Augmented Space Research Articles

2B3-1 Visual search in augmented reality space: Comparison of monocular and binocular presentation

2B3-1 Visual search in augmented reality space: Comparison of monocular and binocular presentation

Optimization of damped outrigger systems subject to stochastic excitation

High-rise buildings have attracted much attention with the continuing growth of world population and increasing urbanization. As buildings become taller, suppressing excessive vibration due to lateral excitation becomes more challenging. One of the promising solutions that has been proposed is the damped outrigger system, where dampers are vertically installed between outriggers attached to the core and perimeter columns, leading to a significant increment of structural damping. To gain insight into the design of damped outrigger systems subject to seismic excitation, a stochastic optimization procedure is proposed. The seismic excitation is modeled as a filtered white noise, following the Kanai-Tajimi spectrum. To better represent the interstory drift, the core of the building is modeled using a Timoshenko beam, and a Kelvin model of the damper is considered. The equations of motion of the structure-damper-excitation system are combined into an augmented state space representation. The variance of the responses of the structure are calculated via solution of the Lyapunov equation. A genetic algorithm is used to determine the damper placement and associated parameters so as to minimize objective function, which is in terms of the structural responses. A Pareto optimal front is used to examine the tradeoffs with respect to minimizing interstory drift, floor accelerations, base shear, and base overturning moment. The results demonstrate the efficacy of the proposed approach, reveal the range of application for such damped outrigger systems, and provide insight into the system design in the presence of seismic excitation.

Fuzzy Reduced-Order Compensator-Based Stabilization for Interconnected Descriptor Systems via Integral Sliding Modes

This paper investigates the integral sliding mode control problem of T–S fuzzy interconnected descriptor system based on a reduced-order compensator. A canonical equivalent form for T–S fuzzy interconnected descriptor system is first introduced to facilitate the reduced-order compensator design. In terms of the measurable output variables of the T–S fuzzy interconnected descriptor system and the state variables of the proposed reduced-order compensator, an integral sliding mode control scheme is then developed for the T–S fuzzy interconnected descriptor system. The sliding motion is defined in the augmented space formed by the state variables of the original T–S fuzzy interconnected descriptor system and the resulting error system. It is shown that the designing parameters in the switching function can be simultaneously solved and the original T–S fuzzy interconnected descriptor system has no requirement to be relative degree one. Since the estimate error is bounded by an auxiliary dynamical system, when ideal sliding mode occurs, the resulting closed-loop system is asymptotically stable rather than uniformly ultimately bounded. Finally, a double-inverted pendulum system and a ball and beam system are numerically simulated to demonstrate the effectiveness and merits of the method proposed.

Time-variant reliability analysis based on high dimensional model representation

Time-variant reliability analysis aims at estimating the probability that an engineering system successfully performs intended missions over a certain period of time under various sources of uncertainty. In order to perform the time-variant reliability analysis efficiently, this paper presents a high dimensional model representation (HDMR) model combined with an active learning strategy to estimate the failure probability of dynamic problem. Firstly, the HDMR meta-model is established in the augmented input space (random variables and time) based on Gaussian process regression technique. Then, a reliability analysis approach incorporating epistemic uncertainty is proposed, and a learning function is introduced to update the experimental design sequentially. Finally, the Monte Carlo simulation method is applied for time-variant failure probability assessment based on the well-developed HDMR meta-model. Two engineering applications are used to demonstrate the effectiveness of the proposed method for time-variant reliability analysis.

Application of the Second-Order Comprehensive Adjoint Sensitivity Analysis Methodology to Compute First- and Second-Order Sensitivities of Flux Functionals in a Multiplying System with Source

This work presents an application of the Second-Order Adjoint Sensitivity Analysis Methodology (2nd-ASAM) to the neutron transport Boltzmann equation that models a multiplying subcritical system comprising a nonfission neutron source to compute efficiently and exactly all of the first- and second-order functional derivatives (sensitivities) of a detector’s response to all of the model’s parameters, including isotopic number densities, microscopic cross sections, fission spectrum, sources, and detector response function. As indicated by the general theoretical considerations underlying the 2nd-ASAM, the number of computations required to obtain the first and second orders increases linearly in augmented Hilbert spaces as opposed to increasing exponentially in the original Hilbert space. The results presented in this work are currently being implemented in several production-oriented three-dimensional neutron transport code systems for analyzing specific subcritical systems.

