Design of interval networks based on neural network and Choquet integral

Design and learning of networks best suited for a particular application is a never-ending process. But this process is restricted due to problems like stability, plasticity, computation and memory consumption. In this paper, we try to overcome these problems by proposing two interval networks (INs), based on a simple feed-forward neural network (NN) and Choquet integral (CI). They have simple structures that reduce the problems of computation and memory consumption. The use of Lyapunov stability (LS) in combination with fuzzy difference (FD) based learning algorithm evolve the converging and diverging process which in turn assures the stability. FD gives a range of variation of parameters having the lower and the upper bounds within which the system is stable thus defining the plasticity. Effectiveness and applicability of the NN and CI based network models are investigated on several benchmark problems dealing with both identification and control.     

SENFIS: a Sensor Node File System for increasing the scalability and reliability of Wireless Sensor Networks applications

In the last years the Wireless Sensor Networks’ (WSN) technology has been increasingly employed in various application domains. The extensive use of WSN posed new challenges in terms of both scalability and reliability. This paper proposes Sensor Node File System (SENFIS), a novel file system for sensor nodes, which addresses both scalability and reliability concerns. SENFIS can be mainly used in two broad scenarios. First, it can transparently be employed as a permanent storage for distributed TinyDB queries, in order to increase the reliability and scalability. Second, it can be directly used by a WSN application for permanent storage of data on the WSN nodes. The experimental section shows that SENFIS implementation makes an efficient use of resources in terms of energy consumption, memory footprint, flash wear levelling, while achieving execution times similarly with existing WSN file systems.     

Empirical validation of object-oriented metrics for predicting fault proneness models

Empirical validation of software metrics used to predict software quality attributes is important to ensure their practical relevance in software organizations. The aim of this work is to find the relation of object-oriented (OO) metrics with fault proneness at different severity levels of faults. For this purpose, different prediction models have been developed using regression and machine learning methods. We evaluate and compare the performance of these methods to find which method performs better at different severity levels of faults and empirically validate OO metrics given by Chidamber and Kemerer. The results of the empirical study are based on public domain NASA data set. The performance of the predicted models was evaluated using Receiver Operating Characteristic (ROC) analysis. The results show that the area under the curve (measured from the ROC analysis) of models predicted using high severity faults is low as compared with the area under the curve of the model predicted with respect to medium and low severity faults. However, the number of faults in the classes correctly classified by predicted models with respect to high severity faults is not low. This study also shows that the performance of machine learning methods is better than logistic regression method with respect to all the severities of faults. Based on the results, it is reasonable to claim that models targeted at different severity levels of faults could help for planning and executing testing by focusing resources on fault-prone parts of the design and code that are likely to cause serious failures.     

Dynamic content-page identification for media-rich websites

Knowledge of the information goal of users is critical in website design, analyzing the efficacy of such designs, and in ensuring effective user-access to desired information. Determining the information goal is complex due to the subjective and latent nature of user information needs. This challenge is further exacerbated in media-rich websites since the semantics of media-based information is context-based and emergent. A critical step in determining information goals lies in the identification of content pages. These are the pages which contain the information the user seeks. We propose a method to automatically determine the content pages by taking into account the organization of the web site, the media-based information content, as well as the influence of a specific user browsing pattern. Given a specific browsing pattern, in our method, putative content pages are identified as the pages corresponding to the local minima of page-content entropy values. For an (unknown) user information goal this intuitively corresponds to modeling the progressive transition of the user from pages with generic information to those with specific information. Experimental investigations on media rich sites demonstrate the effectiveness of the technique and underline its potential in modeling user information needs and actions in a media-rich web.     

Uncertainty propagation in puff-based dispersion models using polynomial chaos

Atmospheric dispersion is a complex nonlinear physical process with numerous uncertainties in model parameters, inputs, source parameters, initial and boundary conditions. Accurate propagation of these uncertainties through the dispersion models is crucial for a reliable prediction of the probability distribution of the states and assessment of risk. A simple three-dimensional Gaussian puff-based dispersion model is used as a test case to study the effect of uncertainties in the model parameters and initial conditions on the output concentration. A polynomial chaos based approach is used to numerically investigate the evolution of the model output uncertainties due to initial condition and parametric uncertainties. The polynomial chaos solution is found to be an accurate approximation to ground truth, established by Monte Carlo simulation, while offering an efficient computational approach for large nonlinear systems with a relatively small number of uncertainties.     

