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Cognate Production Using Character-Based Neural Machine Translation Without Segmentation

IEEE ACCESS 2025年 13卷 34824-34830页

作者： Zundui, Tsolmon Batsuren, Khuyagbaatar Munkhdalai, Tsendsuren Ganbold, Amarsanaa Natl Univ Mongolia Ulaanbaatar 14200 Mongolia Google Res Mountain View CA 94043 USA

Cognates are words that share a common origin or have been borrowed across languages, often exhibiting similarities in both sound and meaning. In this work, we introduce a fully character-level neural sequence-to-sequence model for cognate production that does not require any segmentation. Our model operates at the character-level to transform a source word into its corresponding cognate in the target language, thereby obviating out-of-vocabulary issues and alleviating the need for subword segmentation. We evaluated our approach on a novel dataset and found that it outperforms both statistical machine translation baselines and prior neural methods on the same training dataset, as measured by standard coverage and mean reciprocal rank metrics. These results underscore the effectiveness of character-level sequence-to-sequence architectures for cognate generation in diverse language settings, including cross-alphabetic transformations.

关键词： Production Translation Decoding Computational modeling Multilingual Attention mechanisms Measurement Vocabulary Symbols Phonetics Cognate production sequence-to-sequence model recurrent neural network

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An attention-augmented bidirectional LSTM-based encoder-decoder architecture for electrocardiogram heartbeat classification

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2025年第3期47卷 506-515页

作者： Degachi, Oumayma Ouni, Kais Univ Carthage Natl Engn Sch Carthage Res Lab Smart Elect & ICT SE&ICT LabLR18ES44 45 Rue EntrepreneursCharguia 2 Tunis 2035 Tunisia

Electrocardiogram (ECG) records a series of heart depolarization and repolarization of the atria and ventricles that manifest in waves. They are systematically deployed as a standard non-invasive tool to monitor the cardiac activity and reliably detect eventual heart diseases or any abnormal heart activity. To relief the medical staff of the burden of diagnosing long ECG records, classification algorithms have been tested and explored to give a new way for heartbeat failure detection. This paper proposes a method based on a convolutional neural network (CNN) to extract time-invariant features and a bidirectional long short-term memory (BiLSTM) network-based sequence-to-sequence (seq2seq) architecture augmented with an attention mechanism (AM) to classify the heartbeats into five classes according to the ANSI/AAMI/ISO EC57, 1998-(R)2008 standard. We also use the adaptive synthetic sampling (ADASYN) which is a data augmentation technique to reduce the bias caused by imbalanced data. Finally, to avoid skewing the classification results, we use the inter-patient paradigm diagnosis. For verification, we used the MIT-BIH arrhythmia database, the experiment achieved an accuracy rate of 99.87%. The evaluation results demonstrate that the proposed method obtains excellent performances for heartbeat classification task. The introduction of an AM improves the efficiency of the encoder-decoder architecture.

关键词： Convolutional neural network bidirectional long-term short-term memory sequence-to-sequence model cardiac arrhythmia electrocardiogram class-imbalances adaptive synthetic sampling deep learning Bahdanau attention mechanism

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Real-time fatigue assessment of Floating Offshore Wind Turbine Mooring employing sequence-to-sequence-based deep learning on indirect fatigue response

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OCEAN ENGINEERING 2025年 315卷

作者： Kumar, Rohit Sen, Subhamoy Keprate, Arvind Indian Inst Technol Mandi i4S Lab Mandi HP India Oslo Metropolitan Univ Dept Mech Elect & Chem Engn GronnMet Green Energy Lab Oslo Norway

Mooring lines in Floating Offshore Wind Turbines (FOWT) are subjected to significant environmental loading, leading to fatigue degradation over prolonged usage which necessitates real-time monitoring to ensure operational safety. Traditional fatigue assessment measures stress fluctuations under operational loads with expensive underwater sensors, which are costly and complex to install, especially for monitoring mooring at higher depths. This eventually increases the levelized cost of energy and maintenance expenses. This study proposes a cost-efficient fatigue monitoring method for catenary mooring using a novel sequence-to-sequence deep-learning (DL) approach that leverages platform motions as indirect fatigue response to infer fatigue assessment. By correlating FOWT platform motions with their impact on the fatigue life of mooring lines, the proposed method offers an alternative to costly underwater sensor measurements. In the absence of real data, the DL model is trained using simulated datasets for an OC4 semi-submersible wind turbine model in the OpenFAST open-source simulation package, capturing a wide range of tension fluctuations under diverse metocean conditions. Accordingly, this synthetic data has been treated as "real data" throughout this study. The trained network effectively captures the nonlinear relationship between platform motion and mooring fatigue response in real-time, enabling efficient assessment of fatigue-induced damage on mooring lines.

