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.

     

Incorporation of a Regularization Term to Control Negative Correlation in Mixture of Experts

Combining accurate neural networks (NN) in the ensemble with negative error correlation greatly improves the generalization ability. Mixture of experts (ME) is a popular combining method which employs special error function for the simultaneous training of NN experts to produce negatively correlated NN experts. Although ME can produce negatively correlated experts, it does not include a control parameter like negative correlation learning (NCL) method to adjust this parameter explicitly. In this study, an approach is proposed to introduce this advantage of NCL into the training algorithm of ME, i.e., mixture of negatively correlated experts (MNCE). In this proposed method, the capability of a control parameter for NCL is incorporated in the error function of ME, which enables its training algorithm to establish better balance in bias-variance-covariance trade-off and thus improves the generalization ability. The proposed hybrid ensemble method, MNCE, is compared with their constituent methods, ME and NCL, in solving several benchmark problems. The experimental results show that our proposed ensemble method significantly improves the performance over the original ensemble methods.     

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.     

Analysis of impersonation attacks on systems using RF fingerprinting and low-end receivers

Recently, physical layer security commonly known as Radio Frequency (RF) fingerprinting has been proposed to provide an additional layer of security for wireless devices. A unique RF fingerprint can be used to establish the identity of a specific wireless device in order to prevent masquerading/impersonation attacks. In the literature, the performance of RF fingerprinting techniques is typically assessed using high-end (expensive) receiver hardware. However, in most practical situations receivers will not be high-end and will suffer from device specific impairments which affect the RF fingerprinting process. This paper evaluates the accuracy of RF fingerprinting employing low-end receivers. The vulnerability to an impersonation attack is assessed for a modulation-based RF fingerprinting system employing low-end commodity hardware (by legitimate and malicious users alike). Our results suggest that receiver impairment effectively decreases the success rate of impersonation attack on RF fingerprinting. In addition, the success rate of impersonation attack is receiver dependent.     

Development of the Acidity of Zirconia-Supported Niobia Catalysts

A series of NbO _x /ZrO₂ catalysts containing up to 2.67wt Nb (ca. 80 nominal surface coverage) was prepared by incipient wetness impregnation from niobium oxalate and oxalic acid solution. The structure of the catalysts was monitored by X-ray diffraction and Raman spectroscopy. The results indicated the presence of a surface Nb phase. No evidence for the formation of crystalline Nb₂O₅ species was found. The development of the acidity as a function of Nb loading was monitored by adsorption of a basic probe molecule followed by infrared spectroscopy. The results indicated the appearance of Brnsted acid sites for a threshold of Nb loading. The abundance of Brnsted acid sites correlated well with the isopropanol dehydration activity. The overall behavior was very similar to that reported earlier for the WO _x /ZrO₂ system.     

Mechanical properties of composites from sawdust and recycled plastics

Mechanical properties of wood plastic composites (WPCs) manufactured from sawdust and virgin and/or recycled plastics, namely high density polyethylene (HDPE) and polypropylene (PP), were studied. Sawdust was prepared from beech industrial sawdust by screening to the desired particle size and was mixed with different virgin or recycled plastics at 50% by weight fiber loading. The mixed materials were then compression molded into panels. Flexural and tensile properties and impact strength of the manufactured WPCs were determined according to the relevant standard specifications. Although composites containing PP (virgin and recycled) exhibited higher stiffness and strength than those made from HDPE (virgin and recycled), they had lower unnotched impact strengths. Mechanical properties of specimens containing recycled plastics (HDPE and PP) were statistically similar and comparable to those of composites made from virgin plastics. This was considered as a possibility to expand the use of recycled plastics in the manufacture of WPCs. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 100: 3641–3645, 2006     

Preparation and characterization of cellulose acetate organic/inorganic hybrid films

A series of organic/inorganic hybrid (OIH) films were prepared using cellulose acetate (CA) as the organic component and tetraethyl orthosilicate (TEOS) as the inorganic component. The chemical, morphological, and mechanical properties of these films were evaluated with a variety of analytical techniques. The results of these evaluations showed that crosslinked CA OIH films were formed during the sol-gel reactions. The structure of OIH films was very sensitive to the CA/TEOS ratio and film formation conditions. All of the films with added TEOS were two phase on a molecular level, i.e., inorganic TEOS domains surrounded by a CA matrix. Under some film formation conditions the presence of TEOS, a nonsolvent for CA, led to solvent/nonsolvent phase separation on the micron scale. © 1995 John Wiley & Sons, Inc.     

Privacy-preserving biometric verification with outsourced correlation filter computation

Multimedia Tools and Applications - In traditional biometric verification systems, personal computer stores biometric database and performs verification process. Because of limited storage,...