A text mining-based approach for understanding Chinese railway incidents caused by electromagnetic interference

[1]  Shiwu Yang,et al.  Using text mining to establish knowledge graph from accident/incident reports in risk assessment , 2022, Expert Syst. Appl..

[2]  Nusrat Jahan Prottasha,et al.  Transfer Learning for Sentiment Analysis Using BERT Based Supervised Fine-Tuning , 2022, Sensors.

[3]  M. Bentoumi,et al.  Improvement of emotion recognition from facial images using deep learning and early stopping cross validation , 2022, Multimedia Tools and Applications.

[4]  Handong He,et al.  Deep learning-based methods for natural hazard named entity recognition , 2022, Scientific reports.

[5]  B. Jabir,et al.  Dropout, a basic and effective regularization method for a deep learning model: a case study , 2021, Indonesian Journal of Electrical Engineering and Computer Science.

[6]  Shiwu Yang,et al.  An improved quantitative assessment method on hazardous interference of power lines to the signal cable in high‐speed railway , 2021, IET Electrical Systems in Transportation.

[7]  Zofia Wróbel,et al.  Selected Issues of Safe Operation of the Railway Traffic Control System in the Event of Exposition to Damage Caused by Lightning Discharges , 2021, Energies.

[8]  Vítor Basto Fernandes,et al.  Novel Tools for the Management, Representation, and Exploitation of Textual Information , 2021, Sci. Program..

[9]  Zhigang Lu,et al.  Cybersecurity named entity recognition using bidirectional long short-term memory with conditional random fields , 2021, Tsinghua Science and Technology.

[10]  P. Kirawanich A Numerical Technique for Estimating High-Frequency Radiated Emissions From Railway System , 2021, IEEE Transactions on Electromagnetic Compatibility.

[11]  Mauridhi Hery Purnomo,et al.  Named entity recognition for extracting concept in ontology building on Indonesian language using end-to-end bidirectional long short term memory , 2021, Expert Syst. Appl..

[12]  Alejandro Molina-Villegas,et al.  Geographic Named Entity Recognition and Disambiguation in Mexican News using word embeddings , 2021, Expert Syst. Appl..

[13]  Rui Jiao,et al.  Analytical Comparison of Two Emotion Classification Models Based on Convolutional Neural Networks , 2021, Complex..

[14]  Shufeng He,et al.  Named entity recognition for Chinese marine text with knowledge-based self-attention , 2021, Multimedia Tools and Applications.

[15]  Chunhua Weng,et al.  UMLS-based data augmentation for natural language processing of clinical research literature , 2020, J. Am. Medical Informatics Assoc..

[16]  Dongrun Liu,et al.  The effect of continuously varying wind speed on high-speed train overturning safety , 2020 .

[17]  E. Warmerdam,et al.  Validation of IMU-based gait event detection during curved walking and turning in older adults and Parkinson’s Disease patients , 2020, Journal of neuroengineering and rehabilitation.

[18]  Chang Liu,et al.  Optimization method of switch jumper setting based on strategies for reducing conductive interference in railway , 2020 .

[19]  Chang Liu,et al.  An improved risk assessment method based on a comprehensive weighting algorithm in railway signaling safety analysis , 2020 .

[20]  Haitao Pu,et al.  Domain knowledge graph-based research progress of knowledge representation , 2020, Neural Computing and Applications.

[21]  Ajay Kumar,et al.  A Systematic Review of Hidden Markov Models and Their Applications , 2020, Archives of Computational Methods in Engineering.

[22]  Chihyun Park,et al.  Combinatorial feature embedding based on CNN and LSTM for biomedical named entity recognition , 2020, J. Biomed. Informatics.

[23]  Ling Wang,et al.  Analogue circuit fault diagnosis based on convolution neural network , 2019, Electronics Letters.

[24]  Ruiming Ren,et al.  Study on typical failure forms and causes of high-speed railway wheels , 2019, Engineering Failure Analysis.

[25]  Juan Sequeda,et al.  Knowledge graphs: Construction, management and querying , 2019, Semantic Web.

[26]  Annika Hinze,et al.  Manual semantic annotations: User evaluation of interface and interaction designs , 2019, J. Web Semant..

[27]  Ming Guo,et al.  Cognition and driving safety: How does the high-speed railway drivers’ cognitive ability affect safety performance? , 2019, Transportation Research Part F: Traffic Psychology and Behaviour.

[28]  Tae Joon Jun,et al.  TRk-CNN: Transferable Ranking-CNN for image classification of glaucoma, glaucoma suspect, and normal eyes , 2019, Expert Syst. Appl..

[29]  Youngjoong Ko,et al.  Effective vector representation for the Korean named-entity recognition , 2019, Pattern Recognit. Lett..

[30]  G. Lucca Influence of railway line characteristics in inductive interference on railway track circuits , 2019, IET Science, Measurement & Technology.

[31]  Xing Wu,et al.  Conditional BERT Contextual Augmentation , 2018, ICCS.

[32]  Minwu Chen,et al.  Effects and Characteristics of AC Interference on Parallel Underground Pipelines Caused by an AC Electrified Railway , 2018, Energies.

[33]  Pekka Matilainen,et al.  Evaluation of the confusion matrix method in the validation of an automated system for measuring feeding behaviour of cattle , 2018, Behavioural Processes.

[34]  Charalambos A. Charalambous,et al.  Effects of Electromagnetic Interference on Underground Pipelines Caused by the Operation of High Voltage AC Traction Systems: The Impact of Harmonics , 2018, IEEE Transactions on Power Delivery.

[35]  M. Hanif,et al.  Accelerating Viterbi algorithm on graphics processing units , 2017, Computing.

[36]  Maryam Habibi,et al.  Deep learning with word embeddings improves biomedical named entity recognition , 2017, Bioinform..

[37]  Paolo Atzeni,et al.  Data modeling in the NoSQL world , 2016, Comput. Stand. Interfaces.

[38]  Michael S. Lew,et al.  Deep learning for visual understanding: A review , 2016, Neurocomputing.

[39]  George Bahouth,et al.  Disaster complexity and the Santiago de Compostela train derailment , 2016, Disaster health.

[40]  Jialiang Liu,et al.  Research on the modeling of the impedance match bond at station track circuit in Chinese high-speed railway , 2015 .

[41]  Yu Sun,et al.  Safety threshold of high-speed railway pier settlement based on train-track-bridge dynamic interaction , 2015 .

[42]  Lei Chen,et al.  Case study: Feature-based analysis of electric arc damage to railway signal cables , 2015 .

[43]  Bing Zhang,et al.  High Speed Railway Environment Safety Evaluation Based on Measurement Attribute Recognition Model , 2014, Comput. Intell. Neurosci..

[44]  Jason Weston,et al.  Natural Language Processing (Almost) from Scratch , 2011, J. Mach. Learn. Res..

[45]  Ankur Priyadarshi,et al.  The first named entity recognizer in Maithili: Resource creation and system development , 2021, J. Intell. Fuzzy Syst..

[46]  Jian Feng,et al.  Analysis and Research on Electromagnetic Compatibility of High Speed Railway Traction Current Harmonics to Track Circuit , 2021, IEEE transactions on applied superconductivity.

[47]  Gülsen Eryigit,et al.  Extending a CRF-based named entity recognition model for Turkish well formed text and user generated content , 2017, Semantic Web.

[48]  Clive Roberts,et al.  A novel train control approach to avoid rear-end collision based on geese migration principle , 2017 .

[49]  Jens Lehmann,et al.  DBpedia - A large-scale, multilingual knowledge base extracted from Wikipedia , 2015, Semantic Web.