检索结果-内蒙古大学图书馆

16th Annual Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2025

作者： Sabo, Aliyu Jibia, Kabir Garba Shahinzadeh, Hossein Hayati, Mohammad Mohsen Abapour, Mehdi Gharehpetian, Gevork B. Department of Electrical and Electronic Engineering Malaysia Amirkabir University of Technology Tehran Polytechnic Department of Electrical Engineering Tabriz Iran Faculty of Electrical and Computer Engineering Tabriz Iran

ISBN: (纸本)9798331533946

The growing demand for reliable and sustainable energy has driven researchers and engineers to explore renewable energy sources to complement traditional power grids. Wind energy conversion systems (WECS), particularly those using permanent magnet synchronous generators (PMSG), are among the most widely adopted renewable energy solutions. Integrating WECS into traditional grids introduces challenges, including power system oscillations, especially during disturbances or faults. Inertia control and power electronics converters are pivotal in mitigating these oscillations and ensuring grid stability. This review investigates the design and optimization of damping controllers, emphasizing their integration with excitation systems to enhance the dynamic performance of PMSG-based wind integrated grids. Additionally, the role of power electronics in control strategies and inertia emulation for stable grid operation is discussed. The findings highlight the critical importance of advanced control measures in addressing the inherent uncertainties of renewable energy systems integrated with conventional grids. © 2025 IEEE.

关键词： Synchronous generators

来源：评论

学校读者我要写书评

暂无评论

Enhancing voltage stability and load shedding optimization through a fusion of gravitational search algorithm and particle swarm optimization with deep learning

引用

Soft Computing 2025年第2期29卷 701-721页

作者： Ahmadipour, Masoud Ali, Zaipatimah Ridha, Hussein Mohammed School of Electrical Engineering College of Engineering Universiti Teknologi MARA Selangor Shah Alam40450 Malaysia Selangor Kajang43000 Malaysia Department of Computer Engineering University of Al-Mustansiriyah Baghdad14022 Iraq Department of Electrical and Electronic Engineering Faculty of Engineering Universiti Putra Malaysia Selangor Serdang43400 Malaysia

In this research, A novel approach for optimizing load shedding during power system stress conditions is introduced by combining gravitational search and particle swarm optimization (GSA-PSO) with Deep Learning. This approach aims to determine the most effective load-shedding strategy for specific buses, to prevent revenue loss and voltage instability in power systems. The smallest eigenvalue sensitivity of the load flow Jacobian matrix as an indicator is used to identify the buses for load shedding. Furthermore, inequality constraints are calculated for the current operational state and the anticipated load in the next interval. To evaluate the performance of the proposed approach, experiments were conducted on two different power systems: the IEEE 30-bus and 14-bus systems. The GSAPSO-Deep Learning approach was implemented in the Python environment. The results obtained using the proposed method were compared with those of other models, as well as their variants, using statistical inference. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Eigenvalues and eigenfunctions

来源：评论

学校读者我要写书评

暂无评论

Optimal Operation of Dedicated Urban Charging Stations in Modern Power Distribution Networks with High Electric Public Transport Vehicle Penetration 19

Optimal Operation of Dedicated Urban Charging Stations in Mo...

引用

19th Iranian Conference on Intelligent Systems, ICIS 2024

作者： Zare, Peyman Mohammadian, Amir Seyedshenava, Seyedjalal Davoudkhani, Iraj Faraji University of Mohaghegh Ardabili Faculty of Engineering Department of Electrical and Computer Engineering Ardabil Iran

