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International Joint Conference on Neural Networks (IJCNN)

作者： Du, Shengdong Li, Tianrui Yang, Yan Gong, Xun Homg, Shi-Jinn Southwest Jiaotong Univ Natl Engn Lab Integrated Transportat Big Data App Sch Informat Sci & Technol Chengdu Peoples R China Natl Taiwan Univ Sci & Technol Dept Comp Sci & Informat Engn Taipei Taiwan

ISBN: (纸本)9781728119854

Traffic flow prediction has been regarded as a key research problem in the intelligent transportation system. In this paper, we propose an encoder-decoder model with temporal attention mechanism for multi-step forward traffic flow prediction task, which uses LSTM as the encoder and decoder to learn the long dependencies features and nonlinear characteristics of multivariate traffic flow related time series data, and also introduces a temporal attention mechanism for more accurately traffic flow prediction. Through the real traffic flow dataset experiments, it has shown that the proposed model has better prediction ability than classic shallow learning and baseline deep learning models. And the predicted traffic flow value can be well matched with the ground truth value not only under short step forward prediction condition but also under longer step forward prediction condition, which validates that the proposed model is a good option for dealing with the realtime and forward-looking problems of traffic flow prediction task.

关键词： traffic flow prediction long short-term memory networks encoder-decoder temporal attention mechanism

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Innovative Disease Management: An encoder-decoder Solution for Tomato Black Mold Detection 4

Innovative Disease Management: An Encoder-Decoder Solution f...

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4th IEEE Global Conference for Advancement in Technology, GCAT 2023

作者： Vashisht, Divyam Kukreja, Vinay Sharma, Rishabh Jain, Ayushi Choudhury, Ankur Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350305258

The worldwide spread of tomato black mold disease is a major concern since it reduces crop output and quality. Effective disease control and environmentally responsible farming methods depend on rapid and precise disease identification. Using a dense dilated convolution methodology based on an encoder-decoder pair, the suggested presents a novel method for detecting the black mold illness that can occur in tomatoes in this study. The well-known VGG-16 model is used for feature extraction, and 25, 000 photos of tomatoes that the authors collected themselves are used to train and evaluate the model. Accurate segmentation and localization of infected areas are shown to have been achieved by the model, as evidenced by the high values of the Dice Similarity Coefficient (DSC) and the Intersection over Union (IoU). The model's success in approximating ground truth bounds is further supported by the small Hausdorff Distance. To better ensure the safety of our food supply, our studies aid in the development of precision agriculture by providing farmers with a reliable method of disease detection. Our suggested method offers a viable path for early and precise detection of tomato black mold disease by leveraging the power of modern computer vision techniques and deep learning (DL) models, which will help the agricultural community and promote sustainable crop management practices. © 2023 IEEE.

关键词： Deep Learning Dense Dilated Convolution encoder-decoder Segmentation Tomato black mold disease

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Deep encoder-decoder Network for Lane-Following on Autonomous Vehicle

Deep Encoder-Decoder Network for Lane-Following on Autonomou...

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IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW)

作者： Khanum, Abida Lee, Chao-Yang Yang, Chu-Sing Natl Cheng Kung Univ Dept Elect Engn Tainan Taiwan Natl Formosa Univ Dept Aeronaut Engn Huwei Yunlin Taiwan

ISBN: (数字)9781665470506

ISBN: (纸本)9781665470506

Nowadays there is a vast interest in a self-driving car from both academia and industry. The main reason behind recently enormous progress in deep learning approaches for an autonomous vehicle. The main objective of this research is to propose a deep hybrid encoder-decoder network with input multi-modal data to predict the decision-making task. Therefore, the proposed approaches are tested by both real and simulation data but in the real data single camera image and simulator data three-camera image data. The proposed method analyzes the effects of input data. The experiment results in analyses in terms of Computational time as-well-as parameters in which values of the steering wheel and brake both real and simulated data are (6ms and 9ms) respectively. The analysis shows that our method performs well in driving action prediction.

关键词： Deep learning encoder-decoder Autonomous Vehicle Lane-following

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IDM-Net: A Multi-Task Supported encoder-decoder Framework for Magnetic Field Inverse Design

IDM-Net: A Multi-Task Supported Encoder-Decoder Framework fo...

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2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices, ASEMD 2023

作者： Wang, Jiaqi Zhang, Qiankun Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan China

ISBN: (纸本)9798350301571

This study presents a novel end-to-end trainable network named IDM-Net (Inverse Design Network for Magnetic Fields) that facilitates multi-task supported inverse design of magnetic fields. Employing the encoder-decoder idea, IDM-Net accomplishes the inverse design of magnet parameters by leveraging magnetic field data for backpropagation. The encoder is harnessed to extract magnetic field features and classify magnet shapes, while the decoder predicts other properties, including magnet size and position. This innovative approach breaks the constraints of single magnet types in existing research, enabling the inverse design of properties for magnets with diverse shapes. Our experimental results demonstrate remarkable performance, with a 95.2% accuracy in magnet shape classification and a mere 0.28% error in magnet property prediction. By introducing the encoder-decoder idea in the field of inverse design for magnetic fields, we showcase significantly enhanced accuracy and pave the way for broader applications of this technology. © 2023 IEEE.

