Depression is the leading mental illness/disorder around the world with global number reaching up to 300 million worldwide. This mental disorder is more prevalent in youngster of age group of 18-25 years especially in...
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Depression is the leading mental illness/disorder around the world with global number reaching up to 300 million worldwide. This mental disorder is more prevalent in youngster of age group of 18-25 years especially in developing countries. Early diagnosis and treatment are required to this alarming problem specifically for an adolescent student suffering from these disorders who more often goes undiagnosed and hamper their progress at the critical movement of their life. The cheaper automatous system such as electrodermal (EDA) can help in with early diagnosis of depression disorder. In this paper, EDA based machine learning using autoencoder network (AEN) and deep neural networks (DNN) was developed for detecting level of depression among 38 university students. Developed AEN and DNN algorithm was able to classify five categories of depression with training of 96.5% and 94.5%, testing accuracy 95.2% and 94.2% while overall network accuracy was 94.0% and 92.0% with high sensitivity and specificity rate.
The binarization step for old documents is still a challenging task even though many hand-engineered and deep learning algorithms have been offered. In this research work, we address foreground and background segmenta...
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ISBN:
(纸本)9783030861599;9783030861582
The binarization step for old documents is still a challenging task even though many hand-engineered and deep learning algorithms have been offered. In this research work, we address foreground and background segmentation using a convolutional autoencoder network with 3 supporting components. The assessment of several hyper-parameters including the window size, the number of convolution layers, the kernel size, the number of filters as well as the number of encoder-decoder layers on the network is conducted. In addition, the skip connections approach is considered in the decoding procedure. Moreover, we evaluated the summation and concatenation function before the up-sampling process to reuse the previous low-level feature maps and to enrich the decoded representation. Based on several experiments, we determined that kernel size, the number of filters, and the number of encoder-decoder blocks have a little impact in term of binarization performance. While the window size and multiple convolutional layers are more impactful than other hyper-parameters. However, they require more storage and may increase computation costs. Moreover, a careful embedding of batch normalization and dropout layers also provides a contribution to handle overfitting in the deep learning model. Overall, the multiple convolutional autoencoder network with skip connection successfully enhances the binarization accuracy on old Sundanese palm leaf manuscripts compared to preceding state of the art methods.
Power quality is main concern for the electrical energy consumptions and electrical equipment. Hence, the power quality disturbances needed to monitor, improve and control. However, most of the research are focusing t...
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ISBN:
(纸本)9781665403382
Power quality is main concern for the electrical energy consumptions and electrical equipment. Hence, the power quality disturbances needed to monitor, improve and control. However, most of the research are focusing to the accuracy of the classification analysis. In this paper, an approach to classify the power quality disturbances is presented using the deep neural network algorithm. A raw data containing various types of the power quality disturbances, like swell, interruption, harmonics, and normal signal is evaluated. This several types of power quality disturbance will be extracted using the Sparse autoencoder (SAE). The various values of weight decay parameter, A and sparsity parameter, p are applied to determine which features give optimal values. Optimal features learned from the SAE are then used to train a neural network classifier for identifying power quality disturbances.
Dynamic information is a non-negligible part of time-correlated process data, and its full utilization can improve the performance of fault detection. Traditional dynamic methods concatenate the current process data w...
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Dynamic information is a non-negligible part of time-correlated process data, and its full utilization can improve the performance of fault detection. Traditional dynamic methods concatenate the current process data with a certain number of previous process data into an extended vector before performing feature extraction. However, this simple way of using dynamic information inevitably increases the input dimensionality and it is inappropriate to treat previous process data as equally important. To address these problems, this paper proposes a novel nonlinear dynamic method, called graph dynamic autoencoder (GDAE), for fault detection. GDAE utilizes a graph structure to model the dynamic information between different data points. GDAE firstly embeds the current data point and previous data points as the features of the central node and its neighbors, respectively, then convolves the feature of the central node with the features of its neighbors to derive the updated feature for the central node, and finally, an encoder-decoder structure is adopted to extract the key low-dimensional feature. Due to the utilization of the graph structure, the extended high-dimensional vectors utilized by traditional dynamic fault detection methods are avoided in GDAE. Furthermore, with the dynamically constructed graph, GDAE is able to adaptively assign different weights to its neighbors by updating the adjacency matrix of the graph. Experimental results obtained from a numerical simulation and the Tennessee Eastman process illustrate the superiority of GDAE in terms of missed detection rate (MDR) and false alarm rate (FAR). The source code of GDAE can be found in https://***/luliu-fighting/Graph-Dynamic-autoencoder. (c) 2022 Elsevier Ltd. All rights reserved.
Fault detection constitutes a fundamental task for predictive maintenance, requiring mathematical models that can be conveniently provided by data -driven techniques. autoencoders are a particular type of unsupervised...
