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Two-Level Battery Health Diagnosis Using encoder-decoder framework and Gaussian Mixture Ensemble Learning Based on Relaxation Voltage

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IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 2024年第2期10卷 3966-3975页

作者： Xiang, Haoxiang Wang, Yujie Zhang, Xingchen Chen, Zonghai Univ Sci & Technol China Dept Automat Hefei 230027 Peoples R China

Accurate diagnosis of the state-of-health (SOH) of the lithium-ion battery is crucial for its safe and reliable operation. In this article, a two-level battery health diagnosis model is proposed using relaxation voltage. First, the health features of the relaxation voltage sequence are extracted using the autoencoder based on the encoder-decoder framework, and the Gaussian mixture model (GMM) is used to cluster distinct aging levels of the battery, thereby enabling a preliminary diagnosis of battery SOH. Then, a novel Gaussian mixture ensemble learning (GMEL) method is presented that leverages prior knowledge for accurate diagnosis of battery SOH. Furthermore, the performance of the ensemble model is enhanced by the sequential model-based algorithm configuration (SMAC) algorithm to optimize the hyperparameters, resulting in mean-absolute-error (MAE) and root-mean-square-error (RMSE) of 0.736% and 1.013%, respectively. In addition, a data reconstruction model is developed using the encoder-decoder framework to address the challenge of obtaining complete relaxation voltage sequences in the real world. Utilizing only 4 min of incomplete relaxation voltage data, the presented model achieves the MAE of 1.265% and RMSE of 1.681% in diagnosing the battery SOH. Finally, the superiority of the proposed method is verified by several sets of experiments.

关键词： Batteries Feature extraction Aging Voltage Integrated circuit modeling Ensemble learning Lithium-ion batteries Battery health diagnosis data reconstruction model encoder-decoder framework Gaussian mixture ensemble learning (GMEL) relaxation voltage

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Variational mode decomposition coupled LSTM with encoder-decoder framework: an efficient method for daily streamflow forecasting

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EARTH SCIENCE INFORMATICS 2025年第1期18卷 1-19页

作者： Liu, Jiadong Xu, Teng Lu, Chunhui Yang, Jie Xie, Yifan Hohai Univ Natl Key Lab Water Disaster Prevent Nanjing Peoples R China Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing Peoples R China Hohai Univ Yangtze Inst Conservat & Dev Nanjing Peoples R China

Accurate daily streamflow forecasting is crucial for effective flood control and water management. Decomposition ensemble models have proven to be effective in daily streamflow forecasting. However, it is important to recognize that the performance of streamflow forecasting within the decomposition ensemble models is significantly influenced by the choice of forecast model used. Therefore, a thoughtful selection of streamflow forecast models can greatly enhance the performance of decomposition ensemble models. Nevertheless, simple forecast models, such as long short-term memory, are susceptible to gradient disappearance or explosion, resulting in suboptimal peak streamflow forecasting performance, especially when dealing with long streamflow series. This limitation ultimately hinders the predictive accuracy of the decomposition ensemble model. Furthermore, while single-model forecasting (SF) schemes have demonstrated effectiveness, there has been limited exploration of the potential for coupling VMD and encoder-decoder framework in mid- and long-term daily streamflow forecasting. To address these issues and further enhance the capability of common VMD-LSTM model in mid and long-term streamflow forecasting, we have developed a novel and efficient forecast model VMD-LSTM-ED by integrating VMD and LSTM with a robust encoder-decoder structure in the SF scheme. Our proposed method was tested in the Fish River Basin in Maine, USA. To demonstrate its superiority, we also compared VMD-LSTM-ED against LSTM-ED, VMD-LSTM, and LSTM models. The results indicate that the proposed VMD-LSTM-ED exhibits outstanding performance across all aspects, with its advantage becoming more prominent as lead times increase, particularly in peak streamflow forecasting. For example, compared to VMD-LSTM, the Nash-Sutcliffe Efficiency (NSE) of VMD-LSTM-ED increased by 2.91%, 8.45%, 20.2% and 51.2% respectively, at lead times of 1, 3, 5, and 7 days;thus significantly enhancing the long-term predict

关键词： Variational mode decomposition SF scheme Streamflow forecasting Data leakage encoder-decoder framework

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Prediction of jaywalker-vehicle conflicts based on encoder-decoder framework utilizing multi-source data

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ACCIDENT ANALYSIS AND PREVENTION 2024年 195卷 107403页

作者： Zhang, Ziqian Li, Haojie Ren, Gang Southeast Univ Sch Transportat Nanjing Peoples R China Jiangsu Key Lab Urban ITS Nanjing Peoples R China Jiangsu Prov Collaborat Innovat Ctr Modern Urban T Nanjing Peoples R China

