Design,Modeling and Control of a Simulator of an Aircraft Maneuver in the Wind Tunnel Using Cable Robot

International Journal of Control, Automation and Systems - In this paper, a new controllable simulator is proposed and modeled by which, experimental tests of the aircraft’s models can be...     

Progressive failure analysis of notched composite plate by utilizing macro mechanics approach

In this study, gradual and sudden reduction methods were combined to simulate a progressive failure in notched composite plates using a macro mechanics approach. Using the presented method, a progressive failure is simulated based on a linear softening law prior to a catastrophic failure, and thereafter, sudden reduction methods are employed for modeling a progressive failure. This combination method significantly reduces the computational cost and is also capable of simultaneously predicting the first and last ply failures (LPFs) in composite plates. The proposed method is intended to predict the first ply failure (FPF), LPF, and dominant failure modes of carbon/epoxy and glass/epoxy notched composite plates. In addition, the effects of mechanical properties and different stacking sequences on the propagation of damage in notched composite plates were studied. The results of the presented method were compared with experimental data previously reported in the literature. By comparing the numerical and experimental data, it is revealed that the proposed method can accurately simulate the failure propagation in notched composite plates at a low computational cost.     

A Tradeoff Between Accuracy and Speed for K-Means Seed Determination

With a sharp increase in the information volume, analyzing and retrieving this vast data volume is much more essential than ever. One of the main techniques that would be beneficial in this regard is called the Clustering method. Clustering aims to classify objects so that all objects within a cluster have similar features while other objects in different clusters are as distinct as possible. One of the most widely used clustering algorithms with the well and approved performance in different applications is the k-means algorithm. The main problem of the k-means algorithm is its performance which can be directly affected by the selection in the primary clusters. Lack of attention to this crucial issue has consequences such as creating empty clusters and decreasing the convergence time. Besides, the selection of appropriate initial seeds can reduce the cluster’s inconsistency. In this paper, we present a new method to determine the initial seeds of the k-mean algorithm to improve the accuracy and decrease the number of iterations of the algorithm. For this purpose, a new method is proposed considering the average distance between objects to determine the initial seeds. Our method attempts to provide a proper tradeoff between the accuracy and speed of the clustering algorithm. The experimental results showed that our proposed approach outperforms the Chithra with 1.7% and 2.1% in terms of clustering accuracy for Wine and Abalone detection data, respectively. Furthermore, achieved results indicate that comparing with the Reverse Nearest Neighbor (RNN) search approach, the proposed method has a higher convergence speed.     

An optimized skin texture model using gray-level co-occurrence matrix

Neural Computing and Applications - Texture analysis is devised to address the weakness of color-based image segmentation models by considering the statistical and spatial relations among the group...     

An integrated approach for aircraft turbofan engine fault detection based on data mining techniques

The present study proposes an algorithm for fault detection in terms of condition‐based maintenance with data mining techniques. The proposed algorithm is applied on an aircraft turbofan engine using flight data and consists of two main sections. In the first section, the relationship between engine exhaust gas temperature (EGT) as the main engine health monitoring criterion and other operational and environmental parameters of the engine was modelled using the data‐driven models. In the second section, a data set including EGT residuals, that is, the difference between the actual EGT of the system and the EGT estimated by the developed model in the health conditions of the engine, was created. Finally, faults occurring in each flight were detected based on the identification of abnormal events by a one‐class support vector machine trained by the health condition EGT residual data set. The results indicated that the proposed algorithm was an effective approach for inspecting aircraft engine conditions and detecting faults, with no need for technical knowledge on the interior characteristics of the aircraft engine.     

A survey and taxonomy on virtual data center embedding

The Journal of Supercomputing - Data center network virtualization is being considered as a promising technology to provide a performance guarantee for cloud computing applications. One important...     

An accurate analysis of the nonlinear power amplifier effects on SC-FDMA signals

Wireless Networks - Single carrier-frequency division multiple access (SC-FDMA) is a multiple access technique in broadband wireless networks which has been adapted by 3GPP for uplink transmission...     

Inspection and Monitoring of Concrete Structures via Radiography and Weighted Nuclear Norm Minimization Method

Russian Journal of Nondestructive Testing - The location of reinforcement bar in concrete, the bar corrosion, diameter and the depth below the surface are important factors in the evaluation of the...     

Synthesizing a uniformly spaced array pattern using integral operator for removing progressive phase shift

In this article, an analytical technique is introduced to obtain the excitation coefficients of uniformly spaced linear antenna arrays in order to achieve a desired array factor. By integration of the prescribed array factor, the array factor dependency to the progressive phase shift is eliminated. A new system of linear equations is consequently obtained whose solution represents the excitation coefficients of the array. Some examples are presented to verify the accuracy of the introduced method. The performance of this strategy is compared with those obtained by the other well‐known techniques such as Woodward‐Lawson and Fourier transform. It is shown that the presented method estimates the desired array pattern with a very good precision.     

Influence of Ni/Co binders and Mo2C on the microstructure evolution and mechanical properties of (Ti0.93W0.07)C–based cermets

In this research, solid–solution powder of (Ti_0.93W_0.07)C was synthesized by high–energy ball mill method followed by carbothermal reduction process. Subsequently, the acquired powder was blended with Ni/Co and Mo₂C secondary carbide, and sintered under the optimized temperature (1510?°C) for 1?h to produce the modulated cermets. A typical core–rim structure formation with solid–solution phases was confirmed by backscattered electrons studies using a Field Emission electron scanning microscope. The hardness of the synthesized cermets was enhanced by increasing the specific amount of Mo₂C. The acquired results demonstrate that the binder type has a prominent influence on the microstructure and hardness of the prepared cermets. The hardness of (Ti_0.93W_0.07)C–xMo₂C–Ni cermet increased ~ 9%, when nickel was partially substituted by cobalt.