A non-linear estimation and model predictive control algorithm based on ant colony optimization

A new heuristic controller, called the continuous ant colony controller, is proposed for non-linear stochastic Gaussian/non-Gaussian systems. The new controller formulates the state estimation and the model predictive control problems as a single stochastic dynamic optimization problem, and utilizes a colony of virtual ants to find and track the best estimated state and the best control signal. For this purpose, an augmented state space is defined. An integrated cost function is also defined to evaluate the points of the augmented state space, explored by the ants. This function minimizes simultaneously the state estimation error, tracking error, control effort and control smoothness. Ants search the augmented state space dynamically in a similar scheme to the optimization algorithm, known as the continuous ant colony system. Performance of the new model predictive controller is evaluated for three non-linear problems. The problems are a non-linear continuous stirred tank reactor, a non-linear cart and spring system, and the attitude control of a non-linear quadrotor. The results verify successful performance of the proposed algorithm from both estimation and control points of view.

Second-order sensitivities of a general functional of the forward and adjoint fluxes in a multiplying nuclear system with source

This work presents an application of the Second-Order Adjoint Sensitivity Analysis Methodology (2nd-ASAM) to compute efficiently and exactly all of the 1st- and 2nd-order functional derivatives (“sensitivities”) of a generic scalar-valued response to parameters for a multiplying subcritical system comprising a non-fission neutron source. The response is defined to be a nonlinear functional of the forward and adjoint particle fluxes while the system parameters include isotopic number densities, microscopic cross sections, fission spectrum, sources, and detector response functions. As indicated by the general theoretical considerations underlying the 2nd-ASAM, the number of computations required to obtain the 1st- and 2nd-order increases linearly in augmented Hilbert spaces as opposed to increasing exponentially in the original Hilbert space. This unique feature provides the fundamental reason for the unmatched efficiency of the 2nd-ASAM for computing exactly all of the 1st- and 2nd-order responses sensitivities to model parameters. The results presented in this work are currently being implemented in several production-oriented three dimensional neutron transport code systems for analyzing specific subcritical systems.

“I Am Sitting Here Right Now with You”

This article investigates the uncanny logics of space, time, and voice in augmented reality by theorizing and illustrating how augmented space can serve as a formal medium for writing. Critical analyses of the audio and video walks of artists Janet Cardiff and George Bures Miller, and of student work on a “Writing in Augmented Space” assignment, demonstrate how the literate and literary possibilities afforded by these logics, which first appear as difficulties, identify techniques of an emergent genre of writing in the English studies curriculum.

Robust Fuzzy Predictive Control For Discrete-Time Systems with Interval Time-varying Delays and Unknown Disturbances

A robust fuzzy predictive control (RFPC) based on Takagi–Sugeno (T-S) fuzzy model is proposed for systems with uncertainties, time-varying delays, unknown disturbances, as well as strong nonlinearity. First, the T-S fuzzy model is built by a number of linear submodels and nonlinear membership functions. Then, by introducing the output tracking error to this fuzzy model, the novel augmented state space model is presented to independently regulate the process state variables and output tracking error. The control law of the proposed RFPC is further designed based on this extended model, which can guarantee the process state to be fast convergent and make the process output track the set-point well. Moreover, it increases the ability of adjustment for the proposed controller. Utilizing Lyapunov–Krasovskii method, optimized control theory, and control method, the stable sufficient conditions are given for the designed control law to make sure the asymptotical stability of the nonlinear uncertain system with the time-varying delay and unknown disturbances. The gains of the controller can be obtained by solving these stabilized conditions in form of linear matrix inequality constraints. At last, a case study of continuous stirred tank reactor manifests that the proposed RFPC method can bear a larger range of time delay, overcome the uncertainties and unknown disturbances well, and have better tracking performance.

Steady-State Performance Improvement for LQR-Based PMSM Drives

Considering the periodic disturbances caused by nonideal factors like dead time effect, permanent magnet flux harmonics, and current sampling errors in the permanent magnet synchronous motor (PMSM) drives, this paper establishes an augmented state space model (SSM), which contains information of periodic disturbances, and then more accurate description for the PMSM system is achieved. Meanwhile, since the three augmented state variables related to periodic disturbances in the augmented SSM cannot be measured directly, a novel periodic disturbance observer (PDO) depending on the Kalman filter is designed to estimate these state variables. On the basis of the augmented SSM and PDO, a linear-quadratic-regulator (LQR)-based state feedback controller is established, which can achieve a successful suppression of periodic disturbances and at the same time ensure a more smooth speed output. Finally, the proposed PDO-LQR is tested in a 6 kW PMSM, and the experimental results show that the proposed controller can efficiently improve the steady-state performance of the PMSM system without sacrificing dynamic-state performance.

