Robotics is a highly developed field in industry, and there is a large research effort in terms of humanoid robotics, including the development of multi-functional empathetic robots as human companions. An important function of a robot is to find an optimal coverage path planning, with obstacle avoidance in dynamic environments for cleaning and monitoring robotics. This paper proposes a novel approach to enable robotic path planning. The proposed approach combines robot reasoning with knowledge reasoning techniques, hedge algebra, and the Spiral Spanning Tree Coverage (STC) algorithm, for a cleaning and monitoring robot with optimal decisions. This approach is used to apply knowledge inference and hedge algebra with the Spiral STC algorithm to enable autonomous robot control in the optimal coverage path planning, with minimum obstacle avoidance. The results of experiments show that the proposed approach in the optimal robot path planning avoids tangible and intangible obstacles for the monitoring and cleaning robot. Experimental results are compared with current methods under the same conditions. The proposed model using knowledge reasoning techniques in the optimal coverage path performs better than the conventional algorithms in terms of high robot coverage and low repetition rates. Experiments are done with real robots for cleaning in dynamic environments.

The aim of this study is to compare the performance of various deep learning models in predicting the arrival and charging time of an electric vehicle at a charging station. The objective is to identify the most precise model capable of predicting the time from the driver’s location to the arrival at the charging station. Initially, an effort was made to ascertain which model type offers superior prediction accuracy, characterized by low error rates and high success scores. To this end, the study examined the prediction capabilities of the LSTM (Long Short-Term Memory), GRU (Gated Recurrent Unit), Prophet, and ARIMA (Autoregressive Integrated Moving Average) models, which were trained with historical data using various metrics. These metrics included the R² (R-squared)success metric, MAE (Mean Absolute Error) and MSE (Mean Squared Error) error metrics, DTW (Dynamic Time Warping) metric scores, and model selection. In MAE metric scores, GRU has an error rate of 0.38, LSTM 0.50, Prophet 0.61, and ARIMA 2.31. For MSE metric scores, GRU has an error rate of 2.92, LSTM 3.05, Prophet 4.21, and ARIMA 65.54. In R² metric scores, GRU has a success rate of 0.99, LSTM 0.99, Prophet 0.13, and ARIMA −2.19. In DTW metric scores, GRU has a distance ratio of 125.5, LSTM 126.9, Prophet 185.9, and ARIMA 454.9. Based on these score values, it was decided that the GRU model made the most accurate time estimation. After observing the superior performance of the GRU model, the time prediction capability of this model is demonstrated through an interface program for charging stations in the province of Kocaeli, which serves as the model’s real-world application area. The main contribution of this article is that LSTM, GRU, Prophet, and ARIMA deep learning approaches, which are preferred in many studies, are used for the first time in the process of estimating electric vehicle charging time.

In this study, design principles required for fuel cells to be used in systems that need inrush current are disclosed. The most important difference that separates this study from others is the consideration of utilizing Li-ion batteries to provide power where inrush current is needed in order to spread. In previous studies, lead acid batteries were used instead of Li-ion batteries. Noiselessness and effective electric production ability make fuel cells a good alternative for military applications. The Li-ion battery is the state of the art technology and while advantages include their light weight and the facts that they occupy less space, charge quickly, and have high power intensity, one can experience problems charging because of their charge characteristics. The desired voltage and current can be obtained through serial and parallel implementation of Li-ion batteries. Li-ion batteries and fuel cells are the technology of our day and it is desirable to obtain a more effective system by bringing the advantages of both to the same system. A high efficiency and silent energy is obtained through the use of fuel cells, and Li-ion batteries will enable their use in systems requiring inrush current. Noiseless maneuvering capability in the operational area for a battle ship is a very important factor to preserve. In this study, a way in which these two technologies can be used in cooperation and in an efficient way is described. Fuel cells that have output power 1000 W, output voltage 115 V, and output frequency 60 Hz were tested in the radio room of a corvette of the Turkish Navy.

