Automatic crack detection from images is an important task that is adopted to ensure road safety and durability for Portland cement concrete (PCC) and asphalt concrete (AC) pavement. Pavement failure depends on a numb...
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Automatic crack detection from images is an important task that is adopted to ensure road safety and durability for Portland cement concrete (PCC) and asphalt concrete (AC) pavement. Pavement failure depends on a number of causes including water intrusion, stress from heavy loads, and all the climate effects. Generally, cracks are the first distress that arises on road surfaces and proper monitoring and maintenance to prevent cracks from spreading or forming is important. Conventional algorithms to identify cracks on road pavements are extremely time-consuming and high cost. Many cracks show complicated topological structures, oil stains, poor continuity, and low contrast, which are difficult for defining crack features. Therefore, the automated crack detection algorithm is a key tool to improve the results. Inspired by the development of deep learning in computer vision and object detection, the proposed algorithm considers an encoder-decoder architecture with hierarchical feature learning and dilated convolution, named U-Hierarchical Dilated Network (U-HDN), to perform crack detection in an end-to-end method. Crack characteristics with multiple context information are automatically able to learn and perform end-to-end crack detection. Then, a multi-dilation module embedded in an encoder-decoder architecture is proposed. The crack features of multiple context sizes can be integrated into the multi-dilation module by dilation convolution with different dilatation rates, which can obtain much more cracks information. Finally, the hierarchical feature learning module is designed to obtain a multi-scale features from the high to low- level convolutional layers, which are integrated to predict pixel-wise crack detection. Some experiments on public crack databases using 118 images were performed and the results were compared with those obtained with other methods on the same images. The results show that the proposed U-HDN method achieves high performance because it can
Accurate skin lesion segmentation (SLS) is an important step in computer-aided diagnosis of melanoma. Automatic detection of skin lesions in dermoscopy images is challenging because of the presence of artifacts and as...
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This paper proposes an encoder-decoder based sequence-to-sequence model for Grapheme-to-Phoneme (G2P) conversion in Bangla (Exonym: Bengali). G2P models are key components in speech recognition and speech synthesis sy...
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This paper proposes an encoder-decoder based sequence-to-sequence model for Grapheme-to-Phoneme (G2P) conversion in Bangla (Exonym: Bengali). G2P models are key components in speech recognition and speech synthesis systems as they describe how words are pronounced. Traditional, rule-based models do not perform well in unseen contexts. We propose to adopt a neural machine translation (NMT) model to solve the G2P problem. We used gated recurrent units (GRU) recurrent neural network (RNN) to build our model. In contrast to joint-sequence based G2P models, our encoder-decoder based model has the flexibility of not requiring explicit grapheme-to-phoneme alignment which are not straight forward to perform. We trained our model on a pronunciation dictionary of (approximately) 135,000 entries and obtained a word error rate (WER) of 12.49% which is a significant improvement from the existing rule-based and machine-learning based Bangla G2P models.
State-of-charge (SOC) estimation of lithium-ion batteries based on deep learning techniques has been receiving considerable attention. However, most deep-learning-based methods focus on SOC estimation at fixed ambient...
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State-of-charge (SOC) estimation of lithium-ion batteries based on deep learning techniques has been receiving considerable attention. However, most deep-learning-based methods focus on SOC estimation at fixed ambient temperatures and cannot provide useful indications for battery state in real-world scenarios because batteries usually experience varying temperatures during operation. In this study, an encoder-decoder with bidirectional long short-term memory (LSTM) is proposed for estimating the SOC at different temperature conditions. This end-to-end model can learn sequential information from the measurement sequences to characterize battery dynamics for sequence estimation. Introducing the bidirectional LSTMs into the encoder-decoder enables the model to capture the long-term dependencies of the measurement sequences from both past and future directions to increase the estimation accuracy. The proposed method is evaluated on public battery datasets under dynamic loading profiles. Validation with an experimental dataset shows that this method of considering the sequential contexts and bidirectional dependencies of battery measurement data can accurately estimate the SOC at different ambient temperatures. In particular, the mean absolute errors are as low as 1.07% at varying temperatures. The proposed method can improve the reliability and availability of battery management systems for monitoring the battery state under varying ambient conditions.
Cultivated land extraction is essential for sustainable development and *** this paper,the network we propose is based on the encoder-decoder structure,which extracts the semantic segmentation neural network of cultiv...
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Cultivated land extraction is essential for sustainable development and *** this paper,the network we propose is based on the encoder-decoder structure,which extracts the semantic segmentation neural network of cultivated land from satellite images and uses it for agricultural automation *** encoder consists of two part:the first is the modified Xception,it can used as the feature extraction network,and the second is the atrous convolution,it can used to expand the receptive field and the context information to extract richer feature *** decoder part uses the conventional upsampling operation to restore the original *** addition,we use the combination of BCE and Loves-hinge as a loss function to optimize the Intersection over Union(IoU).Experimental results show that the proposed network structure can solve the problem of cultivated land extraction in Yinchuan City.
A novel encoder-decoder model based on deep neural networks is proposed for the prediction of remaining useful life (RUL) in this work. The proposed model consists of an encoder and a decoder. In the encoder, the Bi-d...
