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Travel Mode Identification for Non-Uniform Passive Mobile Phone Data

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第9期25卷 11103-11116页

作者： Zeng, Jiaqi Huang, Yulang Zhang, Guozheng Cai, Zhengyi Wang, Dianhai Zhejiang Univ Inst Intelligent Transportat Syst Coll Civil Engn & Architecture Hangzhou 310058 Peoples R China Zhejiang Univ Zhongyuan Inst Zhengzhou 450000 Peoples R China Hangzhou City Univ Intelligent Transportat Syst Res Ctr Hangzhou 310058 Peoples R China Zhejiang Univ Coll Civil Engn & Architecture Hangzhou 310058 Peoples R China

The collection of individual GPS data, as a substitute for traditional travel surveys, is hindered by low response rates and high costs. Meanwhile, passive mobile phone data, such as Location-Based Service (LBS) data, has high user penetration but remains unexplored for travel behavior analysis. The irregular frequency of data collection results in distorted features and non-uniformly distributed information of trajectories in the Travel Mode Identification (TMI) task. The diversity of trajectories also poses challenges for TMI models. To fill the gap, we propose a TMI framework named Trajectory-as-a-sequence for Non-uniform data (TaaSN). Specifically, we incorporate GIS features to address the sparsity of motion features. Then, we design a model structure that accounts for time gaps, capturing non-uniform information of trajectory due to irregular frequency. To further enhance model's generalizability to diverse trajectory data, we propose a trajectory point dropout training strategy. The experimental results demonstrate that the TaaSN framework can greatly exploit the potential of LBS data in travel behavior mining. The proposed model achieves high accuracy on both non-uniform and uniform trajectories. On pseudo-LBS data, the accuracy reaches 84.9% when applied to the trajectories of existing travelers, and achieves 83.5% when applied to the trajectories of new travelers. On uniform data, it reaches 86.8% and 85.4%, respectively. Furthermore, we carry out comprehensive experiments and conclude valuable insights for future TMI framework design.

关键词： Trajectory Global Positioning System Data models Mobile handsets Task analysis Training Motion segmentation Travel mode identification location-based service data GPS data deep learning sequence-to-sequence model

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Cross-domain machine transfer learning for capacity aging trajectory prediction of lithium-ion batteries

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JOURNAL OF POWER SOURCES 2024年 624卷

作者： Zhang, Caiping Du, Jingcai Li, Shuowei Zhang, Linjing Zhang, Weige Beijing Jiaotong Univ Natl Act Distribut Network Technol Res Ctr NANTEC Beijing 100044 Peoples R China

Machine transfer learning-based methods have emerged as a promising solution to capacity aging trajectory prediction for different types of batteries. However, the methods lack transferring interpretability, coupled with obvious variations in data across diverse batteries, presenting challenges to early lifespan prediction. A voltage- capacity (V-Q) curve reconstruction-based transfer learning method across source and target domains is proposed to predict the aging trajectories for various batteries. Initially, a generalized mathematical model is established for the V-Q curves of three types of batteries after a two-step transformation. Based on the model, a partial loss function for parameter optimization within deep learning is constructed. Subsequently, a Global Attention- sequence-to-sequence model is developed to reconstruct the V-Q curves in the target domain. The reconstructed data distribution in the target domain exhibits high similarity to that in the source domain, significantly reducing the detrimental impact of domain differences on transfer learning. Ultimately, a predictive model for aging trajectories, applicable to different types of batteries, is formulated with the transfer method. The model's accuracy is validated across three types of batteries. It is demonstrated that the mean absolute percentage error of aging trajectory for Lithium Cobalt Oxide battery and Ternary Lithium-ion battery are 1.09 % and 1.71 %, respectively.

关键词： Lithium-ion batteries EOL prediction Aging trajectory prediction Cross domains sequence-to-sequence model Transfer learning

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URS: An Unsupervised Radargram Segmentation Network Based on Self-Supervised ViT With Contrastive Feature Learning Framework

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2024年 17卷 15512-15524页

作者： Ghosh, Raktim Bovolo, Francesca Fdn Bruno Kessler Ctr Digital Soc I-38123 Trento Italy Univ Trento Dept Informat Engn & Comp Sci I-38123 Trento Italy

