The effect of soil salinity on the use of the universal triangle method to estimate saline soil moisture from Landsat data: application to the SMAPEx-2 and SMAPEx-3 campaigns

In this article, a method based on UTM called salinity-based soil moisture content (S_SMC) is developed. Since the soil moisture depends on the soil salinity (SS) in semi-arid regions, the S_SMC method employs the SS as an effective and augmented variable in conventional UTM to estimate SMC in these areas. In calibration step, initially, a linear regression model between the land surface temperature (LST), the normalized difference vegetation index (NDVI), and the SS is applied using in situ measurements to assess the influence of the SS in SMC estimation. Then, a non-linearity model is conducted through insertion of more terms in the linear equation and an optimal model of S_SMC is yielded. Moreover, the SS is obtained using a linear model from two selected salinity indices derived from Landsat images and in situ measurements. In estimation step, the LST, NDVI, and the SS are obtained using Landsat data. The S_SMC method is evaluated in the Soil Moisture Active Passive Experiment (SMAPEx)-2 and SMAPEx-3 campaigns in wet and dry conditions, respectively, over two scenes of Landsat images. The results demonstrated that the S_SMC method is appropriate in non-irrigated areas. In these areas, the S_SMC method improves R² (coefficient of determination) from 22% to 65% in SMAPEx-2 and from 24% to 50% in SMAPEx-3. Moreover, the results have shown that the SMC can be estimated at satellite level with a root mean square error of 0.06 and 0.02 (m³ m^?3) in wet and dry condition, respectively. Therefore, the SS is a key parameter to adjust conventional UTM to improve the SMC estimation by the S_SMC method.     

Application of energies of optimal frequency bands for fault diagnosis based on modified distance function

Low-dimensional relevant feature sets are ideal to avoid extra data mining for classification. The current work investigates the feasibility of utilizing energies of vibration signals in optimal frequency bands as features for machine fault diagnosis application. Energies in different frequency bands were derived based on Parseval's theorem. The optimal feature sets were extracted by optimization of the related frequency bands using genetic algorithm and a Modified distance function (MDF). The frequency bands and the number of bands were optimized based on the MDF. The MDF is designed to a) maximize the distance between centers of classes, b) minimize the dispersion of features in each class separately, and c) minimize dimension of extracted feature sets. The experimental signals in two different gearboxes were used to demonstrate the efficiency of the presented technique. The results show the effectiveness of the presented technique in gear fault diagnosis application.     

An alternative modal combination rule for adaptive pushover analysis

The main purpose of the present study is to develop an alternative modal combination rule for use in the adaptive pushover analysis. Since the quadratic modal combination rules do not take into account the sign reversals of the modal load vectors in the higher modes, the accuracy of the advanced pushover methods are decreased. The proposed modal combination rule is a direct vectorial addition technique in which the relative contribution of each mode and its sign are taken into account. The proposed modal combination rule is employed within the displacement‐based adaptive pushover technique, and an alternative pushover procedure is developed. In order to verify the accuracy of the proposed method, two reference buildings are used, and the obtained results from the proposed method and nonlinear time history analysis are compared. It is concluded that the proposed method can estimate the benchmark responses with remarkable accuracy. Copyright © 2016 John Wiley & Sons, Ltd.     

A new complete model of switch-mode plasma cutting power supply

A complete model of switch-mode plasma cutting power supply and its simulation are developed. The full bridge isolated pulse width modulation (PWM) buck converter in continuous conduction mode (CCM) for high watt plasma power supply is approached. Reduced ripple current and improved power factor are achieved in the plasma power supply. With a PID control strategy, circuit responses become more stable and faster with low overshoot during load and current changing. The converter achieved high efficiency under 3 to 15kW load conditions.     

Probabilistic safety assessment of self-centering steel braced frame

The main drawback of conventional braced frames is implicitly accepting structural damage under the design earthquake load, which leads to considerable economic losses. Controlled rocking self-centering system as a modern low-damage system is capable of minimizing the drawbacks of conventional braced frames. This paper quantifies main limit states and investigates the seismic performance of self-centering braced frame using a Probabilistic Safety Assessment procedure. Margin of safety, confidence level, and mean annual frequency of the self-centering archetypes for their main limit states, including PT yield, fuse fracture, and global collapse, are established and are compared with their acceptance criteria. Considering incorporating aleatory and epistemic uncertainties, the efficiency of the system is examined. Results of the investigation indicate that the design of low- and mid-rise self-centering archetypes could provide the adequate margin of safety against exceeding the undesirable limit-states.     

