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In this paper, the first in a series, we study the deformed Hermitian–Yang–Mills (dHYM) equation from the variational point of view as an infinite dimensional GIT problem. The dHYM equation is mirror to the special Lagrangian equation, and our infinite dimensional GIT problem is mirror to Thomas’ GIT picture for special Lagrangians. This gives rise to infinite dimensional manifold $${\mathcal {H}}$$ H closely related to Solomon’s space of positive Lagrangians. In the hypercritical phase case we prove the existence of smooth approximate geodesics, and weak geodesics with $$C^{1,\alpha }$$ C 1 , α regularity. This is accomplished by proving sharp with respect to scale estimates for the Lagrangian phase operator on collapsing manifolds with boundary. As an application of our techniques we give a simplified proof of Chen’s theorem on the existence of $$C^{1,\alpha }$$ C 1 , α geodesics in the space of Kähler metrics. In two follow up papers, these results will be used to examine algebraic obstructions to the existence of solutions to dHYM [26] and special Lagrangians in Landau–Ginzburg models [27].

In the present paper, the Schrodinger type involutory differential equation is considered which is stated as $$ i\frac{dv(t)}{dt}+Av(t)+bAv(-t)=f(t),t\in I=(-\infty ,\infty ),v\left( 0\right) =\varphi $$ in a Hilbert space H with a self-adjoint positive definite operator A. Here, operator approach enables us to apply the results on abstract problem on multi-dimensional or nonlocal problems which deserve a studious treatment. Throughout the paper, the main theorem on stability estimates for the solution of the abstract problem under the condition \(\left| b\right

In this paper, we consider a multi-class classifier for problems where unknown classes are included in testing phase. Previous classifiers consider the “closed-set” case where the classes used for training and the classes used for testing are the same. A more realistic case is the “open-set” recognition, in which only a limited number of classes appear at training time, and unknown classes appear during testing. To handle such problems, we need classifiers that accurately classify data belonging to not only known classes but also unknown classes. In this paper, We introduce a Support Vector Machine (SVM) based Extreme Value Machine (EVM) to determine a compact class region. Any data outside of such class regions is rejected as being in unknown classes. To construct a class region, we approach the class decision boundary found by SVM towards the samples, by removing some support vectors close to the boundary. This SVM based EVM resolves the three problems that EVM possesses: unfair size of class regions, excessive sensibility to certain points and fragmentation of a class region.

An inclusion is said to be neutral to uniform fields if upon insertion into a homogenous medium with a uniform field it does not perturb the uniform field at all. It is said to be weakly neutral if it perturbs the uniform field mildly. Such inclusions are of interest in relation to invisibility cloaking and effective medium theory. There have been some attempts lately to construct or to show existence of such inclusions in the form of core-shell structure or a single inclusion with the imperfect bonding parameter attached to its boundary. The purpose of this paper is to review recent progress in such attempts. We also discuss about the over-determined problem for confocal ellipsoids which is closely related with the neutral inclusion, and its equivalent formulation in terms of Newtonian potentials. The main body of this paper consists of reviews on known results, but some new results are also included.

Based on previous results in digital topology, this paper focuses on algorithms related to topological invariants of objects in 2D and 3D Digital Spaces. Specifically, we are interested in hole counting objects in 2D and closed surface genus calculation in 3D. We also present a proof of the hole counting formula in 2D. This paper includes fast algorithms and implementations for topological invariants such as connected components, hole counting in 2D, and boundary surface genus for 3D. For 2D images, we designed a linear time algorithm to solve the hole counting problem. In 3D, we also designed a O(n) time algorithm to obtain the genus of a closed surface. These two algorithms are both in \(O(\log n)\) space complexity.

In histopathological image analysis, cell nucleus segmentation plays an important role in the clinical analysis and diagnosis of cancer. However, due to the different morphology of cells, uneven staining and the existence of a large number of dense nuclei, it is still challenging to accurately segment the nucleus. In order to learn more specific key feature information during the training process, this paper proposed a network model called the CAB-Net that uses channel attention to enhance the learning of feature information on each channel. The network uses the channel attention mechanism to extract the key features in each channel, generates weights to judge the importance of the features, and then weights them into the original image. This aims to strengthen the extraction of key information, thereby generating a more characteristic feature map, and enabling the model to make more accurate judgments. We added a boundary smoothness constraint, in order to better identify cell nuclei with unclear boundaries and achieve more accurate cell nucleus segmentation. The experimental results show that the method in this paper achieves good performance on the cell segmentation data set.

