检索结果-内蒙古大学图书馆

Long-term solar radiation forecasting in India using EMD, EEMD, and advanced machine learning algorithms

Environmental Monitoring and Assessment 2025年第3期197卷 1-36页

作者： RajasundrapandiyanLeebanon, T. Murugan, N. S. Sakthivel Kumaresan, K. Jeyabose, Andrew Department of Electrical and Electronics Engineering TamilNadu College of Engineering Tamil Nadu Coimbatore India Department of Mechanical Engineering Park College of Engineering & Technology TamilNadu Coimbatore India Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

Solar radiation plays a critical role in the carbon sequestration processes of terrestrial ecosystems, making it a key factor in environmental sustainability among various renewable energy sources. This study integrates two advanced signal processing techniques—empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD)—with machine learning (ML) algorithms, including multilayer perceptron (MLP), random forest regression (RFR), support vector regression (SVR), and ridge regression, to forecast long-term solar radiation. Meteorological data spanning 13 years (2000–2012) from seven locations across India (Bhubaneswar, Chennai, Delhi, Hyderabad, Nagpur, Patna, and Trivandrum) were used for training and testing. The optimal model was identified based on performance metrics, including the highest linear correlation coefficient (R), and the lowest mean absolute error (MAE) and root mean square error (RMSE). The results indicate that EEMD integrated with ML algorithms consistently outperformed EMD-based approaches. Among the ML models evaluated, EEMD integrated with MLP achieved the best performance across all locations, with RMSE = 0.332, MAE = 0.26, and R2 = 0.99. Furthermore, a comparative analysis with previous studies demonstrated that the proposed approach provides superior accuracy, underscoring its efficacy in solar radiation forecasting. © The Author(s) 2025.

关键词： Empirical mode decomposition

来源：评论

学校读者我要写书评

暂无评论

Artificial Intelligence Enabled Cognitive Computing for AlN/β-Ga2O3 HEMT 2

Artificial Intelligence Enabled Cognitive Computing for AlN/...

引用

2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Verma, Yogesh Kumar Bhatia, Kamal Khushboo Bhanot, Aastha Roy, Trijeeta Sinha, Supriya Laksh School of Electronics and Electrical Engineering Lovely Professional University Jalandhar India School of Computer Science and Engineering Lovely Professional University Jalandhar India

ISBN: (纸本)9798331530389

HEMT devices have potential to handle the fast processing in applications such as real-time diagnostics utilizing the artificial-intelligence (AI) enhanced abilities. Along-with this HEMT devices can be extensively used in radar and defense systems to obtain faster response time and threat detection. This integration of AI and cognitive computing enables pattern-recognition and adaptive learning. The cognitive computing techniques integrated with HEMT enables the real-time analysis and predictions. For precise sensor data the amplification should be done at either no or very less noise levels. © 2025 IEEE.

关键词： Gallium compounds

来源：评论

学校读者我要写书评

暂无评论

Highly sensitive optical biosensor based on electromagnetically induced transparency in a graphene-dielectric metasurface

引用

Optical and Quantum electronics 2025年第4期57卷 1-17页

作者： Bagheritabar, Mohsen Molaei Zarasvand, Mahdi Yazdani, Afsoon Molaei Zarasvand, Melika Salemizadeh Parizi, Mohammad Bahadori-Haghighi, Shahram Department of Electrical Engineering University of Cincinnati CincinnatiOH45221-0030 United States Department of Electrical and Computer Engineering Clemson University ClemsonSC29634 United States Department of Electrical and Computer Engineering K. N. Toosi University of Technology Tehran Iran Department of Electrical Engineering Sharif University of Technology Tehran Iran Department of Biomedical Engineering Georgia Institute of Technology Emory University AtlantaGA30332 United States Department of Communication and Electronics Engineering Shiraz University Shiraz Iran

In this paper, a graphene-dielectric metasurface is proposed for effective optical biosensing. The structure is composed of a periodic array of double-slit split-ring resonators (SRRs) adjacent to silicon bars over a sheet of graphene and silicon dioxide substrate. Interactions between electric dipoles (bright modes) in silicon bars and magnetic dipoles (dark modes) excited in SRRs lead to a high quality factor resonance based on the phenomenon of electromagnetically induced transparency (EIT). High electric field confinement is achieved within the two gaps of SRRs at the resonance frequency, which result in strong light-analyte interaction. The structural parameters of the proposed metasurface are optimized to achieve the best performance. The biosensor is analyzed using the finite-element method (FEM) and the results are presented. Owing to the high Q-factor EIT resonance and the enhanced light-matter interaction inside the gaps, a high sensitivity of 496 nm/RIU and figure-of-merit (FOM) of as high as 741 RIU−1 are achieved that are higher than those reported in recently published works. The resolution and the linearity R2 value of the proposed biosensor are calculated to be 2.02e-4 and 0.999, respectively. The reported characteristics could be promising for sensing of various biomolecules such as hemoglobin. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Q factor measurement

