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Öğe A modified salp swarm optimization algorithm based on the load frequency control of multiple-source power system(Altınbaş Üniversitesi, 2023) Al-Zubaidi, Anas Mahdi; Cansever, GalipThis work proposes a modified Salp Swarm Optimization Algorithm (SSA) for addressing a multi-source power state's Load Frequency Control (LFC). A controller parameter tuning of the SSA method and its application to the LFC of a multi-source power system with several power generating sources. Derive to the controller parameters, a single area telecommunications device that permits two power system with integrated controlles according to each unit is considered first, and the SSA approach is used. The tunned SSA algorithm is used to optimize the integral (I), proportional integral (PI), and proportional integral derivative (PID) parameters. The research is expanded to include a multi-area multi-source power system, as well as an HVDC link is proposed for connectivity of two regions in addition to the current AC point of intersection. This same tunned SSA method is used to improve the parameters of the Integral (I), Proportional Integral (PI), and Proportional - integral - derivative Derivative (PID). Consequently, the suggested system is shown to be resilient and unaffected by changes of the loading situation, system parameters, or SLP size.Öğe Automated generation control of multiple-area electrical system with an availability-based tariff pricing scheme regulated by whale optimized fuzzy PID controller(Hindawi, 2021) Hamodat, Zaid; Cansever, Galip; Cansever, GalipIn this research, a whale-optimized fuzzy PID controller was developed to manage automatic generation control in multiple-area electrical energy systems with an availability-based tariff (ABT) pricing scheme. The objective of this work is to minimize the power production costs, area control errors (ACEs), and marginal costs of the multiple-area electrical energy system with real-time load and frequency variation conditions. The generation of power, deviation of power in the tie line, and deviation of frequency of the interconnected three-area electrical energy system, including the hydrothermal steam power plant and gas power plant, will be measured and analyzed rigorously. Based on the output from the whale optimization, the fuzzy PID controller regulates the deviation of power in the tie line and the deviation of frequency of the interconnected three-area electrical energy system. The reliability and suitability of the proposed optimization, i.e., whale-optimized fuzzy PID controller, are investigated against already presented methods such as particle swarm optimization and genetic algorithms.Öğe Control of a quadruple tank system using two PID controllers(Institute of Electrical and Electronics Engineers Inc., 2024) Mohammed, Aseel Assim; Cansever, GalipThis work details the mathematical modeling of a nonlinear multi-input multiple-output (MIMO) quadruple tank system (QTS) using the linearization principle and forming a Jacobean matrix to represent the system in a state space model. Using simulation tools in MATLAB software (Matlab /Simulink), a quadruple tank system is designed as well as a proportional-integral-derivative (PID) controller design to control the required liquid (water) level in the quadruple tank system (QTS).Initially, the parameters of the two PID controllers were tuned using a trial and error method, which involved setting the controller parameters manually, and the Automatically Tune the two PID controller method was then employed to tune the gain parameters of each PID controller. Finally, the genetic algorithm was used, which is a research and improvement method used to obtain the optimal values for the PID parameters. In addition, the effect of PID controllers on the steady-state response and transient response of a quadruple tank system was analysed. The results showed that using genetic algorithm-based optimization (GA) is easy to implement for both linear and nonlinear systems and that a response of a system with the presence of the PID controller, which was adjusted using a genetic algorithm (GA) with the size of population 50 and the ISE fitness function, shows the best response compared to other the classical PID controllers’ responses.Öğe Design and simulation of smart parking system using image segmentation and CNN(Institute of Electrical and Electronics Engineers Inc., 2022) Haji Alsaedi, Ali Jameel; Cansever, GalipReducing the congestion of busy parking lots by giving people in the vicinity an accurate idea of how many spots are open is a feature that smart parking systems can provide. so far, these systems have been deployed mostly for indoor locations using expensive sensor-based technology. As research and development of image-based detection techniques has increased, it follows that many commercial products are using smart parking technology, and thus, the need for the systems is growing. This research uses a binary Support Vector Machine (SVM) classifier with an image classifier trained using a Convolutional Neural Network (CNN) to identify the presence of vehicles in parking spots. Classifier training and testing used deep CNN features drawn from public datasets with varying light and weather conditions. So, we check how well the technique does with regard to transfer learning using a dataset designed for our study. We've concluded that our approach is good for solving issues in outdoor settings, as shown by our 99.7 percent detection accuracy and 96.7 percent accuracy for the public dataset and our dataset, respectively.