Radar sounders are air and space-borne nadir-looking sensors operating in high-frequency (HF) or very high-frequency (VHF) bands and collect subsurface backscattered returns by transmitting electromagnetic pulses. The backscatter echoes are coherently integrated to generate radargrams for investigating and identifying geophysical characteristics of subsurface targets. While recent efforts have been made to develop supervised or semisupervised deep learning models for segmenting radargrams, obtaining accurate labeled information is often a challenging task. Therefore, it is of paramount importance to develop automatic unsupervised semantic segmentation methods to characterize the subsurface targets without labeled information. Unsupervised segmentation methods learn to discover meaningful semantic contents and decompose them into distinct semantic segments with known ontology. Here, we propose an unsupervised radargram segmentation network that uses a convolution-based expansive network as a proxy decoder and a progressive stepwise reconstruction strategy of the input signal from the latent space to measure the spatial similarity with the input radar sounder signal. After designing a unique training strategy by bootstrapping the randomness inside the minibatch and combining the spatial similarity loss along with the contrastive correlation loss, the proposed architecture outperformed the state of the art in fully unsupervised settings. Experiments were conducted on the multichannel coherent radar depth sounder to test the robustness of the proposed method. We carried out a comparative analysis with the state-of-the-art unsupervised and supervised segmentation methods. MIoU is improved by 23.47%.

关键词： Ice Training Sensors Radar Task analysis Noise Semantics Multichannel coherent radar depth sounder (MCoRDS) radar sounder semantic segmentation sequence-to-sequence model TransFuse TransUNet transformers

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Machine learning based state-of-charge prediction of electrochemical green hydrogen production:Zink-Zwischenschritt-Elektrolyseur(ZZE)

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Energy and AI 2024年第2期16卷 269-276页

作者： Daniel Vila Elisabeth Hornberger Christina Toigo STOFF2 GmbH–Gebäude H/Flughafen Tegel 1 13405 BerlinGermany University of Applied Sciences Upper Austria Stelzhamerstraße 23A-4600 WelsAustria Centre for Economic Research on Inclusivity and Sustainability at the University of Galway University RoadGalwayIreland

The intermittency of renewable energy is a key limiting factor for the successful decarbonization of both energy producing and consuming sectors. Green hydrogen has the potential to act as the central energy vector connecting hard-to-abate sectors to renewable power. However, combining energy storage and conversion for a holistic electrolyzer system remains challenging. Here, we show the innovative Zink-Zwischenschritt Elektrolyseur (ZZE), or Zinc Intermediate step Electrolyzer in English, that temporarily decouples the water splitting reaction and uses zinc to store electrical energy in chemical form. To perform optimal operation of a ZZE system, machine learning models were applied to predict the state of charge of a lab scale ZZE system. Using various models, we were able to determine the effectiveness of the prediction and contrast it to state of charge predictions of other energy storage systems. We show that a bi-directional long short-term memory neural network approach has the lowest error within the testing environment. This work serves to perform further ZZE development as well as state of charge prediction for other novel energy storage technologies.

关键词： Electrolyzer Battery ZZE Energy storage State of charge Machine learning sequence-to-sequence model

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Solving Turkish math word problems by sequence-to-sequence encoder-decoder models

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2023年第2期31卷 431-447页

作者： Gedik, Esin Güngör, Tunga Bogazici Univ Dept Comp Engn Istanbul Turkiye

Solving math word problems (MWP) is a challenging task due to the semantic gap between natural language texts , mathematical equations. The main purpose of the task is to take a written math problem as input and produce a proper equation as output for solving that problem. This paper describes a sequence-to-sequence (seq2seq) neural model for automatically solving Turkish MWPs based on their semantic meanings in the text. It comprises a bidirectional encoder to comprehend the semantics of the problem by encoding the input sequence and a decoder with attention to extract the equation by tracking the semantic meanings of the output symbols. We investigate the success of several embedding types, pretrained language models , neural models. Our research is novel in the sense that there exist no studies in Turkish on this natural language processing task that utilizes pretrained language models and neural models. There is also no Turkish dataset that can be used to train the neural models for the MWP task. As the first large-scale Turkish MWP dataset, we translated the well-known English MWP datasets into Turkish using a machine translation system. Although Turkish is an agglutinative and grammatically challenging language, the proposed models achieve around 72% accuracy on the dataset compiled from three English datasets.

