Bilayer Graphene Conductance Analysis based on FET Channel

Graphene is considered as a famous nanomaterial because of some parameters such as its large surface–to–volume ratio, high conductivity, high mobility, and strong mechanical and elasticity properties. Therefore, in this work the conductance of two dimensional bilayer graphene (BG) is developed using the Fermi Dirac distribution function. For bilayer graphene two, various stacking structures (AA and AB) have been reported, which have armchair edge. Quantum gradient emerged between the channel and the gate and carrier movement of bilayer graphene is considered as FET channel, which is an important property of FET. Besides, band gap energy and resistance of BG have been modelled in this study. The impact of temperature on the resistance is extensively studied. It is demonstrated that the resistance of BG is the function of temperature and the conductance is increased at higher values of temperature

Improving the efficiency of photovoltaic cells embedded in floating buoys

Solar cells are used to power floating buoys, which is one of their applications. Floating buoys are devices that are placed on the sea and ocean surfaces to provide various information to the floats. Because these cells are subjected to varying environmental conditions, modeling and simulating photovoltaic cells enables us to install cells with higher efficiency and performance in them. The parameters of the single diode model are examined in this article so that the I-V, P-V diagrams, and characteristics of the cadmium telluride (CdTe) photovoltaic cell designed with three layers (CdTe, CdS, and SnOx) can be extracted using A solar cell capacitance simulator (SCAPS) software, and we obtain the parameters of the single diode model using the ant colony optimization (ACO) algorithm. In this paper, the objective function is root mean square error (RMSE), and the best value obtained after 30 runs is 5.2217×10-5 in 2.46 seconds per iteration, indicating a good agreement between the simulated model and the real model and outperforms many other algorithms that have been developed thus far. The above optimization with 200 iterations, a population of 30, and 84 points was completed on a server with 32 gigabytes of random-access memory (RAM) and 30 processing cores

Spiking ink drop spread clustering algorithm and its memristor crossbar conceptual hardware design

In this study, a novel neuro-fuzzy clustering algorithm is proposed based on spiking neural network and ink drop spread (IDS) concepts. The proposed structure is a one-layer artificial neural network with leaky integrate and fire (LIF) neurons. The structure implements the IDS algorithm as a fuzzy concept. Each training data will result in firing the corresponding input neuron and its neighboring neurons. A synchronous time coding algorithm is used to manage input and output neurons firing time. For an input data, one or several output neurons of the network will fire; confidence degree of the network to outputs is defined as the relative delay of the firing times with respect to the synchronous pulse. A memristor crossbar-based hardware is utilized for hardware implementation of the proposed algorithm. The simulation result corroborates that the proposed algorithm can be used as a neuro-fuzzy clustering and vector quantization algorithm

Comparative detection and fault location in underground cables using Fourier and modal transforms

In this research, we create a single-phase to ground synthetic fault by the simulation of a three-phase cable system and identify the location using mathematical techniques of Fourier and modal transforms. Current and voltage signals are measured and analyzed for fault location by the reflection of the waves between the measured point and the fault location. By simulating the network and line modeling using alternative transient programs (ATP) and MATLAB software, two single-phase to ground faults are generated at different points of the line at times of 0.3 and 0.305 s. First, the fault waveforms are displayed in the ATP software, and then this waveform is transmitted to MATLAB and presented along with its phasor view over time. In addition to the waveforms, the detection and fault location indicators are presented in different states of fault. Fault resistances of 1, 100, and 1,000 ohms are considered for fault creation and modeling with low arch strength. The results show that the proposed method has an average fault of less than 0.25% to determine the fault location, which is perfectly correct. It is varied due to changing the conditions of time, resistance, location, and type of error but does not exceed the above value

Optimization of exponential double-diode model for photovoltaic solar cells using ga-pso algorithm

In this paper, an equivalent electrical circuit based on the photovoltaic effect (PV) is presented with studies on the simulation of the solar energy system. This model consists of exponential double diodes illustrates how solar cells behave in order to generate electricity. By using the MATLAB software, we performed simulations. Our goal is to calculate the minimum error value for the unknown parameters of the model, which is attained by using root mean square of errors (RMSE). Regarding to the offered model, which we intend to investigate with the suggested GA-PSO algorithm, we obtain the minimum error value (RMSE) after achieving unknown parameters and then we will compare the results with other methods. Therefore, it can be shown that the proposed algorithm with a RMSE value of 2.02 provides an optimal result.

The synergistic combination of fuzzy C-means and ensemble filtering for class noise detection

Purpose: The purpose of this study is to enhance data quality and overall accuracy and improve certainty by reducing the negative impacts of the FCM algorithm while clustering real-world data and also decreasing the inherent noise in data sets. Design/methodology/approach: The present study proposed a new effective model based on fuzzy C-means (FCM), ensemble filtering (ENS) and machine learning algorithms, called an FCM-ENS model. This model is mainly composed of three parts: noise detection, noise filtering and noise classification. Findings: The performance of the proposed model was tested by conducting experiments on six data sets from the UCI repository. As shown by the obtained results, the proposed noise detection model very effectively detected the class noise and enhanced performance in case the identified class noisy instances were removed. Originality/value: To the best of the authors’ knowledge, no effort has been made to improve the FCM algorithm in relation to class noise detection issues. Thus, the novelty of existing research is combining the FCM algorithm as a noise detection technique with ENS to reduce the negative effect of inherent noise and increase data quality and accuracy