ISBN: (纸本)9798331507565

The rapid integration and expanding use of Electric Public Transport Vehicles (EPTVs) have become pivotal in the evolution of Modern Urban Power Distribution Networks (MUPDNs). As urban centers transition towards electrified public transportation, MUPDNs face both significant challenges and promising opportunities. The widespread deployment of EPTVs, coupled with the high penetration of renewable energy sources (RESs), including Photovoltaic Systems (PVs) and Wind Turbine Generators (WTGs), introduces complexities due to the variability and uncertainty of energy production. This impacts the delicate balance between load and generation. This study investigates the strategic integration of EPTV Dedicated Urban Charging Stations (EPTVDUCSs) alongside RESs, such as PVs and WTGs, with the objective of mitigating these challenges and enhancing the technical performance of MUPDNs. Reconfiguring MUPDNs is identified as a cost-effective strategy for optimizing network parameters and fully leveraging the potential of these emerging technologies. Accordingly, the reorganization of MUPDNs and the optimal timing of the EPTV charging process, along with the maximum utilization of RESs, are the primary objectives of this article. These challenges are formulated as a probabilistic exploitation problem, structured as a multi-objective optimization challenge. The focus is on minimizing operational costs while maximizing power quality. To address this complex issue, a Pareto front-based optimization framework is employed, utilizing the Polar Fox Optimization Algorithm (PFA), an advanced meta-heuristic method. The IEEE 33-bus distribution system serves as a case study to evaluate the proposed probabilistic operation model. The results demonstrate the effectiveness of this approach in improving MUPDN performance amidst increasing EPTV integration and significant RES penetration. © 2024 IEEE.

关键词： Multiobjective optimization

来源：评论

学校读者我要写书评

暂无评论

Plug-and-Play ADMM Based Radar Range Profile Reconstruction Using Deep Priors

引用

Progress In Electromagnetics Research C 2024年 142卷 183-193页

作者： Akçapınar, Kudret Önhon, Naime Özben Gürbüz, Özgür Çetin, Müjdat Faculty of Engineering and Natural Sciences Sabancı University İstanbul Tuzla34956 Turkey Faculty of Engineering Turkish-German University Turkey Department of Electrical and Computer Engineering University of Rochester RochesterNY14627 United States

Reconstructing a range profile from radar returns, which are both noisy and band-limited, presents a challenging and ill-posed inverse problem. Conventional reconstruction methods often involve employing matched filters in pulsed radars or performing a Fourier transform of the received signal in continuous wave radars. However, both of these approaches rely on specific models and model-based inversion techniques that may not fully leverage prior knowledge of the range profiles being reconstructed when such information is accessible. To incorporate prior distribution information of the range profile data into the reconstruction process, regularizers can be employed to encourage specific spatial patterns within the range profiles. Nevertheless, these regularizers often fall short in effectively capturing the intricate spatial correlations within the range profile data, or they may not readily allow for analytical minimization of the cost function. Recently, Alternating Direction Method of Multipliers (ADMM) framework has emerged as a means to provide a way of decoupling the model inversion from the regularization of the priors, enabling the incorporation of any desired regularizer into the inversion process in a plug-and-play (PnP) fashion. In this paper, we implement an ADMM framework to address the radar range profile reconstruction problem where we propose to employ a Convolutional Neural Network (CNN) as a regularization method for enhancing the quality of the inversion process which usually suffers from the ill-posed nature of the problem. We demonstrate the efficacy of deep learning networks as a regularization method within the ADMM framework through our simulation results. We assess the performance of the ADMM framework employing CNN as a regularizer and conduct a comparative analysis against alternative methods under different measurement scenarios. Notably, among the methods under investigation, ADMM with CNN as a regularizer stands out as the most successfu

关键词： Matched filters

来源：评论

学校读者我要写书评

暂无评论

LLM-Powered Multi-Actor System for Intelligent Analysis and Visualization of IEC 61499 Control Systems 50

LLM-Powered Multi-Actor System for Intelligent Analysis and ...

引用

50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Xavier, Midhun Laikh, Tatiana Patil, Sandeep Vyatkin, Valeriy Department of Computer Science Electrical and Space Engineering Lulea Tekniska Universitet Sweden Department of Electrical Engineering and Automation Aalto University Espoo Finland

ISBN: (纸本)9781665464543

This paper introduces an innovative multiactor framework that harnesses the potential of LLMs to augment the functionalities of ICS. By integrating conversational AI technologies, this framework significantly improves human-machine interactions, enabling sophisticated analysis and visualization of intricate data sets. The core of the system comprises specialized LLM actors that interact through a LangGraph-based multiactor framework, addressing various aspects of IEC 61499 control systems including PLC code analysis, SQL query execution, and data visualization. This integration enables operators to interact with the control system using natural language, significantly reducing technical barriers and enhancing the accessibility and usability of complex industrial systems. © 2024 IEEE.

