Prediction of the Remaining Useful Life of Supercapacitors at Different Temperatures Based on Improved Long Short-Term Memory

Ma, Ning; Yin, Huaixian; Wang, Kai

doi:10.3390/en16145240

Cited by 66 publications

(22 citation statements)

References 41 publications

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“…On the other hand, because there is no backgate, electrostatic doping is not feasible for graphene-based Hall sensors on foil substrates. − Under these conditions, it becomes more practical to use an encapsulating layer to reduce the amount of carriers or increase sensitivity. − In practical applications, the enclosing layer serves as a barrier to protect the graphene material from external stimuli, ensuring a consistent Hall sensor performance. − …”

Section: Flexible Hall Sensorsmentioning

confidence: 99%

Recent Advances in Nanosensors for Motion Detection

Xiu,

Yin,

Zhang

et al. 2024

ACS Appl. Electron. Mater.

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The continuous development of nanosensor technology has brought revolutionary changes to motion detection in the field of sports. The small size and high sensitivity of nanosensors make them ideal for real-time motion data acquisition. This paper aims to explore the potential applications of nanosensors in the field of sports, especially in the field of motion monitoring. We first introduce the working principles and types of nanosensors and then discuss the different manufacturing methods and practical applications of each sensor and the technical challenges that these sensors face. We then discuss its application in different aspects. The application of these nanosensors has intriguing prospects in the realm of motion detection as well as a plethora of other options for human−computer interaction. Research on the resilience of these sensors and the trend toward lower energy use will remain crucial. Future developments in nanosensors include obtaining energy from the human body, shrinking and using less power, increasing robustness and dependability, and lowering costs. These developments could encourage the broad usage of nanosensors for motion detection, opening up application possibilities and improving user satisfaction all around.

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Section: Flexible Hall Sensorsmentioning

confidence: 99%

Recent Advances in Nanosensors for Motion Detection

Xiu,

Yin,

Zhang

et al. 2024

ACS Appl. Electron. Mater.

Self Cite

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“…The predictions demonstrate that the suggested method is more accurate and robust than conventional gated recurrent unit (GRU) and LSTM models. [ 29 ] Athanasios et al (2023) proposed a novel data preprocessing strategy applied to ANN training data. Then, they evaluated the efficiency of the proposed model produced to show the advantages obtained.…”

Section: Related Workmentioning

confidence: 99%

Electricity Load Demand Prediction for Microgrid Energy Management System Using Hybrid Adaptive Barnacle‐Mating Optimizer with Artificial Neural Network Algorithm

Indira,

Bhavani,

Brinda

et al. 2024

Energy Tech

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In recent years, one of the primary causes of high electricity consumption is the growing global population and the availability of power‐demanding smart devices. As a result, accurate load forecasting tools are required for effective energy conservation in microgrids. Various simulation tools and artificial intelligence (AI)‐based methods have been used to make the best forecasts of electricity demand. Furthermore, conventional systems are static and based solely on historical data. To address this practical need, this work aims to create a machine learning (ML) model for short‐term load forecasting based on feature selection and parameter optimization. This research proposes a hybrid short‐term load demand prediction approach that combines an adaptive barnacle‐mating optimizer (ABMO) and an artificial neural network (ANN). When compared to the regression tree (RT), support vector machine (SVM), ANN, and particle swarm optimization‐based ANN (PSO‐ANN) algorithms, the proposed ABMO‐ANN algorithm reduces mean absolute percentage errors by 67.69%, 64.58%, 59.18%, and 42.02%, respectively. Compared to conventional RT, SVM, ANN, and PSO‐ANN algorithm, the hybrid ABMO with ANN outperforms them based on mean absolute percentage error, root mean square error, correlation coefficient , symmetric mean absolute percentage error, and agreement index (d).

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“…Moreover, in the mode of nondirect power supply, the nanogenerator can also input the converted electrical energy into the battery of the acoustic device (headphones, speakers, etc. ), providing convenience for predicting the health of the battery. – Therefore, the combination of nanogenerator and acoustic equipment also has great application potential and high research significance in other fields.…”

Section: Introductionmentioning

confidence: 99%

Application of Nanogenerators in the Field of Acoustics

Yu,

Shang,

Zheng

et al. 2023

ACS Appl. Electron. Mater.

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The nanogenerator is a device that can effectively convert the extra mechanical energy released by the environment or the human body into electrical energy. The device collects energy from the environment, such as wind, sound, and small mechanical energy, efficiently and with a low threshold. Because of their unique advantages and excellent output performance, nanogenerators have received wide attention in the field of energy harvesting. Coupled with its high flexibility, low power consumption, and the need for external energy storage devices to achieve complete self-powered characteristics, nanogenerators in other fields, such as acoustics, have high research and application value. The nanogenerators can not only greatly improve the flexibility, portability, and efficiency of the acoustic devices but also act as acoustic devices such as microphones and speakers. The structure and working principle of the triboelectric nanogenerators, piezoelectric nanogenerators, and pyroelectric nanogenerators and the successful combination of various nanogenerators with acoustic devices are reviewed. The feasibility of the application of the nanogenerator technology in the field of acoustics and the flexibility of the combination of nanogenerators and acoustic equipment are described, and the advantages of the equipment produced in combination of the two fields are briefly introduced. Finally, the development prospect and challenges of the nanogenerators in the field of acoustics are discussed, and personal suggestions are put forward.

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Prediction of the Remaining Useful Life of Supercapacitors at Different Temperatures Based on Improved Long Short-Term Memory

Cited by 66 publications

References 41 publications

Recent Advances in Nanosensors for Motion Detection

Recent Advances in Nanosensors for Motion Detection

Electricity Load Demand Prediction for Microgrid Energy Management System Using Hybrid Adaptive Barnacle‐Mating Optimizer with Artificial Neural Network Algorithm

Application of Nanogenerators in the Field of Acoustics

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