Convolutional Neural Network Based Target Recognition for Marine Search

Ashwin, J. S.; Manoharan, N.

doi:10.11591/ijeecs.v8.i2.pp561-563

Cited by 4 publications

(3 citation statements)

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“…CNN is multiple layers fully connected layers. The most beneficial aspect of CNN is reducing the number of parameters [27], [28]. It was first introduced by LeCun with the LeNet-5 architecture in the early 1980s [29].…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

A hybrid of convolutional neural network and long short-term memory network approach to predictive maintenance

Nasser

Al-Khazraji

2022

IJECE

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<p>Predictive maintenance (PdM) is a successful strategy used to reduce cost by minimizing the breakdown stoppages and production loss. The massive amount of data that results from the integration between the physical and digital systems of the production process makes it possible for deep learning (DL) algorithms to be applied and utilized for fault prediction and diagnosis. This paper presents a hybrid convolutional neural network based and long short-term memory network (CNN-LSTM) approach to a predictive maintenance problem. The proposed CNN-LSTM approach enhances the predictive accuracy and also reduces the complexity of the model. To evaluate the proposed model, two comparisons with regular LSTM and gradient boosting decision tree (GBDT) methods using a freely available dataset have been made. The PdM model based on CNN-LSTM method demonstrates better prediction accuracy compared to the regular LSTM, where the average F-Score increases form 93.34% in the case of regular LSTM to 97.48% for the proposed CNN-LSTM. Compared to the related works the proposed hybrid CNN-LSTM PdM approach achieved better results in term of accuracy.</p>

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Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

A hybrid of convolutional neural network and long short-term memory network approach to predictive maintenance

Nasser

Al-Khazraji

2022

IJECE

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“…CNN is one of the popular deep learning methods that has been successfully applied in the classification of high dimensional data especially for image [14], [15]. In signal processing, the signal can be transformed into time-frequency representation (TFR) using the time-frequency distribution (TFD).…”

Section: Introductionmentioning

confidence: 99%

Featureless EMG pattern recognition based on convolutional neural network

Too

Abdullah

Saad

et al. 2019

IJEECS

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In this paper, the performance of featureless EMG pattern recognition in classifying hand and wrist movements are presented. The time-frequency distribution (TFD), spectrogram is employed to transform the raw EMG signals into time-frequency representation (TFR). The TFRs or spectrogram images are then directly fed into convolutional neural network (CNN) for classification. Two CNN models are proposed to learn the features automatically from the images without the need of manual feature extraction. The performance of CNN with different number of convolutional layers is examined. The proposed CNN models are evaluated using the EMG data from 10 intact and 11 amputee subjects through the publicly access NinaPro database. Our results show that CNN classifier offered the best mean classification accuracy of 88.04% in recognizing hand and wrist movements.

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“…Deep learning has emerged as a new area of machine learning research, which enables a system to automatically learn complex information and extract representations at multiple levels of abstraction. Convolutional Neural Network (CNN) is recognized as one of the most promising types of Artificial Neural Networks (ANN) and has become the dominant approach for almost all recognition and detection tasks [1] such as face recognition [2], handwritten digit recognition [3], target recognition [4], and image classification [5]. To achieve acceptable classification results, CNN performs a massive number of convolutions and subsampling operations with significant amounts of intermediate data results.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Computing Correlation Utilization in Convolutional Neural Network Basic Functions

et al. 2018

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In recent years, many applications have been implemented in embedded systems and mobile Internet of Things (IoT) devices that typically have constrained resources, smaller power budget, and exhibit "smartness" or intelligence. To implement computation-intensive and resource-hungry Convolutional Neural Network (CNN) in this class of devices, many research groups have developed specialized parallel accelerators using Graphical Processing Units (GPU), Field-Programmable Gate Arrays (FPGA), or Application-Specific Integrated Circuits (ASIC). An alternative computing paradigm called Stochastic Computing (SC) can implement CNN with low hardware footprint and power consumption. To enable building more efficient SC CNN, this work incorporates the CNN basic functions in SC that exploit correlation, share Random Number Generators (RNG), and is more robust to rounding error. Experimental results show our proposed solution provides significant savings in hardware footprint and increased accuracy for the SC CNN basic functions circuits compared to previous work.

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Convolutional Neural Network Based Target Recognition for Marine Search

Cited by 4 publications

References 0 publications

A hybrid of convolutional neural network and long short-term memory network approach to predictive maintenance

A hybrid of convolutional neural network and long short-term memory network approach to predictive maintenance

Featureless EMG pattern recognition based on convolutional neural network

Stochastic Computing Correlation Utilization in Convolutional Neural Network Basic Functions

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