In modern warfare, the role of airborne early warning aircraft is becoming more and more significant. The airborne early warning radar can accurately detect moving targets in a chaotic environment. Its suppression of strong clutter and jamming by radar is mainly through space-time adaptive processing (STAP) technology to achieve. The STAP algorithm uses the two domains of time and space to perform two-dimensional joint processing on the echo data, adaptively forming a notch that matches the clutter and jamming, and its excellent jamming suppression capability increases the difficulty of radar countermeasures. In this case, it is very necessary to conduct a comprehensive jamming study about the technology. At present, relevant jamming methods at home and abroad are still in the preliminary research stage. In this paper, we take genetic algorithm as an example, and a new distributed jamming method for STAP radar based on improved penalty-based boundary intersection decomposition multiobjective evolutionary algorithm (MOEA/D-PBI) is introduced in the many-to-many confrontation. Through simulation experiments, the optimized distributed jamming scheme is compared with the distributed jamming scheme before optimization, verifying the feasibility of the method proposed in this paper, which simplifies the problem of jamming resource allocation and realizes a fast search for the optimal allocation strategy, and complete real-time jamming decision-making on the battlefield.

The Cutting Path Problem (CPP) is a complex continuous and combinatorial optimization problem that is about finding an optimal tool path for CNC technologies equipment. The problem has many valuable industrial applications arising from the Industry 4.0 strategy, such as those, related to tool path routing for the sheet metal cutting machines. The CPP is strongly NP-hard enclosing variants of the well-known Traveling Salesman Problem (TSP) as sub-problems. In this paper, we for the first time propose an open access library of sample instances (CPPLib) for the CPP to facilitate the benchmarking of optimization algorithms, most of them are heuristics or metaheuristics. Each instance is obtained as an image of a finite set of mutually nested industrial parts on a metal sheet and is presented in the DXF vector format that is induced by a solution result of the well-known 2D nesting problem. For the first time we propose geometric and quantitative principles for constructing different groups (classes) of such image instances. Along with continuous CPP settings, the library contains their discrete counterparts presented in the form of instances of the Precedence Constraints Generalized Traveling Salesman Problem (PCGTSP), since the solution processes for the CPP mostly based on discretizing boundary contours of parts. In addition, the paper presents examples of testing some optimization algorithms for solving the cutting path problem on test instances from the developed CPPLib library.

Recently, learning-based image inpainting methods have made inspiring progress with squared or irregular holes. The generative adversarial networks (GANs) have been able to produce visually realistic and semantically correct results. However, most existing methods generate the results by one stage. They may have a slight advantage in computation time, but more information is lost during the inpainting process. Due to the lack of sufficient context information, these inpainting approaches cannot inpaint large holes in natural images very well. This paper proposes a progressive image inpainting algorithm for solving the above problem. This algorithm synthesizes different image components in a parallel manner within one stage. Moreover, this paper design a branch, which transmits the image features to the generative model iteratively. In each iteration, we adopt a mask auto-updating mechanism to shrink the boundary of a hole. Finally, the generative component can shrink the large corrupted regions in natural images and yield promising inpainting results.

The paper deals with the issues concerning the application of bithreshold-like neural units in the solving of the classification tasks. The fully connected 2-layer feedforward network architecture with the hidden layer consisting of bithreshold neurons is considered in the paper. We design the neural network multicategory classifier based on such an architecture. The upper bound is given on the size of the bithreshold network, which is capable to recognize an arbitrary partition of the given finite set of patterns into a predefined number of classes. The synthesis algorithm is proposed for such neural networks. We also introduce the model of centered bithreshold neuron whose two boundary surfaces get closer as far as the distance to the center of the neuron increases, which is often more convenient in the design of multicategory classifiers. The synthesis algorithm is described for neural network classifier whose hidden layer consists of centered bithreshold neurons. For both type of networks, we give simulation results of the classifier performance on the real data sets and discuss the influence of algorithm parameters on the process of synthesis, quality of the network performance and the representational capacity of classifier.

In this paper we consider the problem of tracking a mobile target using adaptive estimation while circumnavigating it with a system of Unmanned Surface Vehicles (USVs). The mobile target considered is an irregular dynamic shape approximated by a circle with moving centre and varying radius. The USV system is composed of n USVs of which one is equipped with an Unmanned Aerial Vehicle (UAV) capable of measuring both the distance to the boundary of the target and to its centre. This USV equipped with the UAV uses adaptive estimation to calculate the location and size of the mobile target. The USV system must circumnavigate the boundary of the target while forming a regular polygon. We design two algorithms: One for the adaptive estimation of the target using the UAV’s measurements and another for the control protocol to be applied by all USVs in their navigation. The convergence of both algorithms to the desired state is proved up to a limit bound. Two simulated examples are provided to verify the performance of the algorithms designed in this paper.

This paper considers the problem of image segmentation for medical images, in particular, cutaneous lesions. Given a digital image of a skin lesion, our goal is to compute the border curve separating the lesion from the image background. This problem can be formulated as an optimization problem, where the border curve is computed through data fitting from a set of points lying on the lesion boundary. Some recent papers have applied artificial intelligence techniques to tackle this issue. However, they usually focus on the polynomial case, ignoring the more powerful (but also more difficult) case of rational curves. In this paper, we address this problem with rational Bezier curves by applying functional networks, a powerful extension of the classical neural networks. Experimental results on some benchmark medical images show that this method performs well and can be successfully applied to this problem.

