جستجوی مقالات مرتبط با کلیدواژه « genetic algorithm » در نشریات گروه « برق »

Air travel has significantly grown as a fast and safe means of transportation. Therefore, creating a smooth air traffic and proper flight scheduling for safe landings with minimal time changes is necessary to avoid wasting time and money. In most studies, the aircraft landing scheduling problem has been considered a static issue. However, this challenge has a dynamic nature in real-world problems. One of the optimizing approaches in this scope is swarm intelligence optimization algorithms, which are simple and highly capable in solving optimization problems. Among these algorithms, Spider-Monkey optimization algorithm is more efficient than traditional algorithms by using few parameters, maintaining search history, controlling searches, and grouping members of the population if needed. In this study, an active scheduling method for aircraft landing scheduling using Spider-Monkey optimization algorithm and genetic algorithm has been presented. The proposed method is examined by some datasets of single and multi-runways (single and multi-objective aircraft landing). The achieved results show an improvement in flight schedules and reduced costs.

In this research, a new approach for image segmentation based on genetic algorithm and random forest is presented. Image segmentation can be done using supervised learning. In this learning, there are a number of images from data set with their labels. In image segmentation, different parts of the image separate from each other. In this process, all the pixels in the image are given a label, so that the pixels with the same label have common characteristics with each other. To provide a model that can perform image segmentation, it is necessary to extract features from input images and perform segmentation using a suitable classifier and these features. Image feature extraction is done using image filters. In this research, a hybrid combination of 4 Gabor filter banks and Sobel, Prewitt, Canny edge, Scharr, Gaussian, median, and Roberts filters are used for effective feature extraction. One of the most important of these filters, which also has a degree of freedom, is the Gabor filter. This filter has a number of   hyperparameters that change the efficiency of the classifier by changing these hyperparameters. In this research, an attempt has been made to adjust these hyperparameters using genetic algorithm. The fitness function proposed in this research is f1-score. random forest classifier is utilized for image segmentation and classification. The results of the experiments show that the hyperparameters found by the genetic algorithm have been able to perform a satisfactory segmentation on data set.

Insufficient training data is one of the main challenges of utilizing deep Convolutional Neural Networks (CNNs) for Environmental Sound Classification (ESC). As a promising solution, Transfer Learning (TL) has addressed this issue by adapting a network pre-trained on a large-scale dataset to the target task. In this paper, we demonstrate that not all neurons/kernels of every layer in CNN networks are equally utilized to process the inputs of different classes, but there is a specific subgroups of neurons/kernels in every layer that play the key role in classification of every output class. Based on this observation and due to similarities that exist between feature spaces of some source and target classes, we propose to concentrate the fine-tuning process only on those neurons/kernels that do need changes and have the greatest impact on misclassifying target data. To identify these neurons/kernels, we pose a nested optimization problem for which we propose an effective evolutionary approach as solution.  Compared to the conventional fine-tuning approach, our proposed method achieves absolute improvements of about 1.9% and 2.3% in accuracy on ESC-50 and DCASE-17, respectively; remarkable improvements produced not by adding augmented data but with a more efficient utilization of knowledge stored in the pre-trained network. It is noteworthy that the computation time overhead of the proposed evolutionary method is rather small (about one third of the time required to train the model from scratch.

Improving reliability indices, increasing the grid production capacity, reducing power loss during peak periods and fuel cost, and economic and environmental considerations on the one hand, and the progresses made in the field of semiconductor devices on the other hand, are some of the factors that increase the penetration of the renewable energy sources to the distributed network. When the photovoltaic panels are present at the level of the distribution network, there are periods of time when the voltage exceeds its allowable area. Regarding this issue, it is necessary to consider solutions to overcome this challenge. This paper suggests an online dynamic voltage restorer whose control algorithm is designed according to the genetic algorithm. The simulations are carried out according to the voltage profile of a part of Mashhad. They have been investigated by DIgSILENT software. For this purpose, the real data of energy consumption in the residential sector with a time step of one hour has been used and the simulations have been implemented in the form of three scenarios. The first scenario is when there is no solar cell. The second is when the solar cell is online. And the third state is when both the cell and the dynamic voltage recovery in the line help the stability of the system and improve its power quality. According to the obtained results, by applying the mentioned control method, not only more than 7% of the range of voltage variations is reduced, but by reducing the level of voltage imbalance, the level of efficiency and reliability of the network is also increased.

Wireless Body Area Network (WBAN) is a pioneer trend in healthcare technology. Since any cyber-attack on a WBAN could jeopardize the patient's health, securing the WBAN plays a crucial role in healthcare applications. An intrusion detection system (IDS), as a second-line defense, is one of the security methods in computer networks. In this paper, a new IDS has been presented which is able to detect denial of service (DoS) attacks in a WBAN. In the proposed IDS, a genetic algorithm is used to select features of collected data, in a way that increases the performance of the IDS and as a result the WBAN. Then, using support vector machine and k nearest neighbor techniques, the data classification is performed to detect DoS traffic from regular data traffic. Simulation results indicate that the proposed IDS has effective performance with a 90% detection rate.