关键词： FOWT Fatigue assessment Mooring lines sequence-to-sequence model Virtual sensing

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Multistep prediction for egg prices: An efficient sequence-to-sequence network

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EGYPTIAN INFORMATICS JOURNAL 2025年 29卷

作者： Jiang, Minlan Mo, Liyun Zeng, Lingguo Zhang, Azhi Du, Youhai Huo, Yizhi Shi, Xiaowei Al-qaness, Mohammed A. A. Zhejiang Normal Univ Coll Phys & Elect Informat Engn Jinhua Peoples R China Zhejiang Inst Optoelect Jinhua Peoples R China Aksu Educ Inst Aksu Peoples R China Hangzhou Hikvis Digital Technol Co Hangzhou Peoples R China

Egg price has the characteristics of non-stationary, non-linear, and high volatility, which is more difficult to predict accurately. In this paper, we comprehensively consider the multiple factors affecting egg prices and construct a sequence-to-sequence (Seq2seq) model to study the multi-step prediction method of egg prices. Seasonal-trend Decomposition Procedure Based on Loess (STL) is first used to decompose the historical egg price series into trend, seasonal, and residual terms to reduce the interference of sample noise on forecasting performance. Then, Principal Component Analysis (PCA) is used to analyze and downscale the multidimensional factors affecting egg prices, such as feed price, laying hen seedling price, culled chicken price, duck egg price, and consumer index, to eliminate the redundant information in the data. Finally, the above-processed data were introduced into the Seq2seq network for training to establish a multi-step prediction model for egg prices. The experimental results show that the STL-PCA-Seq2seq model proposed in this paper can broadly capture the longterm dependence information of the input series and model the complex nonlinear relationships among the multidimensional factors affecting egg prices with the lowest prediction errors compared to the Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), the Informer model, the Seq2seq model, and the STL-Seq2seq model. The method proposed in this paper can reach R2 of 0.9867, 0.9569, and 0.9106 at prediction steps 6, 12, and 18. With a prediction step size of 6, the RMSE is 0.131, MAE is 0.086, and MAPE is 0.813, respectively, which realizes the accurate prediction of egg price at any number of steps, and the results of the study provide a reference for the multi-step prediction of egg prices.

关键词： Time-series forecasting Egg price forecasting Multistep prediction Principal component analysis sequence-to-sequence model

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Identifying wagon numbers using transformers

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 143卷

作者： Gladys, Bartlomiej Wojakowicz, Bartlomiej Bober, Michal Nalepa, Jakub Euvic SA Ul Przewozowa 32 PL-44100 GLIWICE Silesia Poland Silesian Tech Univ Akad 16 PL-44100 GLIWICE Silesia Poland

Identifying wagons in the railway industry from image data is essential in streamlining the process of wagon inspection and fleet monitoring. However, this is a laborious and time-consuming task, hence automating this process is pivotal in accelerating the identification process and removing human bias. This challenge is addressed in this work, and a multi-stage approach for identifying wagons by detecting and assembling their unique International Union of Railways (UIC) numbers from image data is introduced. Employing a Transformer architecture, the methodology surpasses other methods in accuracy obtained over real-world images and offers fast operation. The model's effectiveness is demonstrated through extensive experiments performed on a unique dataset of more than 3,000 images captured in various weather conditions. The top-performing machine learning model exhibits exceptional speed and real-time performance, processing an average of 100 UIC-containing frames in less than 0.1 son an NVIDIA A10G Tensor Core Graphics Processing Unit, and offering the accuracy of assembling UIC numbers exceeding 95% in scenarios where 1 to 4 digits are missing. The findings presented in this article not only open avenues to apply sequence-to-sequence models to various industrial object detection tasks, but can also significantly improve railway operations.

