Journal of Artificial Intelligence in Electrical Engineering
Volume:9 Issue: 34, Summer 2020

In this work, we calculate optimum thickness of bulk active layer for Ga As/〖Al〗_x 〖Ga〗_(1-x) As laser diodes. We have done these calculations for fundamental oscillation mode of laser with different aluminium contents (fractional percents) in confinement layers. Our calculations were based on the analytical solution of Maxwell equations. The results indicate that the optimum thickness for fundamental mode is dependent on difference of refractive indices of active and confinement layers. The results reveal that the best active layer thicknesses for fundamental mode of laser are d_0=0.63,0.44,0.36 and 0.32 μm for x=0.1,0.2,0.3 and 0.4 aluminium percents in separate confinement heterostructure (SCH) layers respectively.

The application of artificial intelligence in the construction industry is widespread and its development in recent decades has had a tremendous impact on human life. These changes are so fundamental and profound that they require the need to change the design thinking in order to adapt to the new stream of thought and benefit from the benefits of technological tools. Designing modern homes based on artificial intelligence and using car models can take big steps. The purpose of this study is to investigate artificial intelligence, smart homes, control and optimization of energy consumption and raise the level of culture of people's lives and urbanization. Building intelligence (BMS) and consequently smart homes did not have a real structure at first and were just an idea. But now homes have interactions that intelligently control remote control and security systems. How people interact with the environment and feel comfortable in home spaces are two examples of the goals of home architecture that is achieved using artificial intelligence. The research method is applied and the data collection tool is library. The results show that the smart home is focused on smart grid technology to reduce energy purchase tariffs, increase comfort and increase the reliability of energy distribution to consumers.

Landslide susceptibility analysis is beneficial information for a wide range of applications. We aimed to explore and compare three machine learning (ML) techniques, namely the random forests (RF), support vector machine (SVM) and multiple layer neural networks (MLP) for landslide susceptibility assessment in the Ahar county of Iran. To achieve this goal, 10 landslide occurrence-related influencing factors were pondered. A sum of 266 locations with landslide potentiality was recognized in the context of the study, and the Pearson correlation technique utilized in order to select the influencing factors in landslide models. The association between landslides and conditioning factors was also evaluated using a probability certainty factor (PCF) model. Three landslide models (SVM, RF, and MLP) were structured by the training dataset. Lastly, the receiver operating characteristic (ROC) and statistical procedures were employed to validate and contrast the predictive capability of the obtained three models. The findings of the study in terms of the Pearson correlation technique method for the importance ranking of conditioning factors in the context area uncovered that slope, aspect, normalized difference vegetation index (NDVI), and elevation have the highest impact on the occurrence of the landslide. All in all, the MLP model had the utmost rate of prediction capability (85.22 %), after which, the SVM model (78.26 %) and the RF model (75.22 %) demonstrated the second and third rates. Besides, the study revealed that benefiting the optimal machine with the proper selection of the techniques could facilitate landslide susceptibility modeling.

In the current study, the refractive index sensor based on Plasmonic Induced Transparency which is composed of graphene meta-material has been investigated. In this sensor one graphene flat layer has been employed it is worth mentioning that two rods have come out of it Plasmonic Induced Transparency was examined by means of symmetry failure. In addition, through using an important feature of graphene in controlled electrical conductivity by changing the voltage range of the frequency range, the Plasmonic Induced Transparency is controlled, besides light slowing was increased to 690. Its sensitivity is 14.88 um/RIU. The structural shape value is calculated as 48. This nanostructure, in addition to its bio-sensing capabilities, can be used for light slowing, optical switches, modulators.

The most important goal of power system operators is to provide the power required by the consumer at a constant voltage, without harmonics and with a certain frequency. The optimal situation in the production and transmission system is that this system should be able to produce the desired power and the desired consumer. This demand is usually considered in the initial design, but over time due to changes such as: consumption growth, connection of other networks to the previous network, construction of new lines and power plants, etc., upset this balance and put some restrictions. They create power in the operation of the network. In the early days of power systems, they were relatively simple and were designed to be able to operate independently. In addition, transmission system designers have found that AC transmission systems must be controlled by devices to respond to dynamic conditions. Transmission systems were first controlled by classical power control devices under constant conditions or slow load changes and current power control and voltage changes. The dynamics of the system were performed with large stability ranges to cover the critical conditions predicted by faults, line and generator outages, and equipment faults. These factors made transmission systems less efficient and the only solution needed to power them was to build new lines. Therefore, the use of FACTS and D-FACTS devices was proposed, which solved many of the above problems and has a higher efficiency.

A comprehensive feature selection and weighting combination method with novel learning of ANN were introduced, for biomedical RETINA images retrieval. Modified Radon, and modified Hu Moments operators with weighting combinational methods were proposed for achieving higher percentage of retrieval. Besides that, these characteristics are re-composed for presenting outstanding statistic specification and spatial signals. This spatial and frequency information is obtained for all RETINA image dataset. Composition of shape & Textural features present robust vectors for retrieval of biomedical database. In addition, a ANN framework is proposed and applied to measure the similarity between the query and biomedical database. This novel scheme illustrates higher and better specialty in the RETINAI dataset. The results were compared and understood to be remarkable.