An LSTM with Differential Structure and Its Application in Action Recognition

Chen, Weifeng; Zheng, Fei; Gao, Shanping; Hu, Kai

doi:10.1155/2022/7316396

Cited by 4 publications

(3 citation statements)

References 33 publications

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“…This block allows the processing of sequence information through the cumulative linear form to prevent the gradient disappearance problem and learn long-term information. Therefore, it is practical for learning long-term sequence information [27,28].…”

Section: Lstmmentioning

confidence: 99%

EEG-Based Classification of Epileptic Seizure Types Using Deep Network Model

Alshaya,

Hussain

2023

Mathematics

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Accurately identifying the seizure type is vital in the treatment plan and drug prescription for epileptic patients. The most commonly adopted test for identifying epileptic seizures is electroencephalography (EEG). EEG signals include important information about the brain’s electrical activities and are widely used for epilepsy analysis. Among various deep network architectures, convolutional neural networks (CNNs) have been widely used for EEG signal representation learning for epilepsy analysis. However, most of the existing CNN-based methods suffer from the overfitting problem due to a small number of EEG trials and the huge number of learnable parameters. This paper introduces the design of an efficient, lightweight, and expressive deep network model based on ResNet theory and long short-term memory (LSTM) for classifying seizure types from EEG trials. A 1D ResNet module is adopted to train a deeper network without encountering vanishing gradient problems and to avoid the overfitting problem of CNN models. The LSTM module encodes and learns long-term dependencies over time. The synthetic minority oversampling technique (SMOTE) is applied to balance the data by increasing the trials of minority classes. The proposed method was evaluated using the public domain benchmark TUH database. Experimental results revealed the superior performance of the proposed model over other state-of-the-art models with an F1-score of 97.4%. The proposed deep learning model will help neurologists precisely interpret and classify epileptic seizure types and enhance the patient’s life.

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Section: Lstmmentioning

confidence: 99%

EEG-Based Classification of Epileptic Seizure Types Using Deep Network Model

Alshaya,

Hussain

2023

Mathematics

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“…Thus, the discretization time-step ∆t must be chosen to ensure stability, and to guarantee convergence, that is, a numerical solution close enough to the reference solution of the problem. In what follows, we revisit, and discuss, different machine learning techniques that enable learning dynamical systems and applying them for the integration of dynamical systems, as recurrent NN -rNN-and long short time memory -LSTM-techniques perform [10,37].…”

Section: Learning Integratorsmentioning

confidence: 99%

Learning Data-Driven Stable Corrections of Dynamical Systems. Application to the Simulation of the Top-Oil Temperature Evolution of a Power Transformer

Ghnatios¹,

Kestelyn²,

Denis³

et al. 2023

Preprint

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Many engineering systems are described by using differential models whose solutions, generally obtained after discretization, can exhibit a noticeable deviation with respect to the response of the physical systems that those models are expected to represent. In those circumstances one possibility consists of enriching the model in order to reproduce the physical system behavior. The present paper considers a dynamical system, and proposes enriching the model solution by learning the dynamical model of the gap between the system response and the model-based prediction, while ensuring that the time integration of the learnt model remains stable. The proposed methodology is applied on the simulation of the top-oil temperature evolution of a power transformer, whose data is provided by RTE, the French Electricity Transmission System Operator.

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“…Long short-term Memory Networks (LSTM) are one of the most common recurrent neural networks [178]. LSTM is a variant of RNN, which remembers a controllable amount of previous training data or forgets it more properly [179]. As shown in Figure 15, the structure of LSTM and the equations of state of its different modules are given.…”

Section: Rnn With Vslammentioning

confidence: 99%

An Overview on Visual SLAM: From Tradition to Semantic

Chen

Shang

et al. 2022

Remote Sensing

Self Cite

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Visual SLAM (VSLAM) has been developing rapidly due to its advantages of low-cost sensors, the easy fusion of other sensors, and richer environmental information. Traditional visionbased SLAM research has made many achievements, but it may fail to achieve wished results in challenging environments. Deep learning has promoted the development of computer vision, and the combination of deep learning and SLAM has attracted more and more attention. Semantic information, as high-level environmental information, can enable robots to better understand the surrounding environment. This paper introduces the development of VSLAM technology from two aspects: traditional VSLAM and semantic VSLAM combined with deep learning. For traditional VSLAM, we summarize the advantages and disadvantages of indirect and direct methods in detail and give some classical VSLAM open-source algorithms. In addition, we focus on the development of semantic VSLAM based on deep learning. Starting with typical neural networks CNN and RNN, we summarize the improvement of neural networks for the VSLAM system in detail. Later, we focus on the help of target detection and semantic segmentation for VSLAM semantic information introduction. We believe that the development of the future intelligent era cannot be without the help of semantic technology. Introducing deep learning into the VSLAM system to provide semantic information can help robots better perceive the surrounding environment and provide people with higher-level help.

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An LSTM with Differential Structure and Its Application in Action Recognition

Cited by 4 publications

References 33 publications

EEG-Based Classification of Epileptic Seizure Types Using Deep Network Model

EEG-Based Classification of Epileptic Seizure Types Using Deep Network Model

Learning Data-Driven Stable Corrections of Dynamical Systems. Application to the Simulation of the Top-Oil Temperature Evolution of a Power Transformer

An Overview on Visual SLAM: From Tradition to Semantic

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