Probabilistic least squares approach to ordinal regression

This paper proposes a novel approach to solve the ordinal regression problem using Gaussian processes. The proposed approach, probabilistic least squares ordinal regression (PLSOR), obtains the probability distribution over ordinal labels using a particular likelihood function. It performs model selection (hyperparameter optimization) using the leave-one-out cross-validation (LOO-CV) technique. PLSOR has conceptual simplicity and ease of implementation of least squares approach. Unlike the existing Gaussian process ordinal regression (GPOR) approaches, PLSOR does not use any approximation techniques for inference. We compare the proposed approach with the state-of-the-art GPOR approaches on some synthetic and benchmark data sets. Experimental results show the competitiveness of the proposed approach.

Effect of equal channel angular pressing on grain refinement and texture evolution in a biomedical alloy Ti-13Nb-13Zr

Evolution of texture and concomitant grain refinement during Equal Channel Angular Pressing (ECAP) of Ti - 13Nb - 13Zr alloy has been presented. Sub-micron sized equiaxed grains with narrow grain size distribution could be achieved after eight pass at 873 K. A characteristic ECAP texture evolved in alpha phase till four passes while the evolution of characteristic ECAP texture in the beta phase could be observed only beyond the fourth pass. On increasing the deformation up to eight passes, the texture in alpha phase weakens while the beta phase shows an ideal ECAP texture. A weaker texture, low dislocation density and high crystallite size values in alpha phase suggest the occurrence of dynamic recrystallization. The absence of texture evolution in beta phase till four passes can be attributed to local lattice rotations. The characteristic ECAP texture in the eight pass deformed sample is attributed to delayed dynamic recrystallization in the beta phase. (C) 2013 Elsevier Inc. All rights reserved.

Response of vertically loaded pile in clay: a probabilistic study

This study presents the response of a vertically loaded pile in undrained clay considering spatially distributed undrained shear strength. The probabilistic study is performed considering undrained shear strength as random variable and the analysis is conducted using random field theory. The inherent soil variability is considered as source of variability and the field is modeled as two dimensional non-Gaussian homogeneous random field. Random field is simulated using Cholesky decomposition technique within the finite difference program and Monte Carlo simulation approach is considered for the probabilistic analysis. The influence of variance and spatial correlation of undrained shear strength on the ultimate capacity as summation of ultimate skin friction and end bearing resistance of pile are examined. It is observed that the coefficient of variation and spatial correlation distance are the most important parameters that affect the pile ultimate capacity.

Structural refinement, optical and electrical properties of Ba1-x Sm-2x/3](Zr0.05Ti0.95)O-3 ceramics

Samarium doped barium zirconate titanate ceramics with general formula Ba1-x Sm-2x/3](Zr0.05Ti0.95)O-3 x = 0, 0.01, 0.02, and 0.03] were prepared by high energy ball milling method. X-ray diffraction patterns and micro-Raman spectroscopy confirmed that these ceramics have a single phase with a tetragonal structure. Rietveld refinement data were employed to model BaO12], SmO12], ZrO6], and TiO6] clusters in the lattice. Scanning electron microscopy shows a reduction in average grain size with the increase of Sm3+ ions into lattice. Temperature-dependent dielectric studies indicate a ferroelectric phase transition and the transition temperature decreases with an increase in Sm3+ ion content. The nature of the transition was investigated by the Curie-Weiss law and it is observed that the diffusivity increases with Sm3+ ion content. The ferroelectric hysteresis loop illustrates that the remnant polarization and coercive field increase with an increase in Sm3+ ions content. Optical properties of the ceramics were studied using ultraviolet-visible diffuse reflectance spectroscopy.

Efficient Adaptive Mesh Refinement for MoM-based Package-Board 3D Full-wave Extraction

The accurate solution of 3D full-wave Method of Moments (MoM) on an arbitrary mesh of a package-board structure does not guarantee accuracy, since the discretizations may not be fine enough to capture rapid spatial changes in the solution variable. At the same time, uniform over-meshing on the entire structure generates large number of solution variables and therefore requires an unnecessarily large matrix solution. In this work, a suitable refinement criterion for MoM based electromagnetic package-board extraction is proposed and the advantages of the adaptive strategy are demonstrated from both accuracy and speed perspectives.

