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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Shukla, Sakshi Gupta, Kshitij Pasari, Sumanta Gupta, Vishal Birla Institute of Technology and Science Department of Mathematics Rajasthan Pilani India Birla Institute of Technology and Science Department of Computer Science and Information Systems Rajasthan Pilani India

ISBN: (纸本)9798350370249

Accurate forecasting of wind speed is crucial for optimal extraction of energy, enabling integration of power grid, planning and management of renewable energy resources. To overcome the unpredictability of long-term trends and seasonal variation of wind, this study proposes a deterministic framework utilizing a hybrid model based on empirical wavelet transform (EWT), bidirectional gated recurrent neural network (BiGRU), and Bayesian optimization algorithm (BOA) for an hour-ahead wind speed prediction. Firstly, the EWT is used for preprocessing the wind speed data with enabling wavelet charaterticstics. Then, the BiGRU model is employed for regression using optimal values determined by the BOA method. The robustness of the proposed integrative framework is regressively evaluated over seven years (2015-2021) of hourly wind speed data across four locations in India. The evidence of numerical results of the proposed model demonstrates its effectiveness with a maximum improvement of 70% - 80% in terms of RMSE values across all the studied regions. Furthermore, the model evaluation and pictorial results indicate that the proposed model is a potent tool for generating wind energy and its integration into the smart grid. © 2024 IEEE.

关键词： Wind forecasting

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Implementation of Variable Capacitance to Improve Efficiency for Asymmetric LCC-LCC Compensated Wireless Power Transfer Systems

Implementation of Variable Capacitance to Improve Efficiency...

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2024 Energy Conversion Congress and Expo Europe, ECCE Europe 2024

作者： Zhu, Gangwei Dong, Jianning Wang, Jundong Deng, Zichen Li, Yongpeng Bauer, Pavol Delft University of Technology Mathematics and Computer Science Department of Electrical Engineering Delft Netherlands

ISBN: (纸本)9798350364446

This paper proposes a variable-capacitance-based control strategy to improve efficiency for asymmetric LCC-LCC compensated wireless power transfer (WPT) systems. While the existing triple-phase-shift (TPS) method can achieve power regulation and wide-range zero-voltage-switching (ZVS), it results in significantly increased reactive power under asymmetric LCC-LCC compensation topology. To this end, this paper incorporates a switch-controlled-capacitor (SCC) on the primary side. The impact of variable capacitance on the system characteristics is first investigated. Furthermore, the optimal capacitor tuning factor is derived to achieve the inverter ZVS with minimal reactive power. Through the implementation of variable capacitance, the primary inductor current is notably reduced within a wide range of power. Moreover, the turn-off currents of power switches are minimized. These factors contribute to a reduction in inductor and inverter losses, thus improving the overall efficiency. Experimental results confirm that the proposed method improves the efficiency of an asymmetric LCC-LCC compensated WPT prototype, with a maximum efficiency improvement of up to 1.8%. © 2024 IEEE.

关键词： Capacitor bank

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Understanding Syntax Errors in C Sharp Using Game-Based Techniques at Tertiary Level

Understanding Syntax Errors in C Sharp Using Game-Based Tech...

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International Conference on Big Data Intelligence and Computing, DataCom 2022

作者： Raj, Ravishek Sharma, Neeraj Anand Department of Computer Science and Mathematics School of Science and Technology The University of Fiji Lautoka Fiji

ISBN: (纸本)9789819922321

Programming is one of the most popular and demanding courses in universities around the world for students to build a strong foundation for their future in the field of computer science. Unlike other subjects, computer science is more of a practical field than a theoretical one, which requires practical experience to learn rather than theoretical knowledge, which can be forgotten if the teachings are not put into practice by students. When new students enroll in a programming course for the first time, about half of the students pass the course on the first try, while most of the students that pass obtain the minimum grade for passing the course. This is because students find it hard to cope with programming with limited guidance in a short period of time, whereas not all students can get lecturers to help when they get stuck on an error while learning to code. The purpose of this study is to see if there is a need for a strategy to help students new to programming learn about and identify common errors in C-sharp programming and improve their coding abilities using a game-based learning application. The study will also discuss which type of game application will be the most effective in gaining the interest of students, allowing them to actively use the application to improve their experience in coding while outlining a rough idea of what a game-based learning application should contain for others to use as a guide to help in their research. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Students

