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A Video Captioning Method by Semantic Topic-Guided Generation

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Computers, Materials & Continua 2024年第1期78卷 1071-1093页

作者： Ou Ye Xinli Wei Zhenhua Yu Yan Fu Ying Yang College of Computer Science and Technology Xi’an University of Science and TechnologyXi’an710054China

In the video captioning methods based on an encoder-decoder,limited visual features are extracted by an encoder,and a natural sentence of the video content is generated using a decoder.However,this kind ofmethod is dependent on a single video input source and few visual labels,and there is a problem with semantic alignment between video contents and generated natural sentences,which are not suitable for accurately comprehending and describing the video contents.To address this issue,this paper proposes a video captioning method by semantic topic-guided generation.First,a 3D convolutional neural network is utilized to extract the spatiotemporal features of videos during the encoding.Then,the semantic topics of video data are extracted using the visual labels retrieved from similar video data.In the decoding,a decoder is constructed by combining a novel Enhance-TopK sampling algorithm with a Generative Pre-trained Transformer-2 deep neural network,which decreases the influence of“deviation”in the semantic mapping process between videos and texts by jointly decoding a baseline and semantic topics of video contents.During this process,the designed Enhance-TopK sampling algorithm can alleviate a long-tail problem by dynamically adjusting the probability distribution of the predicted words.Finally,the experiments are conducted on two publicly used Microsoft Research Video Description andMicrosoft Research-Video to Text datasets.The experimental results demonstrate that the proposed method outperforms several state-of-art approaches.Specifically,the performance indicators Bilingual Evaluation Understudy,Metric for Evaluation of Translation with Explicit Ordering,Recall Oriented Understudy for Gisting Evaluation-longest common subsequence,and Consensus-based Image Description Evaluation of the proposed method are improved by 1.2%,0.1%,0.3%,and 2.4% on the Microsoft Research Video Description dataset,and 0.1%,1.0%,0.1%,and 2.8% on the Microsoft Research-Video to Text dataset,respectively,compared with the existing video c

关键词： Video captioning encoder-decoder semantic topic jointly decoding Enhance-TopK sampling

Longs short-term based semi-supervised memory encoder-decoder for early prediction of failures in self-lubricating bearings

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Friction 2023年第1期11卷 109-124页

作者： Vigneashwara PANDIYAN Mehdi AKEDDAR Josef PROST Georg VORLAUFER Markus VARGA Kilian WASMER Laboratory for Advanced Materials Processing(LAMP) Swiss Federal Laboratories for Materials Science and Technology(Empa)Thun CH-3602Switzerland AC2T research GmbH Wiener Neustadt 2700Austria

The existing knowledge regarding the interfacial forces,lubrication,and wear of bearings in real-world operation has significantly improved their designs over time,allowing for prolonged service life.As a result,self-lubricating bearings have become a viable alternative to traditional bearing designs in industrial machines.However,wear mechanisms are still inevitable and occur progressively in self-lubricating bearings,as characterized by the loss of the lubrication film and seizure.Therefore,monitoring the stages of the wear states in these components will help to impart the necessary countermeasures to reduce the machine maintenance downtime.This article proposes a methodology for using a long short-term memory(LSTM)-based encoder-decoder architecture on interfacial force signatures to detect abnormal regimes,aiming to provide early predictions of failure in self-lubricating sliding contacts even before they occur.Reciprocating sliding experiments were performed using a self-lubricating bronze bushing and steel shaft journal in a custom-built transversally oscillating tribometer setup.The force signatures corresponding to each cycle of the reciprocating sliding motion in the normal regime were used as inputs to train the encoder-decoder architecture,so as to reconstruct any new signal of the normal regime with the minimum error.With this semi-supervised training exercise,the force signatures corresponding to the abnormal regime could be differentiated from the normal regime,as their reconstruction errors would be very high.During the validation procedure for the proposed LSTM-based encoder-decoder model,the model predicted the force signals corresponding to the normal and abnormal regimes with an accuracy of 97%.In addition,a visualization of the reconstruction error across the entire force signature showed noticeable patterns in the reconstruction error when temporally decoded before the actual critical failure point,making it possible to be used for early predictions of failure.