来源：评论

学校读者我要写书评

暂无评论

SINR-Delay Constrained Node Localization in RIS-Assisted Time-Varying IoT Networks Using ML Frameworks

引用

IEEE Transactions on Network and Service Management 2025年第2期22卷 1544-1557页

作者： Bhardwaj, Vikash Kumar Mundada, Gagan Sahoo, Omm Prakash Shukla, Mahendra K. Pandey, Om Jee Varanasi Dept. of Electronics Engineering 221005 India University of California San Diego Computer Science and Engineering Dept United States Varanasi Dept. of Electrical Engineering 221005 India ABV-IIITM Gwalior Dept. of Information Technology India

Node localization in time-varying Internet of Things (IoT) networks is an essential problem due to increased delay and poor Signal-to-Interference plus Noise Ratio (SINR) at the Base Station (BS). To improve the received signal strength at the BS, Reconfigurable Intelligent Surface (RIS) has recently been used between transmitter and receiver. Additionally, novel phase prediction methods and optimal weight assignment frameworks have been proposed over RIS and BSs, respectively. Nevertheless, these methods suffer from poor performance due to their heuristic approach, resulting in more time consumption and poor SINR. Motivated by the aforementioned challenges, we propose a novel node localization method over a RIS-assisted time-varying IoT network using Machine Learning (ML) frameworks in this work. Firstly, the method computes the optimal phase configuration over the RIS corresponding to each element using coeff2phaseNN, which has been trained on channel coefficients among the transmitter, receiver, and RIS. Subsequently, the weight of the individual antenna element at the BS is optimized using the proposed VectorSync model. The results confirm that the coeff2phaseNN method demonstrates a reduction of 89.79% in total MSE loss compared to the Artificial Neural Network-RIS (ANN-RIS) method. Additionally, it demonstrates a 71.04% reduction in the absolute RIS phase prediction deviation from the optimal phase compared to the ANN-RIS method. Moreover, the proposed VectorSync method attains a 79.28% and 92.29% reduction in time required for optimal weight assignment compared to the Bartlett and Capon methods, respectively. Finally, the Localization Error(LR) using the proposed method is compared to conventional methods in a time-varying experimental scenario and found to be the minimum, i.e., 6.156%. © 2004-2012 IEEE.

关键词： Signal to noise ratio

来源：评论

学校读者我要写书评

暂无评论

Intelligent Motion Control to Enhance the Swimming Performance of a Biomimetic Underwater Vehicle Using Reinforcement Learning Approach

引用

IEEE Access 2025年 13卷 37128-37146页

作者： Algarin-Pinto, Juan A. Garza-Castanon, Luis E. Vargas-Martinez, Adriana Minchala-Avila, Luis I. Payeur, Pierre Tecnológico de Monterrey School of Engineering and Sciences Nuevo Leon Monterrey64849 Mexico Universidad de Cuenca Department of Electrical Engineering Electronics and Telecommunications Azuay Cuenca010101 Ecuador University of Ottawa School of Electrical Engineering and Computer Science OttawaK1N 6N5 Canada

This article develops an intelligent motion control strategy using reinforcement learning to regulate a biomimetic autonomous underwater vehicle (BAUV) swimming performance. The BAUV is driven by the oscillatory motion of a lunate-shaped caudal fin. To navigate effectively, the vehicle must regulate its propeller’s motion to swim toward goals. The caudal fin’s oscillatory motion is defined by sine functions, where the amplitude, frequency, bias, and time shifting are the main flapping parameters that must be regulated. The developed algorithm, based on the n-step state-action-reward-state-action on-policy learning, fine-tunes these parameters. Further, deep Q learning network speed adjusters were designed to enhance BAUV guidance. By integrating waypoint guidance systems and intelligent path trackers, the vehicle reduces its heading deviation and distance to goals. The effectiveness of these methods was validated through simulations featuring various disturbances like longitudinal, lateral, and swirl currents. The proposed scheme allowed the vehicle to reach desired targets efficiently, even in the presence of strong currents. © 2025 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://***/licenses/by/4.0/

