检索结果-内蒙古大学图书馆

Enhancing photovoltaic energy forecasting:a progressive approach using wavelet packet decomposition

Clean Energy 2024年第3期8卷 95-108页

作者： Khaled Ferkous Mawloud Guermoui Abderahmane Bellaour Tayeb boulmaiz Nadjem Bailek LMTESE Laboratory of Materials Technology of Energy Systems and EnvironmentUniversitéde Ghardaia47000Algeria Renewable Energy Applied Research Unit(URAER) Ghardaia47000Algeria Laboratory of Mathematics Modeling and Applications Department of Mathematics and Computer ScienceFaculty of Sciences and TechnologyAhmed Draia University of AdrarAdrar 01000Algeria

Accurate photovoltaic(PV)energy forecasting plays a crucial role in the efficient operation of PV power *** study presents a novel hybrid machine-learning(ML)model that combines Gaussian process regression with wavelet packet decomposition to forecast PV power half an hour *** proposed technique was applied to the PV energy database of a station located in Algeria and its performance was compared to that of traditional forecasting *** evaluations demonstrate the superiority of the proposed approach over conventional ML methods,including Gaussian process regression,extreme learning machines,artificial neural networks and support vector machines,across all *** proposed model exhibits lower normalized root mean square error(nRMSE)(2.116%)and root mean square error(RMSE)(208.233 kW)values,along with a higher coefficient of determination(R^(2))of 99.881%.Furthermore,the exceptional performance of the model is maintained even when tested with various prediction ***,as the forecast horizon extends from 1.5 to 5.5 hours,the prediction accuracy decreases,evident by the increase in the RMSE(710.839 kW)and nRMSE(7.276%),and a decrease in R2(98.462%).Comparative analysis with recent studies reveals that our approach consistently delivers competitive or superior *** study provides empirical evidence supporting the effectiveness of the proposed hybrid ML model,suggesting its potential as a reliable tool for enhancing PV power forecasting accuracy,thereby contributing to more efficient grid management.

关键词： short photovoltaic power forecasting wavelet packet decomposition sub-series reconstruction machine learning in energy forecasting sustainable power stations renewable energy

来源：评论

学校读者我要写书评

暂无评论

An improved KNN algorithm based on ensemble methods and correlation 7

An improved KNN algorithm based on ensemble methods and corr...

引用

7th IEEE International Congress on Information science and technology, CiSt 2023

作者： Manzali, Youness Barry, Khalidou Abdoulaye Far, Mohamed El Faculty of Sciences Dhar El Mahraz Sidi Mohammed Ben Abdellah University Applied Physics Computer Science and Statistics Laboratory Fez Morocco

ISBN: (纸本)9781665461337

K-Nearest Neighbors is a widely used algorithm due to its simplicity and efficacity. However, KNN suffers from many drawbacks, such as it does not work well with datasets with a high number of features. Also, not all the features contribute to the classification process. To resolve these issues, we present an improved KNN algorithm which uses KNN as a base learner in an ensemble method and correlation for selecting the features subsets. The experiment results show that the proposed algorithm performed better than other machine learning algorithms. © 2023 IEEE.

关键词： Learning algorithms

来源：评论

学校读者我要写书评

暂无评论

Prediction of Hypertension Disease Using Machine Learning Techniques: Case Study from Jordan 25

Prediction of Hypertension Disease Using Machine Learning Te...

引用

25th International Arab Conference on Information technology, ACIT 2024

作者： Alazaidah, Raed Samara, Ghassan Katrawi, Anwar Hadi, Wael Al-Safarini, Maram Y. Al-Mamoori, Farah Elrashidi, Ali Alqaraleh, Muhyeeddin Alzoubi, Haneen Zarqa University Faculty of Information Technology Department of Data Science and Ai Zarqa Jordan Zarqa University Faculty of It Department of Computer Science Zarqa Jordan Petra University Faculty of It Information Security Department Amman Jordan Zarqa University Faculty of Pharmacy Department of Pharmaceutical Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Zarqa University Faculty of It Department of Software Engineering Zarqa Jordan Applied Science Private University Facultty of Information Technology Department of Computer Science Amman Jordan

