Probabilistic techniques for intrusion detection based on computeraudit data

This paper presents a series of studies on probabilistic properties of activity data in an information system for detecting intrusions into the information system. Various probabilistic techniques of intrusion detection, including decision tree, Hotelling's T² test, chi-square multivariate test, and Markov chain are applied to the same training set and the same testing set of computer audit data for investigating the frequency property and the ordering property of computer audit data. The results of these studies provide answers to several questions concerning which properties are critical to intrusion detection. First, our studies show that the frequency property of multiple audit event types in a sequence of events is necessary for intrusion detection. A single audit event at a given time is not sufficient for intrusion detection. Second, the ordering property of multiple audit events provides additional advantage to the frequency property for intrusion detection. However, unless the scalability problem of complex data models taking into account the ordering property of activity data is solved, intrusion detection techniques based on the frequency property provide a viable solution that produces good intrusion detection performance with low computational overhead     

Prediction of Hydrate Phase Equilibria in Aqueous Solutions of Salt and Organic Inhibitor Using a Combined Equation of State and Activity Coefficient‐Based Model

A thermodynamic model based on combination of the Valderrama modification of the Patel‐Teja equation of state with non‐density dependent mixing rules and a modification of a Debye‐Hückel electrostatic term is extended to systems containing salt and organic inhibitor by correcting the properties of the aqueous phase such as dielectric constant, density and molecular weight. A linear mixing rule is used for determining the dielectric constant of salt‐free mixture by introducing an interaction parameter (in dielectric constant mixing rule), which is tuned using the freezing point data of aqueous solutions containing salt and organic inhibitor. The binary interaction parameter between salt and organic inhibitor is adjusted using water vapour pressure data in the presence of salt and organic inhibitor. The predicted hydrate dissociation conditions are in acceptable agreement with the experimental data, demonstrating the reliability of the model developed in this work.     

Direct and gradient-based average strain estimation by using weighted nearest neighbor cross-correlation peaks

In this paper, two novel approaches, gradientbased and direct strain estimation techniques, are proposed for high-quality average strain imaging incorporating a cost function maximization. Stiffness typically is a continuous function. Consequently, stiffness of proximal tissues is very close to that of the tissue corresponding to a given data window. Hence, a cost function is defined from exponentially weighted neighboring pre- and post-compression RF echo normalized cross-correlation peaks in the lateral (for displacement estimation) or in both the axial and the lateral (for direct strain estimation) directions. This enforces a controlled continuity in displacement/strain and average displacement/strain is calculated from the corresponding maximized cost function. Axial stress causes lateral shift in the tissue. Therefore, a 1-D post-compression echo segment is selected by incorporating Poisson's ratio. Two stretching factors are considered simultaneously in gradient-based strain estimation that allow imaging the lesions properly. The proposed time-domain gradient-based and direct-strain-estimation-based algorithms demonstrate significantly better performance in terms of elastographic signal-to-noise ratio (SNRe), elastographic contrast-to-noise ratio (CNRe), peak signal-to-noise ratio (PSNR), and mean structural similarity (MSSIM) than the other reported time-domain gradientbased and direct-strain-estimation techniques in finite element modeling (FEM) simulation and phantom experiments. For example, in FEM simulation, it has been found that the proposed direct strain estimation method can improve up to approximately 2.49 to 8.71, 2.2 to 6.63, 1.5 to 5, and 1.59 to 2.45 dB in the SNRe, CNRe, PSNR, and MSSIM compared with the traditional direct strain estimation method, respectively, and the proposed gradient-based algorithm demonstrates 2.99 to 16.26, 18.74 to 23.88, 3 to 9.5, and 0.6 to 5.36 dB improvement in the SNRe, CNRe, PSNR, and MSSIM, respectively, compared with a recently reported time-domain gradient-based technique. The range of improvement as noted above is for low to high applied strains. In addition, the comparative results using the in vivo breast data (including malignant or benign masses) also show that the lesion size is better defined by the proposed gradient-based average strain estimation technique.     

Effect of primary parameters on the performance of PEM fuel cell

A one-dimensional, steady-state and isothermal model for a proton exchange membrane (PEM) fuel cell has been developed to investigate the effects of various parameters such as the molar fraction of nitrogen gas, relative humidity, temperature, pressure, membrane thickness, anode and cathode stoichiometric flow ratio and the distribution of oxygen in the cathode catalyst while water transfer in membrane is produced by diffusion, pressure gradient and electro-osmotic drag. The most critical problems to overcome in the proton exchange membrane (PEM) fuel cell technology are the water and thermal management. The results show that the cell performance increases as operating pressure and temperature are increased. The performance of cell can decrease by decreasing the relative humidity of inlet gases and increasing the membrane thickness. Increasing the anode and cathode stoichiometric flow ratio can also improve the cell performance. As the oxygen concentration becomes zero in about 8 percent depth of cathode catalyst layer, the thickness of cathode catalyst layer can be reduced 92 percent without any potential loss in output voltage. The cathode activation loss also becomes high by increasing the molar fraction of nitrogen gas. The modeling results agree very well with experimental results.     

