检索结果-内蒙古大学图书馆

2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Li, Mingze Ren, Weihang Fan, Lei Guan, Yongpei University of Houston Dept. of Electrical and Computer Engineering TX United States Engineering Technology University of Houston TX United States University of Florida Dept. of Industrial and Systems Engineering FL United States

ISBN: (纸本)9798331541125

This paper presents a preprocessing method to solve the security-constrained unit commitment with AC power flows (SCUC-ACPF), which is a large scale mixed-integer non-convex, nonlinear optimization problem. We introduce clustering algorithms and local search to reduce problem complexity, and obtain local optimal solutions within limited solving time and computational capacity. The clustering algorithms group buses into manageable clusters centered around core buses, creating smaller sub-grids for efficient optimization. The local search algorithms iteratively explore and improve solution quality. Our method ensures balanced clusters that respect physical and operational constraints, significantly enhancing optimization efficiency and contributing to more effective grid management and operation. © 2025 IEEE.

关键词： AC power flow clustering local search Unit commitment

来源：评论

学校读者我要写书评

暂无评论

SB-AGT: A stochastic beam search-enhanced attention-based Gumbel tree framework for drug-drug interaction extraction from biomedical literature

引用

computers in Biology and Medicine 2025年 189卷 110011-110011页

作者： Saad, Eman Kishk, Sherif Ali-Eldin, Amr Saleh, Ahmed I. Communication and Information Technology Center Mansoura University Egypt Electronics and Communication Engineering Dept. Mansoura University Egypt Computer Engineering & Control Systems Dept. Mansoura University Egypt

Drug-Drug Interactions (DDI) and Chemical-Protein Interactions (CPI) detection are crucial for patient safety, as unidentified interactions may lead to severe Adverse Drug Reactions (ADRs). While extensive DDI and CPI information exists within biomedical literature, manual extraction by experts proves time-intensive and resource-demanding. This study presents SB-AGT: Stochastic Beam Search-enhanced Attention-based Gumbel Tree an innovative approach to automated DDI and CPI extraction through an attention-based architecture incorporating a modified Gumbel-Tree method with stochastic beam search optimization. SB-AGT leverages multi-head attention mechanisms and enhanced latent tree structures to effectively capture complex syntactic features and drug relationship patterns. Also, the methodology includes comprehensive preprocessing protocols and addresses dataset imbalance through hybrid sampling techniques. Experimental validation on the DDIExtraction 2013 and CHEMPROT datasets demonstrates the model's effectiveness, achieving precision, surpassing traditional approaches and showing competitive performance compared to contemporary pre-trained models. Through parametric analysis, we establish optimal beam search configurations that maximize the model's extraction accuracy. © 2025 Elsevier Ltd

关键词： Stochastic models

来源：评论

学校读者我要写书评

暂无评论

Multi-Point Positional Insertion Tuning for Small Object Detection

Multi-Point Positional Insertion Tuning for Small Object Det...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Kanoko Goto Takumi Karasawa Takumi Hirose Rei Kawakami Nakamasa Inoue Dept. of Computer Science Institute of Science Tokyo Tokyo Japan Dept. of Systems and Control Engineering Institute of Science Tokyo Tokyo Japan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Small object detection aims to localize and classify small objects within images. With recent advances in large-scale vision-language pretraining, finetuning pretrained object detection models has emerged as a promising approach. However, finetuning large models is computationally and memory expensive. To address this issue, this paper introduces multi-point positional insertion (MPI) tuning, a parameter-efficient finetuning (PEFT) method for small object detection. Specifically, MPI incorporates multiple positional embeddings into a frozen pretrained model, enabling the efficient detection of small objects by providing precise positional information to latent features. Through experiments, we demonstrated the effectiveness of the proposed method on the SODA-D dataset. MPI performed comparably to conventional PEFT methods, including CoOp and VPT, while significantly reducing the number of parameters that need to be tuned.

关键词： Computational modeling Object detection Signal processing Feature extraction Encoding Acoustics Speech processing Tuning

来源：评论

学校读者我要写书评

暂无评论

Predicting Optimal Placement of Electric Vehicle Charge Stations Using Machine Learning: A Case Study in Glasgow, UK 12

Predicting Optimal Placement of Electric Vehicle Charge Stat...

