Hand Gesture Recognition Using Input Impedance Variation of Two Antennas with Transfer Learning

Alnujaim, Ibrahim; Alali, Hashim; Khan, Faisal Nadeem; Kim, Youngwook

doi:10.1109/jsen.2018.2820000

Cited by 47 publications

(16 citation statements)

References 13 publications

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“…Generally, transfer learning provides three kinds of benefits for performance improvements [ 38 , 39 , 40 ], including (1) a higher start with an improved performance at the initial points; (2) a higher slope with a faster performance growth; and (3) a higher asymptote, producing a better final performance. Deep learning generally requires a large amount of data to train deep neural networks and learn the knowledge [ 41 ]. However, transfer learning trains networks with comparatively little data because of the pretrained model.…”

Section: Methodsmentioning

confidence: 99%

Automatic Recognition of Sucker-Rod Pumping System Working Conditions Using Dynamometer Cards with Transfer Learning and SVM

Cheng

Huang

Zeng

et al. 2020

Sensors

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Sucker-rod pumping systems are the most widely applied artificial lift equipment in the oil and gas industry. Accurate and intelligent working condition recognition of pumping systems imposes major impacts on oilfield production benefits and efficiency. The shape of dynamometer card reflects the working conditions of sucker-rod pumping systems, and different conditions can be indicated by their typical card characteristics. In traditional identification methods, however, features are manually extracted based on specialist experience and domain knowledge. In this paper, an automatic fault diagnosis method is proposed to recognize the working conditions of sucker-rod pumping systems with massive dynamometer card data collected by sensors. Firstly, AlexNet-based transfer learning is adopted to automatically extract representative features from various dynamometer cards. Secondly, with the extracted features, error-correcting output codes model-based SVM is designed to identify the working conditions and improve the fault diagnosis accuracy and efficiency. The proposed AlexNet-SVM algorithm is validated against a real dataset from an oilfield. The results reveal that the proposed method reduces the need for human labor and improves the recognition accuracy.

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Section: Methodsmentioning

confidence: 99%

Automatic Recognition of Sucker-Rod Pumping System Working Conditions Using Dynamometer Cards with Transfer Learning and SVM

Cheng

Huang

Zeng

et al. 2020

Sensors

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“…The author did not address on the computational complexity and time. Alnujaim et al [12] achieved an accuracy of 94.6%. Here too, the author did not report on the computational time.…”

Section: Related Workmentioning

confidence: 95%

“…Alnujaim et al [12] proposed a deep CNN (D-CNN) to classify hand gestures by the variation of impedance in monopole antennas. The impedance parameters are transformed into greyscale spectrogram images, and then it is classified.…”

Section: Related Workmentioning

confidence: 99%

Efficient inception V2 based deep convolutional neural network for real‐time hand action recognition

Bose

Kumar

2020

IET Image Processing

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“…Transfer learning supported CNN model has also used for different problems [24,22,26,23,6]. Similarly, pre-trained models have been used in the literature for different purposes besides transfer learning [1,2,4,12,35,37]. The optimization of hyperparameters is an effective way to obtain higher success rates for machine learning methods.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Framework for Coronavirus Disease (COVID-19) Detection in X-Ray Images

Ozcan¹

2020

Preprint

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Coronavirus, a large family of viruses, causes illness in both humans and animals. The novel coronavirus (COVID-19) came up in Wuhan in December 2019. This deadly COVID-19 pandemic has become very fast-spreading and currently present in several countries worldwide. The timely detection of patients who have COVID-19 is vitally important. To this end, scientists are working on different detection methods.In this paper, a grid search (GS) and pre-trained model aided convolutional neural network (CNN) model is proposed to detect COVID-19 in X-Ray images. In the proposed method, the GS method is employed to optimize the hyperparameters of CNN, which directly affects classification performance. Three pre-trained CNN models (GoogleNet, ResNet18 and ResNet50), which can be used for classification, feature extraction and transfer learning purposes were used for transfer learning in this study. The proposed method was trained using the training and validation subdatasets of the collected dataset and detail evaluations are presented according to different performance metrics. According to the experimental studies, the best results were obtained with the GS and ResNet50 aided model.

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Hand Gesture Recognition Using Input Impedance Variation of Two Antennas with Transfer Learning

Cited by 47 publications

References 13 publications

Automatic Recognition of Sucker-Rod Pumping System Working Conditions Using Dynamometer Cards with Transfer Learning and SVM

Automatic Recognition of Sucker-Rod Pumping System Working Conditions Using Dynamometer Cards with Transfer Learning and SVM

Efficient inception V2 based deep convolutional neural network for real‐time hand action recognition

A Deep Learning Framework for Coronavirus Disease (COVID-19) Detection in X-Ray Images

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