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LSTM based encoder-decoder for short-term predictions of gas concentration using multi-sensor fusion

PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 2020年 137卷 93-105页

作者： Lyu, Pingyang Chen, Ning Mao, Shanjun Li, Mei Peking Univ Inst Remote Sensing & Geog Informat Syst Beijing 100871 Peoples R China

Gas is one of the most dangerous byproducts of coal in mines. Before gas accidents occur, an abnormally increased gas concentration can be observed. Therefore, a prediction of the gas concentration in coal mines is of great significance to prevent the gas accident and ensure the production safety in the mines. By calculating the Pearson correlation coefficient for the gas concentration of different sensors, the spatial correlation of the gas concentration that is monitored for each mining face is verified. We present multi-step prediction results for gas concentration time series based on the ARMA model, the CHAOS model and the encoder-decoder model (single-sensor and multi-sensor) and compare these results. The encoder-decoder model provides high robustness in a multi-step prediction and can predict the gas concentration for five different time steps. Its prediction error is significantly lower than those of the ARMA and the CHAOS models. The prediction accuracy is further improved through a fusion with information of other sensors. In this way, this study provides a novel concept and method for gas accident prevention. (C) 2020 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

关键词： LSTM encoder-decoder Gas concentration Multi-Sensor information Multi-Step prediction

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Photovoltaic power station identification using refined encoder-decoder network with channel attention and chained residual dilated convolutions

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JOURNAL OF APPLIED REMOTE SENSING 2020年第1期14卷

作者： Jie, Yongshi Yue, Anzhi Liu, Shunxi Huang, Qingqing Chen, Jingbo Meng, Yu Deng, Yupeng Yu, Zongyang Chinese Acad Sci Aerosp Informat Res Inst Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China China Land Surveying & Planning Inst Beijing Peoples R China Natl Engn Lab Transportat Safety & Emergency Info Beijing Peoples R China

The quantity and distribution of photovoltaic power stations play an important role in policymaking related to clean energy. High-resolution satellite imagery with a wide field of view enables photovoltaic power station monitoring at low cost. The separability varies greatly due to distinct backgrounds in different regions, which makes photovoltaic power station identification a challenging task. We propose an end-to-end semantic segmentation network to identify photovoltaic power stations under multiple complex backgrounds in Gaofen 1 imageries. Our network adopts the encoder-decoder architecture in which three modules between the encoder and decoder are added, i.e., feature refinement residual module (FRRM), chained dilation attention module (CDAM), and global channel attention module (GCAM). FRRM uses the structure of residual connection to refine the features from each stage of the encoder. CDAM consisting of chained residual dilated convolutions with different dilation rates and channel attention can enlarge the receptive field without reducing image resolution and select useful features. GCAM utilizes high-level features containing rich semantic information as a guide to select more discriminative low-level features. Experiments demonstrate the effectiveness of each module and the ability of the proposed network to improve photovoltaic power station identification under complex backgrounds. (C) 2020 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： photovoltaic power station identification remote sensing semantic segmentation encoder-decoder channel attention

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Using An Attention-Based LSTM encoder-decoder Network for Near Real-Time Disturbance Detection

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2020年 13卷 1819-1832页

作者： Yuan, Yuan Lin, Lei Huo, Lian-Zhi Kong, Yun-Long Zhou, Zeng-Guang Wu, Bin Jia, Yan Nanjing Univ Posts & Telecommun Sch Geog & Biol Informat Nanjing 210023 Peoples R China Beijing Qihoo Technol Co Ltd Beijing 100015 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Beijing 100094 Peoples R China Aerosp DongFangHong Satellite Co Ltd Beijing 100094 Peoples R China