ISBN: (纸本)9798331540012

Hypertension is one of the most common diseases in Jordan. It is one of the main reasons of death among Jordanian adult citizens. Worldwide, nearly 13% of all deaths are due to Hypertension with nearly 8 million deaths yearly. Hence, this research is interested in the early prediction of this disease among Jordanian people. To achieve this main objective, Machine Learning (ML) is utilized through a large number of classification models and considering five well-known evaluation metrics. These classification models have been trained on a primary dataset that has been collected for the purpose of the research. The results revealed that Random Forest, Random Committee, and MultilayerPerceptron are the best classification models to handle the task of the early prediction of Hypertension in Jordan. © 2024 IEEE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Ferromagnetism and structural phase transition in rhombohedral PrIr3

引用

Physical Review B 2024年第6期110卷 064431页

作者： Karolina Gornicka Michal J. Winiarski Robert J. Cava Michael A. McGuire Tomasz Klimczuk Materials Science and Technology Division Faculty of Applied Physics and Mathematics and Advanced Materials Centre Department of Chemistry

The synthesis, structural, magnetic, thermal, and transport properties are reported for polycrystalline PrIr3. At room temperature PrIr3 displays the rhombohedral space group R−3m and a PuNi3-type structure. At around 70 K a phase transition to a monoclinic C2/m structure is observed and continued cooling reveals temperature independent behavior of the unit cell volume. Further, PrIr3 undergoes a paramagnetic to ferromagnetic transition with Tc=7.5K. The temperature dependent magnetic susceptibility follows the Curie-Weiss law with a positive Curie-Weiss temperature, and an effective moment that is close to the theoretical effective moment for a free Pr+3 ion. The structural transition introduces further complexity into the behavior of PuNi3-type materials and highlights the importance of temperature-dependent structural studies to complement physical property measurements in intermetallic compounds.

关键词： Ferromagnetism Magnetic phase transitions Structural phase transition

来源：评论

学校读者我要写书评

暂无评论

Prediction of Geopolymer Concrete Compressive Strength Using Convolutional Neural Networks

引用

computer Modeling in Engineering & sciences 2024年第5期139卷 1455-1486页

作者： Kolli Ramujee Pooja Sadula Golla Madhu Sandeep Kautish Abdulaziz S.Almazyad Guojiang Xiong Ali Wagdy Mohamed Department of Civil Engineering VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Information Technology VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Computer Science LBEF Campus(Asia Pacific University of Technology&InnovationMalaysia)Kathmandu44600Nepal Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmmanJordan

Geopolymer concrete emerges as a promising avenue for sustainable development and offers an effective solution to environmental *** attributes as a non-toxic,low-carbon,and economical substitute for conventional cement concrete,coupled with its elevated compressive strength and reduced shrinkage properties,position it as a pivotal material for diverse applications spanning from architectural structures to transportation *** this context,this study sets out the task of using machine learning(ML)algorithms to increase the accuracy and interpretability of predicting the compressive strength of geopolymer concrete in the civil engineering *** achieve this goal,a new approach using convolutional neural networks(CNNs)has been *** study focuses on creating a comprehensive dataset consisting of compositional and strength parameters of 162 geopolymer concrete mixes,all containing Class F fly *** selection of optimal input parameters is guided by two distinct *** first criterion leverages insights garnered from previous research on the influence of individual features on compressive *** second criterion scrutinizes the impact of these features within the model’s predictive *** to enhancing the CNN model’s performance is the meticulous determination of the optimal *** a systematic trial-and-error process,the study ascertains the ideal number of epochs for data division and the optimal value of k for k-fold cross-validation—a technique vital to the model’s *** model’s predictive prowess is rigorously assessed via a suite of performance metrics and comprehensive score ***,the model’s adaptability is gauged by integrating a secondary dataset into its predictive framework,facilitating a comparative evaluation against conventional prediction *** unravel the intricacies of the CNN model’s learning trajectory,a loss plot is deployed to elucidate its learning rat

关键词： Class F fly ash compressive strength geopolymer concrete prediction deep learning convolutional neural network

来源：评论

学校读者我要写书评

暂无评论

Designing Quantum-Resilient Blockchain Frameworks: Enhancing Transactional Security with Quantum Algorithms in Decentralized Ledgers