关键词： predictive maintenance in-situ sensing long short-term memory(LSTM) encoder-decoder wear monitoring tribology

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

Dynamic energy system modeling using hybrid physics-based and machinelearning encoder–decoder models

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能源与人工智能(英文)

能源与人工智能(英文) 2022年第3期9卷 128-138页

作者： Derek Machalek Jake Tuttle Klas Andersson Kody M.Powell Department of Chemical Engineering University of UtahSalt Lake CityUTUnited States of America Taber International LLCUnited States of America Department of Space Earthand EnvironmentUniversity of ChalmersGothenburgSweden Department of Mechanical Engineering University of UtahSalt Lake CityUTUnited States of America

Three model configurations are presented for multi-step time series predictions of the heat absorbed by thewater and steam in a thermal power plant. The models predict over horizons of 2, 4, and 6 steps into thefuture, where each step is a 5-minute increment. The evaluated models are a pure machine learning model, anovel hybrid machine learning and physics-based model, and the hybrid model with an incomplete dataset. Thehybrid model deconstructs the machine learning into individual boiler heat absorption units: economizer, waterwall, superheater, and reheater. Each configuration uses a gated recurrent unit (GRU) or a GRU-based encoder–decoder as the deep learning architecture. Mean squared error is used to evaluate the models compared totarget values. The encoder–decoder architecture is over 11% more accurate than the GRU only models. Thehybrid model with the incomplete dataset highlights the importance of the manipulated variables to the system.The hybrid model, compared to the pure machine learning model, is over 10% more accurate on averageover 20 iterations of each model. Automatic differentiation is applied to the hybrid model to perform a localsensitivity analysis to identify the most impactful of the 72 manipulated variables on the heat absorbed in theboiler. The models and sensitivity analyses are used in a discussion about optimizing the thermal power plant.

关键词： Hybrid model encoder-decoder Time series Automatic differentiation Thermal power plant

Robust encoder–decoder learning framework for offline handwritten mathematical expression recognition based on a multi-scale deep neural network

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Science China(Information Sciences) 2021年第3期64卷 220-222页

作者： Guangcun SHAN Hongyu WANG Wei LIANG Kai CHEN School of Instrumentation Science and Optoelectronics Engineering Beihang University Institute of Electronics Chinese Academy of Sciences School of Electronic Electrical and Communication EngineeringUniversity of Chinese Academy of Sciences Boheng Technology (Hangzhou) Co.Ltd

Dear editor,Mathematical expressions have been widely employed in scientific research, finance, and statistics, and play a significant role in educational activities. For example, if a computer can recognize teachers’ handwritten expressions as standard printed mathematical expressions, this will undoubtedly be more conducive and helpful for improving the effectiveness of lectures. Thus, the question of how to make computers automatically recognize mathematical expressions is highly significant.

关键词： encoder-decoder Multi-Scale Attention Handwritten mathematical expression recognition Attention recurrent neural network Dense Network

基于encoder-decoder框架的城镇污水厂出水水质预测

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中国农村水利水电 2023年第11期 93-99页

作者：史红伟陈祺王云龙李鹏程长春理工大学电子信息工程学院吉林长春130022 长春水务投资发展集团有限公司吉林长春130022

由于污水厂的出水水质指标繁多、污水处理过程中反应复杂、时序非线性程度高,基于机理模型的预测方法无法取得理想效果。针对此问题,提出基于深度学习的污水厂出水水质预测方法,并以吉林省某污水厂监测水质为来源数据,利用多种结合encod... 详细信息