Probabilistic stress variation studies on composite single lap joint using Monte Carlo simulation

The work presented in this paper involves the stochastic finite element analysis of composite-epoxy adhesive lap joints using Monte Carlo simulation. A set of composite adhesive lap joints were prepared and loaded till failure to obtain their strength. The peel and shear strain in the bond line region at different levels of load were obtained using digital image correlation (DIC). The corresponding stresses were computed assuming a plane strain condition. The finite element model was verified by comparing the numerical and experimental stresses. The stresses exhibited a similar behavior and a good correlation was obtained. Further, the finite element model was used to perform the stochastic analysis using Monte Carlo simulation. The parameters influencing stress distribution were provided as a random input variable and the resulting probabilistic variation of maximum peel and shear stresses were studied. It was found that the adhesive modulus and bond line thickness had significant influence on the maximum stress variation. While the adherend thickness had a major influence, the effect of variation in longitudinal and shear modulus on the stresses was found to be little. (C) 2014 Elsevier Ltd. All rights reserved.

Adaptive Mesh Refinement for Fast Convergence of EFIE-Based 3-D Extraction

3-D full-wave method of moments (MoM) based electromagnetic analysis is a popular means toward accurate solution of Maxwell's equations. The time and memory bottlenecks associated with such a solution have been addressed over the last two decades by linear complexity fast solver algorithms. However, the accurate solution of 3-D full-wave MoM on an arbitrary mesh of a package-board structure does not guarantee accuracy, since the discretization may not be fine enough to capture spatial changes in the solution variable. At the same time, uniform over-meshing on the entire structure generates a large number of solution variables and therefore requires an unnecessarily large matrix solution. In this paper, different refinement criteria are studied in an adaptive mesh refinement platform. Consequently, the most suitable conductor mesh refinement criterion for MoM-based electromagnetic package-board extraction is identified and the advantages of this adaptive strategy are demonstrated from both accuracy and speed perspectives. The results are also compared with those of the recently reported integral equation-based h-refinement strategy. Finally, a new methodology to expedite each adaptive refinement pass is proposed.

Probabilistic Prediction based Scheduling for Delay Sensitive Traffic in Internet of Things

This paper proposes a probabilistic prediction based approach for providing Quality of Service (QoS) to delay sensitive traffic for Internet of Things (IoT). A joint packet scheduling and dynamic bandwidth allocation scheme is proposed to provide service differentiation and preferential treatment to delay sensitive traffic. The scheduler focuses on reducing the waiting time of high priority delay sensitive services in the queue and simultaneously keeping the waiting time of other services within tolerable limits. The scheme uses the difference in probability of average queue length of high priority packets at previous cycle and current cycle to determine the probability of average weight required in the current cycle. This offers optimized bandwidth allocation to all the services by avoiding distribution of excess resources for high priority services and yet guaranteeing the services for it. The performance of the algorithm is investigated using MPEG-4 traffic traces under different system loading. The results show the improved performance with respect to waiting time for scheduling high priority packets and simultaneously keeping tolerable limits for waiting time and packet loss for other services. Crown Copyright (C) 2015 Published by Elsevier B.V.

Probabilistic Models for Forecasting Earthquakes in the Northeast Region of India

Northeast India and its adjoining areas are characterized by very high seismic activity. According to the Indian seismic code, the region falls under seismic zone V, which represents the highest seismic-hazard level in the country. This region has experienced a number of great earthquakes, such as the Assam (1950) and Shillong (1897) earthquakes, that caused huge devastation in the entire northeast and adjacent areas by flooding, landslides, liquefaction, and damage to roads and buildings. In this study, an attempt has been made to find the probability of occurrence of a major earthquake (M-w > 6) in this region using an updated earthquake catalog collected from different sources. Thereafter, dividing the catalog into six different seismic regions based on different tectonic features and seismogenic factors, the probability of occurrences was estimated using three models: the lognormal, Weibull, and gamma distributions. We calculated the logarithmic probability of the likelihood function (ln L) for all six regions and the entire northeast for all three stochastic models. A higher value of ln L suggests a better model, and a lower value shows a worse model. The results show different model suits for different seismic zones, but the majority follows lognormal, which is better for forecasting magnitude size. According to the results, Weibull shows the highest conditional probabilities among the three models for small as well as large elapsed time T and time intervals t, whereas the lognormal model shows the lowest and the gamma model shows intermediate probabilities. Only for elapsed time T = 0, the lognormal model shows the highest conditional probabilities among the three models at a smaller time interval (t = 3-15 yrs). The opposite result is observed at larger time intervals (t = 15-25 yrs), which show the highest probabilities for the Weibull model. However, based on this study, the IndoBurma Range and Eastern Himalaya show a high probability of occurrence in the 5 yr period 2012-2017 with >90% probability.