Design of improved‐reliability nanocircuits with mixed NBTI‐ and HCI‐aware gate‐sizing formulation

Negative bias temperature instability (NBTI) and hot carrier injection (HCI) are two important processes of reliability concern in nano‐scale integrated circuits. A circuit‐level design technique to combat NBTI degradation is gate oversizing. This paper presents a new technique based on PMOS and NMOS resistance variation for the NBTI‐ and HCI‐aware gate‐sizing problem for the first time. In this technique, the area of the circuit is minimized with constraints on degraded delay due to NBTI and HCI and the transitor size. Expreimental results for several gates and ISCAS'85 benchmark circuits show that this technique imposes an area overhead of less than 1% with respect to baseline design in most cases. © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Preparation and characterization of an epoxy nanocomposite toughened by a combination of thermoplastic,layered and particulate nano-fillers

This paper reports the development of an epoxy-based nanocomposite toughened by the combination of thermoplastic, layered and particulate nano-fillers. The main objective of this work is to incorporate poly(acrylonitrile-co-butadiene-co-styrene) (ABS), clay (layered nano-filler) and nano-TiO₂ (particulate nano-filler) into epoxy matrix with the aim of obtaining the quaternary nanocomposite with higher impact strength and lower cost without attenuating the other desired mechanical properties such as tensile strength. Taguchi methodology was applied for the optimization and statistical determination of the significant factors influencing the mechanical properties of the quaternary nanocomposite. Impact and tensile strengths of the quaternary nanocomposite with optimum composition increased by 168% and 64% compared to neat epoxy, respectively. Furthermore, synergistic effect was observed with the addition of three type modifiers. It was found that ABS content has the most significant effect on mechanical properties of the obtained quaternary nanocomposite. Also correlation between morphological and mechanical properties of the nanocomposite was investigated. A dispersion of nano-size ABS and TiO₂ particles along with exfoliated clay nano-platelets in epoxy matrix was achieved as main morphological property of the quaternary nanocomposite. A new morphology was obtained for ABS phase in epoxy rich matrix.     

Synthesis,characterization, and swelling kinetic study of porous superabsorbent hydrogel nanocomposite based on sulfonated carboxymethylcellulose and silica nanoparticles

New superabsorbent nanocomposite was synthesized by free-radical graft polymerization of sulfonated-carboxymethyl cellulose (SCMC) with acrylic acid (AA) in the presence of polyvinylpyrrolidone (PVP) and silica nanoparticles. Carboxymethyl cellulose (CMC) was first sulfonated using chlorosulfonic acid, and then AA monomers were grafted onto SCMC. FTIR results confirmed that sulfonation of CMC as well as grafting of AA monomers onto SCMC has been performed successfully. Moreover, the presence of silica nanoparticles into superabsorbent nanocomposite was evaluated with EDX analysis. The element mappings show a homogenous distribution of silica nanoparticles throughout the hydrogel nanocomposite. SEM images exhibited porous morphology for hydrogel nanocomposite, which was due to the incorporation of PVP in its network. The experimental findings from TGA analysis indicated that incorporation of PVP and silica nanoparticles into the hydrogel network improved thermal stability of superabsorbent nanocomposite. Swelling kinetic studies revealed that superabsorbent nanocomposite hydrogel had higher equilibrium swelling capacity and swelling rate compared with the neat hydrogel sample. Besides that, superabsorbent nanocomposite depicted excellent salt and pH-sensitive behavior in different saline and pH solutions. As a consequence, this hydrogel nanocomposite acts as useful water reservoir, which might be most profitable in agricultural applications.     

An EM based probabilistic two-dimensional CCA with application to face recognition

Recently, two-dimensional canonical correlation analysis (2DCCA) has been successfully applied for image feature extraction. The method instead of concatenating the columns of the images to the one-dimensional vectors, directly works with two-dimensional image matrices. Although 2DCCA works well in different recognition tasks, it lacks a probabilistic interpretation. In this paper, we present a probabilistic framework for 2DCCA called probabilistic 2DCCA (P2DCCA) and an iterative EM based algorithm for optimizing the parameters. Experimental results on synthetic and real data demonstrate superior performance in loading factor estimation for P2DCCA compared to 2DCCA. For real data, three subsets of AR face database and also the UMIST face database confirm the robustness of the proposed algorithm in face recognition tasks with different illumination conditions, facial expressions, poses and occlusions.     

Evaluation of initial deposition rate of electroless Ni–P layers by QCM method

Electroless nickel–phosphorus (ENP) initial deposition rates from a glycine bath were studied by means of the quartz crystal microbalance (QCM) method. SEM and EDX methods were also used to study the morphology and elemental analysis of deposits. The effect of pH, temperature and the type of activation process on the initial deposition rate, surface morphology and surface elemental analysis of deposits were evaluated. Increasing the pH and the temperature cause an increase in deposition rate and a decrease in P content of deposits. The phosphorus content of obtained deposits in pre-plate method was greater than in sensitizer–activator process under the same conditions of the alloy deposition. The surface morphology depends on P content of deposits. By decreasing the P content of deposits, the grain size increases.