由于污水厂的出水水质指标繁多、污水处理过程中反应复杂、时序非线性程度高,基于机理模型的预测方法无法取得理想效果。针对此问题,提出基于深度学习的污水厂出水水质预测方法,并以吉林省某污水厂监测水质为来源数据,利用多种结合encoder-decoder结构的神经网络预测水质。结果显示,所提结构对LSTM和GRU网络预测能力都有一定提升,对长期预测能力提升更加显著,ED-GRU模型效果最佳,短期预测中的4个出水水质指标均方根误差(RMSE)为0.7551、0.2197、0.0734、0.3146,拟合优度(R2)为0.9013、0.9332、0.9167、0.9532,可以预测出水质局部变化,而长期预测中的4个指标RMSE为1.7204、1.7689、0.4478、0.8316,R2为0.4849、0.5507、0.4502、0.7595,可以预测出水质变化趋势,与顺序结构相比,短期预测RMSE降低10%以上,R2增加2%以上,长期预测RMSE降低25%以上,R2增加15%以上。研究结果表明,基于encoder-decoder结构的神经网络可以对污水厂出水水质进行准确预测,为污水处理工艺改进提供技术支撑。

关键词：污水厂出水 encoder-decoder 多指标水质预测 GRU模型

基于SBAS-InSAR与神经网络-数据同化机场跑道沉降预测研究

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基于SBAS-InSAR与神经网络-数据同化机场跑道沉降预测研究

作者：王志鹏中国民用航空飞行学院

学位级别：硕士

机场跑道沉降将增加飞机起降风险,使飞机在该过程中遭受震动和冲击,进而导致飞机机身受损,甚至是机毁人亡。尽管既有激光测量技术、GPS监测技术、地面震动测试技术、分布式光纤技术等跑道沉降监测预警技术具有高精度优势,但不是存在成... 详细信息

机场跑道沉降将增加飞机起降风险,使飞机在该过程中遭受震动和冲击,进而导致飞机机身受损,甚至是机毁人亡。尽管既有激光测量技术、GPS监测技术、地面震动测试技术、分布式光纤技术等跑道沉降监测预警技术具有高精度优势,但不是存在成本高就是存在干扰跑道正常运行等问题,因而如何建立低成本、非干扰式的跑道沉降监测预警模型,是当前研究的一个热点。针对该问题,本文提出了一种基于SBAS-In SAR和神经网络-数据同化的机场跑道沉降预测模型。(1)通过SBAS-In SAR技术克服现有沉降监测方法检测范围受监测点影响的缺点,充分利用In SAR技术的监测全面性,为沉降预测提供准确的基础数据。(2)通过三次样条插值方法对SBAS-In SAR获取的沉降时间序列数据进行插值处理,符合实际沉降趋势的同时扩充数据数量,为沉降预测模型提供多量数据。(3)构建LSTM和BP人工神经网络数据驱动模型,实现自主学习数据特征,提高预测模型的泛化能力。(4)引入En KF数据同化方法,构建En KF-LSTM和En KF-BP人工神经网络数据驱动模型。通过En KF代替LSTM和BP模型中使用梯度反向传播的优化方法,避免模型梯度优化产生的梯度爆炸或梯度消失问题,融入观测值实现模型的可持续性学习功能。(5)构建En KF-LSTM-ED模型,通过实现encoder-decoder结构,预测方法自循环预测更改为序列到序列的预测,提高En KF-LSTM模型针对时序数据中短期预测能力。结果表明:本文提出模型具有一定的预测准确性。本模型中,SBAS-In SAR技术获取了康定机场和双流机场跑道沉降时序数据,分析主要沉降区域和数据变化趋势,设计实验案例对比说明,En KF-LSTM模型和En KF-BP模型较未优化模型的预测准确性提升约12%,并且En KF-LSTM模型较En KF-BP模型具有更强的预测能力;序列到序列的En KF-LSTM-ED预测方法较循环预测方法可信预测结果提高到30天。

关键词：机场跑道安全沉降预测 SBAS-InSAR 数据同化神经网络 encoder-decoder

基于encoder-decoder-ILSTM模型的瓦斯浓度预测研究

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能源与节能 2023年第12期 102-105,176页

作者：陈小建山西新景矿煤业有限公司山西阳泉045000

近年来,神经网络在各领域均发挥了巨大作用,同样在煤矿瓦斯浓度预测当中也有应用。为了提高模型的预测精度和实时性,结合encoder-decoder结构、长短期记忆形成、蛇优化算法提出了一种新的神经网络,为促进煤矿安全生产提供了技术支持。

关键词：神经网络 encoder-decoder 蛇优化算法瓦斯浓度预测

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