Unfolding and crossing the augmented space: Metaleptic tools and processes within graphic narrative installations

Abstract Based on the notion of interactional metalepsis as described by Karin Kukkonen, Astrid Ensslin and Alice Bell, this article aims to contribute towards the study of metalepsis in terms of the specificity of installations. Particularly, installations of graphic narratives comics, which, I argue, propose metaleptic effects implied and generated by specific navigational devices and characteristics that suggest an ongoing co-influence between spatial (re)configurations and narrative. In their turn, these (re)configurations occur as a consequence of the conjunction of the actual space with the composition of the installation, which are, at the same time, influenced by, and an influence for, the narrative focalization through the textual, virtual and tridimensional spaces. In this sense, the analysis of Daniel Goodbrey’s The Archivist and Dave McKean’s The Rut will take into account the ways in which the expository and narrative routes inscribe themselves in the physical space of the gallery and the ways in which spaces relate with characters’ and readers’ behaviours. Therefore, by relating the notion of interactional metalepsis with spatial and discursive dynamics, the resulting article will also question the architectural and compositional characteristics not only as constraints but also as promoters of a dynamic context that might function as a kind of interference in the overall narrative. This way, I argue that it is this simultaneous intertwining and interference between space and narrative that, in the end, comply with the specificity of the media and calls for an ‘augmented reading’, which ends up absorbing transgressions as means of expansion, hence contributing towards a blurring of narrative, aesthetic and conceptual borders.

Global sensitivity analysis in the context of imprecise probabilities (p-boxes) using sparse polynomial chaos expansions

Global sensitivity analysis aims at determining which uncertain input parameters of a computational model primarily drives the output quantities of interest. Sobol’ indices are now routinely applied in this context when the input parameters are modelled by classical probability theory using random variables. In many practical applications however, input parameters are affected by both aleatory and epistemic uncertainty. In this respect, imprecise probability theory have become popular in the last decade. In this paper, we consider that the uncertain input parameters are modelled by parametric probability boxes (p-boxes). We propose interval-valued (so-called imprecise) Sobol’ indices as an extension of their classical definition. An original algorithm based on the concepts of augmented space, isoprobabilistic transforms and sparse polynomial chaos expansions is devised to allow for the computation of these imprecise Sobol’ indices at extremely low cost. In particular, phantoms points are introduced to build an experimental design in the augmented space (necessary for the calibration of the sparse PCE) which leads to a smart reuse of runs of the original computational model. The approach is applied to three analytical and engineering examples which illustrate the efficiency of the algorithms against brute-force double-loop Monte Carlo simulation.

A MULTI THREADED FEATURE EXTRACTION TOOL FOR SAR IMAGES USING OPEN SOURCE SOFTWARE LIBRARIES

Abstract. In this paper, we propose a software architecture for a feature extraction tool which is suitable for automatic extraction of sparse features from large remote sensing data capable of using higher order algorithms (computational complexity greater than O(n)). Many features like roads, water bodies, buildings etc in remote-sensing data are sparse in nature. Remote-sensing deals with a large volume of data usually not manageable fully in the primary memory of typical workstations. For these reason algorithms with higher computational complexity is not used for feature extraction from remote sensing images. A good number of remote sensing applications algorithms are based on formulating a representative index typically using a kernel function which is having linear or less computational complexity (less than or equal to O(n)). This approach makes it possible to complete the operation in deterministic time and memory.Feature extraction from Synthetic Aparture Radar (SAR) images requires more computationally intensive algorithm due to less spectral information and high noise. Higher Order algorithms like Fast Fourier Transform (FFT), Gray Level Co-Occurrence Matrix (GLCM), wavelet, curvelet etc based algorithms are not preferred in automatic feature extraction from remote sensing images due to their higher order of computational complexity. They are often used in small subsets or in association with a database where location and maximum extent of the features are stored beforehand. In this case, only characterization of the feature is carried out in the data.In this paper, we demonstrate a system architecture that can overcome the shortcomings of both these approaches in a multi-threaded platform. The feature extraction problem is divided into a low complexity with less accuracy followed by a computationally complex algorithm in an augmented space. The sparse nature of features gives the flexibility to evaluate features in Region Of Interest (ROI)s. Each operation is carried out in multiple threads to minimize the latency of the algorithm. The computationally intensive algorithm evaluates on a ROI provided by the low complexity operation. The system also decouples complex operations using multi-threading.The system is a customized solution developed completely in python using different open source software libraries. This approach has made it possible to carry out automatic feature extraction from Large SAR data. The architecture was tested and found giving promising results for extraction of inland water layers and dark features in ocean surface from SAR data.