Jaywalker-vehicle (J-V) conflicts at mid-blocks without crossing facilities in China are frequent and hazardous. Due to the unexpected and sudden nature of jaywalking activity, it is crucial to develop predictive models for J-V conflicts to offer pre-conflict warnings for road users. This study introduces a novel encoder-decoder framework that utilizes multi-source data to predict J-V conflict severity. We define three encoders to represent three types of input data, (1) J-V interaction encoder (Bi-LSTM), (2) jaywalker motion encoder (Bi-LSTM) and (3) back-ground information encoder (MLP). Subsequently, features extracted by these three encoders are concatenated and transferred to the conflict severity decoder (MLP) to obtain the predicted severity *** further conduct a case study using the surveyed video data at three mid-blocks without crossing facilities in Nanjing, China. The experimental results indicate that, compared to classical models, our proposed encoder-decoder (Proposed ED) model exhibits the best and stable predictive metrics. Furthermore, the results of the ablation study suggest that the incorporation of background information significantly enhances the four evalu-ative metrics of the Proposed ED model, with an average improvement of 24.291%. Additionally, the results of transferability analysis suggest that, when the ratio of added samples from the new mid-block reaches 40% to 50%, the predictive metrics of the updated models could stabilize at around 80% to 95%, indicating a notably good performance. Eventually, we derive several practical suggestions from the above findings, in order to help with J-V conflict prediction and jaywalking safety improvement.

关键词： Jaywalker-vehicle conflict Conflict prediction encoder-decoder framework Mid-blocks without crossing facilities

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PML-ED: A method of partial multi-label learning by using encoder-decoder framework and exploring label correlation

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INFORMATION SCIENCES 2024年 661卷

作者： Wang, Zhenwu Liu, Fanghan Han, Mengjie Tang, Hongjian Wan, Benting China Univ Min & Technol Beijing Sch Artificial Intelligence Beijing 100083 Peoples R China Dalarna Univ Sch Informat & Engn S-79131 Falun Sweden Jiangxi Univ Finance & Econ Sch Software & IoT Engn Nanchang 330013 Peoples R China

Partial multi-label learning (PML) addresses problems where each instance is assigned a candidate label set and only a subset of these candidate labels is correct. The major challenge of PML is that the training procedure can be easily misguided by noisy labels. Current studies on PML have revealed two significant drawbacks. First, most of them do not sufficiently explore complex label correlations, which could improve the effectiveness of label disambiguation. Second, PML models heavily rely on prior assumptions, limiting their applicability to specific scenarios. In this work, we propose a novel method of PML based on the encoder-decoder framework (PML-ED) to address the drawbacks. PML-ED initially achieves the distribution of label probability through a KNN label attention mechanism. It then adopts Conditional Layer Normalization (CLN) to extract the high-order label correlation and relaxes the prior assumption of label noise by introducing a universal encoder-decoder framework. This approach makes PML-ED not only more efficient compared to the state-of-the-art methods, but also capable of handling the data with large noisy labels across different domains. Experimental results on 28 benchmark datasets demonstrate that the proposed PML-ED model, when benchmarked against nine leading-edge PML algorithms, achieves the highest average ranking across five evaluation criteria.

关键词： Partial multi-label learning Label correlation Label disambiguation encoder-decoder framework Conditional layer normalization

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A variable fidelity approach for predicting aerodynamic wall quantities of hypersonic vehicles using the ConvNeXt encoder-decoder framework

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AEROSPACE SCIENCE AND TECHNOLOGY 2024年 155卷

作者： Yang, Yuxin Yao, Shaobo Xue, Youtao Zhao, Wenwen Wu, Changju Zhejiang Univ Sch Aeronaut & Astronaut Hangzhou 310027 Zhejiang Peoples R China Zhejiang Univ Huanjiang Lab Zhuji 311800 Peoples R China