Accurate prediction of future observations based on past data is the key to near real-time disturbance detection using satellite image time series (SITS). To overcome the limitations of existing methods, we present an attention-based long-short-term memory (LSTM) encoder-decoder model in which the historical time series of a pixel is encoded with a bidirectional LSTM encoder while the future time series is produced by another LSTM decoder. An attention mechanism is integrated into the encoder-decoder model to align the input time series with the output time series and to dynamically choose the most relevant contextual information while forecasting. Based on the proposed model, we develop a framework for near real-time disturbance detection and verify its effectiveness in the case of burned area mapping. The prediction accuracy of the proposed model is evaluated using moderate resolution imaging spectroradiometer (MODIS) time series and compared with state-of-the-art models. Experimental results show that our model achieves the best results in terms of lower prediction error and higher model fitness. We also evaluate the disturbance detection ability of the proposed framework. The proposed approach improves the detection rate of disturbances while suppressing false alarms, and increases the temporal accuracy. We suggest that the proposed methods provide new tools for enhancing current early warning systems in real time.

关键词： Time series analysis Predictive models Hidden Markov models Real-time systems Remote sensing Forecasting Satellites Attention mechanism encoder-decoder long-short-term memory (LSTM) near real-time disturbance detection satellite image time series (SITS)

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Multivariate time series forecasting via attention-based encoder-decoder framework

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NEUROCOMPUTING 2020年第0期388卷 269-279页

作者： Du, Shengdong Li, Tianrui Yang, Yan Horng, Shi-Jinn Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 611756 Peoples R China Natl Taiwan Univ Sci & Technol Dept Comp Sci & Informat Engn Taipei 10607 Taiwan

Time series forecasting is an important technique to study the behavior of temporal data and forecast future values, which is widely applied in many fields, e.g. air quality forecasting, power load forecasting, medical monitoring, and intrusion detection. In this paper, we firstly propose a novel temporal attention encoder-decoder model to deal with the multivariate time series forecasting problem. It is an end-to-end deep learning structure that integrates the traditional encode context vector and temporal attention vector for jointly temporal representation learning, which is based on bi-directional long short-term memory networks (Bi-LSTM) layers with temporal attention mechanism as the encoder network to adaptively learning long-term dependency and hidden correlation features of multivariate temporal data. Extensive experimental results on five typical multivariate time series datasets showed that our model has the best forecasting performance compared with baseline methods. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Multivariate time series Temporal attention Multi-step forecasting encoder-decoder Deep learning Long short-term memory networks

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A Gated Dilated Causal Convolution Based encoder-decoder for Network Traffic Forecasting

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IEEE ACCESS 2020年 8卷 6087-6097页

作者： Zhang, Xin You, Jiali Chinese Acad Sci Inst Acoust Natl Network New Media Engn Res Ctr Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China

The accurate estimation of future network traffic is a key enabler for early warning of network degradation and automated orchestration of network resources. The long short-term memory neural network (LSTM) is a popular architecture for network traffic forecasting, and has been successfully used in many applications. However, it has been observed that LSTMs suffer from limited memory capacity problems when the sequence is long. In this paper, we propose a gated dilated causal convolution based encoder-decoder (GDCC-ED) model for network traffic forecasting. The GDCC-ED learns a vector representation in the encoder from historical network traffic series, in which gated dilated causal convolutions are adopted to expand the long-range memory capacity. Moreover, different types of features in various perspectives, including temporal-independent and temporal-related features, are incorporated. In the decoder, the GDCC-ED exploits an RNN with LSTM units to map the vector representation back to a variable-length target sequence. Besides, a sequence data augmentation technique is designed to solve the problem of data scarcity. Experimental results demonstrate that our model achieves superior performance than state-of-the-art algorithms by 11.6 & x0025;.