关键词： deep learning design optimization encoder-decoder inverse design magnetic field

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NASA Technical Reports Server (Ntrs) 19850019880: a Software Simulation Study of a (255,223) Reed-Solomon encoder-decoder

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2017年

NASA Technical Reports Server (Ntrs) 19850019880: a Software Simulation Study of a (255,223) Reed-Solomon encoder-decoder by NASA Technical Reports Server (Ntrs); published by

关键词： (ntrs) 19850019880: algorithms coders computer programs computerized simulation decoders encoder-decoder error detection codes fast fourier transformations information theory nasa technical reports server (ntrs) pollara, f. reed-solomon voyager project

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encoder-decoder with focus-mechanism for sequence labelling based spoken language understanding

Encoder-decoder with focus-mechanism for sequence labelling ...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Su Zhu Kai Yu Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering SpeechLab Department of Computer Science and Engineering Brain Science and Technology Research Center Shanghai Jiao Tong University China

ISBN: (纸本)9781509041183

This paper investigates the framework of encoder-decoder with attention for sequence labelling based spoken language understanding. We introduce Bidirectional Long Short Term Memory - Long Short Term Memory networks (BLSTM-LSTM) as the encoder-decoder model to fully utilize the power of deep learning. In the sequence labelling task, the input and output sequences are aligned word by word, while the attention mechanism cannot provide the exact alignment. To address this limitation, we propose a novel focus mechanism for encoder-decoder framework. Experiments on the standard ATIS dataset showed that BLSTM-LSTM with focus mechanism defined the new state-of-the-art by outperforming standard BLSTM and attention based encoder-decoder. Further experiments also show that the proposed model is more robust to speech recognition errors.

关键词： Spoken language understanding encoder-decoder focus-mechanism robustness spoken language understanding Word Packaging Short-Term Memory Robustness Speech recognition

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基于时空特征融合的encoder-decoder多步4D短期航迹预测

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信号处理 2023年第11期39卷 2037-2048页

作者：石庆研张泽中韩萍中国民航大学天津市智能信号与图像处理重点实验室天津300300

航迹预测在确保空中交通安全、高效运行中扮演着至关重要的角色。所预测的航迹信息是航迹优化、冲突告警等决策工具的输入,而预测准确性取决于模型对航迹序列特征的提取能力。航迹序列数据是具有丰富时空特征的多维时间序列,其中每个变... 详细信息

航迹预测在确保空中交通安全、高效运行中扮演着至关重要的角色。所预测的航迹信息是航迹优化、冲突告警等决策工具的输入,而预测准确性取决于模型对航迹序列特征的提取能力。航迹序列数据是具有丰富时空特征的多维时间序列,其中每个变量都呈现出长短期的时间变化模式,并且这些变量之间还存在着相互依赖的空间信息。为了充分提取这种时空特征,本文提出了基于融合时空特征的编码器-解码器(Spatio-Temporal encoderdecoder,STED)航迹预测模型。在encoder中使用门控循环单元(Gated Recurrent Unit,GRU)、卷积神经网络(Convolutional Neural Network,CNN)和注意力机制(Attention,AT)构成的双通道网络来分别提取航迹时空特征,decoder对时空特征进行拼接融合,并利用GRU对融合特征进行学习和递归输出,实现对未来多步航迹信息的预测。利用真实的航迹数据对算法性能进行验证,实验结果表明,所提STED网络模型能够在未来10 min预测范围内进行高精度的短期航迹预测,相比于LSTM、CNN-LSTM和AT-LSTM等数据驱动航迹预测模型具有更高的精度。此外,STED网络模型预测一个航迹点平均耗时为0.002 s,具有良好的实时性。

关键词： 4D航迹预测时空特征 encoder-decoder 门控循环单元

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The trade-off between topology and content in community detection: An adaptive encoder-decoder-based NMF approach

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 209卷

作者： Zhao, Zhili Ke, Zhengyou Gou, Zhuoyue Guo, Hao Jiang, Kunyuan Zhang, Ruisheng Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Peoples R China

