convolutional autoencoders are making a significant impact on computer vision and signal processing communities. In this work, a convolutional autoencoder denoising method is proposed to restore the corrupted laser st...
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convolutional autoencoders are making a significant impact on computer vision and signal processing communities. In this work, a convolutional autoencoder denoising method is proposed to restore the corrupted laser stripe images of the depth sensor, which directly reduces the external noise of the depth sensor so as to increase its accuracy. To reduce the amount of training data and avoid overfitting, a patch size of the laser stripe image is determined, on the basis of which a small-scale dataset called Laser Stripe Image Patch (LSIP) is created. Also, a 14-layers convolutional autoencoder is constructed to reduce the noise of the image patches, which can learn the most salient features on the LSIP dataset. Moreover, the trained convolutional autoencoder is applied to an omnidirectional structured light system. Experimental results demonstrate that the proposed method obtains useful features and superior performance both visually and quantitatively on denoising tasks, and significantly improves the accuracy of the structured light system.
The most important structural element of prestressed concrete (PSC) bridges is the prestressed tendon, and in order to ensure safety of such bridges, it is very important to determine whether the tendon is damaged. Ho...
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The most important structural element of prestressed concrete (PSC) bridges is the prestressed tendon, and in order to ensure safety of such bridges, it is very important to determine whether the tendon is damaged. However, it is not easy to detect tendon damage in real time. This study proposes a novelty detection approach for damage to the tendons of PSC bridges based on a convolutional autoencoder (CAE). The proposed method employs simulation data from nine accelerometers. The accuracies of CAEs for multi-vehicle are 79.5%-85.8% for 100% and 75% damage severities with all error levels and 50% damage severity without error. However, the accuracies for 50% damage severity with 5% and 10% error levels drop to 69.4%-73.3%. The accuracies of CAEs for single-vehicle ranges from 90.1%-95.1% for all damage severities and error levels that are satisfactory. The findings indicate that the CAE approach for multi-vehicle can be effective when the damages are severe, but not when moderate. Meanwhile, if acceleration data can be obtained for single-vehicle, then the CAE approach can provide a highly accurate and robust method of tendon damage detection in PSC bridges in use, even if the measurement errors are significant.
In recent years, research on detecting disaster areas from synthetic aperture radar (SAR) images has been conducted. When machine learning is used for disaster area detection, a large number of training data are requi...
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In recent years, research on detecting disaster areas from synthetic aperture radar (SAR) images has been conducted. When machine learning is used for disaster area detection, a large number of training data are required;however, we cannot obtain so much training data with correct class labels. Therefore, in this research, we propose an anomaly detection system that finds abnormal areas that deviate from normal ones. The proposed method uses a convolutional autoencoder (CAE) for feature extraction and one-class support vector machine (OCSVM) for anomaly detection. (C) 2019 The Authors. Published by Atlantis Press SARL.
Deep learning methods have been widely used in the field of intelligent fault diagnosis due to their powerful feature learning and classification capabilities. However, it is easy to overfit depth models because of th...
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Deep learning methods have been widely used in the field of intelligent fault diagnosis due to their powerful feature learning and classification capabilities. However, it is easy to overfit depth models because of the large number of parameters brought by the multilayer-structure. As a result, the methods with excellent performance under experimental conditions may severely degrade under noisy environment conditions, which are ubiquitous in practical industrial applications. In this paper, a novel method combining a one-dimensional (1-D) denoising convolutional autoencoder (DCAE) and a 1-D convolutional neural network (CNN) is proposed to address this problem, whereby the former is used for noise reduction of raw vibration signals and the latter for fault diagnosis using the de-noised signals. The DCAE model is trained with noisy input for denoising learning. In the CNN model, a global average pooling layer, instead of fully-connected layers, is applied as a classifier to reduce the number of parameters and the risk of overfitting. In addition, randomly corrupted signals are adopted as training samples to improve the anti-noise diagnosis ability. The proposed method is validated by bearing and gearbox datasets mixed with Gaussian noise. The experimental result shows that the proposed DCAE model is effective in denoising and almost causes no loss of input information, while the using of global average pooling and input-corrupt training improves the anti-noise ability of the CNN model. As a result, the method combined the DCAE model and the CNN model can realize high-accuracy diagnosis even under noisy environment.
A Winner-Take-All convolutional autoencoder is applied to improve the performance on the half space radar high resolution range profile(HRRP) target recognition. Feature extraction is significantly important to the ra...
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ISBN:
(纸本)9781538652183
A Winner-Take-All convolutional autoencoder is applied to improve the performance on the half space radar high resolution range profile(HRRP) target recognition. Feature extraction is significantly important to the radar target recognition based on the HRRP. Conventional deep models of representation learning used for HRRP target recognition commonly use the vanilla autoencoder and deep belief net (DBN), moreover, the simulated HRRP samples used in these related work are mostly under the free space condition which is different from the real world situation. In this paper, convolution architecture autoencoder, which is more efficient in spatial feature extraction and sparse coding, is proposed. Furthermore, the half space HRRP samples, which is much more close to the real world situation and is quite different from the free space HRRP samples, is used as the dataset. Half space simulated HRRP data is used to apply the convolutional architecture on the ground target recognition and got an accuracy promotion about 7% compared to conventional vector-based module.