引用

International Journal of Advanced computer science and Applications 2025年第4期16卷 618-628页

作者： Kale, Meenal R. Baker El-Ebiary, Yousef A. Sathiya, L. Burugari, Vijay Kumar Yulduz, Erkiniy Muniyandy, Elangovan Alanazi, Rakan Department of Humanities Yeshwantrao Chavan College of Engineering Hingna Nagpur India Faculty of Informatics and Computing UniSZA University Malaysia Department of CSE Panimalar Engineering College Chennai India Dept of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Automatic Control and Computer Engineering Department Turin Polytechnic University in Tashkent Tashkent Uzbekistan Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Department of Information Technology Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia

Quantum computing is progressing at a fast rate and there is a real threat that classical cryptographic methods can be compromised and therefore impact the security of blockchain networks. All of the ways used to secure blockchain like Rivest–Shamir–Adleman (RSA), Elliptic Curve Cryptography (ECC) and Secure Hash Algorithm 256-bit (SHA256) are the characteristic of the traditional cryptographic techniques vulnerable to attack by quantum algorithms: Shor’s and Grover’s algorithms: can efficiently break asymmetric encryption and speed up brute force attacks. Because of this vulnerability, there exists a need to develop an advance quantum resilient blockchain framework to protect the decentralized ledgers from the future threats of the quantum. This research proposes Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD) and Quantum Random Number Generation (QRNG) as a formidable architectural integration, to fortify security of blockchain. Classical encryption is replaced with PQC, QKD with secure key exchange by detecting eavesdropping, and QRNG with improving cryptographic randomness to remove the predictable key vulnerability. Only with a small loss of transaction efficiency, we increase transaction encryption accuracy, key exchange security, and resistance to quantum attacks. In this quantum enhanced blockchain design, the idea is to preserve the decentralization, transparency and security and at the same time overcome the future quantum threat. By going through rigorous analysis and comparative evaluation, we demonstrate that the approach saves blockchain networks from the emerging quantum risks to make sure that the decentralized finance, smart contracts and cross chain transactions. © (2025), (science and Information Organization). All rights reserved.

关键词： Quantum cryptography

来源：评论

学校读者我要写书评

暂无评论

Enhancing Timely Graduations: An Explainable AI Approach to Predict Academic Risks in South African Students

Enhancing Timely Graduations: An Explainable AI Approach to ...

引用

2023 IEEE International Conference on Electrical, computer and Energy Technologies, ICECET 2023

作者： Bokgoshi, Lenny Sixhaxa, Khanyisile Jadhav, Ashwini Nyamane, Seipati Ajoodha, Ritesh School of Computer Science and Applied Mathematics University of Witwatersrand Johannesburg South Africa University of Witwatersrand Science Teaching and Learning Unit Faculty of Science Johannesburg South Africa Sol Plaatje University Department of Computer Science and Information Technology Kimberley South Africa

ISBN: (纸本)9798350327816

With rising dropout rates and extended degree completion times in South African institutions, there's a pressing need to better understand and address the hurdles faced by students during their academic journey. This research harnesses the power of explainable AI to predict and classify students based on their likelihood of not completing their degrees on time. Utilizing synthetic data generated via a Bayesian network, we used predictive models that categorize students into four distinct risk profiles. This clarity in prediction not only illuminates the underlying causes of academic delays but also empowers faculty, advisors, and student support services with actionable insights. The goal of this research is to facilitate timely interventions, tailored support, and seamless transitions for students transferring between universities, ensuring more students can realize their academic aspirations within expected time frames. © 2023 IEEE.

关键词： academic performance at-risk students classification models Machine learning student attrition

来源：评论

学校读者我要写书评

暂无评论

AI-Infused Predictive Digital Twin Systems for Resilient IoT Software Architectures 4

AI-Infused Predictive Digital Twin Systems for Resilient IoT...