关键词： Network traffic forecasting dilated causal convolution gated activations encoder-decoder

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Video Summarization With Attention-Based encoder-decoder Networks

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2020年第6期30卷 1709-1717页

作者： Ji, Zhong Xiong, Kailin Pang, Yanwei Li, Xuelong Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Northwestern Polytech Univ Ctr OPT IMagery Anal & Learning Sch Comp Sci Xian 710072 Peoples R China

This paper addresses the problem of supervised video summarization by formulating it as a sequence-to-sequence learning problem, where the input is a sequence of original video frames, and the output is a keyshot sequence. Our key idea is to learn a deep summarization network with attention mechanism to mimic the way of selecting the keyshots of human. To this end, we propose a novel video summarization framework named attentive encoder-decoder networks for video summarization (AVS), in which the encoder uses a bidirectional long short-term memory (BiLSTM) to encode the contextual information among the input video frames. As for the decoder, two attention-based LSTM networks are explored by using additive and multiplicative objective functions, respectively. Extensive experiments are conducted on two video summarization benchmark datasets, i.e., SumMe and TVSum. The results demonstrate the superiority of the proposed AVS-based approaches against the state-of-the-art approaches, with remarkable improvements on both datasets.

关键词： Decoding Visualization Recurrent neural networks Additives Indexes Internet Semantics Video summarization LSTM encoder-decoder attention mechanism

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An encoder-decoder Convolution Network With Fine-Grained Spatial Information for Hyperspectral Images Classification

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IEEE ACCESS 2020年 8卷 33600-33608页

作者： Li, Zhongwei Guo, Fangming Li, Qi Ren, Guangbo Wang, Leiquan China Univ Petr East China Coll Oceanog & Space Informat Qingdao 266580 Peoples R China Minist Nat Resources Inst Oceanog 1 Qingdao 266061 Peoples R China China Univ Petr East China Coll Comp Sci & Technol Qingdao 266580 Peoples R China

Convolutional Neural Network (CNN) is widely used in Hyperspectral Images (HSIs) classification. However, the fine-grained spatial (FGS) details are discarded during a sequence of convolution and pooling operations for most of CNN-based HSIs classification methods. To address this issue, a unified encoder-decoder framework is proposed to integrate high-level semantics and FGS details for HSIs classification, denoted by FGSCNN. The encoder, including a series of convolution and pooling layers, captures the high-level semantic information with low resolution feature maps. The decoder fuses the high-level low-resolution semantic and the fine-grained high-resolution spatial information, namely, to get the FGS features with high-level semantics. The deconvolution layers and skip connection are used in the decoder to retain the FGS details, while, convolution layers are also used to combine the FGS features with high-level semantics. Based on the encoder-decoder framework, a unified loss function is exploited to integrate the high-level semantic information and FGS details with an end-to-end manner for HSIs classification. Experiments conducted on the three public datasets, i.e. the Indian Pines, Pavia University and Salinas, demonstrate the effectiveness of the proposed method on HSIs classification.

关键词： Semantics Feature extraction Convolution Decoding Spatial resolution Hyperspectral imaging Machine learning Convolutional neural networks (CNNs) encoder-decoder hyperspectral image (HSI) classification information fusion

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A multi-task encoder-dual-decoder framework for mixed frequency data prediction

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INTERNATIONAL JOURNAL OF FORECASTING 2024年第3期40卷 942-957页

作者： Lin, Jiahe Michailidis, George Morgan Stanley Machine Learning Res New York NY 10036 USA Univ Calif Los Angeles Dept Stat Los Angeles CA 90095 USA

Mixed -frequency data prediction tasks are pertinent in various application domains, in which one leverages progressively available high -frequency data to forecast/nowcast the low -frequency ones. Existing methods in the literature tailored to such tasks are mostly linear in nature;depending on the specific formulation, they largely rely on the assumption that the (latent) processes that govern the dynamics of the high- and low -frequency blocks of variables evolve at the same frequency, either the low or the high one. This paper develops a neural network -based multi -task shared -encoder -dual -decoder framework for joint multi -horizon prediction of both the low- and high -frequency blocks of variables, wherein the encoder/decoder modules can be either long shortterm memory or transformer ones. It addresses forecast/nowcast tasks in a unified manner, leveraging the encoder-decoder structure that can naturally accommodate the mixed -frequency nature of the data. The proposed framework exhibited competitive performance when assessed on both synthetic data experiments and two real datasets of US macroeconomic indicators and electricity data. (c) 2023 The Authors. Published by Elsevier B.V. on behalf of International Institute of Forecasters. This is an open access article under the CC BY -NC -ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Mixed-frequency data encoder-decoder LSTM Transformer Multi-horizon forecasts Nowcasting