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Enhancing fault detection and predictive maintenance of rotating machinery with Fiber Bragg Grating sensor and machine learning techniques

引用

International Journal of Information Technology (Singapore) 2025年第2期17卷 1225-1234页

作者： Nayak, Narayan Mohapatra, Ambarish G. Khanna, Ashish Talukdar, Jaideep Satapathy, Satyapriya Nayak, Dipak Ranjan Ghuge, Nilam N. Electronics Engineering Silicon University Odisha Bhubaneswar India Basic Sciences & amp Humanities Silicon University Odisha Bhubaneswar India Computer Science and Engineering Maharaja Agrasen Institute of Technology Delhi India Electrical Engineering University of Technology Rajasthan Jaipur India Electrical and Electronics Engineering Silicon University Odisha Bhubaneswar India Electronics and Communication Engineering University of Technology Rajasthan Jaipur India

The reliability and operational efficiency of rotating machinery have become increasingly vital across various industries. Using Fibre Bragg Grating (FBG) vibration sensors, this study investigates the use of Machine Learning (ML) techniques for fault detection and predictive maintenance. The primary goal is to enhance early fault detection and optimize maintenance schedules, thereby reducing both downtime and operational costs in any industrial settings. The research utilizes sophisticated ML models to analyze vibration data acquired from FBG sensors, which are recognized for their exceptional sensitivity and precision in detecting vibrational anomalies that frequently occur prior to mechanical failures. Following the pre-processing phase, which aims to minimize noise and improve signal quality, essential features are extracted to identify critical parameters that reflect the machine’s health. A range of ML models, such as Random Forest (RF) and Radial Basis Function Neural Networks (RBFNN), are trained and validated using this set of features. The results demonstrate that these machine learning models are effective in accurately detecting faults and predicting maintenance (PM) needs based on real-time sensor data. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Fault detection Fiber Bragg Grating (FBG) sensors Machine learning (ML) Predictive maintenance (PM) Rotating machinery

来源：评论

学校读者我要写书评

暂无评论

Design of Polarization-Insensitive Wideband Metamaterial Radar Absorber with Enhanced Bandwidth

引用

Progress in Electromagnetics Research C 2025年 155卷 67-74页

作者： Baqir, Muhammad Abuzar Qadeer, Abdul Altintas, Olcay Draz, Muhammad U. Karaaslan, Muharrem Sampe, Jahariah Department of Electrical and Computer Engineering COMSATS University Islamabad Sahiwal Campus Pakistan Department of Avionics Iskenderun Technical University Hatay Iskenderun31200 Turkey Department of Electrical and Electronics Engineering Iskenderun Technical University Hatay Iskenderun31200 Turkey Malaysia Turkey

Metamaterial-based absorbers offering perfect and broadband absorption are greatly desirable in radar and stealth technology systems. Further, the polarization insensitive feature makes the absorber useful for industrial applications. This study examines the design of a wideband metamaterial-based radar absorber functioning throughout the frequency range of 8 to 16 GHz. The metasurface design comprises two concentric metallic rings and a fan-shaped circular metallic disc at the middle. The rings and central disc are interconnected with surface-mounted chip resistors to enhance the absorption bandwidth. It is depicted that more than 90% absorptivity was attained from 8 to 16 GHz. A wideband absorber with angular stability and polarization insensitivity is an excellent choice for wireless communications, particularly in radar applications. Further, the measured results of the prototype corresponded well with the simulated outcomes. © 2025, Electromagnetics Academy. All rights reserved.

关键词： Metamaterials

来源：评论

学校读者我要写书评

暂无评论

Joint trust-based detection and signature-based authentication technique for secure localization in underwater wireless sensor network

引用

Multimedia Tools and Applications 2025年 1-20页

作者： Kumar, Manni Goyal, Nitin Qaisi, Ramy Mohammed Aiesh Najim, Mohd Gupta, Sachin Kumar Department of Computer Science & Engineering Chandigarh University Gharuan Punjab India Department of Computer Science Engineering School of Engineering and Technology Central University of Haryana Haryana Mahendergarh123031 India Department of Electrical and Electronic Engineering College of Engineering University of Jeddah Jeddah21589 Saudi Arabia Department of Electronics and Communication Engineering Central University of Jammu UT of J&K Jammu181143 India

