Energy management systems (EMSs) are regarded as essential components within smart grids. In pursuit of efficiency, reliability, stability, and sustainability, an integrated EMS empowered by machine learning (ML) has been addressed as a promising solution. A comprehensive review of current literature and trends has been conducted with a focus on key areas, such as distributed energy resources, energy management information systems, energy storage systems, energy trading risk management systems, demand-side management systems, grid automation, and self-healing systems. The application of ML in EMS is discussed, highlighting enhancements in data analytics, improvements in system stability, facilitation of efficient energy distribution and optimization of energy flow. Moreover, architectural frameworks, operational constraints, and challenging issues in ML-based EMS are explored by focusing on its effectiveness, efficiency, and suitability. This paper is intended to provide valuable insights into the future of EMS.

The transition to smart grids has served to transform traditional power systems into data-driven power systems. The purpose of this transition is to enable effective energy management and system reliability through an analysis that is centered on energy information. However, energy theft caused by vulnerabilities in the data collected from smart meters is emerging as a primary threat to the stability and profitability of power systems. Therefore, various methodologies have been proposed for energy theft detection (ETD), but many of them are challenging to use effectively due to the limitations of energy theft datasets. This paper provides a comprehensive review of ETD methods, highlighting the limitations of current datasets and technical approaches to improve training datasets and the ETD in smart grids. Furthermore, future research directions and open issues from the perspective of generative AI-based ETD are discussed, and the potential of generative AI in addressing dataset limitations and enhancing ETD robustness is emphasized.

The accurate segmentation of cracks in structural materials is crucial for assessing the safety and durability of infrastructure. Although conventional segmentation models based on deep learning techniques have shown impressive detection capabilities in these tasks, their performance can be restricted by small amounts of training data. Data augmentation techniques have been proposed to mitigate the data availability issue; however, these systems often have limitations in texture diversity, scalability over multiple physical structures, and the need for manual annotation. In this paper, a novel generative artificial intelligence (GAI)-driven data augmentation framework is proposed to overcome these limitations by integrating a projected generative adversarial network (ProjectedGAN) and a multi-crack texture transfer generative adversarial network (MCT2GAN). Additionally, a novel metric is proposed to evaluate the quality of the generated data. The proposed method is evaluated using three datasets: the bridge crack library (BCL), DeepCrack, and Volker. From the simulation results, it is confirmed that the segmentation performance can be improved by the proposed method in terms of intersection over union (IoU) and Dice scores across three datasets.

The implementation of accurate positioning methods in both line-of-sight (LOS) and non-line-of-sight (NLOS) environments has been emphasized for seamless 6G application services. In LOS environments with unobstructed paths between the transmitter and receiver, accurate tracking essential for seamless 6G services is achievable. However, accurate three-dimensional (3D) outdoor positioning has been challenging to achieve in NLOS environments where positioning accuracy may be severely degraded. In this paper, a novel 3D outdoor positioning method considering both LOS and NLOS environments is proposed. Considering the practical positioning systems, the data received from satellites often contain null values and outliers. Thus, a kernel density estimation (KDE)-based outlier removal method is used for effectively detecting the null values and outliers through temporal correlation analysis. A dilution of precision-based adaptive Kalman filter (DOP-AKF) is proposed to mitigate the effects of an NLOS environment. In the proposed method, the DOP-AKF can optimize the performance of the 3D positioning system that dynamically adapts to complex environments. Experimental results show that the proposed method can improve 3D positioning accuracy by up to 18.84% compared to conventional methods. Therefore, the proposed approach can be suggested as a promising solution for 3D outdoor positioning in 6G wireless systems.

A wide range of research has been conducted on path planning and collision avoidance to enhance the operational efficiency of unmanned aerial vehicles (UAVs). The existing works have mainly assumed an environment with static obstacles and global positioning system (GPS) signals. However, practical environments have often been involved with dynamic obstacles, dense areas with numerous obstacles in confined spaces, and blocked GPS signals. In order to consider these issues for practical implementation, a deep reinforcement learning (DRL)-based method is proposed for path planning and collision avoidance in GPS-denied and dense environments. In the proposed method, robust path planning and collision avoidance can be conducted by using the received signal strength (RSS) value with the extended Kalman filter (EKF). Additionally, the attitude of the UAV is adopted as part of the action space to enable the generation of smooth trajectories. Performance was evaluated under single- and multi-target scenarios with numerous dynamic obstacles. Simulation results demonstrated that the proposed method can generate smoother trajectories and shorter path lengths while consistently maintaining a lower collision rate compared to conventional methods.

An unmanned aerial vehicle (UAV) swarm has emerged as a powerful tool for mission execution in a variety of applications supported by deep neural networks (DNNs). In the context of UAV swarms, conventional methods for efficient data processing involve transmitting data to cloud and edge servers. However, these methods often face limitations in adapting to real-time applications due to the low latency of cloud-based approaches and weak mobility of edge-based approaches. In this paper, a new system called deep reinforcement learning-based resilient layer distribution (DRL-RLD) for distributed inference is designed to minimize end-to-end latency in UAV swarm, considering the resource constraints of UAVs. The proposed system dynamically allocates CNN layers based on UAV-to-UAV and UAV-to-ground communication links to minimize end-to-end latency. It can also enhance resilience to maintain mission continuity by reallocating layers when inoperable UAVs occur. The performance of the proposed system was verified through simulations in terms of latency compared to the comparison baselines, and its robustness was demonstrated in the presence of inoperable UAVs.