Journal of Artificial Intelligence in Electrical Engineering
Volume:10 Issue: 38, Summer 2021

In this paper, we present a self-assembled (pyramidal shaped) QDIPs, which operates in the long wavelength IR. A pyramidal shaped 6×6×3 𝑛𝑚 GaN quantum dot (QD) in a large rectangular cube box of 18×18×9 𝑛𝑚 dimensions. Solves single-band effective mass Schrödinger equation for the gamma conduction band in order to calculate the QD electronic structure. The temperature dependence of the dark current was shown and the amount of dark current at room temperature was equal to 1×〖10〗^(-2) A, which is an acceptable value. The pyramidal GaN QDs has demonstrated exceptionally low dark current, and high detectivity. Detectivity up to 4×〖10〗^9 cm√Hz/W, at room temperature will be the strength of this research.

Regarding the daily increasing development of process and chemical industries and the requirement to improve energy consumption and increasing the output, advanced process control strategies are utilized effectively. Model predictive control as a successful developed process control has been proposed to deal with problems having constraints and multivariable systems. The successful administration of this strategy requires proper adjustment of its’ parameters. In this research, three reservoir system has been modeled as a laboratory plant and predictive control algorithm for first order system with delay has been designed in the form of an unlimited case. The delayed first order model has been achieved though the implementation of a white noise signal into the plant to recognize the system. Then parameters were adjusted using an analytic method. Results of simulations showed that model predictive control has represented an optimal performance through proper adjustment of the parameters.

Due to the widespread use of refrigeration systems in industries, residential and commercial buildings and the major contribution of these systems in the consumption of electrical energy, extensive research is underway to find new technologies for cooling and refrigeration systems. Considering the high consumption of energy in the compression and absorption refrigeration systems, researchers and engineers are developing new refrigeration methods and systems, including modern thermoelectric refrigeration systems. Due to the low coefficient of performance of thermoelectric based systems, extensive research is needed to optimize, develop and find new design methods for these systems. Therefore, in this research, the optimization of the cooling system based on the thermoelectric system has been conducted. For this purpose, the imperial competitive optimization algorithm (ICA) has been used to optimize the thermoelectric system. The objective function of the optimization is the cooling capacity of the refrigeration system. Various parameters of the thermoelectric refrigeration system, such as the amount of electric current on the cold and hot sides of the refrigeration system, were selected as design and optimization variables. The obtained results of the present study have been compared with the results presented by previous researchers. The outlined results revealed that ICA algorithm has a high capability in the optimal design of thermoelectric refrigeration systems.

Mapping the mineralized zones and providing an appropriate distribution pattern of elements for characterizing geochemical system and targeting potentially promising areas of Cu-Au mineralization by utilizing an adequate technique and establishing an optimized exploration tool is the main object of this study in Meshginshahr, NW of Iran. In this respect 144 stream sediments samples were collected and analyzed for Au, Ba, Bi, Cd, Ce, Co, Cr, Cu, Hg, Mo, Ag, As, Sn, Sb, W and Pb. In this study, self-organizing map (SOM) and Fuzzy K-means clustering (FKMC) approaches with the aim of pattern recognition were employed. The SOM as a dimension reduction approach was introduced to recognize geochemical dispersion patterns with high certainty while preserving the originality of data.. During data processing, SOM appropriate structure with a pattern including six clusters was selected and the related elements distribution model was extracted. Results represent two significant sets of elements in clusters for anticipating the mechanism of distribution. In this target pattern, copper and pertaining trace elements formation are localized in the north of the area. Also, Au Anomalies and its associated elements are mostly elongated from NW to SW of the area. To evaluate the SOM results, a comparative study was carried out with the results obtained from Fuzzy K-means clustering (FKMC). FKMC performance showed the proper compliance with the SOM results with respect to the relationship between the elements and their corresponding membership’s probabilities in different clusters. The results illustrated higher performance of the approaches in characterizing geochemical pattern and detecting the element paragenetic sequence in the area for locating the exploration targets..

In this atticle notice to seismicity of the North Tabriz fault which was built many buildings around it, we calculated relevant Stress using GPS observations. We calculated tabriz Fault and the other near faults rupture lenghts using historical earthquakes and GPS data relationship between normal stress and reverse slip region tend by using historical tensions extracted from GPS , which happened in the city of Tabriz and rupture along the fault lines. Also we calculate slip tendency of Tabriz region using slip tendency; normal and reverse stress. The results indicate a high slip tendency of the Tabriz fault specially northwest in Marand to southeast in Miyaneh.

In this paper, energy management in a Micro-grid connected to the distribution network has been done with the aim of reducing the cost of operating the Micro-grids and reducing the environmental pollution index. The Genetic Algorithm method is used to optimize the objective function and find Pareto solutions. The Information Gap Decision-making Method has been used to select the appropriate answers from Pareto's set of answers. In the meanwhile, the participation rate of each of the distributed generator sources and the charge and discharge planning of the energy storage system to provide the Micro-grids load has been calculated. In addition, the interaction between the Micro-grids and the distribution network is important. To analyze the proposed method, a planning problem using multi-priced electricity tariffs is presented as an advantage of the energy storage system. To demonstrate the effectiveness of the multivariate optimization method presented in this thesis, modeling and simulation of diesel generators and energy storage in a Micro-grid have been performed. Mathematical models based on probability density functions, renewable energy sources and consumer load have been investigated. Minimizing the cost of fuel to generate power in the Micro-grids, uncertainty related to renewable sources and Micro-grids load consumption has been modeled using the Information Gap Decision-making Method of maximum uncertainty radius for renewable units and load consumption. The results of the Information Gap Decision Method are compared with conventional methods based on probabilistic function analysis such as the scenario tree method. The results presented in this dissertation show the advantages of the proposed method to improve the overall performance of independent and connected Micro-grids to the distribution network.