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Intelligent Transformation of Lab Based on OpenHarmony 2

Intelligent Transformation of Lab Based on OpenHarmony

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2nd IEEE International Conference on Futuristic Technologies, INCOFT 2023

作者： Wang, Xiaoliang Yin, Wentong College of Mathematics and Computer Science Guangxi Normal University of Science and Technology LaiBin China

ISBN: (纸本)9798350308846

With the continuous development of information technology and artificial intelligence technology, low-cost information technology and artificial intelligence technology can be used to rapidly develop a set of systems to transform computer rooms and multimedia classrooms in colleges and universities, improve the efficiency of management, use and maintenance of instruments and equipment, and ensure the timeliness, accuracy and scientific nature of data. And based on deep learning and other technologies, the teachers' class situation and equipment use situation are predicted, so that multimedia classrooms and computer laboratories in colleges and universities can better play the role of scientific research and talent training platforms. © 2023 IEEE.

关键词： digitization Intelligent LSTM OpenHarmony

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An Automatic ECG Classification for Arrhythmia Detection Using Machine Learning Techniques 4

An Automatic ECG Classification for Arrhythmia Detection Usi...

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4th International Conference on Embedded and Distributed Systems, EDiS 2024

作者： Osmani, Mohammed Nadjib Benhaddouche, Djamila Houari, Nawal Sad University of Science and Technology Mohamed Boudiaf Mathematics and computer science department Oran Algeria University of Science and Technology Mohamed Boudiaf Living and environment department Oran Algeria

ISBN: (纸本)9798331519872

Cardiovascular arrhythmias significantly impact global health, with the electrocardiogram (ECG) serving as a primary diagnostic tool due to its accessibility and cost-effectiveness. Despite its widespread use, ECG misdiagnosis rates remain high, often requiring extended patient monitoring to detect infrequent arrhythmias. Manual diagnosis is time-consuming and heavily reliant on cardiologist expertise. Consequently, automatic arrhythmia detection techniques have been developed worldwide. However, many machine learning-based ECG classification systems face challenges in generalization and transparency, essential for clinical trust and verification. This study presents an automated approach for detecting and classifying eight types of arrhythmias using the MIT-BIH dataset. Two validation protocols, holdout beats and leave-out patients, were employed for data splitting and resampling. The performance of Support Vector Machine (SVM) and MultiLayer Perceptron (MLP) classifiers was compared against other state-of-the-art classifiers. The proposed models demonstrate strong performance, particularly under the leave-out patients' validation protocol, indicating superior generalization capabilities. This research underscores the potential of MLP and SVM in enhancing the accuracy and reliability of arrhythmia detection and classification, thereby improving diagnostic outcomes and patient care. © 2024 IEEE.

关键词： Diseases

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An Authentication Protocol for Federated Learning With Blockchain in Consumer Electronic Assisted Autonomous Driving Environments

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 2231-2242页

作者： Chen, Chien-Ming Hao, Yiru Kumari, Saru Amoon, Mohammed Nanjing University of Information Science and Technology School of Artificial Intelligence Nanjing China Shandong University of Science and Technology Qingdao China Chaudhary Charan Singh University Department of Mathematics India King Saud University Community College Department of Computer Science Riyadh11437 Saudi Arabia

Consumer electronics play a crucial role in the automotive sector by providing the essential hardware infrastructure necessary for developing autonomous vehicles. These vehicles significantly enhance driving comfort and accessibility, particularly benefiting specific populations such as the elderly and individuals with disabilities. Researchers utilize federated learning, employing homomorphic encryption and differential privacy techniques;however, these methods can impede efficiency. Traditional centralized federated learning presents a risk of single points of failure. To address this issue, we propose an authentication protocol for federated learning, integrated with blockchain technology, within consumer electronics-supported autonomous driving environments. This protocol enhances vehicle authentication efficiency while prioritizing data security and user privacy. Formal analysis verifies the integrity of the protocol. Experimental results demonstrate that the communication overhead of our protocol is reduced by 40.3% compared to existing similar authentication protocols. © 1975-2011 IEEE.