引用

4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Gupta, Raj Kumar Dhasmana, Gulshan Benvin, Shalet Bhargav, H.K. Jadhav, Girish Rao, Tarini Department of Physics Bhabua India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India Bgs Institute of Technology Adichunchanagiri University Department of Computer Science and Engineering Mandya India Shridevi Institute of Engineering and Technology Department of Computer Science and Engineering Tumakuru India Parul institute of Engineering and Technology Parul University Faculty of Engineering and Technology Department of Electrical Engineering Post Limda India Faculty of Law Pune India

ISBN: (纸本)9798350352931

The proliferation of Internet of Things (IoT) networks has significantly increased the complexity of software architectures, leading to heightened vulnerabilities and system inefficiencies. AI-infused Predictive Digital Twin Systems provide a promising approach by integrating real-time data analytics with predictive AI models to monitor, forecast, and enhance the health and security of IoT software architectures. This paper introduces an AI-Infused Predictive Digital Twin System designed to create resilient IoT software architectures. Experimental validation was conducted using a large-scale IoT network with 1,000 interconnected devices. The AI-Infused Predictive Digital Twin System achieved a 97.8% accuracy in anomaly detection, with a 28% reduction in system disruptions compared to conventional monitoring solutions. The RL-based optimization module resulted in a 20% improvement in resource utilization efficiency and a 30% increase in overall system resilience under varying load conditions. The integration of the blockchain audit layer significantly improved traceability and reduced security breach recovery time by 40%. The AI-Infused Predictive Digital Twin System provides an innovative solution for developing resilient IoT software architectures. By integrating predictive AI models and real-time digital twin technology, the framework enables continuous monitoring, proactive anomaly detection, and dynamic optimization. The experimental results demonstrate the system's capability to enhance resilience, efficiency, and security in complex IoT environments, making it a valuable tool for managing and securing next-generation IoT architectures. Future work will explore expanding the system's capabilities to support multi-cloud IoT networks and developing more sophisticated AI models for enhanced predictive accuracy and operational efficiency. © 2024 IEEE.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

Significant reduction in friction and wear of a high-entropy alloy via the formation of self-organized nanolayered structure

引用

Journal of Materials science & technology 2021年第14期73卷 1-8页

作者： Lu Yang Zhuo Cheng Weiwei Zhu Cancan Zhao Fuzeng Ren Department of Materials Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Institute of Applied Physics and Materials Engineering Faculty of Science&TechnologyUniversity of MacaoMacaoChina

Sliding wear-induced nanolayering and its positive impact on wear resistance have been observed in conventional binary alloys with a matrix of high stacking fault energy(SFE),but this concept has never been reported in high-entropy alloys(HEAs)with low ***,we design and fabricate a(CoCrFeNi)_(90)Ag_(10)HEA,consisting of a face-center-cubic(fcc)CoCrFeNi HEA matrix with low SFE and uniformly dispersed Ag *** comparison with CoCrFeNi,a significant reduction in friction and wear was found in(CoCrFeNi)_(90)Ag_(10)HEA through the spontaneous formation of nanolayered subsurface microstructure during *** finding suggests a novel approach for designing HEAs that can achieve low friction and wear.

关键词： High-entropy alloy Sliding wear Nanolayering

来源：评论

学校读者我要写书评

暂无评论

Characterization of Sub-Optical-Wavelength Structures through Optically Opaque Films Using Picosecond Ultrasonics

引用

Nano Letters 2025年第22期25卷 8909-8914页

作者： Illienko, Maksym Chaudhary, Komal Velsink, Matthias C. Witte, Stefan Advanced Research Center for Nanolithography Science Park 106 Amsterdam 1098 XG Netherlands Department of Imaging Physics Faculty of Applied Sciences Delft University of Technology Lorentzweg 1 Delft 2628 CK Netherlands

Periodic arrays of nanostructures form an important building block of modern integrated circuits and photonic devices. Functionality of such devices is often critically dependent on the detailed structure. Moreover, multistep lithographic processing requires accurate metrology tools to characterize device morphology noninvasively, often after the deposition of additional layers of material. Here we show that ultrafast picosecond ultrasonics enables the accurate characterization of periodic structures below optically opaque thin films. By optically generating and detecting ultrahigh-frequency ultrasound at the surface of the film, we quantitatively characterize the main features of subsurface gratings with line widths down to 100 nm. We find that the acoustic diffraction is sensitive to the shape of the grating lines on the scale of tens of nanometers. © 2025 The Authors. Published by American Chemical Society.

关键词： acoustic diffraction defect detection optical metrology picosecond ultrasonics subwavelength structures ultrafast photoacoustics

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：