This paper describes a technology for predicting the aggravation of diabetic nephropathy from electronic health record (EHR). For the prediction, we used features extracted from event sequence of lab tests in EHR with...
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ISBN:
(纸本)9781614998525;9781614998518
This paper describes a technology for predicting the aggravation of diabetic nephropathy from electronic health record (EHR). For the prediction, we used features extracted from event sequence of lab tests in EHR with a stacked convolutional autoencoder which can extract both local and global temporal information. The extracted features can be interpreted as similarities to a small number of typical sequences of lab tests, that may help us to understand the disease courses and to provide detailed health guidance. In our experiments on real-world EHRs, we confirmed that our approach performed better than baseline methods and that the extracted features were promising for understanding the disease.
Image compression has been investigated as a fundamental research topic for many decades. Recently, deep learning has achieved great success in many computer vision tasks, and is gradually being used in image compress...
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ISBN:
(纸本)9781538641606
Image compression has been investigated as a fundamental research topic for many decades. Recently, deep learning has achieved great success in many computer vision tasks, and is gradually being used in image compression. In this paper, we present a lossy image compression architecture, which utilizes the advantages of convolutional autoencoder (CAE) to achieve a high coding efficiency. First, we design a novel CAE architecture to replace the conventional transforms and train this CAE using a rate-distortion loss function. Second, to generate a more energy-compact representation, we utilize the principal components analysis (PCA) to rotate the feature maps produced by the CAE, and then apply the quantization and entropy coder to generate the codes. Experimental results demonstrate that our method outperforms traditional image coding algorithms, by achieving a 13.7% BD-rate decrement on the Kodak database images compared to JPEG2000. Besides, our method maintains a moderate complexity similar to JPEG2000.
Developing an automatic detector of plant diseases is one of application fields in machine learning. Ground-truth diagnoses of plant diseases which are conducted by experts in laboratory tests are often inapplicable f...
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ISBN:
(纸本)9781538657416
Developing an automatic detector of plant diseases is one of application fields in machine learning. Ground-truth diagnoses of plant diseases which are conducted by experts in laboratory tests are often inapplicable for fast and cheap implementations. Using machine learning approaches, the images of leaves or fruits are used as input data. From the data, we design discriminative features that are good for diseases classification. However, finding suitable features from the images are often challenging due to high intra-variability and inter-variability of the data. In this paper, we present an unsupervised feature learning algorithm using the convolutional autoencoder for detection of plant diseases. The use of convolutional autoencoder has two main advantages. First, the use of handcrafted features is not necessary as the network itself may learn to produce discriminative features. Secondly, the procedure is conducted in an unsupervised manner and hence, no labeling of the data are required. Here, we use the output of the autoencoder as inputs to SVM-based classifiers for automatic detection of plant diseases. The method indicates to be better than conventional autoencoder with more hidden layers.
Traffic accident is considered as one of main causes for traffic congestion in cities. There are many causal factors that may give rise to traffic accidents, e.g. driver characteristics, road conditions, traffic flows...
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ISBN:
(纸本)9781538680346
Traffic accident is considered as one of main causes for traffic congestion in cities. There are many causal factors that may give rise to traffic accidents, e.g. driver characteristics, road conditions, traffic flows and weather conditions, etc. Due to uncertain factors as well as the contingency of accident occurrences, it is very difficult to predict traffic accidents. Many existing works have utilized classical prediction models to predict the risk of accidents on highways or road segments. However, predicting the risk of citywide accidents remains an open issue. To address this problem, we propose SDCAE, a novel Stack Denoise convolutional Auto-Encoder algorithm to predict the risk of traffic accident in the city-level. First, we divided the city into regions by counting the number of accidents and traffic flows in each region. Second, we employed a deep model of stack denoise convolutional autoencoder which considers spatial dependencies to learn the hidden factors in accidents. Third, we conducted extensive experiments on two real-world cross-domain traffic big datasets from a major city of China for accident risk prediction. Experimental results demonstrate that SDCAE could outperforms five baseline methods.
The uncontrollable weather conditions can cause a serious problem to remote sensing imaginary. One of the weather conditions is a resulting from cloud contamination. As a result, this paper proposed the use of the con...
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ISBN:
(纸本)9781538635551
The uncontrollable weather conditions can cause a serious problem to remote sensing imaginary. One of the weather conditions is a resulting from cloud contamination. As a result, this paper proposed the use of the convolutional autoencoder neural networks to remove clouds from cloud-contaminated images by training on a multi-temporal remote sensing dataset. Here, the observations from different spectral bands are assumed to be independent since their spectral responses are usually non-overlapped. From this assumption, each convolutional autoencoder neural networks are trained with the observation from only one spectral band. In our method, we have three convolutional autoencoder neural networks for red, green and blue spectral bands. The experiments were conducted on both synthesis and real dataset derived from the actual LANDSAT 8 images from the central part of Thailand where our algorithm has shown to have a superb performance.
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