Nonlinear performance evaluation model for throughput of AQM scheme using full factorial design approach

The performance of active queue management (AQM) is measured in terms of throughput, delay, queue size, and loss rate. We have carried out the optimized performance measure of throughput for AQM scheme random early detection (RED) using full factorial design (FDD) technique that is a new approach of performance analysis particularly for congestion control algorithms. We have considered the input factors, viz, buffer size, maximum threshold, and the number of file transfer protocol (FTP) sources for the evaluation of RED that can be used for other AQM schemes, viz, adaptive RED, three‐section RED (TRED), and adaptive queue management with random dropping (AQMRD). The effect of each input factor as well as their interactions are evaluated using factorial design technique that results to obtain the nonlinear equation for performance measure in terms of input factors buffer size, maximum threshold, and the number of FTP sources. Finally, we show the contour plots for variation of performance measure throughput (steady state) from minimum to maximum values with respect to the different setting of input parameters.     

A new controller for boost dc–dc converters based on a novel sliding surface

ABSTRACT

In this paper, two control schemes for boost converters affected by uncertainties in input voltage and load are proposed. The boost converter dynamics is redefined in terms of new state variables to facilitate the use of a disturbance observer that can estimate matched and unmatched disturbances. A sliding surface, which is new in the context of boost converters, is proposed to enable tracking and regulation of output voltage without requiring measurement of input voltage and load current. The stability of the overall system including the disturbance observer, the sliding variable and the output is proved. The performance of the schemes is assessed for regulation of output voltage and tracking of reference voltage by simulation as well as experimentation in which various types of uncertainties and various types of reference voltages are considered.     

Attention-based multimodal contextual fusion for sentiment and emotion classification using bidirectional LSTM

Multimedia Tools and Applications - Due to the availability of an enormous amount of multimodal content on the social web and its applications, automatic sentiment analysis, and emotion detection...     

An intelligent wavelet transform-based framework to detect subsurface fires with NOAA–AVHRR images

Subsurface coal fires (in this article, termed as hotspots), responsible for atmospheric pollution, human fatalities and perilous land subsidence, pose a big threat to major coal-producing countries in the world. The majority of the research performed to date has focused on providing hotspot allocation information for a specific region of interest and most has explored quite expensive high-resolution Landsat Thematic Mapper (TM) satellite images for the same. This article aims to investigate the applicability of a wavelet transform-based model to detect subsurface fires (hotspots) with freely available National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA–AVHRR) images and find out the possibility of extracting novel hotspot features by applying a wavelet transform-based analysis technique. The proposed feature vector consists of wavelet variance coefficients (WVCs) obtained from scale-by-scale decomposition of the AVHRR image variance and builds up a strong base for designing an accurate classification system. Furthermore, the support vector machine (SVM), an efficient machine learning tool, is applied to the proposed feature vector in order to develop a classification model. The demonstrated results successfully prove the effectiveness of the proposed framework as the classified images show a good correspondence with records of subsurface fires mapped by the Bharat Coking Coal Limited (BCCL), India. The effectiveness of the SVM method is also evaluated in comparison with the classical neural network-based approach.     

A hybrid Taguchi-artificial neural network approach to predict surface roughness during electric discharge machining of titanium alloys

In the present study, electric discharge machining process was used for machining of titanium alloys. Eight process parameters were varied during the process. Experimental results showed that current and pulse-on-time significantly affected the performance characteristics. Artificial neural network coupled with Taguchi approach was applied for optimization and prediction of surface roughness. The experimental results and the predicted results showed good agreement. SEM was used to investigate the surface integrity. Analysis for migration of different chemical elements and formation of compounds on the surface was performed using EDS and XRD pattern. The results showed that high discharge energy caused surface defects such as cracks, craters, thick recast layer, micro pores, pin holes, residual stresses and debris. Also, migration of chemical elements both from electrode and dielectric media were observed during EDS analysis. Presence of carbon was seen on the machined surface. XRD results showed formation of titanium carbide compound which precipitated on the machined surface.     

Numerical implementation of drop and tilt method of five-axis tool positioning for tensor product surfaces

The Drop and Tilt tool position method for five-axis machining positions a toroidal tool to have two points of contact with a tensor product Bézier surface. The method results in high order simultaneous non-linear transcendental equations that are slow to solve. In this paper, an efficient numerical implementation of the Drop and Tilt Method of multi-point tool positioning on tensor product surfaces is presented. The method was implemented in C++, and is a direct method that does not convert the tensor product surface into an intermediate representation such as triangles or point clouds. The method is successfully demonstrated on a number of tensor product Bézier surfaces.     

Adaptive Discretization for Computerized Tomography

Two adaptive discretization frameworks are tested for computerized tomography (CT) data reconstruction. Removal of inactive pixels is primary motivation. Efficient and user independent entropy optimized masking is employed for spatial filtering purposes. Density of nodes at high gradient of reconstructed physical property is used as adaptation criterion. An alternative option, independent from noisy projection data and nature of the physical properties, is also discussed. Sensitivity analysis between the uniform and nonuniform (evolved via adaptive route) reconstruction grid reveals the utility of nonuniform grids. Iterative and transform based reconstruction techniques are used. Outcomes are tested successfully on three real world projection data from two different compact CT setups and one commercial high-resolution micro-CT scanner.     

Strategies for software-based hybrid business models

The open source approach to software development has been used by software organizations in tandem with their existing business models, which are based on proprietary software licensing. This led to the creation of hybrid business models that merge open source and proprietary paradigms. This paper explores the practices used by software product vendors using hybrid business models and proposes strategies emerging out of these practices using interpretive, single case study research design.     

Make3D: Learning 3D Scene Structure from a Single Still Image

We consider the problem of estimating detailed 3D structure from a single still image of an unstructured environment. Our goal is to create 3D models that are both quantitatively accurate as well as visually pleasing. For each small homogeneous patch in the image, we use a Markov Random Field (MRF) to infer a set of "plane parameters” that capture both the 3D location and 3D orientation of the patch. The MRF, trained via supervised learning, models both image depth cues as well as the relationships between different parts of the image. Other than assuming that the environment is made up of a number of small planes, our model makes no explicit assumptions about the structure of the scene; this enables the algorithm to capture much more detailed 3D structure than does prior art and also give a much richer experience in the 3D flythroughs created using image-based rendering, even for scenes with significant nonvertical structure. Using this approach, we have created qualitatively correct 3D models for 64.9 percent of 588 images downloaded from the Internet. We have also extended our model to produce large-scale 3D models from a few images.