approximation over the commonly used global harmonic methods and apply it to evaluate the symmetry-forbidden absorption spectrum of benzene, where all of the aforementioned effects contribute.

Discrete topology optimization in augmented space: integrated element removal for minimum size and mesh sensitivity control

A computational strategy is proposed to circumvent some of the major issues that arise in the classical threshold-based approach to discrete topology optimization. These include the lack of an integrated element removal strategy to prevent the emergence of hair-like elements, the inability to effectively enforce a minimum member size of arbitrary magnitude, and high sensitivity of the final solution to the choice of ground structure. The proposed strategy draws upon the ideas used to arrive at mesh-independent solutions in continuum topology optimization and enables efficient imposition of a minimum size constraint onto the set of non vanishing elements. This is achieved via augmenting the design variables by a set of auxiliary variables, called existence variables, that not only prove very effective in addressing the aforementioned issues but also bring in a set of added benefits such as better convergence and complexity control. 2D and 3D examples from truss-like structures are presented to demonstrate the superiority of the proposed approach over the classical approach to discrete topology optimization.

Fenchel–Nielsen coordinates on the augmented moduli space of anti-de Sitter structures

In this paper we combine our recent work on regular globally hyperbolic maximal anti-de Sitter structures with the classical theory of globally hyperbolic maximal Cauchy-compact anti-de Sitter manifolds in order to define an augmented moduli space. Moreover, we introduce a coordinate system in this space that resembles the complex Fenchel–Nielsen coordinates for hyperbolic quasi-Fuchsian manifolds.

A Nonlinear State Observer for Sensorless Speed Control of IPMSM

This paper presents a nonlinear state observer model for sensorless speed control of an interior permanent magnet synchronous motor (IPMSM) drive. First, an augmented state space model of the IPMSM and the system uncertainty dynamics is obtained in the d − q synchronously rotating reference frame. Then, the proposed nonlinear state estimator based on an extended Luenberger observer is constructed by using rotor position, rotor speed, q-axis stator current, and system uncertainty vector as the state variables. Hence, the estimated system parameter uncertainties can be used to dramatically improve the performance of the speed controller without any additional observer. The stability of the proposed observer at low and high speed is asymptotically guaranteed by Lyapunov stability analysis. In addition, the proposed model is simple to carry out, so it does not require a complex calculation. Simulation results obtained via a Matlab/Simulink software indicate that the proposed observer model can assure an excellent state estimation over a wide speed range even when the rotor speed or load torque changes abruptly as well as the difference in the initial values of real and estimated rotor position.

A Waveform-Feature Dual Branch Acoustic Embedding Network for Emotion Recognition

Research in advancing speech emotion recognition (SER) has attracted a lot of attention due to its critical role for better human behaviors understanding scientifically and comprehensive applications commercially. Conventionally, performing SER highly relies on hand-crafted acoustic features. The recent progress in deep learning has attempted to model emotion directly from raw waveform in an end-to-end learning scheme; however, this particular approach remains to be generally a sub-optimal approach. An alternative direction has been proposed to enhance and augment the knowledge-based acoustic representation with affect-related representation derived directly from raw waveform. Here, we propose a complimentary waveform-feature dual branch learning network, termed as Dual-Complementary Acoustic Embedding Network (DCaEN), to effectively integrate psychoacoustic knowledge and raw waveform embedding within an augmented feature space learning approach. DCaEN contains an acoustic feature embedding network and a raw waveform network, that is learned by integrating negative cosine distance constraint in the loss function. The experiment results show that DCaEN can achieve 59.31\% an 46.73\% unweighted average recall (UAR) in the USC IEMOCAP and the MSP-IMPROV speech emotion databases, which improves the performance compared to modeling either acoustic hand-crafted features or raw waveform only and without this particular loss constraint. Further analysis illustrates a reverse mirroring pattern in the learned latent space demonstrating the complementary nature of DCaEN feature space learning.

Robust Receding Horizon Control with Free Control Moves for Polytopic Parametric Uncertainty Systems Subject to Multiple Input Delays

A robust receding horizon control (RHC) with free control moves is applied to polytopic parametric uncertainty systems with multiple input delays and unstable system matrices. A difficulty in the previous robust RHC work is that the free control moves are unsuitable for the system with input time delay, which is overcome in this paper by the design based on the augmented state. As a result, the synthesis of local control based on augmented feedback is given offline to alleviate the online computation burden. The free control moves before the augmented feedback are the online decision variables, which are solved by minimizing a sequence of nonnegative scalars online. The recursive feasibility is guaranteed by adopting the augmented state space equation. By adjusting the robust positively invariant set, the stability of the closed-loop system is guaranteed. Simulation results demonstrate that the proposed algorithm improves the control performance effectively.

Isometric Embeddings of Subsets of Boundaries of Teichmüller Spaces of Compact Hyperbolic Riemann Surfaces

It is known that every finitely unbranched holomorphic covering π:$$\pi :\tilde{S} \to S$$ of a compact Riemann surface S with genus g ≥ 2 induces an isometric embedding $${{\rm{\Phi}}_{\rm{\pi}}}:Teich\left(S \right) \to Teich\left({\widetilde{S}} \right)$$. By the mutual relations between Strebel rays in Teich(S) and their embeddings in $$Teich\left({\widetilde{S}} \right)$$, we show that the 1st-strata space of the augmented Teichmuller space $$\widehat{Teich}\left(S \right)$$ can be embedded in the augmented Teichmuller space $$\widehat{Teich}\left({\widetilde{S}} \right)$$ isometrically. Furthermore, we show that Φπ induces an isometric embedding from the set Teich(S) B (∞) consisting of Busemann points in the horofunction boundary of Teich(S) into $$Teich{\left({\widetilde{S}} \right)_B}\left(\infty \right)$$ with the detour metric.

