检索结果-内蒙古大学图书馆

2025 IEEE PES Grid Edge Technologies Conference and Exposition, Grid Edge 2025

作者： Hussain, Arif Ravikumar, Gelli Iowa State University Department of Electrical and Computer Engineering Ames United States

ISBN: (纸本)9798350352528

This paper presents a online system for detecting and localizing faults in distributed energy resources integrated microgrid (DER-μG) test systems using advanced deep learning methods. We propose a hybrid unsupervised deep learning model that combines long short-term memory (LSTM) and auto-encoders (AE) to detect and localize faults in the DER -μ G system. The LSTM architecture has been carefully configured with optimized encoder and decoder hidden units, as well as window length and threshold parameters to ensure online performance. The Adam optimization algorithm is utilized for training the model, which is continually monitored for optimal performance metrics. Once trained, the model is seamlessly integrated into the Simulink environment for online testing of the proposed model. Our experimental results which include various types of line faults, show that the proposed model performs accurately to detect and localize the faults within an average time of 9 ms. The proposed model provides an accurate and reliable solution for enhancing system reliability and promptly responding to faults in microarid ooerations. © 2025 IEEE.

关键词： Microgrids

来源：评论

学校读者我要写书评

暂无评论

Fake News Encoder Classifier (FNEC) for Online Published News Related to COVID-19 Vaccines

引用

Intelligent Automation & Soft Computing 2023年第7期37卷 73-90页

作者： Asma Qaiser Saman Hina Abdul Karim Kazi Saad Ahmed Raheela Asif Department of Computer Science and Information Technology NED University of Engineering and TechnologyKarachi75270Pakistan Department of Computer Science Iqra UniversityKarachi76400Pakistan Department of Software Engineering NED University of Engineering and TechnologyKarachi75270Pakistan

In the past few years,social media and online news platforms have played an essential role in distributing news content *** of the authenticity of news has become a major *** the COVID-19 outbreak,misinformation and fake news were major sources of confusion and insecurity among the general *** the first quarter of the year 2020,around 800 people died due to fake news relevant to *** major goal of this research was to discover the best learning model for achieving high accuracy and performance.A novel case study of the Fake News Classification using ELECTRA model,which achieved 85.11%accuracy score,is thus reported in this *** addition to that,a new novel dataset called COVAX-Reality containing COVID-19 vaccine-related news has been *** the COVAX-Reality dataset,the performance of FNEC is compared to several traditional learning models i.e.,Support Vector Machine(SVM),Naive Bayes(NB),Passive Aggressive Classifier(PAC),Long Short-Term Memory(LSTM),Bi-directional LSTM(Bi-LSTM)and Bi-directional Encoder Representations from Transformers(BERT).For the evaluation of FNEC,standard metrics(Precision,Recall,Accuracy,and F1-Score)were utilized.

关键词： Deep learning fake news detection machine learning transformer model classification

来源：评论

学校读者我要写书评

暂无评论

Forecasting Smart Grid Stability Using Machine Learning Models

Forecasting Smart Grid Stability Using Machine Learning Mode...

引用

2024 IEEE International Conferences of AI, science, engineering, and Technology, AIxSET 2024

作者： Kandukuri, Raja Banad, Yaser M. Southern University Department of Computer Science Baton RougeLA United States School of Electrical and Computer Engineering University of Oklahoma NormanOK United States

ISBN: (纸本)9798350390995

This paper presents a comprehensive study on designing and evaluating machine learning models for forecasting smart power grid stability. The stability of power grids is crucial for balancing electricity supply and demand, but it is challenged by the increasing use of renewable resources and dynamic pricing. Concepts like Decentralized Smart Grid Control (DSGC) aim to address this issue. We developed and compared machine learning models, including Support Vector Machines (SVM), LightGBM, and deep learning architectures, to identify the most accurate approach for forecasting grid stability. Our results showed that the SVM model achieved the highest accuracy of 99.9%, outperforming other models. These outcomes provide valuable insights and practical recommendations for deploying effective forecasting models in real-world smart grid scenarios, ensuring stability, efficient energy management, and seamless integration of renewable resources. © 2024 IEEE.

关键词： Power grids

来源：评论

学校读者我要写书评

暂无评论

Investigation of Discrepancies Between Induction Motor Test Results and Design Simulation

Investigation of Discrepancies Between Induction Motor Test ...

引用

2024 International Conference on electrical Machines, ICEM 2024

作者： Falekas, Georgios Kolidakis, Zafeirios Karlis, Athanasios Democritus University of Thrace Department of Electrical and Computer Engineering Xanthi Greece

ISBN: (纸本)9798350370607

This work investigates discrepancies in parameters and performance between an induction machine design simulation and the produced unit. The squirrel cage motor is explored analytically and in finite element space based on the complete design blueprints and material properties. The built motor is subjected to the DC, no-load, locked-rotor, and load tests. The simulated model is created parametric, according to the manufacturing process and tolerances. Solution space search results indicate that performance and parameters can deviate with minor structural changes. Reliable simulation prior to production can greatly reduce production time and cost, while improving performance and allowing for optimization. The resulting approach will be used as a basis for a manufacturing digital twin in further collaboration. © 2024 IEEE.

