The rise of social media platforms has revolutionized communication, enabling the exchange of vast amounts of data through text, audio, images, and videos. These platforms have become critical for sharing opinions and...The rise of social media platforms has revolutionized communication, enabling the exchange of vast amounts of data through text, audio, images, and videos. These platforms have become critical for sharing opinions and insights, influencing daily habits, and driving business, political, and economic decisions. Text posts are particularly significant, and natural language processing (NLP) has emerged as a powerful tool for analyzing such data. While traditional NLP methods have been effective for structured media, social media content poses unique challenges due to its informal and diverse nature. This has spurred the development of new techniques tailored for processing and extracting insights from unstructured user-generated text. One key application of NLP is the summarization of user comments to manage overwhelming content volumes. Abstractive summarization has proven highly effective in generating concise, human-like summaries, offering clear overviews of key themes and sentiments. This enhances understanding and engagement while reducing cognitive effort for users. For businesses, summarization provides actionable insights into customer preferences and feedback, enabling faster trend analysis, improved responsiveness, and strategic adaptability. By distilling complex data into manageable insights, summarization plays a vital role in improving user experiences and empowering informed decision-making in a data-driven landscape. This paper proposes a new implementation framework by fine-tuning and parameterizing Transformer Large Language Models to manage and maintain linguistic and semantic components in abstractive summary generation. The system excels in transforming large volumes of data into meaningful summaries, as evidenced by its strong performance across metrics like fluency, consistency, readability, and semantic coherence.展开更多
Two fabricated on-chip transformers under different ground conditions（i.e.,CG and IG types） have been measured to compare their different characteristics.With the aid of the electromagnetic（EM） solver,we have anal...Two fabricated on-chip transformers under different ground conditions（i.e.,CG and IG types） have been measured to compare their different characteristics.With the aid of the electromagnetic（EM） solver,we have analyzed the differences from the electric and magnetic aspects,and different effects in these aspects can be described with the lumped capacitor and inductor from the perspective of the equivalent circuit model.A physicsbased equivalent circuit model is proposed to model transformers under different ground conditions.In addition, the simple parameter extraction procedure for the corresponding model is also provided.All the model parameters are extracted and agree with the analysis.In order to verify the model＇s validity and accuracy,we have compared the modeled and measured S-parameters,and an excellent agreement has been found over a broad frequency range.展开更多
In recent years,Transformer has achieved remarkable results in the field of computer vision,with its built-in attention layers effectively modeling global dependencies in images by transforming image features into tok...In recent years,Transformer has achieved remarkable results in the field of computer vision,with its built-in attention layers effectively modeling global dependencies in images by transforming image features into token forms.However,Transformers often face high computational costs when processing large-scale image data,which limits their feasibility in real-time applications.To address this issue,we propose Token Masked Pose Transformers(TMPose),constructing an efficient Transformer network for pose estimation.This network applies semantic-level masking to tokens and employs three different masking strategies to optimize model performance,aiming to reduce computational complexity.Experimental results show that TMPose reduces computational complexity by 61.1%on the COCO validation dataset,with negligible loss in accuracy.Additionally,our performance on the MPII dataset is also competitive.This research not only enhances the accuracy of pose estimation but also significantly reduces the demand for computational resources,providing new directions for further studies in this field.展开更多
