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International Joint Conference on Neural Networks (IJCNN)

作者： Yuan, Minglei Ji, Xiaozhong Lu, Tong Chen, Pengfei Zhang, Hualu Nanjing Univ Natl Key Lab Novel Software Technol Nanjing Peoples R China Nati Grp Corp Informat Technol & Commun Co Nanjing Peoples R China

ISBN: (纸本)9781728119854

This paper proposes a novel two-factor attention based encoder-decoder model (TwoFactorencoderdecoder) for multivariate weather prediction. The proposed model learns attention weights from two factors, namely, temporal information and prior knowledge inferred information. Here, temporal information contains change patterns hidden in observed time series data, while prior knowledge inferred information gives various types of meteorological observations in weather forecasting. Attention weights of the two factors are used to select the intermediate outputs of the encoder, and then combine the selected result with information inferred by prior knowledge for weather forecasting by a more effective way. In addition, this paper proposes a loss function for multivariate prediction. Compared with Mean Square Error (MSE) loss function, the proposed loss function can fit small variances more accurately in performing multivariate prediction. Compared with the attention model that only uses temporal information or the prior knowledge inferred information, the proposed TwoFactorencoderdecoder model has encouraging improvements in prediction accuracy on the public weather forecasting dataset, namely, the MAPE of t2m is increased by 5.42%, the MAPE of rh2m is increased by 2.92%, and the MAPE of w2m is increased by 1.67%, which shows the effect of the two-factor attention mechanism. Source code for the complete system will be available at https://***/YuanMLer/TFAencoderdecoder.

关键词： two-factor attention weather forecasting encoder-decoder model

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ST-LSTM: SPATIO-TEMPORAL GRAPH BASED LONG SHORT-TERM MEMORY NETWORK FOR VEHICLE TRAJECTORY PREDICTION

ST-LSTM: SPATIO-TEMPORAL GRAPH BASED LONG SHORT-TERM MEMORY ...

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IEEE International Conference on Image Processing (ICIP)

作者： Chen, Guangxi Hu, Ling Zhang, Qieshi Ren, Ziliang Gao, Xiangyang Cheng, Jun Guilin Univ Elect Technol Sch Comp Sci & Informat Secur Guilin Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol CAS Key Lab Human Machine Intelligence Synergy Sy Shenzhen Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781728163956

Autonomous vehicles need the ability to predict the trajectory of surrounding vehicles, so as to make a rational decision planning, improve driving safety and ride comfort. In this paper, a new hierarchical Long Short-Term Memory (LSTM) based on Spatio-Temporal (ST) graph is proposed for vehicle trajectory prediction. Our ST-LSTM uses three layers of different LSTMs to capture the information of spatial, temporal and trajectory data, and LSTM-based encoder-decoder model as a whole, which is capable of accurately predicting future trajectories for vehicles on the highway. Our model trained and validated on the publicly available NGSIM US101 and I-80 datasets. In comparison to state-of-art methods, our method could achieve a more accurate prediction trajectory over 5s time horizon.

关键词： Spatio-Temporal (ST) graph Long Short-Term Memory (LSTM) encoder-decoder model trajectory prediction autonomous vehicles

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DPAST-RNN: A Dual-Phase Attention-Based Recurrent Neural Network Using Spatiotemporal LSTMs for Time Series Prediction 27th

DPAST-RNN: A Dual-Phase Attention-Based Recurrent Neural Net...

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27th International Conference on Neural Information Processing

作者： Shan, Shajia Shen, Ziyu Xi, Bin Liu, Zheng Li, Yun Nanjing Univ Posts & Telecommun Jiangsu Key Lab Fig Data Secur & Intelligent Proc Nanjing Peoples R China Nanjing Univ Posts & Telecommun Sch Comp Sci Nanjing Peoples R China