引用

12th Iranian Conference on Renewable Energies and Distributed Generation, ICREDG 2025

作者： Mousaei, Arash Naderi, Yahya University of Tabriz Dept. Computer and Electrical Engineering Tabriz Iran Ricardo Aea Sustainable Energy Systems Department Glasgow United Kingdom

ISBN: (纸本)9798331525132

Given the reduced costs of establishing Electric Vehicle (EV) charging stations and the need to improve their accessibility, it is essential for EV developers and governments to identify optimized locations for new stations. This study presents a predictive modeling approach using Machine Learning to determine the optimal sites for EV charging stations in Glasgow. While various machine learning methods are available for this purpose, we focus on Linear Regression due to its simplicity and high accuracy. By analyzing a comprehensive dataset of 207 AC charging stations-including factors latitude, longitude, population density, number of kinds of plugs, total number of plugs, price per kWh, traffic conditions, distance to the nearest station, Air Quality Index (AQI), and land value - we aim to provide urban planners and policymakers with actionable insights. The study evaluates the performance of the Linear Regression model in predicting the most suitable locations for charging stations. The results indicate that the proposed model achieved impressive accuracy, with R2, Mean Absolute Error (MAE), and Mean Squared Error (MSE) values of 0.89, 0.05, and 0.08, respectively. This research contributes to the ongoing efforts in urban planning for sustainable mobility by offering a data-driven methodology for EV infrastructure development. © 2025 IEEE.

关键词： Linear regression

来源：评论

学校读者我要写书评

暂无评论

Optimizing 3D Flight Paths for Multiple UAVs with Connectivity Management in Urban Delivery systems 19

Optimizing 3D Flight Paths for Multiple UAVs with Connectivi...

引用

19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Lee, Yeongmin Syahran, Raihan Muhammad Spangenberger, Mael Yang, Huigyu Oh, Jiyong Choo, Hyunseung Dept. of Artificial Intelligence Sungkyunkwan University Suwon Korea Republic of Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of Convergence Research Institute Sungkyunkwan University Suwon Korea Republic of Dept. of AI Systems Engineering Sungkyunkwan University Suwon Korea Republic of Dept. of Informatics Télécom SudParis Évry France

ISBN: (纸本)9798331507817

The dynamic deployment of aerial vehicles in urban delivery scenarios demands precise route planning, reliable data links, and efficient use of network infrastructure. Although prior efforts have explored various aspects of unmanned navigation or communication optimization, existing approaches often overlook the combined impact of three-dimensional path selection and robust connectivity maintenance. To address this gap, this paper proposes a deep reinforcement learning framework employing Independent Proximal Policy Optimization (IPPO) to steer the flight paths of unmanned vehicles while curtailing needless interactions with base stations (BSs). By incorporating communication-specific metrics, Reference Signal Received Power (RSRP), the proposed system adapts to urban conditions and upholds resilient network links. Comprehensive performance evaluations reveal the potential of the framework to reduce excessive BS connections by up to 82.24%, preserve RSRP levels above -80 dBm, and balance handover frequencies across flight trajectories. These findings affirm the scalability and effectiveness of the method for achieving efficient aerial navigation and consistent communication in urban delivery services. © 2025 IEEE.

关键词： Deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Distance Based Single-Channel Target Speech Extraction

Distance Based Single-Channel Target Speech Extraction

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Runwu Shi Benjamin Yen Kazuhiro Nakadai Dept. of Systems & Control Engineering Tokyo Institute of Technology Tokyo Japan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

This paper aims to achieve single-channel target speech extraction (TSE) in enclosures by solely utilizing distance information. This is the first work that utilizes only distance cues without using speaker physiological information for single-channel TSE. Inspired by recent single-channel Distance-based separation and extraction methods, we introduce a novel model that efficiently fuses distance information with time-frequency (TF) bins for TSE. Experimental results in both single-room and multi-room scenarios demonstrate the feasibility and effectiveness of our approach. This method can also be employed to estimate the distances of different speakers in mixed speech. Online demos are available at https://***/distance-demo-page/.

关键词： Time-frequency analysis Fuses Buildings Signal processing Physiology Acoustics Data mining Speech processing

来源：评论

学校读者我要写书评

暂无评论

Closed-Loop System Identification of an Unstable Nonlinear System Using Koopman Operator

Closed-Loop System Identification of an Unstable Nonlinear S...

引用

First International Symposium on Instrumentation, control, Artificial Intelligence, and Robotics (ICA-SYMP)

作者： Benjaset Maneeloke Sudchai Boonto Peerayot Sanposh Yodyium Tipsuwan Natthawut Chinthaned Warit Tanaprasitpattana Dept. of Electrical Engineering Kasetsart University Bangkok Thailand Dept. of Control Systems and Instrumentation Engineering King Mongkut's University of Technology Thonburi Bangkok Thailand Dept. of Computer Engineering Kasetsart University Bangkok Thailand

ISBN: (数字)9798331505400

ISBN: (纸本)9798331505417

This paper presents a novel methodology for closed-loop system identification of unstable nonlinear systems using the Koopman operator with Extended Dynamic Mode Decomposition with control (EDMDc). The study highlights the critical role of selecting appropriate observable functions to develop accurate and efficient Koopman models. We demonstrate that the resulting Koopman models exhibit excellent fitting and validation properties and retain the stabilizability of the original nonlinear systems. These models are verified through time and frequency responses under closed-loop control using a Linear Quadratic Regulator (LQR), confirming their effectiveness. Future work will extend this framework to more complex systems and incorporate machine learning techniques to refine the selection of observable functions. This approach aims further to enhance the adaptability and robustness of Koopman-based control strategies.

