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Titlebook: Recent Trends in Mechatronics Towards Industry 4.0; Selected Articles fr Ahmad Fakhri Ab. Nasir,Ahmad Najmuddin Ibrahim,Anw Conference proc

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41#
發(fā)表于 2025-3-28 17:24:36 | 只看該作者
N. W. Z. Abidin,N. Salim,Mohd Fadzil Faisae Ab. Rashid,N. M. Z. N. Mohamed,A. N. M. Rose,A. Mokhtara maximum total enthalpy of 14.4?MJ/kg for a reservoir pressure of 22?MPa. Useful measuring time varies between 2 and 8?ms, depending on the flow conditions. A slender conical nozzle is preferably in operation for flow acceleration which opposite to contoured nozzles avoids focusing of flow disturba
42#
發(fā)表于 2025-3-28 19:52:27 | 只看該作者
43#
發(fā)表于 2025-3-29 01:42:54 | 只看該作者
44#
發(fā)表于 2025-3-29 04:07:57 | 只看該作者
The Classification of Heartbeat PCG Signals via Transfer Learning,. The data was processed, and the features were extracted using Spectrogram signal representation. They were then split into training and testing data, and the results were compared using the metrics of accuracy and loss. The classification accuracies of the VGG16, VGG19, MobileNet, and inceptionV3
45#
發(fā)表于 2025-3-29 09:41:57 | 只看該作者
The Classification of Wink-Based EEG Signals: An Evaluation of Different Transfer Learning Models fown from the study, that the features extracted via ResNet152 were better than that of ResNet50 and ResNet101. The overall validation and test accuracy attained through the ResNet152 model is approximately 92%. Henceforth, it could be concluded that the proposed pipeline suitable to be adopted to cl
46#
發(fā)表于 2025-3-29 14:30:44 | 只看該作者
47#
發(fā)表于 2025-3-29 16:58:04 | 只看該作者
48#
發(fā)表于 2025-3-29 22:40:57 | 只看該作者
49#
發(fā)表于 2025-3-30 02:40:04 | 只看該作者
50#
發(fā)表于 2025-3-30 06:00:59 | 只看該作者
Investigation of Features for Classification RFID Reading Between Two RFID Reader in Various Supporcts to be located based on the distance of the tag to be located to each reader. The feature of RSSI is extracted to nine single statistical features and three combinations of different statistical features for evaluated the classification performance in different kernel functions of the SVM classif
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