An N400 identification method based on the combination of Soft-DTW and transformer

Ma, Yan; Tang, Yiou; Zhou, Yang; Ding, Tao; Liu, Yifu

doi:10.3389/fncom.2023.1120566

Cited by 5 publications

(3 citation statements)

References 27 publications

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“…On the other hand, WDTW (c) and softDTW (e) do not take shape features into account. Moreover, the window constraints of WDTW [ 41 ] and the softmin [ 42 ] operation in softDTW cause information loss, resulting in slightly lower classification performance compared to the other methods.…”

Section: Methodsmentioning

confidence: 99%

C-DTW for Human Action Recognition Based on Nanogenerator

Xu,

Feng,

Zhang

2023

Sensors

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Sensor-based human action recognition (HAR) is considered to have broad practical prospects. It applies to wearable devices to collect plantar pressure or acceleration information at human joints during human actions, thereby identifying human motion patterns. Existing related works have mainly focused on improving recognition accuracy, and have rarely considered energy-efficient management of portable HAR systems. Considering the high sensitivity and energy harvesting ability of triboelectric nanogenerators (TENGs), in this research a TENG which achieved output performance of 9.98 mW/cm2 was fabricated using polydimethylsiloxane and carbon nanotube film for sensor-based HAR as a wearable sensor. Considering real-time identification, data are acquired using a sliding window approach. However, the classification accuracy is challenged by quasi-periodic characteristics of the intercepted sequence. To solve this problem, compensatory dynamic time warping (C-DTW) is proposed, which adjusts the DTW result based on the proportion of points separated by small distances under DTW alignment. Our simulation results show that the classification accuracy of C-DTW is higher than that of DTW and its improved versions (e.g., WDTW, DDTW and softDTW), with almost the same complexity. Moreover, C-DTW is much faster than shapeDTW under the same classification accuracy. Without loss of generality, the performance of the existing DTW versions can be enhanced using the compensatory mechanism of C-DTW.

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Section: Methodsmentioning

confidence: 99%

C-DTW for Human Action Recognition Based on Nanogenerator

Xu,

Feng,

Zhang

2023

Sensors

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“…The Transformer is a deep learning architecture for sequence processing such as natural language processing, with a multihead self-attention module that captures long-range dependencies within sequences (Vaswani et al, 2017). The Transformer is used not only in language models but also in computer vision, audio processing, and time series forecasting (Lim et al, 2021;Wen et al, 2022;Ma et al, 2023). Recently, the Transformer architecture has also been applied to modeling temporal point processes.…”

Section: Transformer-based Hawkes Processmentioning

confidence: 99%

Mathematical modeling and optimization for real life phenomena

2024

Frontiers Research Topics

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Frontiers is more than just an open access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers journal seriesThe Frontiers journal series is a multi-tier and interdisciplinary set of openaccess, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers journal series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to qualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation. What are Frontiers Research Topics?Frontiers Research Topics are very popular trademarks of the Frontiers journals series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area.

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“…The Transformer is a deep learning architecture for sequence processing such as natural language processing, with a multi-head self-attention module that captures long-range dependencies within sequences (Vaswani et al, 2017 ). The Transformer is used not only in language models but also in computer vision, audio processing, and time series forecasting (Lim et al, 2021 ; Wen et al, 2022 ; Ma et al, 2023 ). Recently, the Transformer architecture has also been applied to modeling temporal point processes.…”

Section: Introductionmentioning

confidence: 99%

Burst and Memory-aware Transformer: capturing temporal heterogeneity

Lee,

Lee

et al. 2023

Front. Comput. Neurosci.

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Burst patterns, characterized by their temporal heterogeneity, have been observed across a wide range of domains, encompassing event sequences from neuronal firing to various facets of human activities. Recent research on predicting event sequences leveraged a Transformer based on the Hawkes process, incorporating a self-attention mechanism to capture long-term temporal dependencies. To effectively handle bursty temporal patterns, we propose a Burst and Memory-aware Transformer (BMT) model, designed to explicitly address temporal heterogeneity. The BMT model embeds the burstiness and memory coefficient into the self-attention module, enhancing the learning process with insights derived from the bursty patterns. Furthermore, we employed a novel loss function designed to optimize the burstiness and memory coefficient values, as well as their corresponding discretized one-hot vectors, both individually and jointly. Numerical experiments conducted on diverse synthetic and real-world datasets demonstrated the outstanding performance of the BMT model in terms of accurately predicting event times and intensity functions compared to existing models and control groups. In particular, the BMT model exhibits remarkable performance for temporally heterogeneous data, such as those with power-law inter-event time distributions. Our findings suggest that the incorporation of burst-related parameters assists the Transformer in comprehending heterogeneous event sequences, leading to an enhanced predictive performance.

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An N400 identification method based on the combination of Soft-DTW and transformer

Cited by 5 publications

References 27 publications

C-DTW for Human Action Recognition Based on Nanogenerator

C-DTW for Human Action Recognition Based on Nanogenerator

Mathematical modeling and optimization for real life phenomena

Burst and Memory-aware Transformer: capturing temporal heterogeneity

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