关键词： Time-frequency analysis Adaptation models Regulators Accuracy Fitting Machine learning Robustness Closed loop systems Complex systems Frequency control

来源：评论

学校读者我要写书评

暂无评论

Enhancing 6G-Satellite Network Integration with Artificial Intelligence: A Future Communication Paradigm 22

Enhancing 6G-Satellite Network Integration with Artificial I...

引用

22nd International Learning and Technology Conference, L and T 2025

作者： Salah, Ibrahim Hussein, Aziza I. Wageh Lotfy, Mostafa Mabrook, Mohamed Mourad Almaaqal University College of Engineering Control and Computer Department Basrah61001 Iraq Effat University College of Engineering Energy and Technology Research Center Saudi Arabia Faculty of Technology and Education Beni-Suef University Dept. of Process Control Technology Beni-Suef Egypt Beni-Suef University Faculty of Navigation Science & Space Technology Space Communication Dept. Beni-Suef Egypt

ISBN: (纸本)9798331532215

The integration of 6G networks and satellite communications is set to revolutionize global connectivity, offering seamless coverage across terrestrial and non-terrestrial environments. Artificial Intelligence (AI) is essential for improving this integration, addressing challenges such as dynamic resource management, latency reduction, and network optimization. AI techniques like machine learning, deep learning, and reinforcement learning offer innovative solutions to handle the complexities of 6G-satellite networks. These advancements promise to significantly improve network efficiency, enhance data transmission reliability, and ensure seamless connectivity across different areas. Potential use cases include smart cities, autonomous vehicles, and Internet of Things (IoT) applications, where AI-driven 6G-satellite integration will be crucial. The proposed AI-enhanced 6G-satellite framework not only addresses current challenges but also lays the groundwork for a resilient, scalable, and globally interconnected communication infrastructure, offering a promising future. © 2025 IEEE.

关键词： Satellite communication systems

来源：评论

学校读者我要写书评

暂无评论

Language Independent Named Entity Recognition via Orthogonal Transformation of Word Vectors

arXiv

引用

arXiv 2025年

作者： Rakha, Omar E. Abbas, Hazem M. Dept. Computer and Systems Engineering Faculty of Engineering Ain Shams University Cairo11571 Egypt

Word embeddings have been a key building block for NLP in which models relied heavily on word embeddings in many different tasks. In this paper, a model is proposed based on using Bidirectional LSTM/CRF with word embeddings to perform named entity recognition for any language. This is done by training a model on a source language (English) and transforming word embeddings from the target language into word embeddings of the source language by using an orthogonal linear transformation matrix. Evaluation of the model shows that by training a model on an English dataset the model was capable of detecting named entities in an Arabic dataset without neither training or fine tuning the model on an Arabic language dataset. © 2025, CC BY.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

Transfer and Bayesian Learning Approaches for Land Use and Land Cover Classification

Transfer and Bayesian Learning Approaches for Land Use and L...

引用

Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Zainab Malik Aisha Sir Elkhatem Gokhan Bilgin Dept. of Computer Engineering Yildiz Technical University Istanbul Turkey Dept. of Control and Automation Engineering Yildiz Technical University Istanbul Turkey

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

Land use and land cover (LULC) classification is essential for understanding the impact of both human activities and natural processes on the Earth’s surface. This classification plays a critical role in deforestation, urban planning, and damage assessment. However, traditional machine learning techniques often face challenges in achieving consistent accuracy in diverse datasets. In this study, we implement transfer learning and Bayesian learning approaches to enhance the accuracy and robustness of LULC classification. Transfer learning leverages knowledge from previous classification problems, while Bayesian learning addresses uncertainties in the classification process. Using the Remote Sensing Image Classification Benchmark (RSI-CB128) dataset, the study evaluates the performance of Bayesian Convolutional Neural Networks (CNN), Mobile Neural Networks (MobileNet), Inception Neural Networks (InceptionNet), Densely Connected Convolutional Networks (DenseNet) and Efficient Neural Networks (EfficientNet). The study also investigates the impact of varying the Bayesian layers on model performance, finding that a medium number of layers optimally captures model uncertainty. The results indicate that the transfer learning models, particularly MobileNet, achieved the highest accuracy of 97. 48% compared to 90. 33% for Bayesian CNN. These findings suggest that transfer learning techniques are highly effective for LULC classification, providing a reliable method for practical applications in remote sensing.

关键词： Deforestation Accuracy Uncertainty Computational modeling Transfer learning Neural networks Urban planning Land surface Bayes methods Convolutional neural networks

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：