Anomaly detection on time series data has been successfully used in power grid operation and maintenance, flow detection, fault diagnosis, and other applications. However, anomalies in time series often lack strict definitions and labels, and existing methods often suffer from the need for rigid hypotheses, the inability to handle high-dimensional data, and highly time-consuming calculation costs. Generative Adversarial Networks (GANs) can learn the distribution pattern of normal data, detecting anomalies by comparing the reconstructed normal data with the original data. However, it is difficult for GANs to extract contextual information from time series data. In this paper, we propose a new method, Transformer-based GAN for Anomaly Detection of Time Series Data (TGAN-AD), The transformer-based generators of TGAN-AD can extract contextual features of time series data to prompt the performance. TGAN-AD’s discriminator can also assist in determining abnormal data. Anomaly scores are calculated through both the generator and the discriminator. We have conducted comprehensive experiments on three public datasets. Experimental results show that our TGAN-AD has better performance in anomaly detection than the state-of-the-art anomaly detection techniques, with the highest $Recall$ and $F1$ values on all datasets. Our experiments also demonstrate the high efficiency of the model and the optimal choice of hyperparameters.

Knowledge graph (KG) technology is a newly emerged knowledge representation method in the field of artificial intelligence. Knowledge graphs can form logical mappings from cluttered data and establish triadic relationships between entities. Accurate derivation and reasoning of knowledge graphs play an important role in guiding power equipment operation and decision-making. Due to the complex and weak relations from multi-source heterogeneous data, the use of KGs has become popular in research to represent potential information in power knowledge reasoning. In this review, we first summarize the key technologies of knowledge graph representation and learning. Then, based on the complexity and real-time changes of power system operation and maintenance, we present multiple data processing, knowledge representation learning, and the graph construction process. In three typical power operation and fault decision application scenarios, we investigate current algorithms in power KG acquisition, representation embedding, and knowledge completion to illustrate accurate and exhaustive recommendations. Thus, using KGs to provide reference solutions and decision guidance has a significant role in improving the efficiency of power system operations. Finally, we summarize the achievements and difficulties of current research and give an outlook for future, promising roles of KG in power systems.

Automatic extraction of domain-related terminology from natural language texts is an important research topic with many practical application scenarios, such as text summarization, knowledge graph construction. It has a great impact on improving the quality of topic construction and the accuracy of semantic retrieval. In recent years, automatic terminology extraction (ATE) has attracted widespread attention from scholars, and rich research results have been achieved. In this paper, we present a survey of terminology extraction from natural language texts. The survey encompasses definitions of pertinent issues and concepts, a systematic classification of proposed methodologies, an exploration of the associated datasets and tools, among other aspects. To the best of our knowledge, this is the first review that systematically summarizes the work of terminology extraction based on language models (LMs), offering comprehensive guidance resources for researchers and practitioners in the field. Consequently, this review offers valuable insights for researchers interested in terminology extraction issues within the Natural Language Processing (NLP) domain.

Anomaly detection has been widely used in grid operation and maintenance, machine fault detection, and so on. In these applications, the multivariate time-series data from multiple sensors with latent relationships are always high-dimensional, which makes multivariate time-series anomaly detection particularly challenging. In existing unsupervised anomaly detection methods for multivariate time series, it is difficult to capture the complex associations among multiple sensors. Graph neural networks (GNNs) can model complex relations in the form of a graph, but the observed time-series data from multiple sensors lack explicit graph structures. GNNs cannot automatically learn the complex correlations in the multivariate time-series data or make good use of the latent relationships among time-series data. In this paper, we propose a new method—masked graph neural networks for unsupervised anomaly detection (MGUAD). MGUAD can learn the structure of the unobserved causality among sensors to detect anomalies. To robustly learn the temporal context from adjacent time points of time-series data from the same sensor, MGUAD randomly masks some points of the time-series data from the sensor and reconstructs the masked time points. Similarly, to robustly learn the graph-level context from adjacent nodes or edges in the relation graph of multivariate time series, MGUAD masks some nodes or edges in the graph under the framework of a GNN. Comprehensive experiments are conducted on three public datasets. According to the experimental findings, MGUAD outperforms state-of-the-art anomaly detection methods.

Sentiment analysis in various languages has been a hot research topic with several applications. Most of the existing models have been reported to work well with widely used language. Were the lass directly applying these models to poor-quality corpo