Angular zones where elements of structures and constructions are connected are characterized by complex stress-strain states and are an object of relevant theoretic-numerical and experimental research. Geometrically non-linear shape of boundaries – cut-outs, cuts, as well as the finite discontinuity of pre-defined forced deformations, emerging into the boundary of elements connection, results in appearance of the stress-strain state singularity. This paper considers local stress-strain state in the apex zone of an angular cut-out on the boundary of a plane domain. The novelty of research stems from the fact that the stress-strain state in neighborhood of the apex of an angular cut-out on the domain boundary is characterized by limit values of stresses and deformations, similar to stress intensity factors used with force criteria in fracture mechanics. A procedure is described for obtaining the expressions for displacements and stresses in neighborhood of the angular cut-out apex using stress intensity factors and deformation intensity factors for a variety of homogeneous boundary conditions. Differences are determined between expressions of stresses and displacements written using the stress and deformation intensity factors, which supports the practical relevance of the paper for carrying out experiments and determining critical values of stress and deformation intensity factors.

This paper deals with the decomposition of a domain into star-shaped blocks, which is motivated by the idea of solving PDEs by means of the scaled boundary isogeometric analysis (SB-IGA). In the first part of the paper an introduction to the SB-IGA is given and we show the necessity for the domain to be star-shaped. Of course not every domain has this property, and for this reason the main focus of the paper is the generation of star-shaped blocks. The approaches that we take into account are the quadtree decomposition and the art gallery decomposition. We highlight the steps of those algorithms and we provide a computable sufficient criterion for the identification of the star-shapedness of a block. Moreover, for the art gallery decomposition an extension of Fisk’s method and the Voronoi diagram are employed. Finally we solve the Poisson equation on different geometries, among them the Yeti footprint domain, and compare the approaches.

The first step towards applying isogeometric analysis techniques to solve PDE problems on a given domain consists in generating an analysis-suitable mapping operator between parametric and physical domains with one or several patches from no more than a description of the boundary contours of the physical domain. A subclass of the multitude of the available parameterization algorithms are those based on the principles of Elliptic Grid Generation (EGG) which, in their most basic form, attempt to approximate a mapping operator whose inverse is composed of harmonic functions. The main challenge lies in finding a formulation of the problem that is suitable for a computational approach and a common strategy is to approximate the mapping operator by means of solving a PDE-problem. PDE-based EGG is well-established in classical meshing and first generalization attempts to spline-based descriptions (as is mandatory in IgA) have been made. Unfortunately, all of the practically viable PDE-based approaches impose certain requirements on the employed spline-basis, in particular global C≥1-continuity.This paper discusses an EGG-algorithm for the generation of planar parameterizations with locally reduced smoothness (i.e., with support for locally only C0-continuous bases). A major use case of the proposed algorithm is that of multipatch parameterizations, made possible by the support of C0-continuities. This paper proposes a specially-taylored solution algorithm that exploits many characteristics of the PDE-problem and is suitable for large-scale applications. It is discussed for the single-patch case before generalizing its concepts to multipatch settings. This paper is concluded with three numerical experiments and a discussion of the results.

This paper classifies roll resonance into two kinds, and points out symbolic solutions of yaw natural frequency at each roll resonance. Firstly, this paper consider change of roll natural frequency ωx due to change of vehicle speed V. This paper points out that ωx is a roll mode around the roll axis on the low speed side, and a roll mode around the center of gravity on the high speed side. Furthermore, we define V of this boundary as Vb; Vb is a V where ωx and yaw resonance frequency ωn coincide. In addition, Vb of passenger cars is estimated to be about 90 [km/h]. Furthermore, this paper considers an influence of ωx change on ωn. As the reference for this consideration, we use the yaw resonance frequency ωn0 of well-known planar 2 DOF model. In the domain of V Vb. Strictly speaking, this ωn is slightly larger than ωn0, which corresponds to the symbolic solution that the author pointed out in the past.

The paper concerns the first approach to interval generalized finite differences. The conventional generalized finite differences are of special interest due to the fact that they can be applied to irregular grids (clouds) of points. Based on these finite differences we can compute approximate values of some derivatives (ordinary or partial). Furthermore, one can formulate the generalized finite difference method for solving some boundary value problems with a complicated boundary of a domain. The aim of this paper is to propose the interval counterparts of generalized finite differences. Under the appropriate assumptions the exact values of the derivatives are included in the interval values obtained.

This paper is devoted to the numerical validation of an explicit finite-difference scheme for the integration in time of Maxwell’s equations in terms of the sole electric field. The space discretization is performed by the standard P1 finite element method assorted with the treatment of the time-derivative term by a technique of the mass-lumping type. The rigorous reliability analysis of this numerical model was the subject of authors’ another paper [2]. More specifically such a study applies to the particular case where the electric permittivity has a constant value outside a sub-domain, whose closure does not intersect the boundary of the domain where the problem is defined. Our numerical experiments in two-dimension space certify that the convergence results previously derived for this approach are optimal, as long as the underlying CFL condition is satisfied.

