Unsupervised deep learning approach for network intrusion detection combining convolutional autoencoder and one-class SVM

Applied Intelligence - With the rapid advancement in network technologies, the need for cybersecurity has gained increasing momentum in recent years. As a primary defense mechanism, an intrusion...     

Single mask fabrication process for movable MEMS devices

The current work reports on the realization of movable micromachining devices using self-aligned single-mask fabrication process. Only dry etching process utilizing inductively coupled plasma reactive ion etching was used to release 3D micro structures from single crystal silicon substrate. No wet etching process is required to release the structures as is the case with silicon on insulator (SOI) wafers. Also the developed process does not require an SOI substrate and accordingly dispensing with the application of a wet etching step, thus yielding uniform structures without stiction. The optimized process was applied to realize thermally actuated microgrippers. The article presents the development of the fabrication process and demonstrates the operation of the fabricated device. The optimized process provides an avenue for low cost fabrication of movable micromachining devices without the use of complicated wet etching steps typically associated with SOI substrates.     

Fuzzy logic controller and cascade inverter for direct torque control of IM

In this paper, a five-level cascaded H-bridge multilevel inverters topology is applied on induction motor control known as direct torque control (DTC) strategy. More inverter states can be generated by a five-level inverter which improves voltage selection capability. This paper also introduces two different control methods to select the appropriate output voltage vector for reducing the torque and flux error to zero. The first is based on the conventional DTC scheme using a pair of hysteresis comparators and look up table to select the output voltage vector for controlling the torque and flux. The second is based on a new fuzzy logic controller using Sugeno as the inference method to select the output voltage vector by replacing the hysteresis comparators and lookup table in the conventional DTC, to which the results show more reduction in torque ripple and feasibility of smooth stator current. By using Matlab/Simulink, it is verified that using five-level inverter in DTC drive can reduce the torque ripple in comparison with conventional DTC, and further torque ripple reduction is obtained by applying fuzzy logic controller. The simulation results have also verified that using a fuzzy controller instead of a hysteresis controller has resulted in reduction in the flux ripples significantly as well as reduces the total harmonic distortion of the stator current to below 4 %.     

Performance evaluation of an importance sampling technique in a Jackson network

Importance sampling is a technique that is commonly used to speed up Monte Carlo simulation of rare events. However, little is known regarding the design of efficient importance sampling algorithms in the context of queueing networks. The standard approach, which simulates the system using an a priori fixed change of measure suggested by large deviation analysis, has been shown to fail in even the simplest network settings. Estimating probabilities associated with rare events has been a topic of great importance in queueing theory, and in applied probability at large. In this article, we analyse the performance of an importance sampling estimator for a rare event probability in a Jackson network. This article carries out strict deadlines to a two-node Jackson network with feedback whose arrival and service rates are modulated by an exogenous finite state Markov process. We have estimated the probability of network blocking for various sets of parameters, and also the probability of missing the deadline of customers for different loads and deadlines. We have finally shown that the probability of total population overflow may be affected by various deadline values, service rates and arrival rates.     

Fabrication of co-PVDF/modacrylic/SiO2 nanofibrous membrane: Composite separator for safe and high performance lithium-ion batteries

Highly porous free-standing co-poly(vinylidene fluoride)/modacrylic/SiO₂ nanofibrous membrane was developed using electrically-assisted solution blow spinning method. The performance and the potential of the membrane as a lithium-ion battery separator were investigated. The addition of modacrylic enhanced the solution spinnability that resulted in defect-free membranes. Moreover, the presence of modacrylic enhanced the dimensional and thermal stabilities, while the addition of hydrophilic SiO₂ nanoparticle enhanced both mechanical property and ionic conductivity. Combustion test results illustrated that the presence of modacrylic provide flame retarding property over a set of different polymeric-based membranes. Electrochemical performance results showed that the developed membrane can increase the battery capacity compared with the commercial separator.     

An Improvement Energy Consumption Policy Using Communication Reduction in Wireless Body Sensor Network

Dedicated short-range communications (DSRC) is an important wireless technology for current and future automotive safety and mitigation of traffic jams. In this work, we have designed a Coplanar waveguide microstrip patch antenna with linear, upper and bottom and side slots for application in DSRC. The patch antenna was designed using glass epoxy substrate (FR4). Various parametric analyses such as the current distribution, reflection coefficient, radiation pattern on E- and H-plane as well as the realized gain (dB) were performed. The results were obtained by simulation using high-frequency structure simulator tool. The proposed antenna covers a frequency band of 5.8–5.9 GHz which is highly dedicated to the DSRC wireless communication technology for enhancement of safety of the automotive transport system. The designed antenna shows a good return loss of ??19 dB at 5.9 GHz.The designed antenna shows a promising gain, return loss and radiation pattern for use in DSRC for automotive transport systems.

     

Eliminating Selective Dropping Attack in Mobile Ad Hoc Network

Wireless Personal Communications - In this paper, we propose a new reputation approach, called I-WD (improved WatchDog). We attempt to eliminate selective dropping attack that ensue when malicious...     

An Iterative Method for Deciding SVM and Single Layer Neural Network Structures

We present two new classifiers for two-class classification problems using a new Beta-SVM kernel transformation and an iterative algorithm to concurrently select the support vectors for a support vector machine (SVM) and the hidden units for a single hidden layer neural network to achieve a better generalization performance. To construct the classifiers, the contributing data points are chosen on the basis of a thresholding scheme of the outputs of a single perceptron trained using all training data samples. The chosen support vectors are used to construct a new SVM classifier that we call Beta-SVN. The number of chosen support vectors is used to determine the structure of the hidden layer in a single hidden layer neural network that we call Beta-NN. The Beta-SVN and Beta-NN structures produced by our method outperformed other commonly used classifiers when tested on a 2-dimensional non-linearly separable data set.     

A robust optimization approach for imprecise data envelopment analysis

Crisp input and output data are fundamentally indispensable in traditional data envelopment analysis (DEA). However, the input and output data in real-world problems are often imprecise or ambiguous. Some researchers have proposed interval DEA (IDEA) and fuzzy DEA (FDEA) to deal with imprecise and ambiguous data in DEA. Nevertheless, many real-life problems use linguistic data that cannot be used as interval data and a large number of input variables in fuzzy logic could result in a significant number of rules that are needed to specify a dynamic model. In this paper, we propose an adaptation of the standard DEA under conditions of uncertainty. The proposed approach is based on a robust optimization model in which the input and output parameters are constrained to be within an uncertainty set with additional constraints based on the worst case solution with respect to the uncertainty set. Our robust DEA (RDEA) model seeks to maximize efficiency (similar to standard DEA) but under the assumption of a worst case efficiency defied by the uncertainty set and it’s supporting constraint. A Monte-Carlo simulation is used to compute the conformity of the rankings in the RDEA model. The contribution of this paper is fourfold: (1) we consider ambiguous, uncertain and imprecise input and output data in DEA; (2) we address the gap in the imprecise DEA literature for problems not suitable or difficult to model with interval or fuzzy representations; (3) we propose a robust optimization model in which the input and output parameters are constrained to be within an uncertainty set with additional constraints based on the worst case solution with respect to the uncertainty set; and (4) we use Monte-Carlo simulation to specify a range of Gamma in which the rankings of the DMUs occur with high probability.