关键词： Squirrel cage motors

来源：评论

学校读者我要写书评

暂无评论

PsychoLex: Unveiling the Psychological Mind of Large Language Models 1

PsychoLex: Unveiling the Psychological Mind of Large Languag...

引用

1st Workshop on NLP for science, NLP4science 2024

作者： Abbasi, Mohammad Amin Mirnezami, Farnaz Sadat Naderi, Hassan Department of Computer Engineering Iran University of Science and Technology Tehran Iran Department of Computer Engineering University of Guilan Rasht Iran

ISBN: (纸本)9798891761858

This paper explores the intersection of psychology and artificial intelligence through the development and evaluation of specialized Large Language Models (LLMs). We introduce PsychoLex1 , a suite of resources designed to enhance LLMs' proficiency in psychological tasks in both Persian and English. Key contributions include the PsychoLexQA dataset for instructional content and the PsychoLexEval dataset for rigorous evaluation of LLMs in complex psychological scenarios. Additionally, we present the PsychoLexLLaMA model, optimized specifically for psychological applications, demonstrating superior performance compared to general-purpose models. The findings underscore the potential of tailored LLMs for advancing psychological research and applications, while also highlighting areas for further refinement. This research offers a foundational step towards integrating LLMs into specialized psychological domains, with implications for future advancements in AI-driven psychological practice. © 2024 Association for Computational Linguistics.

关键词： Psychoacoustic

来源：评论

学校读者我要写书评

暂无评论

A Comparative Analysis of CNN-based Deep Learning Models for Landslide Detection

A Comparative Analysis of CNN-based Deep Learning Models for...

引用

2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Oak, Omkar Nazre, Rukmini Naigaonkar, Soham Sawant, Suraj Vaidya, Himadri Department of Computer Science & Engineering COEP Tech University Pune India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (纸本)9798350374933

Landslides inflict substantial societal and economic damage, underscoring their global significance as recurrent and destructive natural disasters. Recent landslides in northern parts of India and Nepal have caused significant disruption, damaging infrastructure and posing threats to local communities. Convolutional Neural Networks (CNNs), a type of deep learning technique, have shown remarkable success in image processing. Because of their sophisticated architectures, advanced CNN-based models perform better in landslide detection than conventional algorithms. The purpose of this work is to investigate CNNs’ potential in more detail, with an emphasis on comparison of CNN based models for better landslide detection. We compared four traditional semantic segmentation models (U-Net, LinkNet, PSPNet, and FPN) and utilized the ResNet50 backbone encoder to implement them. Moreover, we have experimented with the hyperparameters such as learning rates, batch sizes, and regularization techniques to fine-tune the models. We have computed the confusion matrix for each model and used performance metrics including precision, recall and f1-score to evaluate and compare the deep learning models. According to the experimental results, LinkNet gave the best results among the four models having an Accuracy of 97.49% and a F1-score of 85.7% (with 84.49% precision, 87.07% recall). We have also presented a comprehensive comparison of all pixel-wise confusion matrix results and the time taken to train each model. © 2024 IEEE.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

VMFMAX: Improving Virtual Machine Utilization in VNF Placement

VMFMAX: Improving Virtual Machine Utilization in VNF Placeme...

引用

2024 Systems and Information engineering Design Symposium, SIEDS 2024

作者： Taghizadeh, Seyedreza Emami, Parnian Taleghani, Hamid Reza Tarmah, Ali Taghizadeh, Seyedehnarjes Shiraz University Department of Computer Science Engineering and Information Technology Shiraz Iran Shiraz University Department of Electrical and Computer Engineering Shiraz Iran NASINE Co. Department of Information Technology Shiraz Iran

ISBN: (纸本)9798350385144

In order to improve the performance of contemporary and prospective technologies grounded in Network Function Virtualization (NFV), it is imperative to concentrate on identifying the most effective strategies for the placement of Virtual Network Functions (VNFs) within NFV frameworks. Consequently, the objective of this study is to present a viable solution for VNF placement. We delve into the significance of factors such as Storage Latency, CPU Model, and Network Packet Dropped, among others, which necessitate careful consideration. These parameters wield substantial influence over Virtual Machines (VMs) and overall VNF placements. Through the judicious selection of suitable VMs for VNFs, our aim is to improve the placements of VNFs, employing our proposed methodology termed VMFMAX. Our findings reveal that VMFMAX has efficient and less time and resource-consuming calculations compared to previous studies. VMFMAX conspicuously impacts the outcomes of the VNF placement process, underscoring its critical role in achieving more precise and accurate calculations. © 2024 IEEE.

