检索结果-内蒙古大学图书馆

A semi-supervised deep learning model for ship encounter situation classification

OCEAN ENGINEERING 2021年 239卷 109824-109824页

作者： Chen, Xiang Liu, Yuanchang Achuthan, Kamalasudhan Zhang, Xinyu Chen, Jinhai UCL Dept Civil Environm & Geomat Engn Chadwick Bldg London WC1E 6BT England UCL Dept Mech Engn Torrington Pl London WC1E 7JE England Dalian Maritime Univ Key Lab Maritime Dynam Simulat & Control Minist Transportat Dalian 116026 Peoples R China Jimei Univ Nav Coll Nationallocal Joint Engn Res Ctr Marine Nav Aids Serv Xiamen 361021 Peoples R China

Maritime safety is an important issue for global shipping industries. Currently, most of collision accidents at sea are caused by the misjudgement of the ship's operators. The deployment of maritime autonomous surface ships (MASS) can greatly reduce ships' reliance on human operators by using an automated intelligent collision avoidance system to replace human decision-making. To successfully develop such a system, the capability of autonomously identifying other ships and evaluating their associated encountering situation is of paramount importance. In this paper, we aim to identify ships' encounter situation modes using deep learning methods based upon the Automatic Identification System (AIS) data. First, a segmentation process is developed to divide each ship's AIS data into different segments that contain only one encounter situation mode. This is different to the majority of studies that have proposed encounter situation mode classification using hand-crafted features, which may not reflect the actual ship's movement states. Furthermore, a number of present classification tasks are conducted using substantial labelled AIS data followed by a supervised training paradigm, which is not applicable to our dataset as it contains a large number of unlabelled AIS data. Therefore, a method called Semi Supervised Convolutional encoder-decoder Network (SCEDN) for ship encounter situation classification based on AIS data is proposed. The structure of the network is not only able to automatically extract features from AIS segments but also share training parameters for the unlabelled data. The SCEDN uses an encoder-decoder convolutional structure with four channels for each segment (distance, speed, Time to the Closed Point of Approach (TCPA) and Distance to the Closed Point of Approach (DCPA)) been developed. The performance of the SCEDN model are evaluated by comparing to several baselines with the experimental results demonstrating a higher accuracy can be achieved by o

关键词： Automatic Identification System (AIS) Semi-supervised learning Deep learning Convolutional neural network encoder-decoder Trajectory data Encounter situation classification

来源：评论

学校读者我要写书评

暂无评论

Stroke constrained attention network for online handwritten mathematical expression recognition

引用

PATTERN RECOGNITION 2021年 119卷 108047-108047页

作者： Wang, Jiaming Du, Jun Zhang, Jianshu Wang, Bin Ren, Bo Univ Sci & Technol China Natl Engn Lab Speech & Language Informat Proc Hefei Anhui Peoples R China Tencent Youtu Lab Shenzhen Peoples R China

In this paper, we propose a novel stroke constrained attention network (SCAN) which treats stroke as the basic unit for encoder-decoder based online handwritten mathematical expression recognition (HMER). Unlike previous methods which use trace points or image pixels as basic units, SCAN makes full use of stroke-level information for better alignment and representation. The proposed SCAN can be adopted in both single-modal (online or offline) and multi-modal HMER. For single-modal HMER, SCAN first employs a CNN-GRU encoder to extract point-level features from input traces in online mode and employs a CNN encoder to extract pixel-level features from input images in offline mode, then use stroke constrained information to convert them into online and offline stroke-level features. Using stroke-level features can explicitly group points or pixels belonging to the same stroke, therefore reduces the difficulty of symbol segmentation and recognition via the decoder with attention mechanism. For multi-modal HMER, other than fusing multi-modal information in decoder, SCAN can also fuse multi-modal information in encoder by utilizing the stroke based alignments between online and offline modalities. The encoder fusion is a better way for combining multi-modal information as it implements the information interaction one step before the decoder fusion so that the advantages of multiple modalities can be exploited earlier and more adequately. Besides, we propose an approach combining the encoder fusion and decoder fusion, namely encoder-decoder fusion, which can further improve the performance. Evaluated on a benchmark published by CROHME competition, the proposed SCAN achieves the state-of-the-art performance. Furthermore, by conducting experiments on an additional task: online handwritten Chinese character recognition (HCCR), we demonstrate the generality of our proposed method. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Stroke-level information Multi-modal fusion encoder-decoder Attention mechanism Handwritten mathematical expression recognition