A control scheme to enhance low voltage ride‐through of brushless doubly‐fed induction generators

The use of brushless doubly‐fed induction generator has been recently proposed for wind turbines because of its variable speed operation with fractional size converter without the need to brush and slip ring. This paper introduces a control scheme to improve low voltage ride‐through capability of doubly‐fed induction generator considering grid code requirements. The proposed control strategy is based on analysis of flux linkages and back electromotive forces and intends to retain the control‐winding current below the safety limit (typically 2 pu) during severe voltage dips. The time‐domain simulations validate effectiveness of the proposed scheme to protect the converter against failure as well as support reactive power required by German grid code. Copyright © 2015 John Wiley & Sons, Ltd.     

Modelling and analysis of productivity teamwork based on information technology

Organizations are usually represented and evaluated by their output. Therefore, all procedures and models which may affect and improve output attract the attention of researchers. The effects of organizing work teams as one essential resource of organizations, in addition to the structure, size and effectiveness of the team, are also considered important factors. This paper reports on an investigation performed to define a model to explain how the results of teamwork may improve production, while considering the role of IT as an effective parameter. Also, it is shown that if there is a number of IT improvement options, there may be a mix of IT improvement and team size increase that will yield the most cost efficient increase in team output.     

An efficient neurodynamic model to solve nonconvex nonlinear optimization problems and its applications

This paper presents a recurrent neural network for solving nonconvex nonlinear optimization problems subject to nonlinear inequality constraints. First, the p-power transformation is exploited for local convexification of the Lagrangian function in nonconvex nonlinear optimization problem. Next, the proposed neural network is constructed based on the Karush–Kuhn–Tucker (KKT) optimality conditions and the projection function. An important property of this neural network is that its equilibrium point corresponds to the optimal solution of the original problem. By utilizing an appropriate Lyapunov function, it is shown that the proposed neural network is stable in the sense of Lyapunov and convergent to the global optimal solution of the original problem. Also, the sensitivity of the convergence is analysed by changing the scaling factors. Compared with other existing neural networks for such problem, the proposed neural network has more advantages such as high accuracy of the obtained solutions, fast convergence, and low complexity. Finally, simulation results are provided to show the benefits of the proposed model, which compare to or outperform existing models.     

Synergistic CO2‐Sieving from Polymer with Intrinsic Microporosity Masking Nanoporous Single‐Layer Graphene

High‐flux nanoporous single‐layer graphene membranes are highly promising for energy‐efficient gas separation. Herein, in the context of carbon capture, a remarkable enhancement in the CO₂ selectivity is demonstrated by uniquely masking nanoporous single‐layer graphene with polymer with intrinsic microporosity (PIM‐1). In the process, a major bottleneck of the state‐of‐the‐art pore‐incorporation techniques in graphene has been overcome, where in addition to the molecular sieving nanopores, larger nonselective nanopores are also incorporated, which so far, has restricted the realization of CO₂‐sieving from graphene membranes. Overall, much higher CO₂/N₂ selectivity (33) is achieved from the composite film than that from the standalone nanoporous graphene (NG) (10) and the PIM‐1 membranes (15), crossing the selectivity target (20) for postcombustion carbon capture. The selectivity enhancement is explained by an analytical gas transport model for NG, which shows that the transport of the stronger‐adsorbing CO₂ is dominated by the adsorbed phase transport pathway whereas the transport of N₂ benefits significantly from the direct gas‐phase transport pathway. Further, slow positron annihilation Doppler broadening spectroscopy reveals that the interactions with graphene reduce the free volume of interfacial PIM‐1 chains which is expected to contribute to the selectivity. Overall, this approach brings graphene membrane a step closer to industrial deployment.     

Application of Bernoulli matrix method for solving two-dimensional hyperbolic telegraph equations with Dirichlet boundary conditions

The present article is devoted to develop a new approach and methodology to find the approximate solution of second order two-dimensional telegraph equations with the Dirichlet boundary conditions. We first transform the telegraph equations into equivalent partial integro-differential equations (PIDEs) which contain both initial and boundary conditions and therefore can be solved numerically in a more appropriate manner. Operational matrices of integration and differentiation of Bernoulli polynomials together with the completeness of these polynomials are used to reduce the PIDEs into the associated algebraic generalized Sylvester equations which can be solved by an efficient Krylov subspace iterative (i.e., BICGSTAB) method. The efficiency of the proposed method has been confirmed with several test examples and it is clear that the results are acceptable and found to be in good agreement with exact solutions. We have compared the numerical results of the proposed method with radial basis function method and differential quadrature method. Also, the method is simple, efficient and produces very accurate numerical results in considerably small number of basis functions and hence reduces computational effort. Moreover, the technique is easy to apply for multidimensional problems.