The present paper contains a review of literature data on sources of singlet oxygen in lower atmosphere and methods of its registration. Singlet oxygen gas analyzer developed in OPTEC JSC (St Petersburg, Russia), which operation principle is based on the 9,10-definilantracene chemiluminescent reaction is described in the present paper. The new gas analyzer used for solving a wide range of fundamental and applied problems connected with the processes and phenomena at the boundary between biogenic and abiogenic nature. Its capabilities demonstrated on the examples for monitoring pollutant concentrations in atmospheric air, on the experiments in vivo and in vitro on recording singlet oxygen on the surface of snow and near plants infected by pathogenic fungus, as well as on tests of photocatalytic materials developed for use in medicine and in protection of architectural and sculptural monuments from biodeterioration.

Replicator dynamics, the continuous-time analogue of Multiplicative Weights Updates, is the main dynamic in evolutionary game theory. In simple evolutionary zero-sum games, such as Rock-Paper-Scissors, replicator dynamic is periodic [39], however, its behavior in higher dimensions is not well understood. We provide a complete characterization of its behavior in zero-sum evolutionary games. We prove that, if and only if, the system has an interior Nash equilibrium, the dynamics exhibit Poincare recurrence, i.e., almost all orbits come arbitrary close to their initial conditions infinitely often. If no interior equilibria exist, then all interior initial conditions converge to the boundary. Specifically, the strategies that are not in the support of any equilibrium vanish in the limit of all orbits. All recurrence results furthermore extend to a class of games that generalize both graphical polymatrix games as well as evolutionary games, establishing a unifying link between evolutionary and algorithmic game theory. We show that two degrees of freedom, as in Rock-Paper-Scissors, is sufficient to prove periodicity.

Semantic image segmentation has been one of the fundamental tasks in computer vision, which aims to assign a label to each pixel in an image. Nowadays, approaches based on fully convolutional network (FCN) have shown state-of-the-art performance in this task. However, most of them adopt cross entropy as the loss function, which will lead to poor performance in regions near object boundary. In this paper, we introduce two region-based metrics to quantitatively evaluate the performance of segmentation detail, which provides insights about the bottleneck of model. Based on this analysis, by use of a modified multi-task learning scheme, we combine cross entropy loss with manually defined hard example to propose a simple yet effective loss function named \(\mathcal {L}_\mathrm{{cehe}}\), which helps model focus on the learning of segmentation detail. Experiments show that model using \(\mathcal {L}_\mathrm{{cehe}}\) can better utilize spatial information comparing with the conventional cross entropy loss \(\mathcal {L}_\mathrm{{ce}}\). Statistically, metrics indicate that the proposed method outperforms the widely used \(\mathcal {L}_\mathrm{{ce}}\) by \(1.12\%\) in terms of MIoU on Cityscapes validation set, and by \(4.15\%\) in terms of the region-based metric MIoUiER proposed in this paper, proving that \(\mathcal {L}_\mathrm{{cehe}}\) performs better in segmentation detail.

The present paper is aimed at scrutinizing the contact interaction between the brake shoe and the railway wheel, taking into account the contacting surfaces friction and wear. The Archard’s model was used as a model describing the wear process. The solution to the problem was carried out applying the finite element method and the ANSYS software. The paper contains the results of calculations of wear and contact pressure intensity in the presence of a thin hardened layer on the shoe base, the survey of the influence of the mechanical properties of a layer on these values, as well as on the equivalent stress intensity at the boundary of the base material and the hardened layer. The performed calculations manifest that an entire set parameters must be considered when creating a hardened surface to increase the wear resistance of tribocontact parts.

Image saliency detection plays an important role in the field of computer vision. In order to make the saliency detection achieve better results, this paper proposes an algorithm that combines the global cue with the robust background prior measurement. Specifically, we first get a global ranking of superpixels by absorbing time in Markov chain, which is the absorbing nodes represent the superpixels of the fictitious boundary, and the transient nodes denote the others. Then, the global similarity of transient node can be measured by its absorbing time, so a global ranking for each transient node can be calculated, which is called as global cue in this paper. Finally, considering the remarkable energy optimization model, we integrate the robust background prior measurement with calculated global cue to form a new optimization model for saliency detection. In conclusion, our method is better than some typical significant detection algorithms on several datasets through the experimental verification.

In many engineering problems one has to deal with two materials which can induce local singularities (infinite stresses) as soon as the interface reaches the boundary. This is the case of water pipes with an inner coating for avoiding rusting. Because the wave velocity is smaller in the coating, it appears Love waves which can be used in order to detect defects because they propagate further as in a wave guide. They can be cracks corresponding to a disconnection at the interface between the two materials. In order to detect them, one can use measurements performed at the extremities of the pipes even if the signal in the numerical model, is very much perturbed by the singularities appearing at the interface between the two media at the extremities of the pipe. The phenomenon is amplified when one considers an artificial truncation of the structure and adding transparent boundary conditions in order to avoid reflection for simulating what happens in the full structure. The goal of this paper is to focus on a numerical method which can be used for the analysis of the influence of the singularities on the signal processing analysis. First of all, we give a mathematical description of the singularity met in our problem. Then, we define the extension to our case of the method introduced for cracks by G. Fix. It consists in adding the singular function to finite element functional space used in a classical numerical simulation. The main point of the paper is then to analyze, in a mathematical framework, the error estimates on the coefficients of the singularities with respect to the mesh size. Few numerical tests illustrate the mathematical results obtained for the problem we are dealing with.