来源：评论

学校读者我要写书评

暂无评论

FF-GAN: Feature Fusion GAN for Monocular Depth Estimation 3rd

FF-GAN: Feature Fusion GAN for Monocular Depth Estimation

引用

3rd Chinese Conference on Pattern Recognition and Computer Vision (PRCV)

作者： Jia, Ruiming Li, Tong Yuan, Fei North China Univ Technol Sch Informat Sci & Technol Beijing Peoples R China Chinese Acad Sci Inst Automat Digital Content Technol & Media Serv Res Ctr Beijing Peoples R China

ISBN: (纸本)9783030606329;9783030606336

Since the results of CNN methods for monocular depth estimation generally suffer the problem of visual dissatisfaction, we propose Feature Fusion GAN (FF-GAN) to address this issue. First, an end-to-end network based on encoder-decoder structure is proposed as the generator of FF-GAN, which can exploit the information of different scales. The encoder of our generator fuse features in different levels with a feature fusion module. The component which can obtain the information of multi-scale receptive field is the main part of the decoder of our generator. Second, in order to match the generator, the discriminator of FF-GANis designed to efficiently learn the information of different scales by applying pyramid structure. Experiments on public datasets demonstrate the effectiveness of our generator and discriminator. Compared with the CNN methods, the results predicted by FF-GAN are significantly improved in terms of texture loss and edge blur while ensuring accuracy, and the visual effect is better.

关键词： Conditional Generative Adversarial Network encoder-decoder Monocular depth estimation Receptive field

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Text Classification and Language Generation Model for Automated Summarization of Dutch Breast Cancer Radiology Reports 2

A Hybrid Text Classification and Language Generation Model f...

引用

2nd IEEE International Conference on Cognitive Machine Intelligence (IEEE CogMI)

作者： Nguyen, Elisa Theodorakopoulos, Daphne Pathak, Shreyasi Geerdink, Jeroen Vijlbrief, Onno van Keulen, Maurice Seifert, Christin Univ Twente Fac Elect Engn Math & Comp Sci Enschede Netherlands Hosp Grp Twente Hengelo Netherlands Univ Duisburg Essen Fac Med Essen Germany

ISBN: (纸本)9781728141442

Breast cancer diagnosis is based on radiology reports describing observations made from medical imagery, such as X-rays obtained during mammography. The reports are written by radiologists and contain a conclusion summarizing the observations. Manually summarizing the reports is time-consuming and leads to high text variability. This paper investigates the automated summarization of Dutch radiology reports. We propose a hybrid model consisting of a language model (encoder-decoder with attention) and a separate BI-RADS score classifier. The summarization model achieved a ROUGE-L F1 score of 51.5% on the Dutch reports, which is comparable to results in other languages and other domains. For the BI-RADS classification, the language model (accuracy 79.1%) was outperformed by a separate classifier (accuracy 83.3%), leading us to propose a hybrid approach for radiology report summarization. Our qualitative evaluation with experts found the generated conclusions to be comprehensible and to cover mostly relevant content, and the main focus for improvement should be their factual correctness. While the current model is not accurate enough to be employed in clinical practice, our results indicate that hybrid models might be a worthwhile direction for future research.

关键词： Abstractive Summarization Radiology Reports Breast Cancer Deep Learning encoder-decoder Attention Mechanism

来源：评论

学校读者我要写书评

暂无评论

Image captioning in Hindi language using transformer networks

引用

COMPUTERS & ELECTRICAL ENGINEERING 2021年 92卷 107114-107114页

作者： Mishra, Santosh Kumar Dhir, Rijul Saha, Sriparna Bhattacharyya, Pushpak Singh, Amit Kumar Indian Inst Technol Patna Dept Comp Sci & Engn Patna Bihar India Natl Inst Technol Patna Dept Comp Sci & Engn Patna Bihar India

Neural encoder-decoder architectures have been used extensively for image captioning. Con-volutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) are popularly used in encoder and decoder models. Recurrent Neural Networks are popular architectures in natural language processing used for language modeling, but they are sequential in nature. The transformer model can solve this problem of sequential dependency by using an attention mechanism. Many works are available for image captioning in the English language, but models for generating Hindi captions are limited;hence, we have tried to fill this gap. We have created the Hindi dataset for image captioning by manually translating the popular MSCOCO dataset from English to Hindi. Experimental results show that our proposed model outperforms other models. The proposed model has attained the BLEU-1 score of 62.9, BLEU-2 score of 43.3, BLEU-3 score of 29.1, and BLEU4 score of 19.0.