Model identification of a Marine robot in presence of IMU-DVL misalignment using TUKF

In today’s world, control and navigation of autonomous underwater vehicles (AUVs) are quite challenging issues. In these fields, obtaining an identified dynamic model of AUV is a vital part. In this paper, a method for parameter estimation of an AUV planar model is proposed, which uses augmented state space technique and Square Root Transformed Unscented Kalman Filter (SR-TUKF) as an estimator. Furthermore, by modeling, misalignment between Inertial Measurement Unit (IMU) and Doppler Velocity Log (DVL) is estimated, simultaneously. Parameter identification is conducted using data of an AUV, equipped with Gyroscope, DVL and Encoder for measuring control inputs, in a planar maneuver. According to the experimental results, the output of the identified model has matched appropriately to the output of sensors in validation maneuvers. With and without considering the estimated misalignment between IMU-DVL, navigation is performed by fusing inertial navigation system (INS) with DVL. The results show an improvement in the end point error with respect to the travel distance by 53 percent in a maneuver with a distance of 6557 meters. Therefore, the proposed method for estimation of a planar model of AUV and misalignment of sensors has a plausible result, experimentally verified by raw data of a simple maneuver.

Augmented Shared Spaces: An Application for Exposure Psychotherapy

The latest advances in computational power of small portable devices enables the development of new forms of Augmented Realities, namely Augmented Reality Shared Spaces. In this work, we will explore this technolo- gy to improve the collaborative work between people in the same physical space allowing them to share and visualise virtual contents. The idea is to have a 3D scene shared between several people where everyone can see and interact with and explore its use in psychotherapy context. These live interactions can also be used to improve the quality of collaborative work by presenting in real time ideas and thoughts of any participant directly on top of the 3D model. In the case of a psychotherapy session typically patients describe their feelings and fears to the therapist. This may be centred on virtual representations of their phobic elements, supporting interactions with them, being those actions and re- sults observable by both in their shared space. Our main goal is to apply and evaluate this concept, specially in exposure-based therapies, where it is im- portant for the therapist to control and manipulate the scene that the patient is exposed to in order to obtain the desired emotional activations.

Incidence bicomodules, Möbius inversion and a Rota formula for infinity adjunctions

In the same way decomposition spaces, also known as unital 2-Segal spaces, have incidence (co)algebras, and certain relative decomposition spaces have incidence (co)modules, we identify the structures that have incidence bi(co)modules: they are certain augmented double Segal spaces subject to some exactness conditions. We establish a M\"obius inversion principle for (co)modules, and a Rota formula for certain more involved structures called M\"obius bicomodule configurations. The most important instance of the latter notion arises as mapping cylinders of infinity adjunctions, or more generally of adjunctions between M\"obius decomposition spaces, in the spirit of Rota's original formula.

Estimation of Forcing Function, Mistuning, and Modal Damping in a Bladed Rotor

Abstract This paper deals with the estimation of forcing function, modal damping, and mistuned modal stiffnesses in a bladed rotor. Previous research on parameter estimation in a mistuned bladed rotor relies on the knowledge of the forcing function as well as the vibration data. This paper presents two novel approaches. The first approach relies on knowledge of both the forcing and vibration data. The parameters are treated as states of the system and an augmented state space model is created. Unscented Kalman filter (UKF) is then used on the steady-state data to estimate the parameters. The second approach eliminates the dependence on forcing data. Both the forcing and parameters are now treated as states of the system to construct an augmented state space model. UKF is then used on transient vibration data for estimation. Numerical results are presented for a simple model of a mistuned bladed rotor which considers a single mode of vibration per blade.

Augmented sample-based approach for efficient evaluation of risk sensitivity with respect to epistemic uncertainty in distribution parameters

This paper proposes a novel augmented sample-based approach for efficient evaluation of risk sensitivity with respect to epistemic uncertainty. Calculation of the risk sensitivity (i.e., Sobol’ indices) with respect to uncertain distribution parameters entails significant computational challenges due to the need to evaluate multi-dimensional integrals, e.g., using Monte Carlo Simulation (MCS). The proposed approach addresses the challenges by defining a joint auxiliary density in the augmented space of both the uncertain distribution parameters and input random variables. It first generates one set of samples from the joint auxiliary density and then based on the corresponding marginal samples estimates the marginal auxiliary densities for the uncertain distribution parameters using kernel density estimation (KDE). Then the KDE estimates are used to efficiently calculate the Sobol’ indices. It relies on only one set of simulations to estimate Sobol’ index for all uncertain distribution parameters without the need to repeat MCS for each distribution parameter. It is especially useful and efficient for evaluation of risk sensitivity for systems with expensive models and large number of inputs and uncertain distribution parameters. The good accuracy and high efficiency of the proposed approach are demonstrated through two illustrative examples and also for different risk definitions.