A novel approach to predict shear strength of tilted angle connectors using artificial intelligence techniques

Shear connectors play a prominent role in the design of steel-concrete composite systems. The behavior of shear connectors is generally determined through conducting push-out tests. However, these tests are costly and require plenty of time. As an alternative approach, soft computing (SC) can be used to eliminate the need for conducting push-out tests. This study aims to investigate the application of artificial intelligence (AI) techniques, as sub-branches of SC methods, in the behavior prediction of an innovative type of C-shaped shear connectors, called Tilted Angle Connectors. For this purpose, several push-out tests are conducted on these connectors and the required data for the AI models are collected. Then, an adaptive neuro-fuzzy inference system (ANFIS) is developed to identify the most influencing parameters on the shear strength of the tilted angle connectors. Totally, six different models are created based on the ANFIS results. Finally, AI techniques such as an artificial neural network (ANN), an extreme learning machine (ELM), and another ANFIS are employed to predict the shear strength of the connectors in each of the six models. The results of the paper show that slip is the most influential factor in the shear strength of tilted connectors and after that, the inclination angle is the most effective one. Moreover, it is deducted that considering only four parameters in the predictive models is enough to have a very accurate prediction. It is also demonstrated that ELM needs less time and it can reach slightly better performance indices than those of ANN and ANFIS.

     

Deep learning controller for nonlinear system based on Lyapunov stability criterion

Neural Computing and Applications - For the current paper, the technique of feed-forward neural network deep learning controller (FFNNDLC) for the nonlinear systems is proposed. The FFNNDLC...     

Direct adaptive fuzzy control with membership function tuning

In this paper, a direct self‐structured adaptive fuzzy control is introduced for the class of nonlinear systems with unknown dynamic models. Control is accomplished by an adaptive fuzzy system with a fixed number of rules and adaptive membership functions. The reference signal and state errors are used to tune the membership functions and update them instantaneously. The Lyapunov synthesis method is also used to guarantee the stability of the closed loop system. The proposed control scheme is applied to an inverted pendulum and a magnetic levitation system, and its effectiveness is shown via simulation. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

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Theoretical and experimental analyses of rheological,compatibility and mechanical properties of PVMQ/XNBR-g GMA/XNBR/GO ternary hybrid nanocomposites

Iranian Polymer Journal - Effects of graphene oxide (GO) on various properties of rubber hybrid nanocomposites based on PVMQ/XNBR-g-GMA/XNBR (phenyl-vinyl-methyl-polysiloxane/carboxylated nitrile...     

Facile Hydrothermal Synthesis of Hierarchical Sodium P Zeolite as a Nonphosphate Detergent Builder

This study reports the preparation of hierarchical NaP zeolite with the aim of obtaining a non-phosphate detergent builder as an alternative for environmental remediation from eutrophication phenomenon. Hierarchically structured NaP zeolite was easily synthesized hydrothermally and under different syntheses conditions. Samples were characterized using several standard techniques such as X-ray diffraction (XRD), scanning electron microscopy (SEM), Fourier transform infrared (FT-IR) spectroscopy, and N₂ adsorption–desorption analysis. Three powder detergents were prepared by mixing main components such as linear alkylbenzene sulfonate, sodium sulfate, sodium silicate, and sodium carbonate as well as different amounts of as-synthesized zeolite and sodium tripolyphosphate in the detergent formulation as potential detergency builders. Some different detergency tests as pH value, water insolubility, foam height, moisture content, alcohol insolubility, and surface tension measurement were carried out for all synthetic detergent samples and two commercial ones. The results demonstrated that the high cleaning performance of the powders was obtained as using eco-friendly zeolite builders in comparison with phosphate-based commercial and synthetic detergent samples.     

A review of machine learning applications in IoT-integrated modern power systems

This paper aims to provide a systematic and comprehensive survey of state-of-the-art machine learning techniques and their potential applications in IoT-integrated power systems.     

Analytical Modeling of Short-Channel Fully-Depleted Triple Work Function Metal Gate (TWFMG) SOI MESFET

Silicon - In this paper, a new structure: triple work function metal gate SOI MESFET, intended for integration into the deep-submicron CMOS technology, is proposed. The gate of the device consists...     

Admittance—Frequency Response in Zinc Oxide Varistor Ceramics

The lumped parameter/complex plane analysis technique revealed several contributions to the terminal admittance of the ZnO—Bi₂O₃ based varistor grain-boundary ac response. The terminal capacitance has been elucidated via the multiple trapping phenomena, a barrier layer polarization, and a resonance effect in the frequency range 10⁻²≤ f ≤ 10⁹ Hz. The characterization of the trapping relaxation behavior near ∼ 10⁵ Hz (∼ 10⁻⁶ s) provided a better understanding of a previously reported loss-peak. The possible nonuniformity in this trapping activity associated with its conductance term observed via the depression angle of a semicircular relaxation in the complex capacitance ( C *) plane has been postulated.     

An analytical framework for event mining in video data

With the high availability of digital video contents on the internet, users need more assistance to access digital videos. Various researches have been done about video summarization and semantic video analysis to help to satisfy these needs. These works are developing condensed versions of a full length video stream through the identification of the most important and pertinent content within the stream. Most of the existing works in these areas are mainly focused on event mining. Event mining from video streams improves the accessibility and reusability of large media collections, and it has been an active area of research with notable recent progress. Event mining includes a wide range of multimedia domains such as surveillance, meetings, broadcast, news, sports, documentary, and films, as well as personal and online media collections. Due to the variety and plenty of Event mining techniques, in this paper we suggest an analytical framework to classify event mining techniques and to evaluate them based on important functional measures. This framework could lead to empirical and technical comparison of event mining methods and development of more efficient structures at future.