Active AC-DC rectifiers are widely used, however their presence lowers the quality of the grid current, leading to an increase in non-linearities in electronic equipment. As a result, a three-phase boost power factor ...
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The paper deals with distributed temperature measurements in the core of nuclear reactor. A probe was designed using an ordinary telecommunication optical fiber that can be added to the reactor core without disassembl...
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Optimization of chemo-resistive gas sensors suitable for portable devices to minimize power consumption is always challenging. In this study, we reported the fabrication of SnS2 nanomesh-based nanosensors using the on...
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Rectal cancer remains to be a major issue worldwide, necessitating accurate and precise methods for proper classification of lymph nodes in MRI scan images to guide treatment decisions. The use of deep learning based ...
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The proliferation of commercial unmanned aerial vehicles (UAVs) of various sizes and shapes, equipped with cameras and even signal sabotage devices, has raised concerns regarding privacy and safety. Some websites even...
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This paper presents a novel approach for solving unrelated parallel machine scheduling problems through reinforcement learning. Notably, we consider three main constraints: release date, machine eligibility, and seque...
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The work presents a passive battery-less chipless RFID tag for multi-sensing parameters. The tag designed for 1-bit data encoding, has been examined for humidity as well as temperature sensing simultaneously. The humi...
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This experiment investigates the impact of reducing GRU units and dense layer neurons in a lightweight GRU architecture (LW-GRU-RU) compared to a baseline GRU model for electroencephalogram (EEG) emotion classificatio...
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ISBN:
(数字)9798331543952
ISBN:
(纸本)9798331543969
This experiment investigates the impact of reducing GRU units and dense layer neurons in a lightweight GRU architecture (LW-GRU-RU) compared to a baseline GRU model for electroencephalogram (EEG) emotion classification. A baseline GRU model is used as a reference, with an optimized GRU variant utilizing feature selection through a Random Forest-based algorithm. Experiments are conducted on a labeled emotion dataset, comparing accuracy and training efficiency across five trials. Results highlight the trade-off between model complexity, accuracy, and computational efficiency, providing insights for practical applications. Both models are evaluated on an emotion dataset for accuracy and training efficiency. The lightweight model achieves a competitive accuracy of 97.486% while reducing the average training time to 0.19 seconds per epoch, showcasing its potential for efficient real-world applications.
作者：
Ishihara, ManabuTokyo College
Dept. of Electrical Engineering National Insutiute of Technology Tokyo Hachioji-City193-0997 Japan
Tactile displays can reproduce skin sensations, such as the feel of an object's texture and unevenness, and are expected to have a wide range of applications and a promising future. Motion is important because tac...
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The following article examines the implementation of photogrammetric measuremet method (PhMM) in heavy-duty machinery construction, giving accent to the accuracy of this method and the error analyses methods. The stud...
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