Cyberbullying is a remarkable issue in the Arabic-speaking world,affecting children,organizations,and businesses.Various efforts have been made to combat this problem through proposed models using machine learning(ML)...Cyberbullying is a remarkable issue in the Arabic-speaking world,affecting children,organizations,and businesses.Various efforts have been made to combat this problem through proposed models using machine learning(ML)and deep learning(DL)approaches utilizing natural language processing(NLP)methods and by proposing relevant datasets.However,most of these endeavors focused predominantly on the English language,leaving a substantial gap in addressing Arabic cyberbullying.Given the complexities of the Arabic language,transfer learning techniques and transformers present a promising approach to enhance the detection and classification of abusive content by leveraging large and pretrained models that use a large dataset.Therefore,this study proposes a hybrid model using transformers trained on extensive Arabic datasets.It then fine-tunes the hybrid model on a newly curated Arabic cyberbullying dataset collected from social media platforms,in particular Twitter.Additionally,the following two hybrid transformer models are introduced:the first combines CAmelid Morphologically-aware pretrained Bidirectional Encoder Representations from Transformers(CAMeLBERT)with Arabic Generative Pre-trained Transformer 2(AraGPT2)and the second combines Arabic BERT(AraBERT)with Cross-lingual Language Model-RoBERTa(XLM-R).Two strategies,namely,feature fusion and ensemble voting,are employed to improve the model performance accuracy.Experimental results,measured through precision,recall,F1-score,accuracy,and AreaUnder the Curve-Receiver Operating Characteristic(AUC-ROC),demonstrate that the combined CAMeLBERT and AraGPT2 models using feature fusion outperformed traditional DL models,such as Long Short-Term Memory(LSTM)and Bidirectional Long Short-Term Memory(BiLSTM),as well as other independent Arabic-based transformer models.展开更多
This paper focuses on the research of the main transformer selection and layout scheme for new energy step-up substations.From the perspective of engineering design,it analyzes the principles of main transformer selec...This paper focuses on the research of the main transformer selection and layout scheme for new energy step-up substations.From the perspective of engineering design,it analyzes the principles of main transformer selection,key parameters,and their matching with the characteristics of new energy.It also explores the layout methods and optimization strategies.Combined with typical case studies,optimization suggestions are proposed for the design of main transformers in new energy step-up substations.The research shows that rational main transformer selection and scientific layout schemes can better adapt to the characteristics of new energy projects while effectively improving land use efficiency and economic viability.This study can provide technical experience support for the design of new energy projects.展开更多
As the plasma current power in tokamak devices increases,a significant number of stray magnetic fields are generated around the equipment.These stray magnetic fields can disrupt the operation of electronic power devic...As the plasma current power in tokamak devices increases,a significant number of stray magnetic fields are generated around the equipment.These stray magnetic fields can disrupt the operation of electronic power devices,particularly transformers in switched-mode power supplies.Testing flyback converters with transformers under strong background magnetic fields highlights electromagnetic compatibility(EMC)issues for such switched-mode power supplies.This study utilizes finite element analysis software to simulate the electromagnetic environment of switched-mode power supply transformers and investigates the impact of variations in different magnetic field parameters on the performance of switched-mode power supplies under strong stray magnetic fields.The findings indicate that EMC issues are associated with transformer core saturation and can be alleviated through appropriate configurations of the core size,air gap,fillet radius,and installation direction.This study offers novel solutions for addressing EMC issues in high magnetic field environments.展开更多
X(formerly known as Twitter)is one of the most prominent social media platforms,enabling users to share short messages(tweets)with the public or their followers.It serves various purposes,from real-time news dissemina...X(formerly known as Twitter)is one of the most prominent social media platforms,enabling users to share short messages(tweets)with the public or their followers.It serves various purposes,from real-time news dissemination and political discourse to trend spotting and consumer engagement.X has emerged as a key space for understanding shifting brand perceptions,consumer preferences,and product-related sentiment in the fashion industry.However,the platform’s informal,dynamic,and context-dependent language poses substantial challenges for sentiment analysis,mainly when attempting to detect sarcasm,slang,and nuanced emotional tones.This study introduces a hybrid deep learning framework that integrates Transformer encoders,recurrent neural networks(i.e.,Long Short-Term Memory(LSTM)and Gated Recurrent Unit(GRU)),and attention mechanisms to improve the accuracy of fashion-related sentiment classification.These methods were selected due to their proven strength in capturing both contextual dependencies and sequential structures,which are