关键词： Railway wagon identification Transformer sequence-to-sequence model Automated identification systems Object detection

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Highway non-recurrent congestion prediction using a multi-step spatio-temporal deep learning approach

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TRANSPORTATION LETTERS-THE INTERNATIONAL JOURNAL OF TRANSPORTATION RESEARCH 2025年

作者： Li, Jing Yang, Hao Razavi, Saiedeh Abdulsattar, Harith McMaster Univ Dept Civil Engn Hamilton ON L8S 4L8 Canada

Accurate forecasting of highway Non-Recurrent Congestion (NRC) is critical for modern transportation systems. However, this research remains in its early stages and is frequently constrained to single-step temporal prediction. To address this limitation, this research presents a novel approach that leverages the Dual-Stream Autoencoder sequence-to-sequence model (DS-AE-Seq2Seq) to create an accurate tool for quantitative, spatio-temporal, and multi-step prediction of highway NRC. The proposed model innovatively integrates an Autoencoder and a Seq2Seq encoder to process static and time-series data, respectively. The decoder generates spatio-temporal predictions using the outcomes of both streams. The model is tested with data collected from highway I-5 and I-405, USA. Results show that it not only outperforms benchmarks but also exhibits high prediction accuracy under extreme traffic conditions, including severe injury incidents and various levels of service. Additionally, the model demonstrated reliability, through a sensitivity analysis, across different distances and prediction horizons.

关键词： Non-recurrent congestion congestion prediction sequence-to-sequence model multi-step prediction spatio-temporal prediction

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Advanced process control for sulfur recovery units by artificial intelligence (AI) surrogate model

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JOURNAL OF THE TAIWAN INSTITUTE OF CHEMICAL ENGINEERS 2025年 172卷

作者： Liu, Jialin Chien, Hao-Che Wong, David Shan-Hill Hsieh, Cheng-Ting Tunghai Univ Dept Chem & Mat Engn 1727Sec 4Taiwan Blvd Taichung Taiwan Natl Tsing Hua Univ Dept Chem Engn 101Sect 2Kuang Fu Rd Hsinchu Taiwan China Steel Corp Green Energy & Syst Integrat Res & Dev Dept 1 Chung Kang Rd Kaohsiung Taiwan

Background: The modified Claus process is a major technology used for the recovery of elemental sulfur from acid gases. The process consists of two steps. In the first step, a portion of H2S is oxidized to SO2 in a reaction furnace;the second step comprises the reaction of the remaining H2S with SO2 at lower temperatures over a catalyst. The major manipulated variables (MVs) to maximize the sulfur recovery efficiency (SRE) are the combustion air flow rate and inlet gas temperature for the converter. However, both MVs encounter a process gain reverse problem for the controlled variable (CV), which refers to the outlet total sulfur. In addition, process control is limited by the significant time delay between the air flow rate and tail gas analyzer. Methods: In this study, an artificial intelligence (AI) surrogate model was built and the movement of the MVs was evaluated using to minimize the sum of the predicted total sulfur in the prediction horizon. The assumption of future disturbance data is not necessary for the proposed approach. Significant findings: In field operations, a specific H2S/SO2 in the tail gas is maintained by the combustion air that is inefficient to minimize total sulfur. The total sulfur content can be reduced by approximately 12 % from 1.6 %- 1.4 % compared with that of field operations by the advanced process control.

关键词： Modified Claus process Process gain reverse Nonlinear process control sequence-to-sequence model

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sequence-to-sequence models for Automated Text Simplification 21st

Sequence-to-Sequence Models for Automated Text Simplificatio...

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21st International Conference on Artificial Intelligence in Education (AIED)

作者： Botarleanu, Robert-Mihai Dascalu, Mihai Crossley, Scott Andrew McNamara, Danielle S. Univ Politehn Bucuresti 313 Splaiul Independentei Bucharest 060042 Romania Acad Romanian Scientists Str Ilfov 3 Bucharest 050044 Romania Georgia State Univ Dept Appl Linguist ESL Atlanta GA 30303 USA Arizona State Univ Dept Psychol POB 871104 Tempe AZ 85287 USA

ISBN: (纸本)9783030522407;9783030522391

A key writing skill is the capability to clearly convey desired meaning using available linguistic knowledge. Consequently, writers must select from a large array of idioms, vocabulary terms that are semantically equivalent, and discourse features that simultaneously reflect content and allow readers to grasp meaning. In many cases, a simplified version of a text is needed to ensure comprehension on the part of a targeted audience (e.g., second language learners). To address this need, we propose an automated method to simplify texts based on paraphrasing. Specifically, we explore the potential for a deep learning model, previously used for machine translation, to learn a simplified version of the English language within the context of short phrases. The best model, based on an Universal Transformer architecture, achieved a BLEU score of 66.01. We also evaluated this model's capability to perform similar transformation to texts that were simplified by human experts at different levels.

