Creating Knowledge Graph of Electric Power Equipment Faults Based on BERT–BiLSTM–CRF Model

Meng, Fanqi; Yang, Shuaisong; Wang, Jingdong; Xia, Lei; Liu, Han

doi:10.1007/s42835-022-01032-3

Cited by 89 publications

(30 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e speech recognition system has the characteristics of real-time and high concurrency. erefore, a single-node server cannot meet the basic needs of the system [24][25][26]. e online speech recognition system uses a server cluster architecture and deploys the speech recognition server on each node in the cluster.…”

Section: System Physical Architecturementioning

confidence: 99%

Modeling and Simulation of English Speech Optimization Teaching Recognition Based on Intelligent Edge Detection Algorithm

Tang

Tian

2022

Security and Communication Networks

View full text Add to dashboard Cite

English speech modeling is one of the key problems in the field of speech recognition. Its accuracy directly affects the performance of the English speech recognition system, and how to establish a more accurate acoustic model has always been the focus of researchers. This paper is based on the intelligent edge detection algorithm of the English speech optimization teaching recognition modeling simulation analysis to improve the accuracy of acoustic models such as parameters and the performance of continuous speech recognition system as the main purpose. In this paper, the accuracy of the neural network is improved on the premise of improving the training speed and decoding speed of the model. It proposes a new model and how to use the intelligent edge detection algorithm to build a complete English speech optimization teaching recognition system. The whole system includes the mobile terminal and the server, which realizes the most basic business logic of the speech recognition system. The experimental results of this paper show that from the point of view of the average recognition rate, the recognition effect of the optimized feature set has been further improved compared with the fusion feature. From the point of view of the recognition rate under different SNR environments, the recognition rate of the optimized feature set PCA-Features2 decreased by 0.47% compared with the FFPRLS_D + TEOCC feature set under 10dB10 words. Compared with the FFPLMS_D + TEOCC feature set, the recognition rate under 5dB10 words also drops by 0.47%.

show abstract

Section: System Physical Architecturementioning

confidence: 99%

Modeling and Simulation of English Speech Optimization Teaching Recognition Based on Intelligent Edge Detection Algorithm

Tang

Tian

2022

Security and Communication Networks

View full text Add to dashboard Cite

show abstract

“…In the actual working environment, any type of cable is aging under the combined influence of many complex factors [21,22]. For example, cross-linked polyethylene cables commonly used in power systems are also affected by factors such as high temperature and high pressure, humid environment, and solar radiation.…”

Section: Other Aging Modelsmentioning

confidence: 99%

Aging Law and Life Evaluation Model of Cable Insulation Based on WOA-SVR

Guo

2022

Security and Communication Networks

View full text Add to dashboard Cite

Cables are mainly composed of wires and are used to transmit signals or electrical energy. It can be seen everywhere in real life. However, due to the changeable environment in which it is located, it often leads to the problem of insulation aging. Therefore, it is particularly important to study the law of cable insulation aging and to evaluate its life. Humpback whale hunting behavior is simulated by WOA, a swarming intelligence algorithm. It can intelligently identify the relationship between various data. SVR is a linear regression model with a special computational loss. The aim of this paper is to investigate a WOA-SVR model to evaluate the aging law and life of cable insulation. This paper analyzes a variety of detection models. Finally, the partial discharge detection model and the depolarization detection model are selected for comparative testing with the WOA-SVR model studied in this paper. The test method is to count the aging of cable insulation under different temperature, humidity, and electric field strength environments. The test results show that the evaluation accuracy of the WOA-SVR model in this paper is 92%, 97%, and 98%, respectively, under different temperatures, humidity, and electric field strengths. The average accuracy of its evaluation is higher than the other two models. Therefore, the WOA-SVR model is more accurate and reliable for the cable insulation aging law and life evaluation.

show abstract

“…With the development of machine learning, some scholars try to use classifiers such as RF and SVM to recognize formulaic language through classification technology. However, it is necessary to extract features that can represent samples ( Meng et al, 2022 ). The more appropriate the feature selection, the higher the accuracy of recognition.…”

Section: Related Workmentioning

confidence: 99%

Formulaic language identification model based on GCN fusing associated information

Meng

Zheng

Bao

et al. 2022

PeerJ Computer Science

Self Cite

View full text Add to dashboard Cite

Formulaic language is a general term for ready-made structures in a language. It usually has fixed grammatical structure, stable language expression meaning and specific use context. The use of formulaic language can coordinate sentence generation in the process of writing and communication, and can significantly improve the idiomaticity and logic of machine translation, intelligent question answering and so on. New formulaic language is generated almost every day, and how to accurately identify them is a topic worthy of research. To this end, this article proposes a formulaic language identification model based on GCN fusing associated information. The innovation is that each sentence is constructed into a graph in which the nodes are part-of-speech features and semantic features of the words in the sentence and the edges between nodes are constructed according to mutual information and dependency syntactic relation. On this basis, the graph convolutional neural network is adopted to extract the associated information between words to mine deeper grammatical features. Therefore, it can improve the accuracy of formulaic language identification. The experimental results show that the model in this article is superior to the classical formulaic language identification model in terms of accuracy, recall and F1-score. It lays a foundation for the follow-up research of formulaic language identification tasks.

show abstract

Creating Knowledge Graph of Electric Power Equipment Faults Based on BERT–BiLSTM–CRF Model

Cited by 89 publications

References 13 publications

Modeling and Simulation of English Speech Optimization Teaching Recognition Based on Intelligent Edge Detection Algorithm

Modeling and Simulation of English Speech Optimization Teaching Recognition Based on Intelligent Edge Detection Algorithm

Aging Law and Life Evaluation Model of Cable Insulation Based on WOA-SVR

Formulaic language identification model based on GCN fusing associated information

Contact Info

Product

Resources

About