Öğe Enhanced energy efficiency through path planning for off-road missions of unmanned tracked electric vehicle(Multidisciplinary Digital Publishing Institute (MDPI), 2024) İnal, Taha Taner; Cansever, Galip; Yalçın, Barış; Çetin, Gürkan; Hartavi, Ahu EceThe primary objective of this research is to address the existing gap about the use of a path-planning algorithm that will reduce energy consumption in off-road applications of tracked electric vehicles. The study focuses on examining various off-road terrains and their impact on energy consumption to validate the effectiveness of the proposed solution. To achieve this, a tracked electric vehicle energy model that incorporates vehicle dynamics is developed and verified using real vehicle driving data logs. This model serves as the foundation for devising a strategy that can effectively enhance the energy efficiency of off-road tracked electric vehicles in real-world scenarios. The analysis involves a thorough examination of different off-road terrains to identify strategies that can adapt to diverse landscapes. The path planning strategy employed in this study is a modified version of the A*, called the Energy-Efficient Path Planning (EEPP) algorithm, specifically tailored for the dynamic energy consumption model of off-road tracked electric vehicles. The energy consumption of the produced paths is then compared using the validated energy consumption model of the tracked electric vehicle. It is important to note that the identification of an energy-efficient path heavily relies on the characteristics of the vehicle and the dynamic energy consumption model that has been developed. Furthermore, the algorithm takes into account real-world and practical considerations associated with off-road applications during its development and evaluation process. The results of the comprehensive analysis comparing the EEPP algorithm with the A* algorithm demonstrate that our proposed approach achieves energy savings of up to 6.93% and extends the vehicle’s operational range by 7.45%.Öğe Enhancing frequency deviation of a microgrid in connected mode using particle swarm optimization(Institute of Electrical and Electronics Engineers Inc., 2023) Jumaah, Ali; Cansever, GalipThe demand for electric power has been increasing salient lately, causing carbon emissions from energy goes up by more than half when compared to the period when the industrial revolution began, that raising concerns about global warming. As a result, the researchers focused on the movement toward renewable energy, including solar energy, wind energy, and biomass energy, rather than conventional energy, which depends on fuel. Because the generation point of electric power is relatively far from the point of consumption, therefore electric power transmission lines are the means of delivering energy to the consumer, it has become important to study the electrical system and develop the most appropriate solutions to the problems that occur in it. One of the researchers' top priorities is keeping the system's frequency stable. Various smart solutions were utilized in the researchers' studies to adjust the frequency stability of power systems. An artificial intelligence-based technique was utilized for modeling the proper control of the frequency of the microgrid, like the operation of the human brain. In this study, a model of a microgrid with a solar-energy station connected to the main grid was designed. The control that will use the PSO algorithm to enhance PID was compared and analyzed, and the smart controllers were designed and tested using MATLAB.Öğe Image filtering of impulsive noise using biologically inspired algorithms(Institute of Electrical and Electronics Engineers Inc., 2022) Gharraf, Hadeel Sabti; Cansever, Galip; Ahmed, Ali SaadoonA digital image is a two-dimensional representation of an image in the form of a numerical matrix. In grayscale images, pixels are represented by an integer numeric value that is between 0 and 255. One of the main problems encountered today is the appearance of noise in digital images. The main sources of noise appear during the image acquisition and transmission phases. There are many types of noise and among the best known are some such as Gaussian or impulsive. The main goals of applying filters are to smooth the image, remove noise, enhancement, and edge detection, in this paper we propose the combining of the powerful set of Fuzzy rules with the genetic algorithm pattering recognition features to filter the image from impulsive noise yet retaining the image features unfiltered thus performing a powerful noise filtering on the noise without and destruction to the image itself.Öğe Internet of things based zigbee sniffer for smart and secure home(Altınbaş Üniversitesi, 2022) Albayati, Farah Shakir Mahmood; Cansever, GalipThis paper aims to resolve the Internet of Things (IoT) based ZigBee sniffer for smart home and determine the usage of energy or power with high spectrum allocation in future ZigBee Protocol with the help of clustering in IoT with data mining. The research work starts presenting an overview of the broadband network energy sector and the challenges that face it. It is observed a change in the energy policies promoting energy efficiency, encouraging an active role of the consumer, instructing them about the importance of consumer behavior, and protecting consumer rights. Electricity is gaining room as an energy source. Its share will keep constantly increasing in the following decades. ZigBee Protocol and smart meters’ deployment will benefit both the utility and the consumer in the near future. New services and new businesses appear in this environment, focusing on the energy management field and tools. They require