关键词： Natural language processing Text simplification Paraphrasing sequence-to-sequence model

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sequence-to-sequence deep learning model for building energy consumption prediction with dynamic simulation modeling

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JOURNAL OF BUILDING ENGINEERING 2021年 43卷

作者： Kim, Chul Ho Kim, Marie Song, Yu Jin Korea Univ Res Inst Engn & Technol Coll Engn 145 Anam Ro Seoul 02841 South Korea Elect & Telecommun Res Inst ETRI KSB Convergence Res Dept 218 Gajeong Ro Daejeon 34101 South Korea Korea Inst Energy Res KIER Energy ICT Convergence Res Dept 140 Yuseong Daero Daejeon 34101 South Korea

This study used a more detailed dataset approach to address the limitations of existing building energy prediction methods and to predict building energy demand more accurately. The EnergyPlus dynamic simulation program closely modeled building envelope performance, zone division, and heating, ventilation, and air conditioning systems. Building energy simulation used actual weather data and generated occupancy data. The occupancy, lighting, and equipment schedules for each zone were generated in 5-min intervals using the Lawrence Berkeley National Laboratory occupancy simulator. Summer electric power consumption based on Internet of Things information from the testbed building validated the model. When applying generated occupancy, lighting, and equipment schedules, the mean bias error of simulation similarly improved to 4.73%, and the coefficient of variation of the root-mean-squared error (Cv(RMSE]) improved to 12.26%. Subsequently, a demand prediction model was constructed as a sequence-to-sequence (seq2seq) model using long short-term memory (LSTM) cells in recurrent neural network (RNN) algorithms, then its accuracy was evaluated. In the seq2seq model, the learning performance based on the EnergyPlus data exhibited an RMSE of 4.48% and a weighted average percentage error of 3.07%. As a result of applying prediction methods while changing climate scenarios, prediction performance also satisfied statistically significant levels. The occupancy information and solar radiation were determined to exert the greatest influence on the building energy demand prediction.

关键词： Building energy prediction sequence-to-sequence model EnergyPlus Deep learning Occupancy

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A novel sequence-to-sequence based deep learning model for satellite cloud image time series prediction

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ATMOSPHERIC RESEARCH 2024年 306卷

作者： Lian, Jie Wu, Shixin Huang, Sirong Zhao, Qin Shanghai Normal Univ Shanghai Engn Res Ctr Intelligent Educ & Bigdata 100 Guilin Rd Shanghai 200030 Peoples R China

Satellite cloud imagery is pivotal for meteorologists in characterizing weather patterns, detecting climate anomaly regions, and predicting rain effects. The task of satellite cloud image forecasting is crucial, and while deep learning models have shown promise in predicting spatio-temporal data, traditional methods face challenges with extracting long-term spatio-temporal features and high computation costs. To address these issues, we propose the Re -parameterized sequence -to -sequence Satellite Cloud Imagery Prediction Network (RepSSCIPN). Rep-SSCIPN utilizes Rep -convolution layers to reduce inference -time cost and memory consumption, enhancing efficiency by converting re -parameterized blocks into a single convolution layer during inference. The sequence normalization attention mechanism in Rep-SSCIPN highlights crucial feature sequences and establishes their inter -dependencies. We validate our novel method using a real -world satellite cloud image dataset from the meteorological satellite "Himawari." Experimental results showcase significant improvements in prediction accuracy and reconstruction quality compared to ConvLSTM, PredRNN, FCLSTM, LMC, SimVP and SCSTque models. The efficiency gains make Rep-SSCIPN a promising advancement for satellite cloud image prediction. ARTICLE INFO.

关键词： Satellite cloud image prediction sequence-to-sequence model Re-parameterized convolution Deep learning

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