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An approach to forecasting and filtering noise in dynamic systems using LSTM architectures

NEUROCOMPUTING 2022年第0期500卷 637-648页

作者： Cana, Juan Pedro Llerena Herrero, Jesus Garcia Lopez, Jose Manuel Molina Univ III Madrid Appl Artificial Intelligence Grp GIAA Madrid Spain

Some of the limitations of state-space models are given by the difficulty of modeling certain systems, the filters convergence time, or the impossibility of modeling dependencies in the long term. Having agile and alternative methodologies that allow the modeling of complex problems but still provide solutions to the classic challenges of estimation or filtering, such as the position estimation of a mobile with noisy measurements and unknown motion models, are of high interest. In this work, we address the problem of position estimation of 1-D dynamic systems from a deep learning paradigm, using Long-Short Term Memory (LSTM) architectures designed to solve problems with long term temporal dependencies, in combination with other recurrent networks. A deep neuronal architecture inspired by the encoder decoder language systems is implemented, remarking its limits and finding a solution capable of making predictions of high accuracy with models learnt from training data of a moving object. We use a panel data model for training and validation. In the experimentation, we use sliding overlapping time windows in a recursive and standardized way to avoid the saturation problem of the networks in increasing trend estimates. The results are finally compared with the optimal values from the Kalman filter, obtaining comparable results in error terms. These results show the proposed system has great potential for target tracking.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Deep learning Filtering Forecasting LSTM encoder-decoder Attention

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Data-Driven Voltage Prognostic for Solid Oxide Fuel Cell System Based on Deep Learning

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ENERGIES 2022年第17期15卷 6294页

作者： Li, Mingfei Wu, Jiajian Chen, Zhengpeng Dong, Jiangbo Peng, Zhiping Xiong, Kai Rao, Mumin Chen, Chuangting Li, Xi Sci & Technol Res Inst Co Ltd Guangdong Energy Grp Guangzhou 511466 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Imaging Proc & Intelligent Control Educ Minist Wuhan 430074 Peoples R China Guangdong Huizhou Lng Power Co Ltd Huizhou 516081 Peoples R China Guangdong Energy Grp Co Ltd Guangzhou 510630 Peoples R China Shenzhen Huazhong Univ Sci & Technol Res Inst Shenzhen 518055 Peoples R China

A solid oxide fuel cell (SOFC) is an innovative power generation system that is green, efficient, and promising for a wide range of applications. The prediction and evaluation of the operation state of a solid oxide fuel cell system is of great significance for the stable and long-term operation of the power generation system. Prognostics and Health Management (PHM) technology is widely used to perform preventive and predictive maintenance on equipment. Unlike prediction based on the SOFC mechanistic model, the combination of PHM and deep learning has shown wide application prospects. Therefore, this study first obtains an experimental dataset through short-term degradation experiments of a 1 kW SOFC system, and then proposes an encoder-decoder RNN-based SOFC state prediction model. Based on the experimental dataset, the model can accurately predict the voltage variation of the SOFC system. The prediction results of the four different prediction models developed are compared and analyzed, namely, long short-term memory (LSTM), gated recurrent unit (GRU), encoder-decoder LSTM, and encoder-decoder GRU. The results show that for the SOFC test set, the mean square error of encoder-decoder LSTM and encoder-decoder GRU are 0.015121 and 0.014966, respectively, whereas the corresponding error results of LSTM and GRU are 0.017050 and 0.017456, respectively. The encoder-decoder RNN model displays high prediction precision, which proves that it can improve the accuracy of prediction, which is expected to be combined with control strategies and further help the implementation of PHM in fuel cells.

关键词： solid oxide fuel cell recurrent neural network long short-term memory gated recurrent unit encoder-decoder state prediction

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Research on Image Caption Model Based on Improved Attention Mechanism 9

Research on Image Caption Model Based on Improved Attention ...

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9th International Conference on Intelligent Computing and Signal Processing, ICSP 2024

作者： Zhang, Kun'ao Sun, Jinghua School of Computer Science and Technology Xi'an University of Science and Technology Shaanxi Xi'an710054 China

ISBN: (纸本)9798350376548

The basic explanation of the image caption task is that the sentences generated by the model should comprehensively express the content of an image. Existing image caption models face issues such as inadequate utilization of image features and insufficient correlation between extracted image features and semantic information. To address these challenges, a novel image caption model based on asymmetric convolution attention mechanism is proposed. The improved attention mechanism integrates the concept of asymmetric convolution to better utilize the extracted image features. Experimental results on the Flickr8k and Flickr30k image caption datasets demonstrate a significant enhancement in the generated caption sentences based on the asymmetric attention mechanism, as indicated by the improvement in relevant evaluation metrics BLEU2-BLEU4. © 2024 IEEE.

关键词： attention mechanism convolutional neural network encoder-decoder image caption natural language processing

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AI-assisted Silhouettes Generation from Sparse mmWave Sampling

AI-assisted Silhouettes Generation from Sparse mmWave Sampli...