The need for electricity is increasing and the cost of energy generation is arising. For this reason, it is important to use the generated energy in good quality, safely and efficiently. Reactive power compensation is one of the most effective application to reduce transmission losses, prevent voltage drops, prevent consumers from paying for reactive energy, facilitate operating, increase efficiency and save energy in energy systems. However, due to improvements in semiconductor technology, reactive power compensation systems have gained a new dimension. The power compensation made by the use of semiconductor power elements is called Static VAr Compensation. This compensation is used for the compensation of loads such as arc furnaces, elevators, automotive; paper packaging, food and textile, point welding machines, port cranes, flat welds. Transient events are minimized, losses are reduced, back up possibility, control flexibility and reliability are ensured by using Static VAr Compensation systems in power network. In this study, thyristor triggering angles and power factor values of a system, that contains reactive loads and controlled by Static VAr Compensation, were obtained by using the computer software developed by Microsoft C Sharp (C#) programming language. The results were discussed in terms of importance of using such controlling structures in power networks.

Ulaşım konusunda tüm dünyanın üzerinde durduğu elektrikli araçlar oldukça hızlı yaygınlaşmaktadır. Bu hızlı yayılma ile birlikte aracın sorunsuz yol alabilmesi için belirli aralıklarla şarj edilmesi gerekmektedir. Dolayısıyla, elektrikli araçlar için şarj istasyonları ve bunların enerji şebekesine entegrasyonunun önemi gündeme gelmiştir. Yapılan bu çalışmada Kocaeli Üniversitesi Umuttepe Yerleşkesi için gerçek veriler Digsilent Power Factory programına aktarılarak mevcut enerji dağıtım sisteminin elektrikli araç şarj istasyonu tasarım sorunu detaylı olarak ele alınmıştır. Böylece yakın gelecekte kampüste kurulması beklenen şarj istasyonları için çalışma rehber niteliğinde olacaktır. İlk olarak mevcut dağıtım sisteminin trafolarının % 80 yüklendiği bilindiği için bu yüklenme yüzdesi ile yük akışı elde edilmiştir. Böylece mevcut sisteme herhangi bir iyileştirme yapılmadan ne kadar şarj istasyonu eklenebileceği görülmüştür. Mevcut sisteme eklenebilecek maksimum şarj istasyonu sayısının gelecekte yetersiz olabileceği ön görülerek iki farklı yöntem önerisi ile şarj istasyonu sayısının artırılması hedeflenmiştir. İlk olarak sisteme uygun yeni trafo merkezleri eklenerek şarj istasyonu sayıları ve noktaları gösterilmiştir. Daha sonra ise oldukça popüler olan yenilenebilir enerji kaynaklarından güneş enerjisi ile kampüs için bir güneş panelli şarj istasyonu tasarlanmıştır. Yapılan tasarım Digsilent Power Factory programı ile mevcut sisteme entegre edilerek şarj istasyonu sayısı ve noktaları gösterilmiştir. Yapılan her bir simülasyon çalışması detaylı olarak incelenmiş ve mevcut sisteme etkisi gösterilmiştir.

This study presents models for estimating the charging time and travel time in autonomous electric taxi systems, based on Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) deep learning methods. In addition to these models, two classical time-series forecasting techniques ARIMA and Prophet were also applied to provide a broader comparative baseline. Unlike traditional time-series prediction methods, the proposed system combines artificial intelligence with Internet of Things (IoT) technologies to perform secure charging operations based on multi-layer cybersecurity mechanisms, including IP authentication, encrypted communication, and charger server validation steps. The models were trained and validated using a comprehensive dataset obtained from 100 electric vehicles with different battery capacities at 50 charging stations located in Kocaeli Province. In the predictions considering parameters such as the vehicle type, battery capacity, and charge level, both models showed high accuracy rates, with the GRU model performing better than the LSTM model in terms of the error rate and temporal consistency. ARIMA and Prophet, on the other hand, produced significantly lower performance compared to deep learning models, confirming that GRU is the most suitable approach for real-time duration estimation. Customers can obtain the estimated time, cost, and charging requirements before their trip, and continuous multi-stage IP-based security controls are performed throughout the charging process as part of the cybersecurity framework. If a foreign or unauthorized connection is detected, the charging operation is automatically stopped. The proposed approach not only increases the efficiency in electric vehicle energy management but also presents an innovative framework that contributes to sustainable and smart transportation. By combining deep learning models, classical statistical forecasting methods, IoT integration, and enhanced cybersecurity controls, this work represents a pioneering step toward autonomous, secure, and eco-friendly urban transportation systems.