关键词： Image captioning Transformer model Deep learning encoder-decoder Hindi language

来源：评论

学校读者我要写书评

暂无评论

MH UNet: A Multi-Scale Hierarchical Based Architecture for Medical Image Segmentation

引用

IEEE ACCESS 2021年 9卷 148384-148408页

作者： Ahmad, Parvez Jin, Hai Alroobaea, Roobaea Qamar, Saqib Zheng, Ran Alnajjar, Fady Aboudi, Fathia Huazhong Univ Sci & Technol Natl Engn Res Ctr Big Data Technol & Syst Sch Comp Sci & Technol Serv Comp Technol & Syst LabCluster & Grid Comp Wuhan 430074 Peoples R China Taif Univ Coll Comp & Informat Technol Dept Comp Sci At Taif 21944 Saudi Arabia Madanapalle Inst Technol & Sci Dept Comp Applicat Madanapalle 517325 Andhra Pradesh India United Arab Emirates Univ Coll Informat Technol Al Ain U Arab Emirates Univ Tunis El Manar High Inst Med Technol Tunisia ISTMT LRBTM Res Lab Biophys & Med Technol Tunis 1006 Tunisia

UNet and its variations achieve state-of-the-art performances in medical image segmentation. In end-to-end learning, the training with high-resolution medical images achieves higher accuracy for medical image segmentation. However, the network depth, a massive number of parameters, and low receptive fields are issues in developing deep architecture. Moreover, the lack of multi-scale contextual information degrades the segmentation performance due to the different sizes and shapes of regions of interest. The extraction and aggregation of multi-scale features play an important role in improving medical image segmentation performance. This paper introduces the MH UNet, a multi-scale hierarchical-based architecture for medical image segmentation that addresses the challenges of heterogeneous organ segmentation. To reduce the training parameters and increase efficient gradient flow, we implement densely connected blocks. Residual-Inception blocks are used to obtain full contextual information. A hierarchical block is introduced between the encoder-decoder for acquiring and merging features to extract multi-scale information in the proposed architecture. We implement and validate our proposed architecture on four challenging MICCAI datasets. Our proposed approach achieves state-of-the-art performance on the BraTS 2018, 2019, and 2020 Magnetic Resonance Imaging (MRI) validation datasets. Our approach is 14.05 times lighter than the best method of BraTS 2018. In the meantime, our proposed approach has 2.2 times fewer training parameters than the top 3D approach on the ISLES 2018 Computed Tomographic Perfusion (CTP) testing dataset. MH UNet is available at https://***/parvezamu/MHUnet.

关键词： Image segmentation Biomedical imaging Feature extraction Training Decoding Three-dimensional displays Convolutional neural networks BraTS convolutions dense connections encoder-decoder ISLES MICCAI UNet

来源：评论

学校读者我要写书评

暂无评论

Generating Instructive Questions from Multiple Articles to Guide Reading in E-Bibliotherapy

引用

SENSORS 2021年第9期21卷 3223页

作者： Xin, Yunxing Cao, Lei Wang, Xin He, Xiaohao Feng, Ling Tsinghua Univ Res Inst Data Sci Dept Comp Sci & Technol Ctr Computat Mental Healthcare Beijing 100084 Peoples R China

E-Bibliotherapy deals with adolescent psychological stress by manually or automatically recommending multiple reading articles around their stressful events, using electronic devices as a medium. To make E-Bibliotherapy really useful, generating instructive questions before their reading is an important step. Such a question shall (a) attract teens' attention;(b) convey the essential message of the reading materials so as to improve teens' active comprehension;and most importantly (c) highlight teens' stress to enable them to generate emotional resonance and thus willingness to pursue the reading. Therefore in this paper, we propose to generate instructive questions from the multiple recommended articles to guide teens to read. Four solutions based on the neural encoder-decoder model are presented to tackle the task. For model training and testing, we construct a novel large-scale QA dataset named TeenQA, which is specific to adolescent stress. Due to the extensibility of question expressions, we incorporate three groups of automatic evaluation metrics as well as one group of human evaluation metrics to examine the quality of the generated questions. The experimental results show that the proposed encoder-decoder with Summary on Contexts with Feature-rich embeddings (ED-SoCF) solution can generate good questions for guiding reading, achieving comparable performance on some semantic similarity metrics with that of humans.