This paper shows that constraint programming techniques can successfully be used to solve challenging hex-meshing problems. Schneiders’ pyramid is a square-based pyramid whose facets are subdivided into three or four quadrangles by adding vertices at edge midpoints and facet centroids. In this paper, we prove that Schneiders’ pyramid has no hexahedral meshes with fewer than 18 interior vertices and 17 hexahedra, and introduce a valid mesh with 44 hexahedra. We also construct the smallest known mesh of the octagonal spindle, with 40 hexahedra and 42 interior vertices. These results were obtained through a general purpose algorithm that computes the hexahedral meshes conformal to a given quadrilateral surface boundary. The lower bound for Schneiders’pyramid is obtained by exhaustively listing the hexahedral meshes with up to 17 interior vertices and which have the same boundary as the pyramid. Our 44-element mesh is obtained by modifying a prior solution with 88 hexahedra. The number of elements was reduced using an algorithm which locally simplifies groups of hexahedra. Given the boundary of such a group, our algorithm is used to find a mesh of its interior that has fewer elements than the initial subdivision. The resulting mesh is untangled to obtain a valid hexahedral mesh.

International audience; Recently, a sufficient condition, namely quasi-regularity, has been proposed for preserving the connectivity during the process of digitization of a continuous object whose boundary is not necessarily differentiable. Under this condition, a rigid motion scheme for digital objects of $\mathbb Z^2$ is proposed to guarantee that a well-composed object will remain well-composed, and its global geometry will be approximately preserved.In this paper, we are interested in polygons generated from digital objects and their rigid motions in $\mathbb Z^2$. For this, we introduce a notion of discrete regularity which is a restriction of quasi-regularity for polygons. This notion provides a simple geometric verification, based on the measure of lengths and angles, of quasi-regularity which is originally defined with morphological operators. Furthermore, we present a method for geometry-preserving rigid motions based on convex decomposition of polygons.This paper focuses on the implementation and on the reproduction of the method linking to an online demonstration. The way of using the C++ code source in other contexts is shown as well.

The paper studying the case that blunder intrude the approach path of lead aircraft in paired approach, and advances three escape maneuver procedure by introducing the paired approach. This paper gives five steps to determine the safety front boundary by establishing relative motion reference system. At last, the paper takes a case to make the simulation and calculate the minimum safety front boundary between different lateral spacing. The results show that distance between runway centerlines and the intrusive angle of intrusive plane have effect on minimum follow-up distance. Meanwhile, this method can calculate the safety front boundary of paired approach in real time and quantitatively.

This paper uses Information Geometry in a practical and important applied statistical context: Cox regression in survival analysis. We explore the geometry of the corresponding model space including its potentially complex boundary. The exact manner that frailty terms in Cox’s hazard model are specified has important implications for modelling. For example, it is very common to assume a gamma frailty for reasons of mathematical tractability and convenience. In this paper, we examine if there is a cost to having the gamma as a default option, without further scientific justification. We take a geometric approach to understanding the effect of precise model specification. We use a new, highly flexible but statistically well-behaved, way of specifying the frailty to calibrate modelling assumptions that are very commonly used in practice. We show that the gamma frailty assumption has the effect of considerably under-estimating standard errors when compared to our more general assumptions and, potentially, introducing bias. We comment on the implications of this. The survival times of adult acute myeloid leukaemia patients in northwest England are analyzed.

This paper presents a parametric study of the microphones distribution effect in the identification of the boundary acoustic sources acting in the acoustic cavities through the knowledge of the acoustic energy densities measurement. An energetic approach, also called the simplified energy method (MES) was developed to predict the energy densities distribution for the acoustic applications. MES can also be applied to structures to determine energy densities. This energy method can solve inverse problems in order to localize and quantify the structural and the acoustic boundary sources at medium and high frequency ranges, thanks the inverse formulation of this energetic approach (IMES). The main novelty of this paper is to study the performances of this inverse energetic approach in the quantification and localization of the boundary acoustic sources acting in the acoustic cavities. Numerical investigation concerning 3D acoustic cavity was performed to test the validity of the presented technique using different number of acoustic sources and distance between the microphones repartition and the cavity walls. The numerical results show that the inverse simplified energy method (IMES) has an excellent performance in identifying and detecting the boundary acoustic sources at medium and high frequency ranges from the knowledge of the acoustic energy densities measurement.

