Reinforcement learning (RL) techniques have emerged as powerful tools for optimizing energy systems, offering the potential to enhance efficiency, reliability, and sustainability. This review paper provides a comprehensive examination of the applications of RL in the field of energy system optimization, spanning various domains such as energy management, grid control, and renewable energy integration. Beginning with an overview of RL fundamentals, the paper explores recent advancements in RL algorithms and their adaptation to address the unique challenges of energy system optimization. Case studies and real-world applications demonstrate the efficacy of RL-based approaches in improving energy efficiency, reducing costs, and mitigating environmental impacts. Furthermore, the paper discusses future directions and challenges, including scalability, interpretability, and integration with domain knowledge. By synthesizing the latest research findings and identifying key areas for further investigation, this paper aims to inform and inspire future research endeavors in the intersection of reinforcement learning and energy system optimization.

The article presents the importance of renewable energy in reducing the potential dangers of global warming and climate crises in the context of increase in global energy comp Sumption that will lead to increase of emission of greenhouse gases. to and of the maintenance in solar energy systems. The operation and maintenance activities of photovoltaic systems represent aspects not to be neglected for obtaining the profitability of investments and ensuring their viability and reliability. Currently, the procedures applied mainly refer to simple techniques such as visual inspection and scheduled maintenance strategies. Also, the types of faults that can may appear in photovoltaic panels and the ones can be detected by thermography (single hot spot, multiple hot spots, activation of the bypass diode and a higher temperature of the junction box) are presented with their characteristics and severity. In the last part of the article a thermal imaging processing software based on artificial intelligence technology is proposed for use for the preventive maintenance, in order to detect the photovoltaic(solar) panels with faults to be repaired or changed to increase the efference of the system. The Software will be use to develop an innovative maintenance and repair curricula for the departments related to solar energy in vocational and technical education schools in order to better predict and prevent malfunctions in solar energy systems.

In recent years, there is rise in various human-computer interaction interfaces and automatic text translation services and frameworks. In this paper, we conduc

In this paper we aim to offer a low budget simulator of the M1A2 Abrams battle tank for training and educational purposes. We have focused on modeling the battle tank fire controls, as well as a virtual representation of the tank. In addition, we use modern methods for calculating the external projectile ballistics. Furthermore, wind force and its effect on the trajectory of shell projectiles are simulated and studied. Finally, we have conducted several experiments and draw conclusions.

Education and self-improvement are key features of human behavior. However, learning in the physical world is not always desirable or achievable. That is how simulators came to be. There are domains where purely virtual simulators can be created in contrast to physical ones. In this research we present a novel environment for learning, using a natural user interface. We, humans, are not designed to operate and manipulate objects via keyboard, mouse or a controller. The natural way of interaction and communication is achieved through our actuators (hands and feet) and our sensors (hearing, vision, touch, smell and taste). That is the reason why it makes more sense to use sensors that can track our skeletal movements, are able to estimate our pose, and interpret our gestures. After acquiring and processing the desired – natural input, a system can analyze and translate those gestures into movement signals.

In this paper, we present the usability evolution of human-computer interactions. In addition, we group the various user interfaces of human-computer interaction into two generations with three classes each. The most user interfaces for human-computer interaction are assigned into a class depending on objective criteria scores, such as technical literacy level, level of natural interactions, user learning curve and UI’s ability to adapt. In addition, current tendencies for HCI are presented and future perspectives are discussed. Finally, we summarize the achieved results and draw conclusions.