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Fault detection constitutes a fundamental task for predictive maintenance, requiring mathematical models that can be conveniently provided by data -driven techniques. autoencoders are a particular type of unsupervised Artificial Neural Networks that can be suitable for fault detection applications. Diverse architectures might be used for autoencoders, resulting in different fault detection performances, which are usually compared by means of Fault Detection Rates for a fixed threshold of the False Alarm Rate, limiting the conclusions to particular cases. To improve the comparability, the present work uses the area under the receiver operating characteristic curve to compare autoencoder architectures for a range of false alarm rates using the Tennessee Eastman Process benchmark. Performances obtained for shallow and deep autoencoders were compared with those of the denoising and variational autoencoders for undercomplete and sparse structures. Overall, the results indicate better performances for sparse structures, especially for the variational autoencoder and the deep denoising autoencoder, with area under the curve of 98.35%.
Electrocardiographic (ECG) signals are used to evaluate heart activity and to identify disease-related anomalies. Reliable support systems are useful for analyzing ECG signals, for instance, in long-term data acquisit...
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autoencoder can not only extract features in an unsupervised manner, but also selects samples out that differs significantly from others. However, autoencoder is sensitive to noise and anomalies during training, and t...
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ISBN:
(纸本)9781728176055
autoencoder can not only extract features in an unsupervised manner, but also selects samples out that differs significantly from others. However, autoencoder is sensitive to noise and anomalies during training, and the relationships between pixels are discarded. In order to tackle these problems, we propose a robust graph autoencoder (RGAE) for hyperspectral anomaly detection. To be specific, we first redesign the objective function to encourage the network more robust to noise and anomalies. Meanwhile, a superpixel segmentation-based graph regularization term (SuperGraph) is incorporated into AE to preserve the geometric structure and spatial information simultaneously. Experiments with three real data sets are conducted to evaluate the performance, and the detection results demonstrate that our method outperforms other state-of-the-art hyperspectral anomaly detectors.
In recent years, multi-compartmental models have been widely used to try to characterize brain tissue microstructure from Diffusion Magnetic Resonance Imaging (dMRI) data. One of the main drawbacks of this approach is...
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ISBN:
(纸本)9783030876159;9783030876142
In recent years, multi-compartmental models have been widely used to try to characterize brain tissue microstructure from Diffusion Magnetic Resonance Imaging (dMRI) data. One of the main drawbacks of this approach is that the number of microstructural features needs to be decided a priori and it is embedded in the model definition. However, the number of microstructural features which is possible to obtain from dMRI data given the acquisition scheme is still not clear. In this work, we aim at characterizing brain tissue using autoencoder neural networks in combination with rotation-invariant features. By changing the number of neurons in the autoencoder latent-space, we can effectively control the number of microstructural features that we obtained from the data. By plotting the autoencoder reconstruction error to the number of features we were able to find the optimal trade-off between data fidelity and the number of microstructural features. Our results show how this number is impacted by the number of shells and the b-values used to sample the dMRI signal. We also show how our technique paves the way to a richer characterization of the brain tissue microstructure in-vivo.
Techniques which leverage channel state information (CSI) at a transmitter to adapt wireless signals to changing propagation conditions have been shown to improve the reliability of modern multiple input multiple outp...
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ISBN:
(纸本)9781728181042
Techniques which leverage channel state information (CSI) at a transmitter to adapt wireless signals to changing propagation conditions have been shown to improve the reliability of modern multiple input multiple output (MIMO) communication systems. To reduce overhead, previous works have proposed to compress CSI matrices using a trained deep autoencoder (AE) at the receiver before feeding it back to the transmitter, and recent work has proposed to quantize and perform entropy coding on the compressed CSI to further reduce communication complexity. While these methods are effective, they either do not incorporate quantization and lossless coding into their end-to-end optimization, or do not achieve performance comparable to methods that do not use quantization and entropy coding. In this work, we propose a new AE-based feedback method which uses an entropy bottleneck layer to both quantize and losslessly code the compressed CSI. This bottleneck layer allows us to jointly optimize bit-rate and distortion to achieve a highly-compressed CSI representation which preserves important channel information. Our method achieves better reconstruction quality than existing autoencoder-based CSI feedback methods for a wide range of bit-rates on simulated data, in both indoor and outdoor wireless settings.
Collaborative inference (CI) enhances the inference efficiency of deep neural networks (DNNs) by partitioning a computational workload between an edge device and a cloud platform. Efficient inference using CI requires...
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ISBN:
(纸本)9781665423830
Collaborative inference (CI) enhances the inference efficiency of deep neural networks (DNNs) by partitioning a computational workload between an edge device and a cloud platform. Efficient inference using CI requires searching for the optimal partition layer that minimizes the end-to-end inference latency. In addition, the intermediate feature at the partitioned layer should he effectively compressed. However, recent DNNbased feature compression methods require independent models dedicated for each partition point, resulting in significant storage overhead. In this paper, we propose a novel method that efficiently compresses the features from variable partition layers using a single autoencoder. The proposed method incorporates a weight-sharing technique that shares the weights of autoencoders that compress each partition layer. In addition, dynamic bitwidths quantization is supported for flexibility in compression ratio. The experimental results show that the proposed method reduced the required parameter size by 4x compared to the existing independent model based method, while maintaining the accuracy loss within 0.5%.
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