Reinforced soil structures encompass the advantage of easy and quick construction. In addition, they are highly resistant to stress and earthquakes. Conventional gravity retaining structures are thus being gradually replaced by geosynthetic reinforced soil structures. Bearing in mind that high frequency of earthquakes in Taiwan area, earthquake resistance should be taken into account in the design of reinforced soil structures to consider the effects of additional stress from earthquakes. This paper utilizes PLAXIS finite element program to simulate the seismic behavior of geosynthetic reinforced slope during the earthquake in FoGuang University. Since the repeated seismic reflections and magnified seismic response can occur within the model boundary when a seismic input is directly applied to the base location, the reflection of seismic input may affect the dynamic analysis simulation. Appropriate boundary condition and damping ratio are emphasized in this paper to facilitate further analyses to resolve the effects of seismic response. Although greater distance for model margins may also be employed without the boundary conditions to reduce the seismic reflections, more calculation for processing additional elements and storage space requirements may be demanded. Therefore, dynamic boundary condition provided in Plaxis can be applied to simplify the calculation. Furthermore, it is necessary to properly consider the soil damping ratio for a seismic simulation analysis to enhance accuracy of the predicted response.

This paper proposes two clustering algorithms of twofold memberships for each cluster. One uses a membership similar to that in K-means, while another membership is defined for a core of a cluster, which is compared to the lower approximation of a cluster in rough K-means. Two ideas for the lower approximation are proposed in this paper: one uses a neighborhood of a cluster boundary and another uses a simple circle from a cluster center. By using the two memberships, two alternate optimization algorithms are proposed. Numerical examples show the effectiveness of the proposed algorithms.

In 2004 the second author of the present paper proved that a point set in [0, 1]d which has star-discrepancy at most e must necessarily consist of at least cabsde−1 points. Equivalently, every set of n points in [0, 1]d must have star-discrepancy at least cabsdn−1. The original proof of this result uses methods from Vapnik–Chervonenkis theory and from metric entropy theory. In the present paper we give an elementary combinatorial proof for the same result, which is based on identifying a sub-box of [0, 1]d which has approximately d elements of the point set on its boundary. Furthermore, we show that a point set for which no such box exists is rather irregular, and must necessarily have a large star-discrepancy.

Computational Fluid Dynamics (CFD) is used extensively in modelling high-speed flows for commercial transport aircraft. Current production CFD has deficiencies in modelling specific flow features. In this paper unitary flow cases are considered for specific flow features such as boundary layers, vortical flow, flow separation and shock boundary layer interaction. In the paper new CFD practices in meshing and turbulence modelling are investigated in order to better represent these flow features. In order to measure improvements metrics are introduced which measure an improvement with respect to a reference (from wind tunnel experiment or LES computation). In the paper it is shown that application of new CFD practices give an improved prediction of boundary layer and vortical flow.

Genetic Programming is a specialized form of genetic algorithms which evolve trees. This paper proposes an approach to evolve an expression tree, which is an N-Ary tree that represents a mathematical equation and that describes a given set of points in some space. The points are a set of trade-off solutions of a multi-objective optimization problem (MOOP), referred to as the Pareto Optimal Front (POF). The POF is a curve in a multi-dimensional space that describes the boundary where a single objective in a set of objectives cannot improve more without sacrificing the optimal value of the other objectives. The algorithm, proposed in this paper, will thus find the mathematical function that describes a POF after a multi-objective optimization algorithm (MOA) has solved a MOOP. Obtaining the equation will assist in finding other points on the POF that was not discovered by the MOA. Results indicate that the proposed algorithm matches the general curve of the points, although the algorithm sometimes struggles to match the points perfectly.

Model-based segmentation (MBS) has been successfully used for the fully automatic segmentation of anatomical structures in medical images with well defined gray values due to its ability to incorporate prior knowledge about the organ shape. However, the robust and accurate detection of boundary points required for the MBS is still a challenge for organs with inhomogeneous appearance such as the prostate and magnetic resonance (MR) images, where the image contrast can vary greatly due to the use of different acquisition protocols and scanners at different clinical sites. In this paper, we propose a novel boundary detection approach and apply it to the segmentation of the whole prostate in MR images. We formulate boundary detection as a regression task, where a convolutional neural network is trained to predict the distances between a surface mesh and the corresponding boundary points. We have evaluated our method on the Prostate MR Image Segmentation 2012 challenge data set with the results showing that the new boundary detection approach can detect boundaries more robustly with respect to contrast and appearance variations and more accurately than previously used features. With an average boundary distance of 1.71 mm and a Dice similarity coefficient of 90.5%, our method was able to segment the prostate more accurately on average than a second human observer and placed first out of 40 entries submitted to the challenge at the writing of this paper.

The paper studies the control problem of a class of hyper-redundant robots with uncertain components by using the output control. To avoid the complexity of distributed controllers, in this paper is proposed a lumped output controller. A weighted technique is used to generate a boundary output. A PD controller is proposed and an algorithm for determining the controller gains is discussed. Lyapunov techniques are used to prove the asymptotically stability of the control system. Numerical simulations and prove the efficiency of the method.

The correct usage of phrase boundaries is an important issue for ensuring a natural sounding and easily intelligible speech. Therefore, it is not surprising that the boundary detection is also a part of text-to-speech systems. In the presented paper, large speech corpora are used for a classification based approach in order to improve the phrasing of synthesized sentences. The paper compares results of different classifiers to the deterministic approaches based on punctuation and conjunctions and shows that they are able to outperform the simple algorithms.

