Scientia Iranica
Volume:31 Issue: 5, Mar-Apr 2024

Due to its convenience and flexible services, cloud users have drastically increased during the past decade. Manual configuration for the available resources makes the resource management process potentially error-prone. While optimal scheduling is an NP-complete problem, it becomes more complicated due to other factors such as resource dynamicity and on-demand consumer applications’ requirements. In this research, we have used deep reinforcement learning (DRL) as a sequential decision-making method for automatic resource management that changes its behavior to deal with environmental changes. The proposed approach uses the discrete soft actor-critic algorithm which is a model-free deep reinforcement learning algorithm. The proposed approach is compared to similar reinforcement learning-based automatic resource management researches using Google’s dataset. Results show that the proposed approach improves the slowdown and the balance of slowdown at least, 3 and 5 times in the left-bi-model, 4 and 3 times in the right-bi-model, 3 and 7 times in the normal-model, 4 and 2 times in the balanced-bi-model and 3 and 3 times using the Google's dataset.

A cubic bipolar fuzzy number (CBFN) is extremely useful for conveying ambiguous data in real-world settings. The idea of a priority degree is used in an MCDM in which the parameters have a prioritization relationship. Aggregation operators (AOs) are created by assigning non-negative real numbers called priority degrees to tight priority levels. As a result, "cubic bipolar fuzzy prioritized averaging operator with priority degrees (CBFPDA)" and "cubic bipolar fuzzy prioritized geometric operator with priority degrees (CBFPGD)" are presented as prioritized operators with CBFNs. The proposed approaches results are compared to those of numerous other related studies. The existing method's properties are frequently compared to those of other current methods, emphasizing the superiority of the provided work over currently employed operators. In addition, the impact of priority degrees on information fusion and object ranking is investigated. The discussion of a third party reverse logistic provider (3PRLP) optimization problem's practical implementation is a secondary goal. A numerical example is used to examine the effectiveness, superiority, and logic of the recommended way to refer about 3PRLP. Following the procedure of choosing the best strategy and rating the viable alternatives, a comparison analysis is performed.

An optimization algorithm based on training and learning is formed based on the process of training and learning in a class. A deep neural network is one of the types of feedforward neural networks whose connection pattern among its neurons is inspired by the visual cortex of animals' brain. The present study considers decreasing prediction error for the types of time series and the uncertainty in estimation parameters, improving the structure of the deep neural network and increasing response speed in the proposed neural network method; besides, the competitive performance and the collaboration among the neurons of deep neural network are also increased. Selected data is related to Qeshm weather (suitable weather conditions to study our purpose) prediction during 2016 onwards. In this study, for the purpose of analyzing the prediction issue of power consumption of domestic expenses in the indefinite and severe fluctuation mode, we decided to combine two methods of Long Short-Term Memory and Convolutional Neural Network. For the training of the deep network, the BP algorithm is used.

Fine-grained vehicle type recognition using on-road cameras is among interesting topics in machine vision. It has several challenges like inter-class similarity, different viewing angles, and different lighting and weather conditions. This paper presents a novel approach for vehicle classification based on a novel augmentation method and deep learning. In the proposed smart augmentation, the vehicle images of each class are registered on the reference vehicles of all other classes and then added to the training set of that class. In this way, we will have a lot of new images which are very similar to both reference and target classes. This helps the CNN model to handle inter-class similarities very well. In the test phase, the input image is registered on every reference image in parallel and applied to the model. Finally, the winner is determined by summing up the provided scores of all models. The targeted data augmentation along with the proposed classification strategy has high recognition power and is capable of providing high accuracy using small CNNs or any other classification method without the need for large datasets. The proposed method achieved a recognition rate of 99.8% with only 150K parameters.

Understanding the vibrational characteristics of power transformers is significantly important in their design and monitoring. In this contribution, a model with a multi-physics coupling simulation of the electrical circuit, magnetic field, and solid mechanics is developed to investigate the characteristics of the transformer vibration. After describing the model, the harmonic contents of the vibration signals and their variation in the case of mechanical faults are studied. It is shown that under normal operating conditions, the fundamental vibration frequency of 100 Hz has the maximum amplitude, while in the case of mechanical faults, the amplitudes of 200 Hz and 300 Hz harmonics increase dramatically compared to the fundamental harmonic. The influence of vibration sensor position is investigated too, which indicates that the area near the tank bottom is the best position to gather vibration signals. Moreover, the mechanical resonance frequencies of the transformer, along with their mode shapes, are addressed in this paper. Finally, the influence of mechanical changes on the vibration energy distribution in the tank surface is explored. The results of the paper suggest possible diagnosis methods for condition monitoring of transformers, such as using the vibration energy distribution on the tank surface or analyzing the vibration harmonics.

A compact triple band notched ultra-wideband (UWB) monopole antenna is presented in this paper. Split Ring Resonator (SRR) structure is exploited in various forms like Complementary Split Ring Resonator (CSRR), Split Ring Resonator Pair (SRRP) and CSRR on Electromagnetic Band Gap (EBG) structure to produce triple band notched characteristics in UWB spectrum. The proposed antenna produces triple band notched functions with integration of all three types of SRR on primary antenna. The parametric analysis of each form of SRR is presented along with their current distribution effects on triple band notched antenna. The proposed antenna prototype is fabricated and measured results are also compared with simulated one to understand the discrepancies. The measured and simulated results are presented to investigate the band notching characteristics of suggested antenna in terms of VSWR and radiation characteristics.