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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.
Ramin Niromandfam, Amir Niromandfam,
Volume 9, Issue 1 (9-2022)
Abstract
Today, electricity service providers have to consider economic index as well as social indicators and consumer perspectives. In this paper, by studying the consumer satisfaction function, a new load model based on nonlinear consumer perspective has been developed. The main variable in the proposed model is the coefficient of individual consumer behavior, which requires field and statistical activities to estimate. As an alternative solution in this paper, the relationship between demand elasticity and individual behavior coefficients has been mathematically developed to estimate these coefficients through demand elasticity information. Also, a new demand response program based on individual behavior and satisfaction function has been developed to determine the incentive rate for consumer participation in an optimal way based on their individual perspective. In the numerical results section, the data of Iran's electricity network has been selected for study. Numerical results showed that in order to study the annual consumption change, in addition to increasing prices, inflation rates and increasing consumer incomes also need to be examined. It was also shown that for the Iranian electricity grid, the demand response program implementation for the household consumers has the lowest cost. However, in order to achieve maximum welfare, it is more appropriate these programs have been applied to the public customers.
Dr Ramazan Havangi,
Volume 10, Issue 2 (9-2023)
Abstract
Estimating the state of charge of lithium- ion batteries is of great importance not only for optimal energy management, but also for ensuring safe operation, preventing charging and discharging, and as a result reducing the life of the battery. However, this parameter cannot be measured directly from the battery terminals. Therefore, there is a need to estimate it. In this paper an improved auxiliary marginal particle filter is presented to estimate the state of charge of lithium-ion batteries. In the proposed method, unlike the particle filter, sampling is done on the marginal distribution and the sampling dimensions do not increase with the passage of time. In addition, genetic operators and M-H algorithm have been used in the proposed method to increase diversity among particles. The use of genetic operators and the M-H algorithm causes the resampled particles to asymptotically approximate the samples from the posterior probability density function of the true state and increases the compatibility. The performance of the proposed method for estimating the state of charge of the battery has been compared with the estimation of the state of charge based on the developed particle filter and traceless particle filter. The results show the effective performance of the proposed method in comparison with other methods. The proposed method to obtain the same estimation accuracy as the particle filter requires far fewer particles and the amount of calculations is low. The root mean square error in the proposed method with different particles is close to 0.007, while in other methods, the root mean square error increases with the decrease of particles.
Majid Najjarpour, Behrouz Tousi, Alireza Ebadi Zahedan,
Volume 10, Issue 2 (9-2023)
Abstract
In this article, an efficient method to minimize energy losses is presented. The proposed method uses intermittent load conditions over a future time interval instead of an instantaneous network condition. This method obtains the optimal condition during the given period according to the current value of the condition. A given time interval is divided into many smaller subintervals. By increasing the number of subintervals or load profiles, the dimensions of the problem increase, for which an optimal value must be obtained. In this method, the variables are divided into the group of continuous and discrete control variables. While only continuous control variables are allowed to change in each sub-interval, continuous and discrete variables are set at the beginning of each time interval. This problem is solved by using the GBD general bend decomposition method. Using this method, the load conditions for each subinterval in the NLP subproblem are solved. Then, the results of the NLP subproblem are used in the main subproblem. As shown in the simulation results, the proposed method not only improves the voltage profile but also reduces the total energy wasted in the desired period.
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