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2024 International Conference on Advancements in Power, Communication and Intelligent Systems, APCI 2024

作者： Mohamadi, Fred Tialinx Inc. Irvine United States

ISBN: (纸本)9798350363289

This paper introduces a novel approach leveraging the U-Net algorithm to generate silhouette images of objects using sparse data generated by computers or obtained from mmWave radar. Through this method, exceptional replication of previously unseen images is achieved. The implementation of this algorithm holds significant potential for applications such as collision avoidance on roads or runway incursion. By harnessing sparse data and the power of deep learning, this approach offers promising prospects for enhancing safety measures in various domains. © 2024 IEEE.

关键词： Deep Neural Network encoder-decoder mmWave Object Detection Obscured Environment

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Morphologically Motivated Input Variations and Data Augmentation in Turkish-English Neural Machine Translation

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ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING 2023年第3期22卷 1-31页

作者： Yirmibesoglu, Zeynep Gungor, Tunga Bogazici Univ Comp Engn Istanbul Turkiye

Success of neural networks in natural language processing has paved the way for neural machine translation (NMT), which rapidly became the mainstream approach in machine translation. Significant improvement in translation performance has been achieved with breakthroughs such as encoder-decoder networks, attention mechanism, and Transformer architecture. However, the necessity of large amounts of parallel data for training an NMT system and rare words in translation corpora are issues yet to be overcome. In this article, we approach NMT of the low-resource Turkish-English language pair. We employ state-of-the-art NMT architectures and data augmentationmethods that exploit monolingual corpora. We point out the importance of input representation for the morphologically rich Turkish language and make a comprehensive analysis of linguistically and non-linguistically motivated input segmentation approaches. We prove the effectiveness of morphologically motivated input segmentation for the Turkish language. Moreover, we show the superiority of the Transformer architecture over attentional encoder-decoder models for the Turkish-English language pair. Among the employed data augmentation approaches, we observe back-translation to be the most effective and confirm the benefit of increasing the amount of parallel data on translation quality. This research demonstrates a comprehensive analysis on NMT architectures with different hyperparameters, data augmentation methods, and input representation techniques, and proposes ways of tackling the low-resource setting of Turkish-English NMT.

关键词： Neural machine translation morphology low-resource Transformer encoder-decoder attention data augmentation word segmentation

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Zero-Shot Learning via Discriminative Dual Semantic Auto-encoder

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IEEE ACCESS 2021年 9卷 733-742页

作者： Xing, Nan Liu, Yang Zhu, Hong Wang, Jing Han, Jungong Xian Univ Technol Sch Automat & Informat Engn Xian 710048 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Xian Univ Technol Fac Printing Packaging Engn & Digital Media Techn Xian 710048 Peoples R China Aberystwyth Univ Comp Sci Dept Aberystwyth SY23 3FL Dyfed Wales

Zero-shot learning (ZSL) is an effective method to perform the recognition task without any training samples of specific classes. Most existing ZSL models put emphasis on learning an embedding between visual space and semantic space directly. However, few ZSL models research whether the human-designed semantic features are discriminative enough to recognize different classes. Moreover, one-way mapping suffers from the project domain shift problem. In this article, we propose to learn a Discriminative Dual Semantic Auto-encoder (DDSA) based on the encoder-decoder paradigm to solve this problem. DDSA attempts to construct two bidirectional embeddings to connect the visual space and the semantic space with the help of the learned aligned space which includes discriminative information of the visual features and semantic features. Based on the DDSA, we additionally propose a Deep DDSA to capture deep aligned features that are more conducive to zero-shot classification. The key to the proposed framework is that it implicitly exact the principal information from visual space and semantic space to construct aligned features, which is not only semantic-preserving but also discriminative. Extensive experiments on five benchmarks (SUN, CUB, AWA1, AWA2 and aPY) demonstrate the effectiveness of the proposed framework with state-of-the-art performance obtained on both conventional ZSL and generalized ZSL settings.

关键词： Semantics Visualization Training Task analysis Licenses Supervised learning Neural networks Zero-shot learning discriminative encoder-decoder aligned

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A multi-mode traffic flow prediction method with clustering based attention convolution LSTM

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APPLIED INTELLIGENCE 2022年第13期52卷 14773-14786页

作者： Huang, Xiaohui Ye, Yuming Wang, Cheng Yang, Xiaofei Xiong, Liyan East China Jiaotong Univ Sch Informat Engn Dept Nanchang 330013 Jiangxi Peoples R China Univ Macau Fac Sci & Technol E11 Macau 999078 Peoples R China

Increasing traffic congestion is a major obstacle to the development of cities. The prediction of traffic flow is very important to city planning and dredging. A good model of flow is able to accurately predict future flow by learning historical flow data. Traffic flow is usually affected by macro and micro factors. At the macro level, the whole city can be divided into different subregions according to the similarity in the traffic flow patterns. At the micro-level, there is a temporal and spatial correlation between the traffic flow of different road sections at di fferent times. In this paper, we propose a multi-mode traffic flow prediction method with Clustering based Attention Convolution LSTM (CACLSTM) to model spatial-temporal data of traffic flow. The framework includes three modules: a convolution LSTM encoding-decoding layer which is used to predict the traffic flow of the next time slice by encoding the historical traffic information, a clustering based attention layer which is able to extract different temporal features by clustering based attention, and an additional factors layer which can integrate weather, wind speed, holidays and other factors to improve the prediction accuracy. The experimental results on Beijing taxis data show that the CACLSTM method performs more effective than the six well-known compared methods.

