Results 31 to 40 of about 13,592 (226)

Ship Roll Prediction Algorithm Based on Bi-LSTM-TPA Combined Model

Journal of Marine Science and Engineering, 2021
When ships sail on the sea, the changes of ship motion attitude presents the characteristics of nonlinearity and high randomness. Aiming at the problem of low accuracy of ship roll angle prediction by traditional prediction algorithms and single neural ...
Yuchao Wang, Hui Wang, Dexin Zou, Huixuan Fu   +3 more
doaj   +1 more source

Bidirectional LSTM-CRF Models for Sequence Tagging

CoRR, 2015
In this paper, we propose a variety of Long Short-Term Memory (LSTM) based models for sequence tagging. These models include LSTM networks, bidirectional LSTM (BI-LSTM) networks, LSTM with a Conditional Random Field (CRF) layer (LSTM-CRF) and bidirectional LSTM with a CRF layer (BI-LSTM-CRF).
Zhiheng Huang, Wei Xu 0017, Kai Yu 0001
openaire   +2 more sources

Cybersecurity Named Entity Recognition Using Bidirectional Long Short-Term Memory with Conditional Random Fields

Tsinghua Science and Technology, 2021
Network texts have become important carriers of cybersecurity information on the Internet. These texts include the latest security events such as vulnerability exploitations, attack discoveries, advanced persistent threats, and so on.
Pingchuan Ma, Bo Jiang, Zhigang Lu, Ning Li, Zhengwei Jiang   +4 more
doaj   +1 more source

The Bidirectional Information Fusion Using an Improved LSTM Model [PDF]

Mobile Information Systems, 2021
The information fusion technology is of great significance in intelligent systems. At present, the modern coal-fired power plant has the fully functional sensor network. However, many data that are important for the operation of a power plant, such as the coal quality, cannot be directly obtained.
Tianwei Zheng, Mei Wang 0002, Yuan Guo, Zheng Wang   +3 more
openaire   +1 more source

A Study on Sensor System Latency in VR Motion Sickness

Journal of Sensor and Actuator Networks, 2021
One of the most frequent technical factors affecting Virtual Reality (VR) performance and causing motion sickness is system latency. In this paper, we adopted predictive algorithms (i.e., Dead Reckoning, Kalman Filtering, and Deep Learning algorithms) to
Ripan Kumar Kundu, Akhlaqur Rahman, Shuva Paul   +2 more
doaj   +1 more source

A Hybrid GAS-ATT-LSTM Architecture for Predicting Non-Stationary Financial Time Series

Mathematics
This study proposes a hybrid approach to analyze and forecast non-stationary financial time series by combining statistical models with deep neural networks. A model is introduced that integrates three key components: the Generalized Autoregressive Score
Kevin Astudillo, Miguel Flores, Mateo Soliz, Guillermo Ferreira, José Varela-Aldás   +4 more
doaj   +1 more source

Sequential Modeling for the Recognition of Activities in Logistics

Sukkur IBA Journal of Emerging Technologies, 2021
Activity recognition is an important task in cyber physical system research and has been the focus of researchers worldwide. This paper presents a method for activity recognition in logistic operations using data from accelerometer and gyroscope sensors.
Zafi Sherhan Syed, Muhammad Zaigham Abbas Shah Syed, Muhammad Shehram Shah Syed, Aunsa Shah   +3 more
doaj   +1 more source

Bidirectional Tree-Structured LSTM with Head Lexicalization

CoRR, 2016
Sequential LSTM has been extended to model tree structures, giving competitive results for a number of tasks. Existing methods model constituent trees by bottom-up combinations of constituent nodes, making direct use of input word information only for leaf nodes.
Zhiyang Teng, Yue Zhang 0004
openaire   +2 more sources

Bidirectional Long Short-Term Memory Development for Aircraft Trajectory Prediction Applications to the UAS-S4 Ehécatl

Aerospace
The rapid advancement of unmanned aerial systems in various civilian roles necessitates improved safety measures during their operation. A key aspect of enhancing safety is effective collision avoidance, which is based on conflict detection and is ...
Seyed Mohammad Hashemi, Ruxandra Mihaela Botez, Georges Ghazi   +2 more
doaj   +1 more source

Graph-based Dependency Parsing with Bidirectional LSTM [PDF]

Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2016
Dans cet article, nous proposons un modèle de réseau neuronal pour l'analyse de dépendance basée sur des graphes qui utilise LSTM bidirectionnel (BLSTM) pour capturer des informations contextuelles plus riches au lieu d'utiliser la factorisation d'ordre élevé, et permettre à notre modèle d'utiliser beaucoup moins de fonctionnalités que les travaux ...
Wenhui Wang, Baobao Chang
openaire   +1 more source