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Dual encoder-decoder Based Generative Adversarial Networks for Disentangled Facial Representation Learning

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IEEE ACCESS 2020年 8卷 130159-130171页

作者： Hu, Cong Feng, Zhenhua Wu, Xiaojun Kittler, Josef Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Jiangsu Peoples R China Jiangnan Univ Jiangsu Prov Engn Lab Pattern Recognit & Computat Wuxi 214122 Jiangsu Peoples R China Minjiang Univ Fujian Prov Key Lab Informat Proc & Intelligent C Fuzhou 350121 Peoples R China Univ Surrey Dept Comp Sci Guildford GU2 7XH Surrey England Univ Surrey Ctr Vis Speech & Signal Proc Guildford GU2 7XH Surrey England

To learn disentangled representations of facial images, we present a Dual encoder-decoder based Generative Adversarial Network (DED-GAN). In the proposed method, both the generator and discriminator are designed with deep encoder-decoder architectures as their backbones. To be more specific, the encoder-decoder structured generator is used to learn a pose disentangled face representation, and the encoder-decoder structured discriminator is tasked to perform real/fake classification, face reconstruction, determining identity and estimating face pose. We further improve the proposed network architecture by minimizing the additional pixel-wise loss defined by the Wasserstein distance at the output of the discriminator so that the adversarial framework can be better trained. Additionally, we consider face pose variation to be continuous, rather than discrete in existing literature, to inject richer pose information into our model. The pose estimation task is formulated as a regression problem, which helps to disentangle identity information from pose variations. The proposed network is evaluated on the tasks of pose-invariant face recognition (PIFR) and face synthesis across poses. An extensive quantitative and qualitative evaluation carried out on several controlled and in-the-wild benchmarking datasets demonstrates the superiority of the proposed DED-GAN method over the state-of-the-art approaches.

关键词： Face Gallium nitride Generative adversarial networks Training Generators Face recognition Task analysis Disentangled representation learning encoder-decoder generative adversarial networks face synthesis pose invariant face recognition

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FFEDN: Feature Fusion encoder decoder Network for Crack Detection

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第9期23卷 15546-15557页

作者： Liu, Chuanqi Zhu, Chengguang Xia, Xuan Zhao, Jiankang Long, Haihui Shanghai Jiao Tong Univ Dept Instrument Sci & Engn Shanghai 200240 Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen 518000 Peoples R China

Crack detection plays a crucial role in structural health monitoring tasks to ensure the reliability of the transportation infrastructures. However, the automatic detection of cracks remains a challenging task due to the complicated background. Especially, tiny crack detection should be attached importance because of its weak feature and background interference. Therefore, an end-to-end network Feature Fusion encoder decoder Network (FFEDN) with two novel modules is proposed to improve the crack detection accuracy. For one thing, the representation capability for tiny cracks is enhanced by introducing the attention mechanism, which redistributes and fuses different features of both the encoder and the decoder. For another, because high-level feature contains less interference, a shape semantic prior module is developed to learn the shape prior map that provides the rough shape and location information of cracks. This map is fed into the lower-level feature and helps it focus on crack areas, thereby suppressing background interference. To demonstrate the effectiveness of the proposed network, several experiments are implemented on three publicly available crack datasets. Compared with state-of-the-art crack detection methods, the novel network shows better performance on all the six evaluation metrics.

关键词： Feature extraction Decoding Shape Semantics Interference Task analysis Image edge detection Crack detection encoder-decoder feature fusion attention mechanism shape semantic prior

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