Effects of different modes of hot cross-rolling in 7010 aluminum alloy: part I. evolution of microstructure and texture

The current study describes the evolution of microstructure and texture in an Al-Zn-Mg-Cu-Zr-based 7010 aluminum alloy during different modes of hot cross-rolling. Processing of materials involves three different types of cross-rolling. The development of texture in the one-step cross-rolled specimen can be described by a typical beta-fiber having the maximum intensity near Copper (Cu) component. However, for the multi-step cross-rolled specimens, the as-rolled texture is mainly characterized by a strong rotated-Brass (Bs) component and a very weak rotated-cube component. Subsequent heat treatment leads to sharpening of the major texture component (i.e., rotated-Bs). Furthermore, the main texture components in all the specimens appear to be significantly rotated in a complex manner away from their ideal positions because of non-symmetric deformations in the two rolling directions. Detailed microstructural study indicates that dynamic recovery is the dominant restoration mechanism operating during the hot rolling. During subsequent heat treatment, static recovery dominates, while a combination of particle-stimulated nucleation (PSN) and strain-induced grain boundary migration (SIBM) causes partial recrystallization of the grain structure. The aforementioned restoration mechanisms play an important role in the development of texture components. The textural development in the current study could be attributed to the combined effects of (a) cross-rolling and inter-pass annealing that reduce the intensity of Cu component after each successive pass, (b) recrystallization resistance of Bs-oriented grains, (c) stability of Bs texture under cross-rolling, and (d) Zener pinning by Al3Zr dispersoids.

Effects of different modes of hot cross-rolling in 7010 aluminum alloy: part II. mechanical properties anisotropy

The influence of microstructure and texture developed by different modes of hot cross-rolling on in-plane anisotropy (A (IP)) of yield strength, work hardening behavior, and anisotropy of Knoop hardness (KHN) yield locus has been investigated. The A (IP) and work hardening behavior are evaluated by tensile testing at 0 deg, 45 deg, and 90 deg to the rolling direction, while yield loci have been generated by directional KHN measurements. It has been observed that specimens especially in the peak-aged temper, in spite of having a strong, rotated Brass texture, show low A (IP). The results are discussed on the basis of Schmid factor analyses in conjunction with microstructural features, namely grain morphology and precipitation effects. For the specimen having a single-component texture, the yield strength variation as a function of orientation can be rationalized by the Schmid factor analysis of a perfectly textured material behaving as a quasi-single crystal. The work hardening behavior is significantly affected by the presence of solute in the matrix and the state of precipitation rather than texture, while yield loci derived from KHN measurements reiterate the low anisotropy of the materials. Theoretic yield loci calculated from the texture data using the visco-plastic self-consistent model and Hill's anisotropic equation are compared with that obtained experimentally.

Crop stage classification of hyperspectral data using unsupervised techniques

The presence of a large number of spectral bands in the hyperspectral images increases the capability to distinguish between various physical structures. However, they suffer from the high dimensionality of the data. Hence, the processing of hyperspectral images is applied in two stages: dimensionality reduction and unsupervised classification techniques. The high dimensionality of the data has been reduced with the help of Principal Component Analysis (PCA). The selected dimensions are classified using Niche Hierarchical Artificial Immune System (NHAIS). The NHAIS combines the splitting method to search for the optimal cluster centers using niching procedure and the merging method is used to group the data points based on majority voting. Results are presented for two hyperspectral images namely EO-1 Hyperion image and Indian pines image. A performance comparison of this proposed hierarchical clustering algorithm with the earlier three unsupervised algorithms is presented. From the results obtained, we deduce that the NHAIS is efficient.

Gene expression programming-fuzzy logic method for crop type classification

Crop type classification using remote sensing data plays a vital role in planning cultivation activities and for optimal usage of the available fertile land. Thus a reliable and precise classification of agricultural crops can help improve agricultural productivity. Hence in this paper a gene expression programming based fuzzy logic approach for multiclass crop classification using Multispectral satellite image is proposed. The purpose of this work is to utilize the optimization capabilities of GEP for tuning the fuzzy membership functions. The capabilities of GEP as a classifier is also studied. The proposed method is compared to Bayesian and Maximum likelihood classifier in terms of performance evaluation. From the results we can conclude that the proposed method is effective for classification.

Robust formulations for clustering-based large-scale classification

Chebyshev-inequality-based convex relaxations of Chance-Constrained Programs (CCPs) are shown to be useful for learning classifiers on massive datasets. In particular, an algorithm that integrates efficient clustering procedures and CCP approaches for computing classifiers on large datasets is proposed. The key idea is to identify high density regions or clusters from individual class conditional densities and then use a CCP formulation to learn a classifier on the clusters. The CCP formulation ensures that most of the data points in a cluster are correctly classified by employing a Chebyshev-inequality-based convex relaxation. This relaxation is heavily dependent on the second-order statistics. However, this formulation and in general such relaxations that depend on the second-order moments are susceptible to moment estimation errors. One of the contributions of the paper is to propose several formulations that are robust to such errors. In particular a generic way of making such formulations robust to moment estimation errors is illustrated using two novel confidence sets. An important contribution is to show that when either of the confidence sets is employed, for the special case of a spherical normal distribution of clusters, the robust variant of the formulation can be posed as a second-order cone program. Empirical results show that the robust formulations achieve accuracies comparable to that with true moments, even when moment estimates are erroneous. Results also illustrate the benefits of employing the proposed methodology for robust classification of large-scale datasets.