specialization in fields such as computer science, software development, and data science. This research has segmented the ZigBee Protocol according to the similarities of their electrical load profiles, using the proportion of energy usage per hour (%) as a common framework. This energy consumption segmentation aims to provide personalized recommendations to each group to reduce their energy consumption and the associated costs, fostering energy efficiency measures and improving consumer engagement. The desired segmentation is obtained by an iterative process, based on computational clusters calculation (using a Python programming language) and finalized by a post-clustering analysis applying visualization and statistical data mining technique to detect the energy consumption and reallocate them to a more appropriate group. The K-Means clustering technique was tested and compared, giving the best prediction of accuracy 98.46% for all energy load profiles with a high spectrum of 100GHz. The solution from the K-Means clustering is the one that better adapts to the segmentation sought, which is used as the base of the post-clustering stage to obtain the final energy consumption segmentation.Öğe Intursion detection in Iot networks using feature selection and SVM classificastion(Institute of Electrical and Electronics Engineers Inc., 2022) Hussein Al-Balhawi, Maryam Ali; Cansever, GalipThe steady growth in the number of devices connected to the Internet has attracted cyber criminals looking for vulnerabilities in computer networks and systems. The objective of this paper is to develop a model to identify DDoS, Infiltration, Web and Brute force attacks on computer networks, using Machine Learning (ML) techniques, increasing the accuracy, sensitivity, precision and measurement values. -F in relation to existing work.Öğe LAN based GIS optimization for coverage in wireless networks(Institute of Electrical and Electronics Engineers Inc., 2023) Atiyah, Israa Salman; Cansever, Galip; Ahmed, Ali SaadoonMachine learning is a branch of artificial intelligence based on the idea that systems can learn to identify patterns and make decisions with a minimum of human intervention. In this Paper, demonstration learning will be used, using neural networks in a prototype of a drone built to perform trajectories in controlled environments. To accelerate the training convergence process, a new training data selection approach has been introduced, which picks data from the experience pool based on priority instead of randomness. An autonomous maneuver strategy for dual-UAV olive formation air warfare is provided, which makes use of UAV capabilities such as obstacle avoidance, formation, and confrontation to maximize the effectiveness of the attack.Öğe Management of hybrid electric microgrid using fuzzy logic and adaptive neural network(Institute of Electrical and Electronics Engineers Inc., 2022) Aljanabi, Ihsan Basil; Cansever, GalipThe Energy most countries installed a distributed generation micro-grid which incorporates renewable resources (solar and wind energy) With respect to this system, in this work two topics are addressed; the calculation of the range in which the demand can move and the effect that the demand management signals have on the forecast. For the first topic, fuzzy intervals will be used to determine, based on historical data, the dynamic range. This range provides the limits to the optimizer for the load displacement factor, on which the signals that are sent to the consumers depend.Öğe Obstacle avoidance in mobile robots in RGB-D images using deep neural network and semantic segmentation(Institute of Electrical and Electronics Engineers Inc., 2022) Al-Adhami, Abdulrahman Abdulwahab Kaml; Cansever, GalipAutomating industrial and commercial activities through the use of manipulative robots capable of movement and carrying a variety of products is gaining popularity. When it comes to mobile robots, their operating systems are more complex, which means they are more in demand due to their use in research, which increases their price. These devices run on a variety of operating systems. Obstacle avoidance, line tracking, and mobile explorers are just a few examples. As a result of our findings, we propose a vision system that utilizes a feature extraction and classification algorithm DNN for obstacles in RGB-D images in order to detect and avoid obstacles in known environments.Öğe Resource allocation in cloud-fog systems using genetic algorithm(Institute of Electrical and Electronics Engineers Inc., 2022) Al-Abbasi, Sohaib Naseer Lateef; Cansever, GalipThis paper's proposal is that Cloud administrators are responsible for providing resources, but users are responsible for ensuring that information travels to and from the cloud safely and securely. The supply of resources has to be understood in layers, with each layer representing a distinct genre of goods that may be offered in various ways, and in this context, we suggest the employment of genetic algorithms and fog nodes to accomplish the ideal route to the clouds and back.Öğe Robot localization in RGB-D images using PCA and CNN(IEEE, 2021) Taha, Alwaled Khalid; Cansever, GalipHuman beings have always used the resources at their disposal as tools to help carry out tasks in the most effective, fast and safe way. As technology advances, these tools are being used in increasingly complex machinery capable of performing complicated jobs precisely and often better than a human could have in this paper we aim at performing a robust robot localization by reducing the 3d images into 2d images using PCA algorithm and using CNN for feature extraction and classification of the images.