关键词： E-bibliotherapy instructive question reading guidance encoder-decoder dataset

来源：评论

学校读者我要写书评

暂无评论

Getting More Data for Low-resource Morphological Inflection: Language Models and Data Augmentation 12

Getting More Data for Low-resource Morphological Inflection:...

引用

12th International Conference on Language Resources and Evaluation (LREC)

作者： Sorokin, Alexey Moscow MV Lomonosov State Univ Moscow Inst Phys & Technol Fac Math & Mech Leninskie GoryGSP 1 Moscow Russia

ISBN: (纸本)9791095546344

We investigate the effect of data augmentation on low-resource morphological segmentation. We compare two settings: the pure low-resource one, when only 100 annotated word forms are available, and the augmented one, where we use the original training set and 1000 unlabeled word forms to generate 1000 artificial inflected forms. Evaluating on Sigmorphon 2018 dataset, we observe that using the best among these two models reduces the error rate of state-of-the-art model by 6%, while for our baseline model the error reduction is 17%

关键词： inflection encoder-decoder abstract paradigms language models data augmentation

来源：评论

学校读者我要写书评

暂无评论

Fast and Slow Acoustic Model 21

Fast and Slow Acoustic Model

引用

Interspeech Conference

作者： Kumar, Kshitiz Stoimenov, Emilian Khalil, Hosam Wu, Jian Microsoft Corp Redmond WA 98052 USA

ISBN: (纸本)9781713820697

In this work we layout a Fast & Slow (F&S) acoustic model (AM) in an encoder-decoder architecture for streaming automatic speech recognition (ASR). The Slow model represents our baseline ASR model;it's significantly larger than Fast model and provides stronger accuracy. The Fast model is generally developed for related speech applications. It has weaker ASR accuracy but is faster to evaluate and consequently leads to better user-perceived latency. We propose a joint F&S model that encodes output state information from Fast model, feeds that to Slow model to improve overall model accuracy from F&S AM. We demonstrate scenarios where individual Fast and Slow models are already available to build the joint F&S model. We apply our work on a large vocabulary ASR task. Compared to Slow AM, our Fast AM is 3-4x smaller and 11.5% relatively weaker in ASR accuracy. The proposed F&S AM achieves 4.7% relative gain over the Slow AM. We also report a progression of techniques and improve the relative gain to 8.1% by encoding additional Fast AM outputs. Our proposed framework has generic attributes - we demonstrate a specific extension by encoding two Slow models to achieve 12.2% relative gain.

关键词： ASR LSTM LC-BLSTM encoder-decoder Model Combination

来源：评论

学校读者我要写书评

暂无评论

Forecasting Photovoltaic Power Production using a Deep Learning Sequence to Sequence Model with Attention

Forecasting Photovoltaic Power Production using a Deep Learn...

引用

International Joint Conference on Neural Networks (IJCNN) held as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Kharlova, Elizaveta May, Daniel Musilek, Petr Univ Alberta Elect & Comp Engn Edmonton AB Canada

ISBN: (纸本)9781728169262

Rising penetration levels of (residential) photovoltaic (PV) power as distributed energy resource pose a number of challenges to the electricity infrastructure. High quality, general tools to provide accurate forecasts of power production are urgently needed. In this article, we propose a supervised deep learning model for end-to-end forecasting of PV power production. The proposed model is based on two seminal concepts that led to significant performance improvements of deep learning approaches in other sequence-related fields, but not yet in the area of time series prediction: the sequence to sequence architecture and attention mechanism as a context generator. The proposed model leverages numerical weather predictions and high-resolution historical measurements to forecast a binned probability distribution over the prognostic time intervals, rather than the expected values of the prognostic variable. This design offers significant performance improvements compared to common baseline approaches, such as fully connected neural networks and one-block long short-term memory architectures. Using normalized root mean square error based forecast skill score as a performance indicator, the proposed approach is compared to other models. The results show that the new design performs at or above the current state of the art of PV power forecasting.

关键词： photovoltaic power PV forecasting probabilistic forecasting time-series deep learning sequence to sequence attention encoder-decoder

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：