ISBN: (纸本)9783030638351;9783030638368

For time series forecasting, the weight distribution among multivariables and the long-short-term time dependence are always very important and challenging. Traditional machine forecasting can't automatically select the effective features of multivariable input and can't capture the time dependence of sequences. The key to solve this problem is to capture the spatial correlations at the same time, the spatiotemporal relationships at different times and the long-term dependence of the temporal relationships between different series. In this paper, inspired by human attention mechanism including encoder-decoder model, we propose DPAST-based RNN (DPAST-RNN) for long-term time series prediction. Specifically, in the first phase we use attention mechanism to extract relevant features at each time adaptively then we use stacked LSTM units to extract hidden information of time series both from time and space dimensions. In the second phase, we use another attention mechanism to select the related hidden state in encoder to the hidden state of the decoder at the current time to make context vector which is embed into recurrent neural network in decoder. Thorough empirical studies based upon the VM-Power dataset we collected on OpenStack and the NASDAQ 100 Stock dataset demonstrate that the DPAST-RNN can outperform state-of-the-art methods for time series prediction.

关键词： Time series prediction Spatiotemporal LSTM Attention mechanism encoder-decoder model

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Deep Learning for ICD Coding: Looking for Medical Concepts in Clinical Documents in English and in French 9th

Deep Learning for ICD Coding: Looking for Medical Concepts i...

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9th International Conference of the Conference-and-Labs-of-the-Evaluation-Forum-Association, (CLEF)

作者： Miftahutdinov, Zulfat Tutubalina, Elena Kazan Volga Reg Fed Univ Kazan Russia Neuromation OU EE-10111 Tallinn Estonia

ISBN: (纸本)9783319989327;9783319989310

Medical Concept Coding (MCD) is a crucial task in biomedical information extraction. Recent advances in neural network modeling have demonstrated its usefulness in the task of natural language processing. Modern framework of sequence-to-sequence learning that was initially used for recurrent neural networks has been shown to provide powerful solution to tasks such as Named Entity Recognition or Medical Concept Coding. We have addressed the identification of clinical concepts within the International Classification of Diseases version 10 (ICD-10) in two benchmark data sets of death certificates provided for the task 1 in the CLEF eHealth shared task 2017. A proposed architecture combines ideas from recurrent neural networks and traditional text retrieval term weighting schemes. We found that our models reach accuracy of 75% and 86% as evaluated by the F-measure on the CepiDc corpus of French texts and on the CDC corpus of English texts, respectfully. The proposed models can be employed for coding electronic medical records with ICD codes including diagnosis and procedure codes.

关键词： ICD coding ICD codes Medical concept coding Medical record coding Computer assisted coding Recurrent neural network encoder-decoder model Deep learning Machine learning Death certificates CepiDc corpus CDC corpus Healthcare CLEF eHealth

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Capturing intrinsic features from field data for predicting the production of natural gas

GEOENERGY SCIENCE AND ENGINEERING

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GEOENERGY SCIENCE AND ENGINEERING 2023年 227卷

作者： Wang, Xin Wang, Yong-Sheng Pang, Lan-Su Jiang, Tao Chen, Yu-Fan Wang, Yang Mei, Qing-Yan Qing, Sheng-Lan Jiang, Wei Southwest Petr Univ Sch Comp Sci Chengdu 610500 Peoples R China PetroChina Southwest Oil & Gasfield Co Explorat & Dev Res Inst Chengdu 610043 Peoples R China

Production prediction for gas wells is a popular topic in reservoir engineering as it plays a crucial role in the formulation of development plans. Most traditional techniques can be categorized into two types, i.e., numerical simulation methods and decline curve analysis, while none of them can precisely capture the varying trends of gas production, which leads to poor prediction results. To tackle the issue, we propose a comprehensive approach that works in a pipeline manner to learn intrinsic features from data for production prediction. (1) We propose to group wells with a clustering algorithm which does not need the pre-specified cluster number. To group wells even better, two parameters, i.e., dynamic volatility and static volatility of productions are introduced and involved for clustering. (2) We devise a technique that is based on the maximum likelihood estimation, for well matching. (3) We develop an encoder-decoder model for learning varying trends of well productions, by considering geological, engineering and production data simultaneously. (4) On real-life data, we conduct intensive experiments and find that our approach achieves superior performance and substantially outperforms its counterparts.