关键词： Math word problems sequence-to-sequence model attention mechanism natural language processing

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Turkish Data-to-Text Generation Using sequence-to-sequence Neural Networks

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ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING 2023年第2期22卷 1-27页

作者： Demir, Seniz MEF Univ Dept Comp Engn Maslak Ayazaga Caddesi TR-34396 Istanbul Turkiye

End-to-end data-driven approaches lead to rapid development of language generation and dialogue systems. Despite the need for large amounts of well-organized data, these approaches jointly learn multiple components of the traditional generation pipeline without requiring costly human intervention. End-to-end approaches also enable the use of loosely aligned parallel datasets in system development by relaxing the degree of semantic correspondences between training data representations and text spans. However, their potential in Turkish language generation has not yet been fully exploited. In this work, we apply sequenceto-sequence (Seq2Seq) neural models to Turkish data-to-text generation where the input data given in the form of a meaning representation is verbalized. We explore encoder-decoder architectures with attention mechanism in unidirectional, bidirectional, and stacked recurrent neural network (RNN) models. Our models generate one-sentence biographies and dining venue descriptions using a crowdsourced dataset where all field value pairs that appear in meaning representations are fully captured in reference sentences. To support this work, we also explore the performances of our models on a more challenging dataset, where the content of a meaning representation is too large to fit into a single sentence, and hence content selection and surface realization need to be learned jointly. This dataset is retrieved by coupling introductory sentences of person-related Turkish Wikipedia articles with their contained infobox tables. Our empirical experiments on both datasets demonstrate that Seq2Seq models are capable of generating coherent and fluent biographies and venue descriptions from field value pairs. We argue that the wealth of knowledge residing in our datasets and the insights obtained fromthis study hold the potential to give rise to the development of new end-to-end generation approaches for Turkish and other morphologically rich languages.

关键词： Data-to-text generation sequence-to-sequence model Turkish Wikipedia

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JoinER-BART: Joint Entity and Relation Extraction With Constrained Decoding, Representation Reuse and Fusion

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2023年 31卷 3603-3616页

作者： Chang, Hongyang Xu, Hongfei van Genabith, Josef Xiong, Deyi Zan, Hongying Zhengzhou Univ Zhengzhou 450001 Henan Peoples R China DFKI D-66123 Saarbrucken Germany Saarland Univ Informat Campus D-66123 Saarbrucken Germany Tianjin Univ Tianjin 300350 Peoples R China

Joint Entity and Relation Extraction (JERE) is an important research direction in Information Extraction (IE). Given the surprising performance with fine-tuning of pre-trained BERT in a wide range of NLP tasks, nowadays most studies for JERE are based on the BERT model. Rather than predicting a simple tag for each word, these approaches are usually forced to design complex tagging schemes, as they may have to extract entity-relation pairs which may overlap with others from the same sequence of word representations in a sentence. Recently, sequence-to-sequence (seq2seq) pre-trained BART models show better performance than BERT models in many NLP tasks. Importantly, a seq2seq BART model can simply generate sequences of (many) entity-relation triplets with its decoder, rather than just tag input words. In this article, we present a new generative JERE framework based on pre-trained BART. Different from the basic seq2seq BART architecture: 1) our framework employs a constrained classifier which only predicts either a token of the input sentence or a relation in each decoding step, and 2) we reuse representations from the pre-trained BART encoder in the classifier instead of a newly trained weight matrix, as this better utilizes the knowledge of the pre-trained model and context-aware representations for classification, and empirically leads to better performance. In our experiments on the widely studied NYT and WebNLG datasets, we show that our approach outperforms previous studies and establishes a new state-of-the-art (92.91 and 91.37 F1 respectively in exact match evaluation).

关键词： Joint Entity and Relation Extraction pre-trained BART sequence-to-sequence model

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A multi-step ahead global solar radiation prediction method using an attention-based transformer model with an interpretable mechanism

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2023年第40期48卷 15317-15330页

作者： Zhou, Yong Li, Yizhuo Wang, Dengjia Liu, Yanfeng Xian Univ Architecture & Technol Sch Management 13 Yanta Rd Xian 710055 Peoples R China Xi an Univ Architecture & Technol State Key Lab Green Bldg Western China 13 Yanta Rd Xian 710055 Peoples R China Xian Univ Architecture & Technol Sch Bldg Serv Sci & Engn 13 Yanta Rd Xian 710055 Peoples R China