关键词： Edge computing

来源：评论

学校读者我要写书评

暂无评论

A Review of Modern Computing Paradigms: Cloud, Fog, and Edge

A Review of Modern Computing Paradigms: Cloud, Fog, and Edge

引用

2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Sharma, Neha Choudhary, Monika Srivastava, Prakash Prabha, Chander Sharma, Himanshu Cgc Department of Computer Science and Engineering Punjab Landran India Department of Computer Science & Engineering Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331509828

In the current competitive environment, businesses are looking at how to lower their IT expenses while simultaneously improving their productivity and flexibility to respond quickly to changes in their operational procedures. One of the most widely accepted information technology-based services is cloud computing, which has garnered interest from both the academic and business communities as well as the general public-characteristics such as pay-per-use and subscription options ease of use, elasticity, and low entry costs. Low entry cost in fog, cloud, and edge computing is achieved by utilizing pay-as-you-go pricing models, leveraging open-source technologies, and optimizing resource allocation. By emphasizing how this technology differs from the rest of the current technology and concentrating on its numerous benefits and areas of application-which provide proof of its growing popularity-this paper presents the development of modern computer paradigms and associated areas of research interest. A discussion of each paradigm is done by clearly defining its main ideas and how they relate to the others. The paper presents a comparative assessment of Cloud, Edge, and Fog computing indicating their diverse areas of applicability. © 2025 IEEE.

关键词： Fog computing

来源：评论

学校读者我要写书评

暂无评论

Decoding the Script: A Bi-LSTM Based Journey into Doctor's Handwriting for Precise Prescription Understanding

Decoding the Script: A Bi-LSTM Based Journey into Doctor's H...

引用

2024 International Conference on Smart Devices, ICSD 2024

作者： Khan, Mohd Ayan Arif, Hamza Singh, Vishal Sharma, Dolly Amity University Department of Computer Science and Engineering Noida India 3rd Amity University Department of Computer Science and Engineering Noida India

ISBN: (纸本)9798350394412

Deciphering a doctor's handwritten prescription has been a complex task for human beings. Research has been done to automate the understanding of handwritten text, but transcribing doctor's handwritten prescriptions still requires the advancement to build a method that can be applied in real life. This paper presents a new hybrid model built on a fusion of CNN and Bi-LSTM architectures trained with CTC loss for the difficult task of decoding non-printed handwritten physicians' prescriptions. The unification of signals such as spatial feature extraction (CNN) as well as sequential dependency decrease (Bi-LSTM) with the aim of misinterpretation mitigation retrieves a very high recognition accuracy over a single, segmented test collection. With iterative training methodology, which focuses on computational limitations, the approach results in continuous learning, thus eliminating the problem of overfitting. The given model is very good at understanding the heterogeneity of handwriting and contextual relationships in prescription texts. This scientific addition leads to improvements in patient care, streamlined workflows, and healthcare information management improvements as the ultimate performance of the model is the astonishing 98.40% accuracy level it offers. © 2024 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Enhancing Urban Traffic Management Through Machine Learning Prediction Models for Sensor-Less Roads 8

Enhancing Urban Traffic Management Through Machine Learning ...

引用

8th International Conference on Intelligent Traffic and Transportation, ICITT 2024 - incorporating the 8th International Conference on Communication and Network Technology, ICCNT 2024

作者： de Souza Oliveira, Thamires Di Grande, Sarah Berlotti, Mariaelena Cavalieri, Salvatore Torrisi, Vincenza Calabrò, Giovanni Inturri, Giuseppe Department of Electrical Electronic and Computer Engineering University of Catania Catania Italy

ISBN: (纸本)9781643685601

The process of urbanization on a global scale has generated a significant increase in metropolitan populations, which in turn brings with it a series of challenges for the management of transport infrastructure. In this context, the importance of accurate traffic flow prediction in urban traffic management systems stands out, with the city of Catania being used as the focus of this study. However, predicting traffic flow faces challenges, such as the high costs associated with acquiring extensive real traffic data, particularly when installing sensors on every road is not feasible. To address the issue of missing traffic flow data on roads without sensors, this study proposes an innovative approach based on machine learning, using the Long Short-Term Memory architecture and incorporating both sensor and floating car data. The results obtained are thoroughly analyzed and discussed, highlighting the ability of the LSTM model to provide accurate predictions of urban traffic, even with a limited amount of data available. These findings highlight the relevance of employing advanced machine learning techniques in the efficient management of urban mobility, aiming to improve the quality of life in cities and deal more effectively with the challenges of contemporary urban traffic. © 2024 The Authors.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：