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...     

Trans-differentiation of human adipose-derived mesenchymal stem cells into cardiomyocyte-like cells on decellularized bovine myocardial extracellular matrix-based films

In this study, we aimed at fabricating decellularized bovine myocardial extracellular matrix-based films (dMEbF) for cardiac tissue engineering (CTE). The decellularization process was carried out utilizing four consecutive stages including hypotonic treatment, detergent treatment, enzymatic digestion and decontamination, respectively. In order to fabricate the dMEbF, dBM were digested with pepsin and gelation process was conducted. dMEbF were then crosslinked with N-hydroxysuccinimide/1-Ethyl-3-(3-dimethylaminopropyl)-carbodiimide (NHS/EDC) to increase their durability. Nuclear contents of native BM and decellularized BM (dBM) tissues were determined with DNA content analysis and agarose-gel electrophoresis. Cell viability on dMEbF for 3rd, 7th, and 14th days was assessed by MTT assay. Cell attachment on dMEbF was also studied by scanning electron microscopy. Trans-differentiation capacity of human adipose-derived mesenchymal stem cells (hAMSCs) into cardiomyocyte-like cells on dMEbF were also evaluated by histochemical and immunohistochemical analyses. DNA contents for native and dBM were, respectively, found as 886.11?±?164.85 and 47.66?±?0.09?ng/mg dry weight, indicating a successful decellularization process. The results of glycosaminoglycan and hydroxyproline assay, and sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE), performed in order to characterize the extracellular matrix (ECM) composition of native and dBM tissue, showed that the BM matrix was not damaged during the proposed method. Lastly, regarding the histological study, dMEbF not only mimics native ECM, but also induces the stem cells into cardiomyocyte-like cells phenotype which brings it the potential of use in CTE.     

Thermodynamic performance analysis of three solid oxide fuel cell and gas microturbine hybrid systems for application in auxiliary power units

In this study, three different configurations of a solid oxide fuel cell and gas microturbine hybrid system are evaluated for application in auxiliary power units. The first configuration is a common hybrid system in auxiliary power units, utilizing a fuel cell stack in the structure of the gas turbine cycle. The other configurations use two series and parallel fuel cell stacks in the structure of the gas turbine cycle. The main purpose of this research is thermodynamic analysis, evaluation of the performance of the proposed hybrid systems in similar conditions, and selection of an appropriate system in terms of efficiency, power generation, and entropy generation rate. In this study, the utilized fuel cells were subjected to electrochemical, thermodynamic, and thermal analyses and their working temperatures were calculated under various working conditions. Results indicate that the hybrid system with two series stacks had maximum power generation and efficiency compared with the other two cases. Moreover, the simple hybrid system and the system with two parallel stacks had relatively equal pure power generation and efficiency. According to the investigations, hybrid system with two series fuel cell stacks, which had 3424 and 1712 cells, respectively, can achieve the electrical efficiency of over 48%. A hybrid system with two parallel fuel cell stacks, in which each stack had 2568 cells, had the electrical efficiency of 46.3%. Findings suggested that maximum electrical efficiency occurred between the pressure ratios of 5–6 in the proposed hybrid systems.