关键词： Natural gas Production prediction encoder-decoder model Well clustering & matching

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MS-Pointer Network: Abstractive Text Summary Based on Multi-Head Self-Attention

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IEEE ACCESS 2019年 7卷 138603-138613页

作者： Guo, Qian Huang, Jifeng Xiong, Naixue Wang, Pan Shanghai Normal Univ Coll Informat Mech & Elect Engn Shanghai 200030 Peoples R China Northeastern State Univ Dept Math & Comp Sci Tahlequah OK 74464 USA Huanggang Jinghe Sch Xixian New Area Xian 713702 Shaanxi Peoples R China

.Abstractive text summarization plays an important role in the field of natural language processing. However, the abstractive text summary adopts deep learning research method to predict words often appears semantic inaccuracy and repetition and so on. at the present stage, in order to solve the problem that semantic inaccuracy, we propose an MS-Pointer Network that based on the multi-head self-attention mechanism, which a multi-head self-attention mechanism is introduced in the basic encoder-decoder model. Since multi-head self-attention can combine input words into the encoder-decoder arbitrarily, and given a higher weight of these words that combination of the semantics, thereby achieving the purpose of enhancing the semantic features of the text, so that the abstractive text summary is more semantically structured, And the multi-head self-attention mechanism add the position information of the input text, which can enhance the semantic representation of the text. At the same time, in order to solve the problem of out of vocabulary, a pointer network is introduced on the seqtoseq with a multi-head attention mechanism. The model is referred to as MS-Pointer Network. We used CNN/Daily Mail and Gigaword datasets to validate our model, and uses the ROUGE metric to measure model. Experiments have shown that abstractive text summaries generated using the multi-head self-attention mechanism outperforming current open state-of-the-art two points averagely.

关键词： Abstractive text summarization encoder-decoder model multi-head self-attention mechanism

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SED: Searching Enhanced decoder with switchable skip connection for semantic segmentation

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PATTERN RECOGNITION 2024年 149卷

作者： Zhang, Xian Quan, Zhibin Li, Qiang Zhu, Dejun Yang, Wankou Southeast Univ Sch Automat Nanjing 210096 Peoples R China SenseTime Res Beijing Peoples R China Tsinghua Univ Dept Hydraul Engn State Key Lab Hydrosci & Hydraul Engn Beijing 100084 Peoples R China

Neural architecture search (NAS) has shown excellent performance. However, existing semantic segmentation models rely heavily on pre-training on Image-Net or COCO and mainly focus on the designing of decoders. Directly training the encoder-decoder architecture search models from scratch to SOTA for semantic segmentation requires even thousands GPU days, which greatly limits the application of NAS. To address this issue, we propose a novel neural architecture Search framework for Enhanced decoder (SED). Utilizing the pre-trained hand-designing backbone and the searching space composed of light-weight cells, SED searches for a decoder which can perform high-quality segmentation. Furthermore, we attach switchable skip connection operations to search space, expanding the diversity of possible network structure. The parameters of backbone and operations selected in searching phrase are copied to retraining process. As a result, searching, pruning and retraining can be done in just 1 day. The experimental results show that the SED proposed in this paper only needs 1/4 of the parameters and calculation in contrast to hand-designing decoder, and obtains higher segmentation accuracy on Cityscapes. Transferring the same decoder architecture to other datasets, such as: Pascal VOC 2012, Camvid, ADE20K proves the robustness of SED.

关键词： NAS Semantic segmentation encoder-decoder model

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S-Net: a scalable convolutional neural network for JPEG compression artifact reduction

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JOURNAL OF ELECTRONIC IMAGING 2018年第4期27卷 043037-043037页

作者： Zheng, Bolun Sun, Rui Tian, Xiang Chen, Yaowu Zhejiang Univ Inst Adv Digital Technol & Instrument Hangzhou Zhejiang Peoples R China Sichuan Univ Coll Phys Sci & Technol Chengdu Sichuan Peoples R China Zhejiang Prov Key Lab Network Multimedia Technol Hangzhou Zhejiang Peoples R China Zhejiang Univ State Key Lab Ind Control Technol Hangzhou Zhejiang Peoples R China Zhejiang Univ Embedded Syst Engn Res Ctr Minist Educ China Hangzhou Zhejiang Peoples R China

Recent studies have used deep residual convolutional neural networks (CNNs) for JPEG compression artifact reduction. This study proposes a scalable CNN called S-Net. Our approach effectively adjusts the network scale dynamically in a multitask system for real-time operation with little performance loss. It offers a simple and direct technique to evaluate the performance gains obtained with increasing network depth, and it is helpful for removing redundant network layers to maximize the network efficiency. We implement our architecture using the Keras framework with the TensorFlow backend on an NVIDIA K80 GPU server. We train our models on the DIV2K dataset and evaluate their performance on public benchmark datasets. To validate the generality and universality of the proposed method, we created and utilized a new dataset, called WIN143, for over-processed images evaluation. Experimental results indicate that our proposed approach outperforms other CNN-based methods and achieves state-of-the-art performance. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License.