Quantum stochastic calculus associated with quadratic quantum noises

We first study a class of fundamental quantum stochastic processes induced by the generators of a six dimensional non-solvable Lie dagger-algebra consisting of all linear combinations of the generalized Gross Laplacian and its adjoint, annihilation operator, creation operator, conservation, and time, and then we study the quantum stochastic integrals associated with the class of fundamental quantum stochastic processes, and the quantum Ito formula is revisited. The existence and uniqueness of solution of a quantum stochastic differential equation is proved. The unitarity conditions of solutions of quantum stochastic differential equations associated with the fundamental processes are examined. The quantum stochastic calculus extends the Hudson-Parthasarathy quantum stochastic calculus. (C) 2016 AIP Publishing LLC.

Grain Refinement at the Nanoscale Via Mechanical Twinning and Dislocation Interaction in a Nickel-Based Alloy

A nanostructured surface layer was formed on an Inconel 600 plate by subjecting it to surface mechanical attrition treatment at room temperature. Transmission electron microscopy and high-resolution transmission electron microscopy of the treated surface layer were carried out to reveal the underlying grain refinement mechanism. Experimental observations showed that the strain-induced nanocrystallization in the current sample occurred via formation of mechanical microtwins and subsequent interaction of the microtwins with dislocations in the surface layer. The development of high-density dislocation arrays inside the twin-matrix lamellae provides precursors for grain boundaries that subdivide the nanometer-thick lamellae into equiaxed, nanometer-sized grains with random orientations.

Probabilistic graphical models for semi-supervised traffic classification

Traffic classification using machine learning continues to be an active research area. The majority of work in this area uses off-the-shelf machine learning tools and treats them as black-box classifiers. This approach turns all the modelling complexity into a feature selection problem. In this paper, we build a problem-specific solution to the traffic classification problem by designing a custom probabilistic graphical model. Graphical models are a modular framework to design classifiers which incorporate domain-specific knowledge. More specifically, our solution introduces semi-supervised learning which means we learn from both labelled and unlabelled traffic flows. We show that our solution performs competitively compared to previous approaches while using less data and simpler features. Copyright © 2010 ACM.

Adaptive Mesh Refinement FEM for Damage Evolution of Heterogeneous Brittle Media

Damage evolution of heterogeneous brittle media involves a wide range of length scales. The coupling between these length scales underlies the mechanism of damage evolution and rupture. However, few of previous numerical algorithms consider the effects of the trans-scale coupling effectively. In this paper, an adaptive mesh refinement FEM algorithm is developed to simulate this trans-scale coupling. The adaptive serendipity element is implemented in this algorithm, and several special discontinuous base functions are created to avoid the incompatible displacement between the elements. Both the benchmark and a typical numerical example under quasi-static loading are given to justify the effectiveness of this model. The numerical results reproduce a series of characteristics of damage and rupture in heterogeneous brittle media.

Strain-Induced Grain Refinement of Cobalt During Surface Mechanical Attrition Treatment

The microstructural evolution during surface mechanical attrition treatment of cobalt (a mixture of hexagonal close packed (hep) and face-centered cubic (fcc) phases) was investigated. In order to reveal the mechanism of grain refinement and strain accommodation. The microstructure was systematically characterized by both cross-sectional and planar-view transmission electron microscopy. In the hcp phase, the process of grain refinement. Accompanied by an increase in strain imposed in the surface layer. Involved: (1) the onset of 110 111 deformation twinning, (2) the operation of (1 120) 110 1 0} prismatic and (1 120) (000 1) basal slip, leading to the formation of low-angle dislocation boundaries, and (3) the successive subdivision of grains to a finer and finer scale. Ressulting in the formation of highly misoriented nanocrystalline grains. Moreover. The formation of nanocrystalliies at the grain boundary and triple junction was also observed to occur concurrently with straining. By contrast. The fec phase accommodated strain in a sequence as follows: (1) slip of dislocations by forming intersecting planar arrays of dislocations, (2) {1 1 1} deformation twinning, and (3) the gamma(fcc) --> epsilon(hcp) martensitic phase transformation. The mechanism of grain refinement was interpreted in terms of the structural subdivision of grains together with dynamic recrystallization occurring in the hep phase and the gamma --> E: martensitic transformation in the fcc phase as well.

A probabilistic model for fatigue crack propagation analysis

A simple probabilistic model for predicting crack growth behavior under random loading is presented. In the model, the parameters c and m in the Paris-Erdogan Equation are taken as random variables, and their stochastic characteristic values are obtained through fatigue crack propagation tests on an offshore structural steel under constant amplitude loading. Furthermore, by using the Monte Carlo simulation technique, the fatigue crack propagation life to reach a given crack length is predicted. The tests are conducted to verify the applicability of the theoretical prediction of the fatigue crack propagation.