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This paper presents a novel dynamic adaptive event-triggered mechanism (DAETM) for addressing actuator saturation in leader–follower fractional-order nonlinear multi-agent networked systems (FONMANSs). By utilizing a sector-bounded condition approach and a convex hull representation technique, the proposed method effectively addresses the effects of actuator saturation. This results in less conservative linear matrix inequality (LMI) criteria, guaranteeing asymptotic consensus among agents within the FONMANS framework. The proposed sufficient conditions are computationally efficient, requiring only simple LMI solutions. The effectiveness of the approach is validated through practical applications, such as synchronous generators within a FONMANS framework, where it demonstrates superior performance and robustness. Additionally, comparative studies with Chua’s circuit system enhance the robustness and efficiency of control systems compared to existing techniques. These findings highlight the method’s potential for broad application across various multi-agent systems, particularly in scenarios with limited communication and actuator constraints. The proposed approach enhances system performance and provides a robust, adaptive control solution for dynamic and uncertain environments.

Object detection is a task that performs position identification and label classification of objects in images or videos. The information obtained through this process plays an essential role in various tasks in the field of computer vision. In object detection, the data utilized for training and validation typically originate from public datasets that are well-balanced in terms of the number of objects ascribed to each class in an image. However, in real-world scenarios, handling datasets with much greater class imbalance, i.e., very different numbers of objects for each class, is much more common, and this imbalance may reduce the performance of object detection when predicting unseen test images. In our study, thus, we propose a method that evenly distributes the classes in an image for training and validation, solving the class imbalance problem in object detection. Our proposed method aims to maintain a uniform class distribution through multi-label stratification. We tested our proposed method not only on public datasets that typically exhibit balanced class distribution but also on private datasets that may have imbalanced class distribution. We found that our proposed method was more effective on datasets containing severe imbalance and less data. Our findings indicate that the proposed method can be effectively used on datasets with substantially imbalanced class distribution.

Natural language processing refers to the ability of computers to understand text and spoken words similar to humans. Recently, various machine learning techniques have been used to encode a large amount of text and decode feature vectors of text successfully. However, understanding low-resource languages is in the early stages of research. In particular, Korean, which is an agglutinative language, needs sophisticated preprocessing steps, such as morphological analysis. Since morphological analysis in preprocessing significantly influences classification results, ideal and optimized morphological analyzers must be used. This study explored five state-of-the-art morphological analyzers for Korean news articles and categorized their topics into seven classes using term frequency–inverse document frequency and light gradient boosting machine frameworks. It was found that a morphological analyzer based on unsupervised learning achieved a computation time of 6 s in 500,899 tokens, which is 72 times faster than the slowest analyzer (432 s). In addition, a morphological analyzer using dynamic programming achieved a topic categorization accuracy of 82.5%, which is 9.4% higher than achieve when using the hidden Markov model (73.1%) and 13.4% higher compared to the baseline (69.1%) without any morphological analyzer in news articles. This study can provide insight into how each morphological analyzer extracts morphemes in sentences and affects categorizing topics in news articles.

Recently, transformer-based pretrained language models have demonstrated stellar performance in natural language understanding (NLU) tasks. For example, bidirectional encoder representations from transformers (BERT) have achieved outstanding performance through masked self-supervised pretraining and transformer-based modeling. However, the original BERT may only be effective for English-based NLU tasks, whereas its effectiveness for other languages such as Korean is limited. Thus, the applicability of BERT-based language models pretrained in languages other than English to NLU tasks based on those languages must be investigated. In this study, we comparatively evaluated seven BERT-based pretrained language models and their expected applicability to Korean NLU tasks. We used the climate technology dataset, which is a Korean-based large text classification dataset, in research proposals involving 45 classes. We found that the BERT-based model pretrained on the most recent Korean corpus performed the best in terms of Korean-based multiclass text classification. This suggests the necessity of optimal pretraining for specific NLU tasks, particularly those in languages other than English.