关键词： Multi-mode encoder-decoder Attention mechanism Traffic flow prediction Spatial-temporal data

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Exploring Data and Models in SAR Ship Image Captioning

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IEEE ACCESS 2022年 10卷 91150-91159页

作者： Zhao, Kai Xiong, Wei Space Engn Univ Sci & Technol Complex Elect Syst Simulat Lab Beijing 101400 Peoples R China

In recent years, considerable progress has been made in ship detection in synthetic aperture radar (SAR) images;however, no research has been conducted on translating SAR ship images into flexible and accurate sentences. To explore image captions in SAR ship images, we conduct the following work: first, to better describe SAR ship images, we propose certain principles for SAR image annotation based on the characteristics of SAR images. Second, to make better use of SAR ship images, a large-scale SAR ship image captioning dataset is carefully constructed. Finally, we explore encoder-decoder models and the attention mechanism and apply these methods to the SAR ship image captioning task. We conduct detailed experiments on the proposed dataset and find that the encoder-decoder model and attention mechanism can obtain good results in the SAR ship image captioning task. The experiments also reveal that the generated sentences can accurately describe SAR ship images. This dataset has already been published on https://***/5132210/***.

关键词： Marine vehicles Synthetic aperture radar Radar polarimetry Remote sensing Seaports Feature extraction Decoding Encoding Recurrent neural networks SAR image image captioning encoder-decoder recurrent neural network long short-term memory network

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A deep multivariate time series multistep forecasting network

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APPLIED INTELLIGENCE 2022年第8期52卷 8956-8974页

作者： Yin, Chenrui Dai, Qun Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Peoples R China

Due to that multivariate time series, multistep forecasting technology has a guiding role in many fields, such as electricity consumption, traffic flow detection, and stock price prediction, many approaches have been proposed, seeking to realize accurate prediction based on historical data. However, multivariate time series in real-world applications often contain complex and non-linear interdependencies between time steps and series, for which traditional approaches that just model dependencies in time dimension may fail to make forecasting accurately. In addition, the traditional cyclic multistep prediction strategy leads to error accumulation and consequently results in the reduction of prediction performance. To address these limitations, we propose a novel Deep Multivariate Time Series Multistep Forecasting Network based on the encoder-decoder model, abbreviated as DualMNet. DualMNet uses the temporal patterns module to capture long-term patterns between time steps, and, simultaneously, employs the spatial patterns module to discover the interdependencies between time series. Furthermore, the decoder utilizes the historical data of the target sequence to predict the long time-series sequences at the final forward operation, significantly ameliorating the issue of error accumulation. Finally, the conducted t-test results, based upon the extensive experimental results on the three benchmark multivariate time series datasets, they demonstrate that the multistep predictive performance of DualMNet is significantly superior to those of the comparison models, and the ablation study shows that integrating all the components of DualMNet together results in robust forecasting performance.

关键词： Multivariate time series Multistep prediction Deep learning encoder-decoder Attention mechanism

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MobileXNet: An Efficient Convolutional Neural Network for Monocular Depth Estimation

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第11期23卷 20134-20147页

作者： Dong, Xingshuai Garratt, Matthew A. Anavatti, Sreenatha G. Abbass, Hussein A. Univ New South Wales Sch Engn & Informat Technol Canberra ACT 2612 Australia

Depth estimation from a single RGB image has attracted great interest in autonomous driving and robotics. State-of-the-art methods are usually designed on top of complex and extremely deep network architectures, which require more computational resources. Moreover, the inherent characteristic of the backbone used by the existing approaches results in severe spatial information loss in the produced feature maps, which impairs the accuracy of depth estimation on small sized images. In this study, we aimed to design a novel and efficient Convolutional Neural Network (CNN) to address these problems. Specifically, we stacked two shallow encoder-decoder style subnetworks successively in a unified network. Extensive experiments have been conducted on the NYU depth v2, KITTI, Make3D and Unreal data sets. Experimental results show that the proposed network achieves comparable accuracy to state-of-the-art methods that have extremely deep architectures but runs at a much faster speed on a single, less powerful GPU.

关键词： Estimation Convolution Convolutional neural networks Task analysis Decoding Computer architecture Semantics Monocular depth estimation depth prediction convolutional neural networks encoder-decoder autonomous vehicles

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