essential for interpreting short-form text.Our model was evaluated on a dataset of 20,000 fashion tweets.The experimental results demonstrate a classification accuracy of 92.25%,outperforming conventional models such as Logistic Regression,Linear Support Vector Machine(SVM),and even standalone LSTM by a margin of up to 8%.This improvement highlights the importance of hybrid architectures in handling noisy,informal social media data.This study’s findings offer strong implications for digital marketing and brand management,where timely sentiment detection is critical.Despite the promising results,challenges remain regarding the precise identification of negative sentiments,indicating that further work is needed to detect subtle and contextually embedded expressions.展开更多
文摘The rise of social media platforms has revolutionized communication, enabling the exchange of vast amounts of data through text, audio, images, and videos. These platforms have become critical for sharing opinions and insights, influencing daily habits, and driving business, political, and economic decisions. Text posts are particularly significant, and natural language processing (NLP) has emerged as a powerful tool for analyzing such data. While traditional NLP methods have been effective for structured media, social media content poses unique challenges due to its informal and diverse nature. This has spurred the development of new techniques tailored for processing and extracting insights from unstructured user-generated text. One key application of NLP is the summarization of user comments to manage overwhelming content volumes. Abstractive summarization has proven highly effective in generating concise, human-like summaries, offering clear overviews of key themes and sentiments. This enhances understanding and engagement while reducing cognitive effort for users. For businesses, summarization provides actionable insights into customer preferences and feedback, enabling faster trend analysis, improved responsiveness, and strategic adaptability. By distilling complex data into manageable insights, summarization plays a vital role in improving user experiences and empowering informed decision-making in a data-driven landscape. This paper proposes a new implementation framework by fine-tuning and parameterizing Transformer Large Language Models to manage and maintain linguistic and semantic components in abstractive summary generation. The system excels in transforming large volumes of data into meaningful summaries, as evidenced by its strong performance across metrics like fluency, consistency, readability, and semantic coherence.
基金supported by the National Natural Science Foundation of China(No.61106024)
文摘Two fabricated on-chip transformers under different ground conditions（i.e.,CG and IG types） have been measured to compare their different characteristics.With the aid of the electromagnetic（EM） solver,we have analyzed the differences from the electric and magnetic aspects,and different effects in these aspects can be described with the lumped capacitor and inductor from the perspective of the equivalent circuit model.A physicsbased equivalent circuit model is proposed to model transformers under different ground conditions.In addition, the simple parameter extraction procedure for the corresponding model is also provided.All the model parameters are extracted and agree with the analysis.In order to verify the model＇s validity and accuracy,we have compared the modeled and measured S-parameters,and an excellent agreement has been found over a broad frequency range.
基金supported in part by the Scientific Research Start-Up Fund of Zhejiang Sci-Tech University,under the project titled“(National Treasury)Development of a Digital Silk Museum System Based on Metaverse and AR”(Project No.11121731282202-01).
文摘In recent years,Transformer has achieved remarkable results in the field of computer vision,with its built-in attention layers effectively modeling global dependencies in images by transforming image features into token forms.However,Transformers often face high computational costs when processing large-scale image data,which limits their feasibility in real-time applications.To address this issue,we propose Token Masked Pose Transformers(TMPose),constructing an efficient Transformer network for pose estimation.This network applies semantic-level masking to tokens and employs three different masking strategies to optimize model performance,aiming to reduce computational complexity.Experimental results show that TMPose reduces computational complexity by 61.1%on the COCO validation dataset,with negligible loss in accuracy.Additionally,our performance on the MPII dataset is also competitive.This research not only enhances the accuracy of pose estimation but also significantly reduces the demand for computational resources,providing new directions for further studies in this field.
基金funded by the Deanship of Scientific Research at Northern Border University,Arar,Saudi Arabia,through the project number“NBU-FFR-2025-1197-01”.
