Optimal characterization of a microwave transistor using grey wolf algorithms

Analog Integrated Circuits and Signal Processing - Modern time microwave stages require low power consumption, low size, low-noise amplifier (LNA) designs with high-performance measures. These...     

Machine learning–based piecewise affine model of wind turbines during maximum power point tracking

In this paper, a discrete‐time piecewise affine (PWA) model of a wind turbine during Maximum Power Point Tracking (MPPT) region is identified. A clustering‐based identification method is utilized to create PWA maps for nonlinear aerodynamic torque and thrust force functions. This method exploits the combined use of clustering, pattern recognition, and parameter identification techniques. The well‐known K‐means clustering method is employed along with a perceptron‐based multiclassifier for pattern recognition and the least squared technique for parameter estimation. The identified maps are approximated the nonlinear static functions of the dynamic model of the wind turbine. Characteristics of a 5‐MW wind turbine are considered and the resulting model, which consists of 25 subregions is compared with the nonlinear dynamic model. Two test cases are studied in order to validate the presented model. Simulation results demonstrate the effectiveness and accuracy of the PWA model such that the response of the identified PWA model is fitted well to the nonlinear one. The PWA model identified in this paper can be widely used for advanced control systems design and long‐term performance and security assessment of the power grid.     

Automatic detection of depression symptoms in twitter using multimodal analysis

The Journal of Supercomputing - Depression is the most prevalent mental disorder that can lead to suicide. Due to the tendency of people to share their thoughts on social platforms, social data...     

Preparation of porous PLLA/PCL blend by a combination of PEO phase and NaCl particulate leaching in PLLA/PCL/PEO/NaCl blend

In this study, the ternary blends containing microporosity based on poly(l-lactic acid) (PLLA), poly(ε-caprolactone) (PCL) and polyethylene oxide (PEO) were prepared using an internal mixer via a polymer leaching technique. The particulate leaching is the most widely used technique to create porosity. To introduce macroporosity besides micropores, NaCl particulates were incorporated into the ternary blends at 40–80 wt % and macropores were formed by particulate leaching. Samples porosity were evaluated by calculating the ratio of porous scaffold density (ρ*) to the non-porous material density (ρ _s). The results showed that with an increase in NaCl particulate content, the amount of porosity increased and the distribution of pore size was gradually transformed from monomodal into bimodal form. The porosity plays a key role in governing the compression properties. Mechanical properties are presented by Gibson–Ashby model. Compressive modulus decreased with an increase in NaCl particulate concentration due to the increase in porosity and thinning of pore wall that caused rupture at these weaker spots. Blending and forming of the bio-scaffold can be made using conventional polymer processing equipment. This process seems promising for a large-scale production of porous bio-scaffold of many sizes through an economic method.     

Selecting optimal monitoring site locations for peak ambient particulate material concentrations using the MM5-CAMx4 numerical modelling system

Installation of temporary or long term monitoring sites is expensive, so it is important to rationally identify potential locations that will achieve the requirements of regional air quality management strategies. A simple, but effective, numerical approach to selecting ambient particulate matter (PM) monitoring site locations has therefore been developed using the MM5-CAMx4 air pollution dispersion modelling system. A new method, ‘site efficiency,’ was developed to assess the ability of any monitoring site to provide peak ambient air pollution concentrations that are representative of the urban area. ‘Site efficiency’ varies from 0 to 100%, with the latter representing the most representative site location for monitoring peak PM concentrations. Four heavy pollution episodes in Christchurch (New Zealand) during winter 2005, representing 4 different aerosol dispersion patterns, were used to develop and test this site assessment technique. Evaluation of the efficiency of monitoring sites was undertaken for night and morning aerosol peaks for 4 different particulate material (PM) spatial patterns. The results demonstrate that the existing long term monitoring site at Coles Place is quite well located, with a site efficiency value of 57.8%. A temporary ambient PM monitoring site (operating during winter 2006) showed a lower ability to capture night and morning peak aerosol concentrations. Evaluation of multiple site locations used during an extensive field campaign in Christchurch (New Zealand) in 2000 indicated that the maximum efficiency achieved by any site in the city would be 60-65%, while the efficiency of a virtual background site is calculated to be about 7%. This method of assessing the appropriateness of any potential monitoring site can be used to optimize monitoring site locations for any air pollution measurement programme.     

Prediction of the asphaltene deposition profile along a wellbore during natural production from a reservoir

The potential problem of asphaltene deposition during oil production has motivated both academics and industries to predict the asphaltene deposit profile in wellbores and pipelines. In this work, asphaltene deposition profile along an oil field well with the severe problem of asphaltene deposition was predicted. To do this, a comprehensive simulator for modeling of flow parameters such as pressure, temperature, and composition for a multiphase flow of oil, gas, and asphaltene from the reservoir to the surface was developed and coupled with the deposition model. By applying the simulator to an oil field well, it has been found that 60–70% of the total asphaltene thickness formed after 1 month of production, indicating that the problem of asphaltene deposition is bound to the initial stage of wellbore life. Moreover, the simulator was able to predict the accumulated asphaltene thickness and the time of wellbore plugging properly. This prediction is highly crucial if it is aimed to control the well performance and to optimize the productivity.     

Improving Hydrological Process Modeling Using Optimized Threshold-Based Wavelet De-Noising Technique

The extent of the noise on hydrological data is inevitable, which reduces the efficiency of Data-Driven Models (DDMs). Despite of this fact that the DDMs such as Artificial Neural Network (ANN) are capable of nonlinear functional mapping between a set of input and output variables, but refining of the time series through data pre-processing methods can provide with the possibility to increase the performance of these set of models. The main objective of this study is to propose a new method called Optimized Threshold-Based Wavelet De-noising technique (OTWD) to de-noise hydrological time series and improve the prediction accuracy while the DDM is being used. For this purpose, in the first step, Wavelet-ANN (WNN) model was developed for identifying suitable wavelet function and maximum decomposition level. Afterward, sub-signals of original precipitation time series which were determined in the first step were de-noised by using of OTWD technique. Therefore, these clean sub-signals of precipitation time series were imposed as input data to the ANN to predict the precipitation one time step ahead. The results showed that OTWD technique could improve the efficiency of WNN model dramatically; this outcome was reported by the different efficiency criterions such as Nash-Sutcliffe Efficiency (NSE?=?0.92), Root Mean Squared Error (RMSE?=?0.0103), coefficient correlation of linear regression (R?=?0.93), Peak Value Criterion (PVC?=?0.021) and Low Value Criterion (LVC?=?0.026). The best fitted WNN model in comparison by proposed model showed weaker performance by the NSE, RMSE, R, PVC and LVC values of 0.86, 0.043, 0.87, 0.034 and 0.045, respectively.     

Three-dimensional transient analysis of FGM cylindrical shell subjected to thermal and mechanical loading

In this article, transient analysis of functionally graded material (FGM) cylindrical shell subjected to thermomechanical load is performed. Mechanical and thermal properties of the shell are assumed to be graded in radial direction according to power law distribution. In the case of simply supported edge condition, problem is solved analytically using Fourier series expansions for stresses and displacements along the axial direction and state space technique along the radial direction and Laplace transformation technique for time domain. For other boundary conditions, we use a semianalytical method by applying differential quadrature method along the axial direction and the state space method along radial direction. Accuracy of this approach is validated by comparing the results with the results reported in the literature. Moreover, influence of edge boundary conditions, length to mid radius ratio, FGM direction and time on stresses, and displacements is studied.