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ISBN:
(纸本)9781728119854
A novel encoder-decoder model based on deep neural networks is proposed for the prediction of remaining useful life (RUL) in this work. The proposed model consists of an encoder and a decoder. In the encoder, the Bi-directional Long Short-Term Memory Networks (Bi-LSTM) and Convolutional Neural Networks (CNN) are used to capture the long-term temporal dependencies and important local features from the sequential data, respectively. Besides, single 1*1 convolution filter in the last convolutional layer is used for dimensionality reduction. In the decoder, the fully connected networks are employed to decode the feature information to predict RUL. In addition, the proposed data-driven method can achieve end-to-end prediction, which does not need feature engineering. To evaluate the proposed model, experimental verification is carried out on a commonly used aero-engine C-MAPSS dataset. Compared with other state-of-the-art approaches on the same dataset, the effectiveness and superiority of the proposed framework are demonstrated. For example, the scoring function value of the second subset is reduced by up to 64.99% compared with the best existing result.
Chinese couplets, as one of the traditional Chinese culture, is the treasure of Chinese civilization and the inheritance of Chinese history. Given a sentence (namely an antecedent clause), people reply with another se...
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ISBN:
(纸本)9781728111988
Chinese couplets, as one of the traditional Chinese culture, is the treasure of Chinese civilization and the inheritance of Chinese history. Given a sentence (namely an antecedent clause), people reply with another sentence (namely a subsequent clause) equal in length. Because of the complexity of the semantic and grammatical rules of couplet, it is not easy to create a suitable couplet that meets the requirements of sentence pattern, context, and flatness. In this paper, given the issued antecedent clause, we can automatically generate the subsequent clause by encoder-decoder model. Moreover, to satisfy special characteristics of couplets, we incorporate the attention mechanism into the encoding-decoding process, which greatly improves the accuracy of couplets generated automatically.
Segmentation which is identification of regions of interest (ROIs) in medical images is a very important step for image analysis in computer-aided diagnosis systems. Accurate segmentation of skin lesions images plays ...
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ISBN:
(纸本)9783030283773;9783030283766
Segmentation which is identification of regions of interest (ROIs) in medical images is a very important step for image analysis in computer-aided diagnosis systems. Accurate segmentation of skin lesions images plays a vital role in efficient diagnosis of melanoma skin cancer. Diagnosis of melanoma cancer through the segmentation of skin lesions is a challenging task due to possible presence of noise and artefacts such as hairs, air or oil bubbles on the skin lesion images. Skin lesions images are also sometimes characterized with weak edges, irregular and fuzzy borders, marks, dark corners, skin lines and blood vessels on skin lesions. Recently, segmentation methods based on Fully Convolutional encoder-decoder Architecture (FCEDA) have achieved great success in medical images. This work presents automatic skin lesion segmentation method that is based on Fully Convolutional encoder-decoder Architecture. Two types of FCEDA namely U-Net and SegNet architectures, have been examined and utilized for segmentation of skin lesion images. The performance analysis of the two architectures have been conducted. Evaluation and comparison of these two architectures were also carried out. This work finds out and proposes possible improvements of these methods on the segmentation of skin lesions. It is also a systematic comparison of U-Net and SegNet models on the segmentation of skin lesion images. The paper discovers how deep learning methods can be explored using a supervised approach to get accurate results with less complexity possible. The models were evaluated on skin lesion challenge dataset in ISIC 2018 dermoscopic images archives.
Neural systems are complicated networks connected by a large number of neurons through gap junctions and synapse. At present, for electron microscopy connectomics research, neuron structure recognition algorithms most...
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ISBN:
(纸本)9781538613115
Neural systems are complicated networks connected by a large number of neurons through gap junctions and synapse. At present, for electron microscopy connectomics research, neuron structure recognition algorithms mostly focus on synapses, dendrites, axons and mitochondria, etc. However, effective methods for automatic recognition of neuronal cell bodies are rare. In this paper, we proposed an effective encoder-decoder network, which extracted segmentation features of neural cell bodies and cell nucleus by the modified residual network and pyramid module. The framework is capable of merging multi-scale contextual information and generating efficient segmentation results by integrating multilevel features. We applied this proposed network on two segmentation tasks for electron microscope (EM) images and compared it with other promising methods as U-Net and deeplab v3+. The results demonstrated that our method achieved the state-of-the-art performance on quality metrics. Finally, we visualized two intact neural cell bodies and cell nucleus to provide a close look into these fine structures.
Neural networks have made significant achievements in the field of image restoration. To efficiently repair facial images with large areas damaged, a decoder-encoder structured convolutional neural network is used as ...
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Neural networks have made significant achievements in the field of image restoration. To efficiently repair facial images with large areas damaged, a decoder-encoder structured convolutional neural network is used as a generative model and skip-connection is added between some of its layers to enhance the structure prediction ability of the generated model and well suppressed the problem that the repair network is easy to over-fitting. The global discrimination network mostly uses the image’s edge structure and feature information to ensure that the repaired image, which is the output from the repair network, conforms to visual connectivity, while the local discriminators, not only recognize local consistency but also optimize more details. The network structure proposed in this paper combines the encoder-decoder, skip-connection, and dual discriminator networks to improve the effect of face completion. The experimental results on the CelebA show that the proposed method is superior to other methods in repairing images with large areas of damage.
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