Noisy Hamiltonian Monte Carlo for Doubly Intractable Distributions

ABSTRACTHamiltonian Monte Carlo (HMC) has been progressively incorporated within the statistician’s toolbox as an alternative sampling method in settings when standard Metropolis–Hastings is inefficient. HMC generates a Markov chain on an augmented state space with transitions based on a deterministic differential flow derived from Hamiltonian mechanics. In practice, the evolution of Hamiltonian systems cannot be solved analytically, requiring numerical integration schemes. Under numerical integration, the resulting approximate solution no longer preserves the measure of the target distribution, therefore an accept–reject step is used to correct the bias. For doubly intractable distributions—such as posterior distributions based on Gibbs random fields—HMC suffers from some computational difficulties: computation of gradients in the differential flow and computation of the accept–reject proposals poses difficulty. In this article, we study the behavior of HMC when these quantities are replaced by Monte Carlo estimates. Supplemental codes for implementing methods used in the article are available online.

Numerical experiments with the Generalized Finite Element Method based on a flat-top Partition of Unity

Abstract The Stable Generalized Finite Element Method (SGFEM) is essentially an improved version of the Generalized Finite Element Method (GFEM). Besides of retaining the good flexibility for constructing local enriched approximations, the SGFEM has the advantage of presenting much better conditioning than that of the conventional GFEM. Actually, bad conditioning is well known as one of the main drawbacks of the GFEM, while affecting severely the precision of the numerical scheme used for solving the linear system associated to the problem. Despite of its consistent mathematical basis, the numerical experiments so far conducted on using SGFEM are not yet clearly conclusive, especially regarding the robustness of the method. Therefore, the main purpose of the present paper is to give a contribution in this direction, through further investigating the SGFEM accuracy and stability. In particular, the so called Flat-Top SGFEM is a recent proposed version of the method hereby considered. As a flat-top Partition of Unit (PoU) is used for constructing the augmented approximation space with polynomial enrichments this version of the method is called SGFEM with flat-top PoU, or simply FT-SGFEM. Some computational aspects are briefly addressed, as the ones related to the implementation and integration of the flat-top for 2-D analysis. The numerical simulations consist essentially of linear analysis of panels presenting edge cracks and reentrant corners on its boundaries. Our findings from the numerical tests done are highly relevant regarding accuracy of the SGFEM versions, which present order of convergence similar to the conventional GFEM. Moreover, the measure of stability given by the scaled condition number presented in particular by the FT-SGFEM is comparable to the conventional FEM order.

Radiographic and histologic observations of sequential healing processes following ridge augmentation after tooth extraction in buccal-bone-deficient extraction sockets in beagle dogs.

To evaluate dimensional ridge alterations and sequential healing processes following ridge augmentation after tooth extraction in damaged extraction sockets with buccal-bone-deficiency. Bilateral dental roots of three mandibular premolars were extracted with entire removal of the buccal-bone plate in eight beagle dogs. Unilateral sites were grafted with biomaterials (test group) and contralateral sites were healed without grafting (control group). Observations were made after 1, 2, 4, and 8weeks, and all sites were distributed evenly (n=6 for each group and period). Radiographic/histomorphometric analyses were performed. In spontaneous healing of damaged extraction sockets, the dimension of regenerated alveolar ridge gradually increased until 4weeks and then remained stable, but radiographic/histomorphometric analyses revealed evident dimensional shrinkage compared to the pristine tissue at 8weeks in the coronal and middle areas. Bone grafting retained the pristine dimension of alveolar ridge, and newly formed bone area within the augmented space continuously expanded during the observational period to the outermost border of the space. Spontaneous healing of damaged extraction sockets caused substantial dimensional shrinkage. However, ridge augmentation can provide space into which new bone may grow continuously, resulting in the final dimensions comparable to those of the pristine alveolar ridge.