Tensile flow and work hardening behavior of hot cross-rolled AA7010 aluminum alloy sheets

The present work describes the tensile flow and work hardening behavior of a high strength 7010 aluminum alloy by constitutive relations. The alloy has been hot rolled by three different cross-rolling schedules. Room temperature tensile properties have been evaluated as a function of tensile axis orientation in the as-hot rolled as well as peak aged conditions. It is found that both the Ludwigson and a generalized Voce-Bergstrom relation adequately describe the tensile flow behavior of the present alloy in all conditions compared to the Hollomon relation. The variation in the Ludwigson fitting parameter could be correlated well with the microstructural features and anisotropic contribution of strengthening precipitates in the as-rolled and peak aged conditions, respectively. The hardening rate and the saturation stress of the first Voce-Bergstrom parameter, on the other hand, depend mainly on the crystallographic texture of the specimens. It is further shown that for the peak aged specimens the uniform elongation (epsilon(u)) derived from the Ludwigson relation matches well with the measured epsilon(u) irrespective of processing and loading directions. However, the Ludwigson fit overestimates the epsilon(u) in case of the as-rolled specimens. The Hollomon fit, on the other hand, predicts well the measured epsilon(u), of the as-rolled specimens but severely underestimates the epsilon(u), for the peak aged specimens. Contrarily, both the relations significantly overestimate the UTS of the as-rolled and the peak aged specimens. The Voce-Bergstrom parameters define the slope of e Theta-sigma plots in the stage-III regime when the specimens show a classical linear decrease in hardening rate in stage-III. Further analysis of work hardening behavior throws some light on the effect of texture on the dislocation storage and dynamic recovery.

Capacity bounds for the Gaussian X channel

We consider bounds for the capacity region of the Gaussian X channel (XC), a system consisting of two transmit-receive pairs, where each transmitter communicates with both the receivers. We first classify the XC into two classes, the strong XC and the mixed XC. In the strong XC, either the direct channels are stronger than the cross channels or vice-versa, whereas in the mixed XC, one of the direct channels is stronger than the corresponding cross channel and vice-versa. After this classification, we give outer bounds on the capacity region for each of the two classes. This is based on the idea that when one of the messages is eliminated from the XC, the rate region of the remaining three messages are enlarged. We make use of the Z channel, a system obtained by eliminating one message and its corresponding channel from the X channel, to bound the rate region of the remaining messages. The outer bound to the rate region of the remaining messages defines a subspace in R-+(4) and forms an outer bound to the capacity region of the XC. Thus, the outer bound to the capacity region of the XC is obtained as the intersection of the outer bounds to the four combinations of the rate triplets of the XC. Using these outer bounds on the capacity region of the XC, we derive new sum-rate outer bounds for both strong and mixed Gaussian XCs and compare them with those existing in literature. We show that the sum-rate outer bound for strong XC gives the sum-rate capacity in three out of the four sub-regions of the strong Gaussian XC capacity region. In case of mixed Gaussian XC, we recover the recent results in 11] which showed that the sum-rate capacity is achieved in two out of the three sub-regions of the mixed XC capacity region and give a simple alternate proof of the same.

Unprecedented formation of a 14-membered dihydropyran macrocycle via sequential olefin cross metathesis-intramolecular hetero Diels-Alder reaction

Formation of a 2,3-dihydro-4H-pyran containing 14-membered macrocycle by sequential olefin cross metathesis and a highly regiospecific hetero Diels-Alder reaction was observed in the reaction of a hydroxydienone derived from tartaric acid with Grubbs' second generation catalyst. It was found that the free alcohol in the hydroxyenone led to the macrocycle formation, while protection of the hydroxy group formed the ring closing metathesis product. (C) 2013 Elsevier Ltd. All rights reserved.

Efficient method of moving shadow detection and vehicle classification

Moving shadow detection and removal from the extracted foreground regions of video frames, aim to limit the risk of misconsideration of moving shadows as a part of moving objects. This operation thus enhances the rate of accuracy in detection and classification of moving objects. With a similar reasoning, the present paper proposes an efficient method for the discrimination of moving object and moving shadow regions in a video sequence, with no human intervention. Also, it requires less computational burden and works effectively under dynamic traffic road conditions on highways (with and without marking lines), street ways (with and without marking lines). Further, we have used scale-invariant feature transform-based features for the classification of moving vehicles (with and without shadow regions), which enhances the effectiveness of the proposed method. The potentiality of the method is tested with various data sets collected from different road traffic scenarios, and its superiority is compared with the existing methods. (C) 2013 Elsevier GmbH. All rights reserved.