Modified particle swarm optimization for a multimodal mixed-variable laser peening process

Optimization problems that result in shock, impact, and explosion type disciplines typically have mixed design variables, multiple optimal solutions, and high computational cost of an analysis. In the optimization literature, many researchers have solved problems involving mixed variables or multiple optima, but it is difficult to find multiple optima of a mixed-variable and high computation cost problem using an particle swarm optimization (PSO). To solve such problems, a mixed-variable niching PSO (MNPSO) is developed. The four modifications introduced to the PSO are: Latin Hypercube sampling-based particle generation, a mixed-variable handling technique, a niching technique, and surrogate model-based design space localization. The proposed method is demonstrated on the laser peening (LP) problem. The LP process induces favorable residual stress on the peened surface to improve the fatigue and fretting properties of the material. In many applications of LP, geometric configurations and dimensional integrity requirements of the component can constrain implementation of an optimal solution. In such cases, it is necessary to provide multiple alternatives to the designer so that a suitable one can be selected according to the requirements. It takes 24–72 CPU hours to perform an LP finite element analysis.     

Support vector regression based modeling of pier scour using field data

This paper investigates the potential of support vector machines based regression approach to model the local scour around bridge piers using field data. A dataset of consisting of 232 pier scour measurements taken from BSDMS were used for this analysis. Results obtained by using radial basis function and polynomial kernel based Support vector regression were compared with four empirical relation as well as with a backpropagation neural network and generalized regression neural network. A total of 154 data were used for training different algorithms whereas remaining 78 data were used to test the created model. A coefficient of determination value of 0.897 (root mean square error=0.356) was achieved by radial basis kernel based support vector regression in comparison to 0.880 and 0.835 (root mean square error=0.388 and 0.438) by backpropagation neural and generalized regression neural network. Comparisons of results with four predictive equations suggest an improved performance by support vector regression. Results with dimensionless data using all three algorithms suggest a better performance by dimensional data with this dataset. Sensitivity analysis suggests the importance of depth of flow and pier width in predicting the scour depth when using support vector regression based modeling approach.     

Imperfect fluid cosmology with heat flow

We discuss spatially homogeneous and locally rotationally symmetric Bianchi type V cosmological models under the influence of both bulk and shear viscosity coefficients with heat flow. Some exact solutions of the Einstein field equations are presented by using a law of variation for the generalized Hubble parameter that yields a constant value of the deceleration parameter. The law also generates power-law and exponential forms of the average scale factor in terms of the cosmic time t. To get a complete solution, a linear relation between the shear viscosity and the expansion scalar has been assumed. The presence of viscous terms, however, does not change the fundamental nature of the initial singularity. The solutions presented here satisfy all necessary conditions for physical acceptability. The thermodynamical relations, entropy production and energy condition in viscous fluid model are studied in detail.     

SPACE: A lightweight collaborative caching for clusters

In this paper, we introduce Systematic P2P Aided Cache Enhancement or SPACE, a new collaboration scheme among clients in a computer cluster of a high performance computing facility to share their caches with each other. The collaboration is achieved in a distributed manner, and is designed based on peer-to-peer computing model. The objective is to provide (1) a decentralized solution, and (2) a near optimal performance with reasonably low overhead. Simulation results are given to demonstrate the performance of the proposed scheme. In addition, the results show that SPACE evenly distributes work loads among participators, and entirely eliminates any requirement of a central cache manager.     

Obesity does not reduce maximum acceptable weights of lift

The maximum acceptable weights of lift (MAWL) of obese and non-obese participants were empirically investigated. Three obesity levels were considered: non-obese (18.5 kg/m(2)< or= body mass index (BMI)or= 40 kg/m(2)). Ten male and 10 female participants were recruited for each obesity level. The participants determined their MAWL for 18 different lifting task conditions (six lifting frequencies x three lifting heights). An analysis of variance (ANOVA) was conducted to determine the effects of obesity level, gender, lifting height, lifting frequency and their interactions on MAWL. Overall, the ANOVA results indicated that obesity does not reduce MAWL, and thus, suggested that the existing MAWL data can be used to accommodate both general and obese workers. However, further studies based on the biomechanical and physiological approaches are required to provide more complete understanding of obesity effects on lifting tolerance limits.