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Showing 4 results for Subject: Modeling and Simulation

Amir Habibzadeh-Sharif, Mohammad Soleimani,
Volume 1, Issue 2 (1-2014)
Abstract

Optical interconnects as appropriate alternatives for electrical interconnects in the computer chips and boards can be realized by CMOS-based integrated silicon photonics. Dielectric slot waveguide, as one of the newest optical waveguide structures, can form the infrastructure of the passive and active components in these integrated circuits. The passive components have the linear behavior. In order to realize the all-optical active components such as laser, amplifier, and modulator we can use the nonlinear effects in the silicon photonics waveguides. On the other hand, Si-nc:SiO2 as a new material, has a stronger nonlinear property than Si. The results of the full-wave analyzes of the slot waveguide in the linear and nonlinear regimes show that the slot region of this waveguide can be filled with the Si-nc:SiO2 and also realize a high optical intensity. Therefore, this waveguide intensifies the nonlinear behaviors by two factors.
Farhad Mohajelkazemi, Mohamad Reza Banaei, Mehran Sabahi,
Volume 5, Issue 2 (3-2019)
Abstract

A novel current source multilevel inverter is introduced in this paper which is an appropriate alternative to be employed for low/medium power applications. the proposed converter is formed basic modules which paralleling these modules increse output current levels and improve quality of injected current to load or grid. in order to validate advantages of proposed converter versus the several multilevel current source inverters, a full comparison is provided. the simulation results shows the good performance of the proposed converter in off grid and grid-connected applications. Also experimental results for single-phase load confirm the practicablity of the proposed converter.


Zahra Bounik, Dr Mousa Shamsi, Dr Mohammad Hossein Sedaaghi,
Volume 7, Issue 1 (9-2020)
Abstract

In this paper, a real-time interactive high resolution soft tissue modeling is implemented that enriches a coarse model in a data-driven approach to produce a fine model. As a preprocess step, a set of corresponding coarse and fine models are simulated for the database. In the test step, by using a regressor, the coarse model in the test set is compared to the coarse models in the training set and the blending weights are assigned to the training coarse models. These weights are used for approximating the fine model as a linear combination of the corresponding fine models in the train set. To decrease the computational complexity, assuming that applying a force on the tissue results in a local deformation, a feature extraction algorithm is proposed that considers the displacements of the contact node and its neighbor nodes and ignores the rest. This results in a low dimensional feature vector and decreases the computational complexity. In order to compute the blending weights, a nonlinear regressor with Gaussian kernel is leveraged. To eliminate the artefacts resulting from negative weights, a nonnegative least square algorithm is used for regression. Simulation results of applying the proposed method on two soft tissue models are investigated regarding the reconstruction accuracy, computational complexity and running time.
Ali Ghaemi, Amin Safari,
Volume 7, Issue 2 (3-2021)
Abstract

The high power passing through transmission systems and the high costs due to the fault occurrence in these lines have encouraged researchers to pay special attention to protection issues in this area. The limitations and deficiencies of traditional protection methods and their strong dependencies on the system operating conditions doubles the importance of early fault detection and its prediction utilizing new techniques. Timely detection and warning issuance toward the possibility of fault occurrence can be accomplished by analyzing the data and information obtained from the system and examining the relationships between different parameters. In this paper, machine learning methods are used, which have the ability to predict the occurrence of faults with appropriate accuracy independent of the operating area of the system. To evaluate the performance of the models, a large amount of data has been generated in various operating conditions and applied as input to the algorithms under study. Also, the effects of different weather conditions as one of the important factors have been considered. For the sake of greater generality, accuracy check, and comparability of the results, three methods including KNN, SVM, and decision tree in two modes (unbalanced and balanced data in the existing classes) have been used, and the outcomes have been presented. The simulations and modeling presented in this paper have been implemented using Python and MATLAB.


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سامانه های غیرخطی در مهندسی برق Journal of Nonlinear Systems in Electrical Engineering
نشریه سامانه‌های غیرخطی در مهندسی برق در خصوص اصول اخلاقی انتشار مقاله، از توصیه‌های «کمیته بین‌المللی اخلاق نشر» موسوم به COPE و «منشور و موازین اخلاق پژوهش» مصوب معاونت پژوهش و فناوری وزارت علوم، تحقیقات و فناوری تبعیت می‌کند.
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