Classification of Vehicles Using Magnetic Field Angle Model

This paper presents an efficient approach to the modeling and classification of vehicles using the magnetic signature of the vehicle. A database was created using the magnetic signature collected over a wide range of vehicles(cars). A sensor dependent approach called as Magnetic Field Angle Model is proposed for modeling the obtained magnetic signature. Based on the data model, we present a novel method to extract the feature vector from the magnetic signature. In the classification of vehicles, a linear support vector machine configuration is used to classify the vehicles based on the obtained feature vectors.

Classification, representation, and automatic extraction of deformation features in sheet metal parts

This paper presents classification, representation and extraction of deformation features in sheet-metal parts. The thickness is constant for these shape features and hence these are also referred to as constant thickness features. The deformation feature is represented as a set of faces with a characteristic arrangement among the faces. Deformation of the base-sheet or forming of material creates Bends and Walls with respect to a base-sheet or a reference plane. These are referred to as Basic Deformation Features (BDFs). Compound deformation features having two or more BDFs are defined as characteristic combinations of Bends and Walls and represented as a graph called Basic Deformation Features Graph (BDFG). The graph, therefore, represents a compound deformation feature uniquely. The characteristic arrangement of the faces and type of bends belonging to the feature decide the type and nature of the deformation feature. Algorithms have been developed to extract and identify deformation features from a CAD model of sheet-metal parts. The proposed algorithm does not require folding and unfolding of the part as intermediate steps to recognize deformation features. Representations of typical features are illustrated and results of extracting these deformation features from typical sheet metal parts are presented and discussed. (C) 2013 Elsevier Ltd. All rights reserved.

Classification of Myopathies on Molecular basis in Drosophila using Raman spectroscopy

Myopathies are muscular diseases in which muscle fibers degenerate due to many factors such as nutrient deficiency, infection and mutations in myofibrillar etc. The objective of this study is to identify the bio-markers to distinguish various muscle mutants in Drosophila (fruit fly) using Raman Spectroscopy. Principal Components based Linear Discriminant Analysis (PC-LDA) classification model yielding >95% accuracy was developed to classify such different mutants representing various myopathies according to their physiopathology.

Classification calibration dimension for general multiclass losses

We study consistency properties of surrogate loss functions for general multiclass classification problems, defined by a general loss matrix. We extend the notion of classification calibration, which has been studied for binary and multiclass 0-1 classification problems (and for certain other specific learning problems), to the general multiclass setting, and derive necessary and sufficient conditions for a surrogate loss to be classification calibrated with respect to a loss matrix in this setting. We then introduce the notion of \emph{classification calibration dimension} of a multiclass loss matrix, which measures the smallest `size' of a prediction space for which it is possible to design a convex surrogate that is classification calibrated with respect to the loss matrix. We derive both upper and lower bounds on this quantity, and use these results to analyze various loss matrices. In particular, as one application, we provide a different route from the recent result of Duchi et al.\ (2010) for analyzing the difficulty of designing `low-dimensional' convex surrogates that are consistent with respect to pairwise subset ranking losses. We anticipate the classification calibration dimension may prove to be a useful tool in the study and design of surrogate losses for general multiclass learning problems.

Differentially private stochastic gradient descent algorithm for multiparty classification

We consider the problem of developing privacy-preserving machine learning algorithms in a dis-tributed multiparty setting. Here different parties own different parts of a data set, and the goal is to learn a classifier from the entire data set with-out any party revealing any information about the individual data points it owns. Pathak et al [7]recently proposed a solution to this problem in which each party learns a local classifier from its own data, and a third party then aggregates these classifiers in a privacy-preserving manner using a cryptographic scheme. The generaliza-tion performance of their algorithm is sensitive to the number of parties and the relative frac-tions of data owned by the different parties. In this paper, we describe a new differentially pri-vate algorithm for the multiparty setting that uses a stochastic gradient descent based procedure to directly optimize the overall multiparty ob-jective rather than combining classifiers learned from optimizing local objectives. The algorithm achieves a slightly weaker form of differential privacy than that of [7], but provides improved generalization guarantees that do not depend on the number of parties or the relative sizes of the individual data sets. Experimental results corrob-orate our theoretical findings.

Extension of TSVM to multi-class and hierarchical text classification problems With general losses

Transductive SVM (TSVM) is a well known semi-supervised large margin learning method for binary text classification. In this paper we extend this method to multi-class and hierarchical classification problems. We point out that the determination of labels of unlabeled examples with fixed classifier weights is a linear programming problem. We devise an efficient technique for solving it. The method is applicable to general loss functions. We demonstrate the value of the new method using large margin loss on a number of multi-class and hierarchical classification datasets. For maxent loss we show empirically that our method is better than expectation regularization/constraint and posterior regularization methods, and competitive with the version of entropy regularization method which uses label constraints.