When performing remote sensing image segmentation, practitioners often encounter various challenges, such as a strong imbalance in the foreground–background, the presence of tiny objects, high object density, intra-class heterogeneity, and inter-class homogeneity. To overcome these challenges, this paper introduces AerialFormer, a hybrid model that strategically combines the strengths of Transformers and Convolutional Neural Networks (CNNs). AerialFormer features a CNN Stem module integrated to preserve low-level and high-resolution features, enhancing the model’s capability to process details of aerial imagery. The proposed AerialFormer is designed with a hierarchical structure, in which a Transformer encoder generates multi-scale features and a multi-dilated CNN (MDC) decoder aggregates the information from the multi-scale inputs. As a result, information is taken into account in both local and global contexts, so that powerful representations and high-resolution segmentation can be achieved. The proposed AerialFormer was benchmarked on three benchmark datasets, including iSAID, LoveDA, and Potsdam. Comprehensive experiments and extensive ablation studies show that the proposed AerialFormer remarkably outperforms state-of-the-art methods.

This study proposes a cost-based adaptive (CBA) droop control strategy for use in a voltage source converter (VSC)-based multi-terminal high voltage direct current (MTDC) system. Rather than using a fixed droop gain, we suggest the CBA droop control scheme, which reduces the total incremental generation cost of ac systems, while sharing the burden based on the available capacity of VSCs at the postcontingency- steady-state operating point. Following a certain VSC outage in the MTDC system, unbalanced power is allocated based on the equal incremental cost principle to reduce the total active power generation cost. The results were verified through a transient simulation on an MTDC system with four monopole VSCs, and outage contingency scenarios were presented with three groups of generation cost curves. The results indicate that the CBA droop control strategy can provide greater contributions to the economic operation of the MTDC system, while achieving robust control.

The increasing usage of interconnected devices within the Internet of Things (IoT) and Industrial IoT (IIoT) has significantly enhanced efficiency and utility in both personal and industrial settings but also heightened cybersecurity vulnerabilities, particularly through IoT malware. This paper explores the use of one-class classification, a method of unsupervised learning, which is especially suitable for unlabeled data, dynamic environments, and malware detection, which is a form of anomaly detection. We introduce the TF-IDF method for transforming nominal features into numerical formats that avoid information loss and manage dimensionality effectively, which is crucial for enhancing pattern recognition when combined with n-grams. Furthermore, we compare the performance of multi-class vs. one-class classification models, including Isolation Forest and deep autoencoder, that are trained with both benign and malicious NetFlow samples vs. trained exclusively on benign NetFlow samples. We achieve 100% recall with precision rates above 80% and 90% across various test datasets using one-class classification. These models show the adaptability of unsupervised learning, especially one-class classification, to the evolving malware threats in the IoT domain, offering insights into enhancing IoT security frameworks and suggesting directions for future research in this critical area.

This study proposes an adaptive droop control strategy to be used in hybrid static synchronous compensator (STATCOM) systems. The proposed voltage-sensitivity-based adaptive droop control scheme using a hybrid STATCOM system is introduced to eliminate the possibility of interference with the other voltage regulation devices while achieving better voltage regulation without having to implement a full observation of the corresponding network. The sensitivity-analysis is performed by the voltage control of STATCOM system and the sliding-mode-control is included for control system robustness. A comparison between fixed and adaptive control methods is made through case studies of the IEEE 39-bus test system. The results were further verified through a transient simulation of the hybrid STATCOM system that considers both low and high penetration levels of inverter-based reactive power resources.