Wearing Data: from McLuhan’s “Extended Skin” to the Integration Between Wearable Technologies and a New Algorithmic Sensibility

One of the current issues in fashion studies resides in the area of wearable technologies research. The intersection between fashion and technology has been studied; however, as technology, particularly information and communication technologies (ICTs), evolves at a rapid speed, this topic has become increasingly important in recent years. This paper proposes to apply Marshall McLuhan's ideas on media and fashion as an analytical framework of wearable technologies, particularly focusing on the case of Hussein Chalayan. The analysis of Chalayan’s work suggests the progressive integration between design innovation and increasingly elaborate storytelling techniques. The paper argues that the smartly clothed body produces data as well as wears the data. The extended skin is equipped with artificial sensory systems, extending our natural body and sensory systems into the augmented space; the extended skin is datafied and the body is datafied.

Modification of an implicit approach based on nonstandard rules for structural dynamics analysis

The present study aims to modify a recently suggested implicit approach consisted of the approximate Euler method and closed-form exponential mapping (herein referred to as the Liu scheme) for the dynamic analysis of structures. Such modification has been developed based upon nonstandard rules. The equation of motion is formulated in the augmented dynamic space to apply the exponential mapping as a group preserving scheme. The formulation of the proposed method involves the hyperbolic sine and cosine functions. The method is therefore prone to divergence due to the behavior of the hyperbolic functions in structures with a high ratio of stiffness to mass. In the present study, to consider the properties of the structural equation into the formulation of the time step size and thereby avoid the divergence, a parameter, known as stability parameter, is thus derived from the exact solution of the equation of motion based on nonstandard rules. Embedding this parameter into the proposed method improves its stability. Afterward, for evaluating the performance of the proposed method, it is applied to several structures with different loading patterns while implemented in programing environment of the Matlab software. The results are compared to those of several commonly used numerical methods in structural applications. It is found that the proposed method has acceptable convergence and accuracy, and low time consumption compared to several commonly used methods. Furthermore, its stability is guaranteed by embedding the stability parameter into the proposed method.

Uniformizations of stable $$(\gamma ,n)$$ ( γ , n ) -gonal Riemann surfaces

A $$(\gamma ,n)$$ -gonal pair is a pair (S, f), where S is a closed Riemann surface and $$f:S \rightarrow R$$ is a degree n holomorphic map onto a closed Riemann surface R of genus $$\gamma $$ . If the signature of (S, f) is of hyperbolic type, then it admits a uniformizing pair $$(\varGamma ,G)$$ , where G is a Fuchsian group acting on the unit disc $${{\mathbb {D}}}$$ containing $$\varGamma $$ as an index n subgroup, such that f is induced by the inclusion $$\varGamma \le G$$ . The uniformizing pair is uniquely determined by (S, f), up to conjugation by holomorphic automorphisms of $${{\mathbb {D}}}$$ , and it permits to provide a natural complex orbifold structure on the Hurwitz space parametrizing (twisted) isomorphic classes of pairs topologically equivalent to (S, f). In order to produce certain compactifications of these Hurwitz spaces, one needs to consider the so called stable $$(\gamma ,n)$$ -gonal pairs, which are natural geometrical deformations of $$(\gamma ,n)$$ -gonal pairs. Due to the above, it seems interesting to search for uniformizations of stable $$(\gamma ,n)$$ -gonal pairs, in terms of certain class of Kleinian groups. In this paper we review such uniformizations by using noded Fuchsian groups, obtained from the noded Beltrami differentials of Fuchsian groups that were previously studied by Alexander Vasil’ev and the author, and which provide uniformizations of stable Riemann orbifolds. These uniformizations permit to obtain a compactification of the Hurwitz spaces together a complex orbifold structure, these being quotients of the augmented Teichmuller space of G by a suitable finite index subgroup of its modular group.

Model-based optimal boundary control of selective catalytic reduction in diesel-powered vehicles

This paper is devoted to design a model-based boundary optimal controller for selective catalytic reduction system. The mathematical model consists of coupled parabolic-hyperbolic PDEs with an ODE. The main objective is to manipulate the ammonia gas at the inlet of the SCR in order to reduce the amount of NOx and ammonia slip as much as possible. The augmented infinite-dimensional state space representation has been used in order to solve the corresponding linear-quadratic control problem. The dynamical properties of both the linearized system and its augmented version have been studied. Under some technical conditions, it has been shown that the augmented system generates an exponentially stabilizable and detectable C0-semigroups. The linear-quadratic control problem has been solved for the augmented system. A decoupling technique has been implemented to decouple and solve the corresponding Riccati equation. An algorithm has been developed to describe the steps of solving the Riccati equation. Numerical simulations for the closed-loop system have been implemented to show the controller performances.