关键词： Control system analysis

来源：评论

学校读者我要写书评

暂无评论

Assessing the Suitability of Software Tools for System-Theoretic Process Analysis of Nuclear Instrumentation and Control Systems 29

Assessing the Suitability of Software Tools for System-Theor...

引用

29th IEEE International Conference on Emerging Technologies and Factory automation, ETFA 2024

作者： King, Akira Ovsiannikova, Polina Vyatkin, Valeriy Aalto University Department of Electrical Engineering and Automation Espoo Finland Lulea Tekniska Universitet Department of Computer Science Electrical and Space Engineering Sweden

ISBN: (纸本)9798350361230

Modernization of currently operational nuclear power plants is becoming increasingly important to maintain their performance and safety. Ensuring the safety of newer Instrumentation and Control (I&C) systems used in modernization efforts requires hazard analysis techniques suitable for the analysis of complex and software-heavy systems. System-Theoretic Process Analysis (STPA) has proven to be a suitable hazard analysis method for these complex I&C systems, however, its practical use is still often limited by its labor-intensive and time-consuming nature, partially due to the limitations of the tools used to perform the analysis: common Office tools such as Microsoft Excel or Visio. Conducting an STPA analysis could be simpler and more attractive with software tools specific to the method. This work introduces the requirements for these software tools and lays the foundation for further work, in which software tools will be evaluated against these requirements. © 2024 IEEE.

关键词： Nuclear power plants

来源：评论

学校读者我要写书评

暂无评论

Dynamic U-Net for multi-organ nucleus segmentation

引用

Multimedia Tools and Applications 2024年 1-28页

作者： Traisuwan, Attasuntorn Limsiroratana, Somchai Phukpattaranont, Pornchai Tandayya, Pichaya Department of Computer Engineering Faculty of Engineering Prince of Songkla University Karnjanavanich Rd. Songkhla90110 Thailand Department of Electrical Engineering Faculty of Engineering Prince of Songkla University Karnjanavanich Rd. Songkhla90110 Thailand

Multi-organ nucleus segmentation is an important task for diagnosis, staging, and grading cancer. It is the first step of many quantitative data analysis pipelines. Most existing methods did not apply frequency-related features to extract the key spatial information. In this work, we propose the Dynamic U-Net, a novel method for multi-organ nucleus segmentation. Specifically, we design the Dynamic Multi-spectral module which is a combination of the Dynamic Convolution and the Multi-spectral Channel Attention module. The Dynamic Convolution enhances the representation capability by aggregating multiple parallel convolution kernels dynamically based upon their attentions. The attention applied inside was the Multi-spectral modules. It maintains useful features in the network. Experiments on MoNuSeg datasets show that our Dynamic U-Net performed slightly better than state-of-the-art of U-Nets. In contrast, the network size is far less costly. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Generative AI Co-Pilot for Rapid Prototyping of IEC 61499 Control Applications 29

Generative AI Co-Pilot for Rapid Prototyping of IEC 61499 Co...

引用

29th IEEE International Conference on Emerging Technologies and Factory automation, ETFA 2024

作者： Ovsiannikova, Polina Liakh, Tatiana Jhunjhunwala, Pranay Vyatkin, Valeriy Aalto University Department of Electrical Engineering and Automation Espoo Finland Luleå Tekniska Universitet Department of Computer Science Electrical and Space Engineering Sweden

ISBN: (纸本)9798350361230

This work develops a concept of a natural language co-pilot for the creation of IEC 61499 applications to accelerate initial testing and development. The co-pilot addresses the challenge of generating the correct function blocks that can be opened in popular IEC 61499 IDEs. In our case study, we focus on the generation of the centralised controller that operates particular equipment to perform a defined process. In addition, we have developed a scheme for how the co-pilot will be integrated into the FBME IDE as an AI assistant in the next steps. In the discussion, we provide valuable information for future co-pilot developers on the possible problems they might face in this area and how to overcome them. © 2024 IEEE.