文摘Cyberbullying is a remarkable issue in the Arabic-speaking world,affecting children,organizations,and businesses.Various efforts have been made to combat this problem through proposed models using machine learning(ML)and deep learning(DL)approaches utilizing natural language processing(NLP)methods and by proposing relevant datasets.However,most of these endeavors focused predominantly on the English language,leaving a substantial gap in addressing Arabic cyberbullying.Given the complexities of the Arabic language,transfer learning techniques and transformers present a promising approach to enhance the detection and classification of abusive content by leveraging large and pretrained models that use a large dataset.Therefore,this study proposes a hybrid model using transformers trained on extensive Arabic datasets.It then fine-tunes the hybrid model on a newly curated Arabic cyberbullying dataset collected from social media platforms,in particular Twitter.Additionally,the following two hybrid transformer models are introduced:the first combines CAmelid Morphologically-aware pretrained Bidirectional Encoder Representations from Transformers(CAMeLBERT)with Arabic Generative Pre-trained Transformer 2(AraGPT2)and the second combines Arabic BERT(AraBERT)with Cross-lingual Language Model-RoBERTa(XLM-R).Two strategies,namely,feature fusion and ensemble voting,are employed to improve the model performance accuracy.Experimental results,measured through precision,recall,F1-score,accuracy,and AreaUnder the Curve-Receiver Operating Characteristic(AUC-ROC),demonstrate that the combined CAMeLBERT and AraGPT2 models using feature fusion outperformed traditional DL models,such as Long Short-Term Memory(LSTM)and Bidirectional Long Short-Term Memory(BiLSTM),as well as other independent Arabic-based transformer models.
文摘This paper focuses on the research of the main transformer selection and layout scheme for new energy step-up substations.From the perspective of engineering design,it analyzes the principles of main transformer selection,key parameters,and their matching with the characteristics of new energy.It also explores the layout methods and optimization strategies.Combined with typical case studies,optimization suggestions are proposed for the design of main transformers in new energy step-up substations.The research shows that rational main transformer selection and scientific layout schemes can better adapt to the characteristics of new energy projects while effectively improving land use efficiency and economic viability.This study can provide technical experience support for the design of new energy projects.
基金supported by the Natural Science Foundation of Anhui Province(No.228085ME142)the Comprehensive Research Facility for the Fusion Technology Program of China(No.20180000527301001228)the Open Fund of the Magnetic Confinement Fusion Laboratory of Anhui Province(No.2024AMF04003)。
文摘As the plasma current power in tokamak devices increases,a significant number of stray magnetic fields are generated around the equipment.These stray magnetic fields can disrupt the operation of electronic power devices,particularly transformers in switched-mode power supplies.Testing flyback converters with transformers under strong background magnetic fields highlights electromagnetic compatibility(EMC)issues for such switched-mode power supplies.This study utilizes finite element analysis software to simulate the electromagnetic environment of switched-mode power supply transformers and investigates the impact of variations in different magnetic field parameters on the performance of switched-mode power supplies under strong stray magnetic fields.The findings indicate that EMC issues are associated with transformer core saturation and can be alleviated through appropriate configurations of the core size,air gap,fillet radius,and installation direction.This study offers novel solutions for addressing EMC issues in high magnetic field environments.
文摘X(formerly known as Twitter)is one of the most prominent social media platforms,enabling users to share short messages(tweets)with the public or their followers.It serves various purposes,from real-time news dissemination and political discourse to trend spotting and consumer engagement.X has emerged as a key space for understanding shifting brand perceptions,consumer preferences,and product-related sentiment in the fashion industry.However,the platform’s informal,dynamic,and context-dependent language poses substantial challenges for sentiment analysis,mainly when attempting to detect sarcasm,slang,and nuanced emotional tones.This study introduces a hybrid deep learning framework that integrates Transformer encoders,recurrent neural networks(i.e.,Long Short-Term Memory(LSTM)and Gated Recurrent Unit(GRU)),and attention mechanisms to improve the accuracy of fashion-related sentiment classification.These methods were selected due to their proven strength in capturing both contextual dependencies and sequential structures,which are essential for interpreting short-form text.Our model was evaluated on a dataset of 20,000 fashion tweets.The experimental results demonstrate a classification accuracy of 92.25%,outperforming conventional models such as Logistic Regression,Linear Support Vector Machine(SVM),and even standalone LSTM by a margin of up to 8%.This improvement highlights the importance of hybrid architectures in handling noisy,informal social media data.This study’s findings offer strong implications for digital marketing and brand management,where timely sentiment detection is critical.Despite the promising results,challenges remain regarding the precise identification of negative sentiments,indicating that further work is needed to detect subtle and contextually embedded expressions.