Comunicació presentada al 11th International Joint Conference (VISIGRAPP), celebrat del 27 al 29 de febrer de 2016 a Roma, Itàlia. Estimating 3D structure of the scene from a single image remains a challenging problem in computer vision. This paper proposes a novel approach to obtain a global depth order of objects by incorporating monocular perceptual cues such as T-junctions and object boundary convexity, which are local indicators of occlusions, together with physical cues, namely ground contact points. The proposed combination of these local cues complement each other and creates a more thorough partial depth order relationship. The different partial orders are then robustly aggregated using a Markov random chain approximation to obtain the most plausible global depth order. Experiments show that the proposed method excels in comparison to state of the art methods. The authors acknowledge partial support by the MINECO/FEDER project with reference TIN2015-70410-C2-1-R, the MICINN project with reference MTM2012-30772, and by GRC reference 2014 SGR 1301, Generalitat de Catalunya.

Electrical Impedance Tomography (EIT) is a method used to display, through an image, the conductivity distribution inside a domain by using measurements taken from electrodes placed at its periphery. This paper presents our prototype of a stretchable touch sensor, which is based on the EIT method. We then test its performance by comparing voltage data acquired from testing with two different materials, using the performance parameters Signal-to-Noise Ratio (SNR), Boundary Voltage Changes (BVC) and Singular Value Decomposition (SVD). The paper contributes to the literature by demonstrating that, depending on the present stimuli position over the conductive domain, the selection of electrodes on which current injection and voltage reading are performed, can be chosen dynamically resulting in an improved quality of the reconstructed image and system performance.

This paper presents a boundary control design for a single point mooring line system with input saturation in three-dimensional (3D) space. The system is described by some partial differential equations (PDEs) and ordinary differential equations (ODEs). The control strategy proposed in this paper at the tip payload of the mooring line and the control design uses Lyapunov’s direct method (LDM) to ensure the stability of the system. In order to compensate the input saturation, we propose an auxiliary system. With the proposed boundary control, the mooring system’s uniform boundedness under the effect of external environment is obtained. The presented boundary control is implementable with feasible equipment because all information in the system can be gained and calculated through various sensors or by applying a backward difference algorithm. Simulation results are provided to prove that the controller is effective in regulating the vibration of the system.

This work-in-progress paper explores the use of complex variables to define the design sensitivities of high-speed aircraft components modeled by nonlinear reduced-order models (NLROMs). Extreme conditions are expected to be seen by high-speed flight vehicles and it is anticipated that portions of the structure are likely to exhibit significant nonlinearity in their response. Accurate prediction of the path-dependent response requires direct time-integration of nonlinear models. Large finite element models of the structural components would require prohibitively large amounts of computer time to properly simulate. Methodologies have been proposed that use NLROMs to model the component level, dynamic response. The nonlinear ROMs are linear modal models that have been coupled through the addition of nonlinear modal stiffness terms. The nonlinearity in these models is sensitive to the connectivity of the components with the assembly. Recent work has investigated the use of complex variables to update NLROMs based on the boundary stiffness of the adjoining structure. This paper will explore complex methods to determine component design sensitivities to the thermal expansion and stiffness of the surrounding structure.

In this paper, we present a novel algorithm to convert the contour in a raster image into its vector form. Different from the state-of-art methods, we explore the potential parallelism that exists in the problem and propose an algorithm suitable to be accelerated by the graphics hardware. In our algorithm, the vectorization task is decomposed into four steps: detecting the boundary pixels, pre-computing the connectivity relationship of detected pixels, organizing detected pixels into boundary loops and vectorizing each loop into line segments. The boundary detection and connectivity pre-computing are parallelized owing to the independence between scanlines. After a sequential boundary pixels organizing, all loops are vectorized concurrently. With a GPU implementation, the vectorization can be accomplished in real-time. Then, the image can be represented by the vectorized contour. This real-time vectorization algorithm can be used on images with multiple silhouettes and multi-view videos. We demonstrate the efficiency of our algorithm with several applications including cartoon and document vectorization.

A new framework of clustering is proposed inspired by the theory of three-way decisions, which is an alternative formulation different from the ones used in the existing studies. The novel three-way representation intuitively shows which objects are fringe to the cluster and it is proposed for dealing with uncertainty clustering. Instead of using two regions to represent a cluster by a single set, a cluster is represented using three regions through a pair of sets, and there are three regions such as the core region, fringe region and trivial region. A cluster is therefore more realistically characterized by a set of core objects and a set of boundary objects. In this paper, we also illustrate an algorithm for incomplete data by using the proposed evaluation-based three-way cluster model. The preliminary experimental results show that the proposed method is effective for clustering incomplete data which is one kind of uncertainty data. Furthermore, this paper reviews some three-way clustering approaches and discusses some future perspectives and potential research topics based on the three-way cluster analysis.