Ensuring secure and accurate node localization in Underwater Wireless Sensor Networks (UWSN) is a significant challenge, as conventional methods tend to neglect the security risks associated with malicious node interference. This research addresses this issue by proposing a Joint Trust-based Detection and Signature-based Authentication (JTDSA) technique, aimed at improving both the security and accuracy of node localization in UWSNs. The technique utilizes Intrusion Detection System (IDS) methods to calculate the trust values of secondary-anchor nodes (SNs), ensuring only the most trusted SNs are selected. Furthermore, a digital signature algorithm secures the Received Signal Strength (RSS) data from tampering. The method incorporates a hybrid approach combining Angle of Arrival (AoA) and RSS ranging techniques to achieve robust localization. Simulation results demonstrate that JTDSA improves localization accuracy by 4% compared to existing methods, achieving an overall accuracy of 99.2%. The technique also reduces packet drop by 35% and enhances attack detection accuracy by 3%. These results indicate that the proposed JTDSA technique offers an effective solution for secure and reliable localization in UWSNs. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Sensor nodes

来源：评论

学校读者我要写书评

暂无评论

AI and IoT in Energy Conservation: Challenges, Opportunities, and Trends 12

AI and IoT in Energy Conservation: Challenges, Opportunities...

引用

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

作者： Shirinabadi, Parisa Bonab, Amirreza Samadi Siddiqui, Khadim Moin University of Tabriz Faculty of Electrical and Computer Engineering Tabriz Iran S.R. Institute of Management & Technology Electrical & Electronics Engineering Department Lucknow India

ISBN: (纸本)9798331525132

The rapid advancements in Artificial Intelligence (AI) and the Internet of Things (IoT) have paved the way for innovative solutions to address global energy challenges. This paper explores the integration of AI and IoT in energy conservation, focusing on their combined potential to optimize energy consumption, enhance system efficiency, and support sustainable development. AI-driven predictive analytics and machine learning algorithms enable accurate energy demand forecasting and real-time decision-making, while IoT devices facilitate seamless data collection, monitoring, and control of energy systems. The synergy of these technologies has been instrumental in transforming energy management across various sectors, including smart grids, industrial automation, and smart buildings. However, their adoption comes with significant challenges, such as data security, interoperability, and the high cost of deployment. This review discusses the current state of AI and IoT in energy conservation, identifies key challenges, highlights emerging opportunities, and examines future trends, such as the role of blockchain and edge computing in decentralized energy systems. The paper concludes by emphasizing the need for interdisciplinary research and collaboration to unlock the full potential of AI and IoT in creating sustainable energy solutions. © 2025 IEEE.

关键词： Energy efficiency

来源：评论

学校读者我要写书评

暂无评论

DACS: User Association and TDMA Framing for Low-latency Services on Integrated Access and Backhaul Networks

引用

IEEE Transactions on Vehicular Technology 2025年第5期74卷 8110-8125页

作者： Baek, Seungwoo Choi, Siyoung Bahk, Saewoong Seoul National University Department of Electrical and Computer Engineering INMC Seoul08826 Korea Republic of LG Electronics 5G Wireless Protocol Standard Task Seoul06772 Korea Republic of

To realize a ubiquitous communication environment, network providers are currently attaching great importance to wireless backhaul options for the dense deployment of network nodes in urban areas. However, the increase in hop count due to wireless backhaul results in higher multi-hop delays. Network providers need an efficient UE association strategy to minimize multi-hop delays, considering the demand for future low-latency network services. To this end, we consider realistic TDMA frame configuration for downward integrated access and backhaul (IAB) communication and formulate the problem of end-to-end (E2E) delay minimization for UE association. To reduce the complexity of the problem, we apply a piecewise linear function to approximate the queueing delay part and consider an approximate optimization-based scheme (AOPT), which can be computed offline, as a reference scheme. To make the problem solvable online, we simplify the problem and propose a Delay-based user Association Control Scheme, termed DACS, that adaptively enables peak delay minimization by avoiding congested links and selecting routing paths with fewer hops for user association. DACS shows comparable performance to AOPT and outperforms other comparison schemes in terms of the 98th percentile E2E tail delay, helping each user experience acceptable low-latency services. © 1967-2012 IEEE.

关键词： Delays Backhaul networks Millimeter wave communication Wireless communication Time division multiple access Spread spectrum communication Routing Resource management Relays Indexes

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：