关键词： Rapid prototyping

来源：评论

学校读者我要写书评

暂无评论

Confidentiality Preserving Data Sharing for Life Cycle Assessment in Process Industries 29

Confidentiality Preserving Data Sharing for Life Cycle Asses...

引用

29th IEEE International Conference on Emerging Technologies and Factory automation, ETFA 2024

作者： Perera, Hansani Atmojo, Udayanto Dwi Vyatkin, Valeriy Aalto University Department of Electrical Engineering and Automation Espoo Finland Luleå Tekniska Universitet Department of Computer Science Computer and Space Engineering Sweden

ISBN: (纸本)9798350361230

The pulp and paper industry faces significant en-vironmental challenges, such as air pollution, greenhouse gas emissions, and wastewater discharge, requiring smarter and more sustainable operations. Regulatory bodies are imposing stringent measures to mitigate these impacts, prompting the industry to adopt sustainable practices and technologies. Life Cycle Assessment (LCA) models are crucial in this effort, pro-viding a comprehensive evaluation of environmental impacts and aiding decision making for sustainable manufacturing. However, organisations prioritise the confidentiality of their sensitive data, which can hinder collaborative efforts and LCA calculations. This paper addresses organisational requirements for improving confidentiality, tamper-proof data transfer, and ensuring data sovereignty. The ongoing proof-of-concept introduces a novel approach in LCA, employing Secure Multiparty Computation (SMPC) and data spaces to enable confidentiality-preserving LCA. Our solution ensures data sovereignty and accurate LCA calculations, promoting sustainable practices across the value chain. This paper lays the foundation for a collaborative data platform that meets the critical needs of confidentiality, security, and sustainability in the process manufacturing industry. © 2024 IEEE.

关键词： Life cycle assessment

来源：评论

学校读者我要写书评

暂无评论

Secure speaker identification in open and closed environments modeled with symmetric comb filters

引用

Multimedia Tools and Applications 2025年第18期84卷 19147-19189页

作者： Shafik, Amira Monir, Mohamad El-Shafai, Walid Khalaf, Ashraf A. M. Nassar, M.M. El-Fishawy, Adel S. El-Din, M. A. Zein Dessouky, Moawad I. El-Rabaie, El-Sayed M. Abd El-Samie, Fathi E. Department Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Laboratory Department of Computer Science Prince Sultan University Riyadh11586 Saudi Arabia Electrical Engineering Department Faculty of Engineering Minia University Minia61519 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Speech is a fundamental means of human interaction. Speaker Identification (SI) plays a crucial role in various applications, such as authentication systems, forensic investigation, and personal voice assistance. However, achieving robust and secure SI in both open and closed environments remains challenging. To address this issue, researchers have explored new techniques that enable computers to better understand and interact with humans. Smart systems leverage Artificial Neural Networks (ANNs) to mimic the human brain in identifying speakers. However, speech signals often suffer from interference, leading to signal degradation. The performance of a Speaker Identification System (SIS) is influenced by various environmental factors, such as noise and reverberation in open and closed environments, respectively. This research paper is concerned with the investigation of SI using Mel-Frequency Cepstral Coefficients (MFCCs) and polynomial coefficients, with an ANN serving as the classifier. To tackle the challenges posed by environmental interference, we propose a novel approach that depends on symmetric comb filters for modeling. In closed environments, we study the effect of reverberation on speech signals, as it occurs due to multiple reflections. To address this issue, we model the reverberation effect with comb filters. We explore different domains, including time, Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and Discrete Sine Transform (DST) domains for feature extraction to determine the best combination for SI in case of reverberation environments. Simulation results reveal that DWT outperforms other transforms, leading to a recognition rate of 93.75% at a Signal-to-Noise Ratio (SNR) of 15 dB. Additionally, we investigate the concept of cancelable SI to ensure user privacy, while maintaining high recognition rates. Our simulation results show a recognition rate of 97.5% at 0 dB using features extracted from speech signals and their DCTs. Fo

关键词： Speech enhancement

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：