关键词： compression artifact reduction encoder-decoder model scalable convolutional neural network depth evaluation

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Chaotic time series prediction of nonlinear systems based on various neural network models

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CHAOS SOLITONS & FRACTALS 2023年第Part1期175卷

作者： Sun, Ying Zhang, Luying Yao, Minghui Beijing Informat Sci & Technol Univ Sch Appl Sci Beijing 100192 Peoples R China Tiangong Univ Sch Aeronaut & Astronaut Tianjin 300387 Peoples R China

This paper discusses the chaos prediction of nonlinear systems using various neural networks based on the modified substructure data-driven modeling architecture. In the modeling step, we construct two-coefficient loss functions according to the linear multi-step method to improve the prediction accuracy of neural networks. Then, the predicted response data of the system is given by the forward Euler method and neural networks. Under such architecture, chaos forecasting is carried out on a five-degree-of-freedom duffing oscillator system via the feedforward neural network (FNN), long short-term memory (LSTM) network and LSTM encoder-decoder (LSTM ED). The numerical simulation results show that the model can predict chaotic time series even if a small amount of information and samples are known, and the prediction window is twice that of the observation window. Among these models, LSTM ED exhibits the highest accuracy in both short-term and long-term chaos prediction. Furthermore, the prediction results mainly involve three evaluation indicators: absolute error, mean absolute error, normalized root mean square error. Through error analysis and noise processing, LSTM ED shows superior stability, robustness and extrapolation ability. Its prediction error is about half of FNN and the maximum increase in accuracy is 71.3 %.

关键词： Neural networks LSTM model encoder-decoder model Chaos prediction Time series

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DeepLabV3-Refiner-Based Semantic Segmentation model for Dense 3D Point Clouds

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REMOTE SENSING 2021年第8期13卷 1565-1565页

作者： Kwak, Jeonghoon Sung, Yunsick Dongguk Univ Seoul Dept Multimedia Engn 30 Pildong Ro 1 Gil Seoul 04620 South Korea

Three-dimensional virtual environments can be configured as test environments of autonomous things, and remote sensing by 3D point clouds collected by light detection and range (LiDAR) can be used to detect virtual human objects by segmenting collected 3D point clouds in a virtual environment. The use of a traditional encoder-decoder model, such as DeepLabV3, improves the quality of the low-density 3D point clouds of human objects, where the quality is determined by the measurement gap of the LiDAR lasers. However, whenever a human object with a surrounding environment in a 3D point cloud is used by the traditional encoder-decoder model, it is difficult to increase the density fitting of the human object. This paper proposes a DeepLabV3-Refiner model, which is a model that refines the fit of human objects using human objects whose density has been increased through DeepLabV3. An RGB image that has a segmented human object is defined as a dense segmented image. DeepLabV3 is used to make predictions of dense segmented images and 3D point clouds for human objects in 3D point clouds. In the Refiner model, the results of DeepLabV3 are refined to fit human objects, and a dense segmented image fit to human objects is predicted. The dense 3D point cloud is calculated using the dense segmented image provided by the DeepLabV3-Refiner model. The 3D point clouds that were analyzed by the DeepLabV3-Refiner model had a 4-fold increase in density, which was verified experimentally. The proposed method had a 0.6% increase in density accuracy compared to that of DeepLabV3, and a 2.8-fold increase in the density corresponding to the human object. The proposed method was able to provide a 3D point cloud that increased the density to fit the human object. The proposed method can be used to provide an accurate 3D virtual environment by using the improved 3D point clouds.

关键词： dynamic object deep learning encoder-decoder model 3D point cloud LiDAR data fusion dense semantic segmentation

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