Community detection is an important research field of complex network analysis and focuses on the study of networks' aggregation behaviours. Different from traditional methods that only consider network structural topology, many efforts have been put into combining network structural topology with node content attributes to achieve better community detection performance. However, it is critical to make an appropriate trade-off between structural topology and node content. In this paper, we propose an adaptive trade-off approach, called ANMF, which not only considers both structural topology and node content, but also provides a flexible parameter to balance their contribution. Compared with other related approaches, ANMF is a kind of non-negative matrix factorization (NMF)-based community detection method, but it imposes more constraints on the network reconstruction. More precisely, ANMF simultaneously employs a decoder that reconstructs a network from its community membership space and an encoder that transforms the network into the community membership space. Moreover, compared with the most related state-of-the-art effort adaptive semantic community detection (ASCD), which considers the topology part always has more contribution if there is a mismatch, ANMF considers the mismatch in two different situations, i.e., the topology part contributes more than the node content part and the node content part contributes more than the topology part. Based on the intensive evaluation on both real and artificial networks, ANMF provides higher normalized mutual information (NMI) values of 4.95%similar to 126.41% than the models without considering node content information on 13 out of 14 experimental networks. ANMF also presents higher NMI values of 7.38%similar to 201.01% than ASCD on 13 out of 14 experimental networks. Moreover, ANMF shows good convergence performance, and it can converge after 100 iterations on all of the networks. ANMF also provides stability alike to s

关键词： Non-negative matrix factorization Community detection encoder-decoder Network topology Node attributes

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基于encoder-decoder网络的列车轮对激光曲线提取

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光电子．激光 2021年第6期32卷 602-612页

作者：杨凯罗帅王勇高晓蓉蒋天赐李春江西南交通大学光电工程研究所四川成都610031 早稻田大学机械工程学院日本福冈8080135 浙江大学力学学院浙江杭州310058

提出了一种基于encoder-decoder网络的列车轮对激光曲线精确分割的算法。针对列车轮对激光曲线数据集局部特征丰富、语义信息简单的特点,设计了具有深度较浅、分辨率较高、细节表现良好的网络。设计的网络很好的利用了密集链接机制和上... 详细信息

提出了一种基于encoder-decoder网络的列车轮对激光曲线精确分割的算法。针对列车轮对激光曲线数据集局部特征丰富、语义信息简单的特点,设计了具有深度较浅、分辨率较高、细节表现良好的网络。设计的网络很好的利用了密集链接机制和上采样模块,加强了特征复用以及特征传播,具有较少参数的同时,能多尺度提取上下文语义信息。实验证明,encoder-decoder网络相比于其他网络在列车轮对激光曲线提取上表现出优异的性能。基于encoder-decoder的网络在列车轮对激光曲线数据集上交并比、召回率、准确率和F1score指标分别达到了86.5%、89.2%、99.9%、85.0%,能够比较精准提取列车轮对激光条纹。同时encoder-decoder网络在进行列车轮对激光条纹分割时能在一定程度上改善噪声对条纹提取的影响。因此在铁路安全方面具有良好的应用前景。

关键词：深度学习图像处理目标分割结构光测量激光条纹分割 encoder-decoder

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Effective Multi-Step PM2.5 and PM10 Air Quality Forecasting Using Bidirectional ConvLSTM encoder-decoder With STA Mechanism

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IEEE ACCESS 2024年 12卷 179628-179647页

作者： Lakshmi, S. Krishnamoorthy, A. Vellore Inst Technol Sch Comp Sci & Engn Vellore 632014 India

Effective prediction of PM2.5 and PM10 levels is essential for preserving public health and informing governmental actions. Nevertheless, the unpredictable behavior of air fluxes makes it difficult to forecast these concentrations accurately. The objective is to create advanced technology for accurately evaluating the air quality in Delhi, which is essential for implementing effective measures to reduce pollution. Meteorological and air quality data were gathered hourly from 6 monitoring sites in Delhi, as part of the Central Pollution Control Board (CPCB) initiative. The data collection period spanned from June 1, 2018, to October 1, 2019, with measurements conducted at hourly intervals. Address air quality prediction as an issue of predicting spatiotemporal sequences, since individual models have difficulties in successfully capturing both spatial and temporal relationships effectively. This study proposed the hybrid of an innovative encoder-decoder model based on a Bidirectional Convolutional Long-Short term network (BiConvLSTM) with a Spatial-Temporal Attention mechanism that could achieve this by developing spatial-temporal characteristics and, a prediction system that accurately forecasts PM2.5 and PM10 concentrations across several time steps. This model performed extremely well in forecasting PM2.5 and PM10 levels, achieving impressive error metrics. For PM2.5 MSE of 0.0298, MAE of 0.1511, RMSE of 0.1728, and the coefficient of determination (R2) of 0.999. Similarly, for PM10 MSE of 0.0582, MAE of 0.1833, RMSE of 0.3134, and the coefficient of determination (R2) was 0.999. Analyze the model's efficacy over prediction periods of 6,12,24,36,48 hours concerning the distribution of errors and the level of accuracy. This study emphasizes the effectiveness of the proposed method using an extensive comparison with state-of-the-art models such as Transformer, GNN+LSTM, and Seq2Seq with attention.

关键词： Air quality prediction BiConvLSTM deep learning deep learning encoder-decoder encoder-decoder spatial-temporal attention mechanism spatial-temporal attention mechanism prediction of PM2.5 and PM10 prediction of PM2.5 and PM10 prediction of PM2.5 and PM10

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