In the automotive industry components are often connected through rubber bushings. The bushingsʼ material properties are usually not well known. In computational models these properties are parametrised and their spread can be considerable. A good estimate of these parameters is important in various applications, including substructuring, and for uncertainty quantification of systems with connected components. This paper deals with the calibration of an industrial size finite element model of a car subframe with parametrised bushing models. Mass loading is used on the bushings to bring local bushing modes to a lower frequency region and impose a more realistic boundary condition in component testing. The model parameters can be calibrated in different ways. In this paper two approaches are considered. They are based on two test configurations, one with and one without mass loaded boundaries. In the first case only the bushing parameters are considered for the mass loaded boundary configuration. In the second case, consisting of two steps, the configuration without mass loaded boundaries is considered in which the bushing parameters are first fixed and other model parameters considered, and in the last step a subset of all parameters is considered. The calibration, validation and uncertainty quantification, using bootstrapping, have been performed using the open-source MATLAB tool FEMcali.

Many “theoretical” frameworks have been proposed for software systems design with a plethora of techniques, scopes and degrees of sophistication. However, a clear delineation of the forbidden in software design terms is almost universally absent in all these frameworks. This absence is surprising, as other engineering disciplines obviously display forbidden regions. This paper claims that an acceptable software design theory should clearly demarcate the forbidden in contrast to the possible. Algebra is argued to be the mathematical field appropriate to determine boundaries of forbidden regions. To this end, a spectral approach is demonstrated, in which matrix eigenvectors play a central role. Such boundaries of forbidden regions are illustrated by a case study.

This paper presents a numerical result for generalized eigenvalue problems in the optimum shape obtained by conducting the maximizing and minimizing problems of the energy dissipation. The shape optimization problem addressed in this paper is defined as a two-dimensional lid-driven cavity flow. As an objective cost function, the energy dissipation is used, and the domain volume is used as a constraint cost function, where the shape of the boundaries aside the top boundary is optimized. By solving a generalized eigenvalue problem, linear neutral curves between the initial domain \( \Omega _0\) and the optimum shape domains \(\Omega _1\) and \( \Omega _2\) for maximizing and minimizing problems respectively are compared. Numerical results reveal that the shape is optimized by satisfying the volume constraint. Based on generalized eigenvalue problems, the critical Reynolds numbers of the optimum shapes \(\Omega _1\) and \( \Omega _2\) are larger and smaller than that of the initial shape \(\Omega _0\).

In this paper, a new methodology for reconstructing spatially varying random material properties is presented by combining stochastic finite element (SFE) models with Self-Optimizing Inverse Method (Self-OPTIM). The Self-OPTIM can identify model parameters based on partial boundary force and displacement data from experimental tests. Statistical information (i.e. spatial mean, variance, correlation length and Gaussian normal random variables) of spatially varying random fields (RFs) are parameterized by Karhunen-Loeve (KL) expansion method and integrated into SFE models. In addition, a new software framework is also presented that can simultaneously utilize any number of remote computers in a network domain for the Self-OPTIM simulation. This can result in a significant decrease of computational times required for the optimization task. Two important issues in the inverse reconstruction problem are addressed in this paper: (1) effects of the number of internal measurements and (2) non-uniform reaction forces along the boundary on the reconstruction accuracy. The proposed method is partially proven to offer new capabilities of reconstructing spatially inhomogeneous material properties and estimating their statistical parameters from incomplete experimental measurements.

In this paper we propose an explicit finite-difference scheme to solve the American option pricing problem. It is based on front-fixing transformation that involves unknown free boundary to the equation. The proposed stable and consistent numerical scheme preserves positivity and monotonicity of the solution in accordance with the behavior of the exact solution. Numerical examples and comparison with other methods are included. This technique can be applied to some types of two-asset options after reducing the dimension. In the paper the front-fixing method is applied to exchange option pricing.

The design of numerical boundary conditions in high order schemes is a challenging problem that has been tackled in different ways depending on the nature of the problem and the scheme used to solve it numerically. In this paper we propose a technique to extrapolate the information from the computational domain to ghost cells for schemes with structured Cartesian Meshes on complex domains. This technique is based on the application of Lagrange interpolation with weighted filters for the detection of discontinuities that permits a data dependent extrapolation, with high order at smooth regions and essentially non oscillatory properties near discontinuities. This paper is a sequel of Baeza et al. (J Sci Comput, 2015), where a boundary extrapolation procedure with Boolean filters was developed. We show that weighted extrapolation can tackle discontinuities more robustly than the procedure introduced in Baeza et al. (J Sci Comput, 2015).

The paper discusses some practical problems occurring at ISO 10816-1 application in technical practice. Technical diagnosticians find out frequently that the zone boundaries proposed by the standard are not sensitive adequately to the changes in vibrations during operation resulting from a change of technical condition of the machine. The principle of statistical determination of individual operational limits from the measured overall level of RMS vibration velocity (overall level) with utilization of statistical regulation of individual values is also described in the paper. The alarm limit for the overall level (boundary between the B and C zones) in the range 10–1000 Hz is specified in the paper on the basis of measuring of vibrations on the electric motor of Bosch Rexroth hydraulic unit.