The conventional multi-step ahead solar radiation prediction method ignores the time -dependence of a future solar radiation time series. Therefore, according to sequence-to -sequence (seq2seq) model theory, this paper proposes the seq2seq long-and short-term memory model (seq2seq-LSTM), the seq2seq-LSTM model with an attention mechanism (seq2seq-at-LSTM), and a transformer model, which consists only of the attention mech-anism. The hourly global solar radiation data between 2016 and 2018 from Shaanxi, China, is used to train and validate the models. The results show that the introduction of the attention mechanism can effectively improve the prediction accuracy of the seq2seq-LSTM model. However, the model is still not very good at capturing the long-distance depen-dence of the solar radiation time series due to the inherent properties of LSTM. In com-parison, the transformer model, which is based entirely on the attention mechanism, performs much better at capturing the long-distance dependence of the solar radiation time series. Furthermore, as the number of time-steps increases, the performance of the solar radiation prediction decreases relatively smoothly and slowly. The obtained average coefficient of determination, root mean square error (RMSE), relative RMSE, and mean bias error are 0.9788, 72.91 W/m2, 25.25%, and 38.35 W/m2, respectively. In addition, the average skill score of the transformer model is around 44.9%, which is 20.54% higher than that of the seq2seq-at-LSTM model and about 40.84% higher than that of the seq2seq-LSTM model. Besides, the use of the attention mechanism can explain the improved prediction compared to other models. This model developed in this study could also be used for

关键词： Multi-step ahead Global solar radiation model predictions sequence-to-sequence model Transformer model

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Discontinuous grammar as a foreign language

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NEUROCOMPUTING 2023年 524卷 43-58页

作者： Fernandez-Gonzalez, Daniel Gomez-Rodriguez, Carlos Univ A Coruna Dept Ciencias Comp & Tecnol Informac FASTPARSE Lab CITICLyS Grp Campus de Elvina s-n La Coruna 15071 Spain

In order to achieve deep natural language understanding, syntactic constituent parsing is a vital step, highly demanded by many artificial intelligence systems to process both text and speech. One of the most recent proposals is the use of standard sequence-to-sequence models to perform constituent parsing as a machine translation task, instead of applying task-specific parsers. While they show a competitive performance, these text-to-parse transducers are still lagging behind classic techniques in terms of accuracy, coverage and speed. To close the gap, we here extend the framework of sequence-to-sequence models for constituent parsing, not only by providing a more powerful neural architecture for improving their performance, but also by enlarging their coverage to handle the most complex syntactic phenomena: discontinuous structures. To that end, we design several novel linearizations that can fully produce discontinuities and, for the first time, we test a sequence-to-sequence model on the main discontinuous benchmarks, obtaining competitive results on par with task-specific discontinuous constituent parsers and achieving state-of-the-art scores on the (discontinuous) English Penn Treebank. (c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Natural language processing Computational linguistics Parsing Discontinuous constituent parsing Neural network Deep learning sequence-to-sequence model

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LongT5-Mulla: LongT5 With Multi-Level Local Attention for a Longer sequence

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IEEE ACCESS 2023年 11卷 138433-138444页

作者： Zhou, Le Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200240 Peoples R China

Efficient Transformer models typically employ local and global attention methods, or utilize hierarchical or recurrent architectures, to process long text inputs in natural language processing tasks. However, these models face challenges in terms of sacrificing either efficiency, accuracy, or compatibility to develop their application in longer sequences. To maintain both the accuracy of global attention and the efficiency of local attention, while keeping a good compatibility to be easily applied to an existing pre-trained model, in this paper, we propose multi-level local attention (Mulla attention), which is a hierarchical local attention that acts on both the input sequence and multiple pooling sequences of different granularity simultaneously, thus performing long-range modeling while maintaining linear or log-linear complexity. We apply Mulla attention to LongT5 and implement our LongT5-Mulla sequence-to-sequence model, without introducing new parameters except for positional embeddings. Experiments show that our model can surpass all baseline models, including two original variants of LongT5, in the 8 similar to 16k-input long text summarization task on the Multi-News, arXiv and WCEP-10 datasets, with improvements of at least +0.22, +0.01, +0.52 percentage points (pp) averaged Rouge scores respectively, while at the meantime being able to effectively process longer sequences that have 16 similar to 48k tokens with at least 52.6% lower memory consumption than LongT5-tglobal, and +0.56 similar to 1.62 pp averaged Rouge scores higher than LongT5-local. These results demonstrate that our proposed LongT5-Mulla model can effectively process long sequences and extend the maximum input length for long text tasks from 16k to 48k while maintaining accuracy and efficiency.

关键词： Efficient transformer long-range modeling natural language processing sequence-to-sequence model text summarization

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