Computational fluid dynamics (CFD) simulations for obtaining three-dimensional hypersonic vehicle aerodynamic characteristics are resource-intensive. Deep learning offers a promising alternative for predicting aerodynamic wall quantities but typically requires a large dataset, which conflicts with the intention to reduce CFD costs. To address this, we propose a novel prediction model leveraging variable fidelity data to alleviate computational demands. The model utilizes an encoder-decoder architecture with ConvNeXt blocks as operators, processing variable fidelity data as inputs and outputs. We also developed a U-Net with residual blocks (ResUNet) for performance comparison. Transformation and topology patching techniques were applied to tackle the challenges posed by large grid volumes and complex topologies in three-dimensional vehicles. Results demonstrate that the ConvNeXt encoder-decoder predicts peak regions more accurately than ResUNet and aligns closely with CFD in low-value areas. The ConvNeXt encoder-decoder maintains maximum heat flux errors below 1 % and viscous drag errors below 3.81 % across varying angles of attack. It exhibits superior fitting performance with minimal deviation from CFD compared to ResUNet. Prediction accuracy decreases under multiple inflow changes compared to the angle of attack variations alone. Heat flux predictions show high consistency with CFD, with relative errors below 6.38 %, whereas friction predictions exhibit higher errors with 10.28 % maximum error and 0.28 % minimum error. The model accurately predicts friction variation trends in peak regions at the blunt edge's central plane but performs poorly in low-value areas. In summary, the ConvNeXt encoder-decoder accurately predicts the wall quantities under varying multiple inflow conditions.

关键词： Variable fidelity data Aerodynamic wall quantities prediction ConvNeXt block encoder-decoder framework Deep learning

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Exploring EfficientNet Variants for Image Encoding in Auxiliary Signal Guided Knowledge encoder-decoder framework

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Procedia Computer Science 2024年 245卷 391-398页

作者： Matthew Martianus Henry Nur Adhianti Heryanto Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

The growing number of medical images has led to radiologist burnout, which seriously impacts the radiologist's performance. To address the previously mentioned issue, an Auxiliary Signal Guided Knowledge (ASGK) multimodal encoder-decoder framework was designed to automatically generate the medical report based on the proposed medical graph and natural language decoder. It utilizes DenseNet-121 as the image encoder. With DenseNet-121 lack of computational and memory efficiency, this study aims to explore the potential of EfficientNetB0 to EfficientNetB4 as an ASGK image encoder substitute. The framework is trained with IU X-Ray dataset for 30 epochs, with Adam optimizer, a learning rate of 0.01 with 0.8 decay rate, binary cross entropy loss for the medical tags, and cross-entropy loss for the generated medical captions. During the framework training process with each image encoder, the parameter that achieves the highest CIDEr score on the validation set is considered the best image encoder parameter and will be used on the test set. On the test set, EfficientNetB3 as an ASGK image encoder has been shown to increase the CIDEr score to 0.35, a significant increase from the 0.28 CIDEr score obtained by the ASGK using DenseNet-121. This score is only a 1% decrease from the best validation score. It suggests that not only EfficientNetB3 increases the framework's performance, it is also less prone to overfitting. This study has demonstrated that EfficientNetB3 is a potential image encoder substitute for DenseNet-121 in the ASGK framework.

关键词： Automated medical report generation DenseNet-121 EfficientNet encoder-decoder framework

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Parallel encoder-decoder framework for image captioning

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KNOWLEDGE-BASED SYSTEMS 2023年 282卷

作者： Saeidimesineh, Reyhane Adibi, Peyman Karshenas, Hossein Darvishy, Alireza Univ Isfahan Fac Comp Engn Artificial Intelligence Dept Esfahan Iran Zurich Univ Appl Sci ZHAW Sch Engn Zurich Switzerland

Recent progress in deep learning has led to successful utilization of encoder-decoder frameworks inspired by machine translation in image captioning models. The stacking of layers in encoders and decoders has made it possible to use several modules in encoders and decoders. However, just one type of module in encoder or decoder has been used in stacked models. In this research, we propose a parallel encoder-decoder framework that aims to take advantage of multiple of types modules in encoders and decoders, simultaneously. This framework contains augmented parallel blocks, which include stacking modules or non-stacked ones. Then, the results of the blocks are integrated to extract higher-level semantic concepts. This general idea is not limited to image captioning and can be customized for many applications that utilize encoder-decoder frameworks. We evaluated our proposed method on the MS-COCO dataset and achieved state-of-the-art results. We got 149.92 for CIDEr-D metric outperforming state-of-the-art image captioning models.

关键词： Parallelization encoder-decoder framework Image captioning Natural language processing

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Into the Unobservables: A Multi-range encoder-decoder framework for COVID-19 Prediction 21

Into the Unobservables: A Multi-range Encoder-decoder Framew...

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30th ACM International Conference on Information and Knowledge Management (CIKM)

作者： Cui, Yue Zhu, Chen Ye, Guanyu Wang, Ziwei Zheng, Kai Univ Elect Sci & Technol China Chengdu Peoples R China

ISBN: (纸本)9781450384469

The ongoing COVID-19 pandemic has dramatically changed people's daily lives. A robust forecasting model for COVID-19 infections is essential for governments and institutions to plan timely and perform accurate interventions. Mainstream solutions for COVID-19 prediction fit reported data only by considering observed cases. However, the neglected facts that positive samples are incomplete and many facts of the novel disease are unknown may be prone to cause severe error accumulation, especially in long-term predictions. To fully understand the spreading patterns of the virus, we propose an encoder-decoder framework: (i) in the encoder we embed historical case data into multiple expose-infection ranges and learn message passing between time slices and across ranges with coarse-grained human mobility data incorporated;(ii) in the decoder, we decode the embedded features based on reported cases as well as deaths to jointly consider the effect of both observed and hidden data. We model the spreading of disease in over 60 counties of California and New York, which are two of the most metropolitan areas in the US. The proposed framework significantly outperforms state-of-the-art baselines on JHU COVID-19 dataset on both weekly prediction and daily prediction tasks. We design detailed ablation studies to verify the effectiveness of each key module and find the model not only works with the assistance of mobility data but also with purely cases and deaths, which implies its broad application scenarios.

关键词： COVID-19 prediction encoder-decoder framework

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Nonautoregressive encoder-decoder Neural framework for End-to-End Aspect-Based Sentiment Triplet Extraction

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第9期34卷 5544-5556页

作者： Fei, Hao Ren, Yafeng Zhang, Yue Ji, Donghong Wuhan Univ Sch Cyber Sci & Engn Wuhan 430072 Peoples R China Guangdong Univ Foreign Studies Lab Language & Artificial Intelligence Guangzhou 510420 Peoples R China Westlake Univ Sch Engn Hangzhou 310024 Peoples R China

Aspect-based sentiment triplet extraction (ASTE) aims at recognizing the joint triplets from texts, i.e., aspect terms, opinion expressions, and correlated sentiment polarities. As a newly proposed task, ASTE depicts the complete sentiment picture from different perspectives to better facilitate real-world applications. Unfortunately, several major challenges, such as the overlapping issue and long-distance dependency, have not been addressed effectively by the existing ASTE methods, which limits the performance of the task. In this article, we present an innovative encoder-decoder framework for end-to-end ASTE. Specifically, the ASTE task is first modeled as an unordered triplet set prediction problem, which is satisfied with a nonautoregressive decoding paradigm with a pointer network. Second, a novel high-order aggregation mechanism is proposed for fully integrating the underlying interactions between the overlapping structure of aspect and opinion terms. Third, a bipartite matching loss is introduced for facilitating the training of our nonautoregressive system. Experimental results on benchmark datasets show that our proposed framework significantly outperforms the state-of-the-art methods. Further analysis demonstrates the advantages of the proposed framework in handling the overlapping issue, relieving long-distance dependency and decoding efficiency.

关键词： Task analysis Decoding Sentiment analysis Predictive models Labeling Analytical models Transformers Bipartite matching loss encoder-decoder framework natural language processing (NLP) nonautoregressive decoding pointer network sentiment analysis

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AEDmts: An Attention-Based encoder-decoder framework for Multi-Sensory Time Series Analytic

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IEEE ACCESS 2020年 8卷 37406-37415页

作者： Fan, Jin Wang, Hongkun Huang, Yipan Zhang, Ke Zhao, Bei Hangzhou Dianzi Univ Dept Comp Sci & Technol Hangzhou 310018 Peoples R China

Numerous IoT applications have emerged in human healthcare area with advances in wearable electronics. Multiple physical and physiological data bearing strong spatial-temporal characteristic collected by the wearable sensors is sent to the smartphones where it is aggregated and transferred to back-end applications for further processing. Analyzing the multivariate time series is of great importance yet very challenging as it is affected by many complex factors, i.e., dynamic spatial-temporal correlations and external factors. In this paper, we propose an attention-based encoder-decoder framework for multi-sensory time-series analytic. It consists of four parts: data collection, data mining, time-series analytic part and user interaction. A temporal-attention based encoder-decoder model is proposed to make a long-term prediction of multiple time series to realize the real-time user interaction. The proposed model uses the LSTM model to learn the long-term dependence of the time series related to certain motion sequence. The attention mechanism connects the encoder and the decoder to make long-term predictions for future time series. Through extensive experiments, the proposed model has achieved better results in short-term and long-term predictions compared with the state of art methods. An activity recognition algorithm based on LSTM is also proposed in this framework to identify daily human activities and sports activities accurately. Through five-fold and ten-fold cross-validation strategies and comparison with six baseline machine learning models, the activity recognition algorithm has a recognition rate of 98.89% and 99.28% for human activity.

关键词： encoder-decoder framework multi-sensory time series analytic motion prediction

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