关键词： Protocols Blockchains Authentication Computational modeling Autonomous vehicles Federated learning Security Data models Consumer electronics Privacy

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The Formulation of Finite Difference RBFWENO Schemes for Hyperbolic Conservation Laws:An Alternative Technique

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Advances in Applied mathematics and Mechanics 2023年第4期15卷 1023-1055页

作者： Rooholah Abedian Mehdi Dehghan School of Engineering Science College of EngineeringUniversity of TehranIran Department of Applied Mathematics Faculty of Mathematics and Computer ScienceAmirkabir University of TechnologyNo.424Hafez Ave.TehranIran

To solve conservation laws,efficient schemes such as essentially nonoscillatory(ENO)and weighted ENO(WENO)have been introduced to control the Gibbs *** on radial basis functions(RBFs)with the classical WENO-JS weights,a new type of WENO schemes has been proposed to solve conservation laws[*** et al.,***.,70(2017),pp.551–575].The purpose of this paper is to introduce a new formulation of conservative finite difference RBFWENO schemes to solve conservation *** the usual method for reconstructing the flux functions,the flux function is generated directly with the conservative *** with Guo and Jung(2017),the main advantage of this framework is that arbitrary monotone fluxes can be employed,while in Guo and Jung(2017)only smooth flux splitting can be used to reconstruct flux *** 1D and 2D benchmark problems are prepared to demonstrate the good performance of the new scheme.

关键词： Weighted essentially non-oscillatory scheme radial basis functions interpolation ﬁnite difference method hyperbolic conservation laws

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EEDL-based Detection and Classification of Apple Foliar Leaf Disease 15

EEDL-based Detection and Classification of Apple Foliar Leaf...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Kumar, Satish Kumar, Rakesh Gupta, Meenu Obaid, Ahmed J. Chandigarh University Department of Computer Science and Engineering Punjab India University of Kufa National University of Science and Technology Faculty of Computer Science and Mathematics Dhi Qar Najaf Iraq

ISBN: (纸本)9798350370249

Apple farmers constantly grapple with the challenge of increasing their yield and safeguarding apple trees from diseases. The prevalence of diseases and pests significantly hampers apple production, resulting in substantial financial losses for the industry annually Detecting apple leaf diseases swiftly and accurately is crucial for effectively handling and curbing these issues within orchards. Specifically, advancements in computer vision methods that utilize deep learning have opened avenues for identifying and understanding these diseases at an early stage directly on the leaves Here, the EEDL(Enhanced Efficient Deep Learning)model is proposed for the detection and classification of five types of apple foliar diseases. In the EEDL model, we modify and fine-tune the Efficient Net model variant B0 by adding four additional layers. These layers consist of the augmentation layer, dense layer, dropout layer and the final output classifier dense layer. The AFD-7C dataset(8000 images are gathered in the real filed environment of apple research Centre and apple orchards of Himachal Pradesh. 9037 images are obtained from the Plant Village public dataset) is utilized in the experimental procedure and achieves an accuracy of 97.82%. © 2024 IEEE.

关键词： Fruits

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The Best of Both Worlds: Model-Driven Engineering Meets Model-Based Testing 34

The Best of Both Worlds: Model-Driven Engineering Meets Mode...

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34th International Conference on Concurrency Theory, CONCUR 2023

作者： van Spaendonck, P.H.M. Willemse, Tim A.C. Department of Mathematics and Computer Science Eindhoven University of Technology Netherlands Department of Mathematics and Computer Science Eindhoven University of Technology The Netherlands ESI [TNO Eindhoven Netherlands

ISBN: (纸本)9783959772990

We study the connection between stable-failures refinement and the ioco conformance relation. Both behavioural relations underlie methodologies that have gained traction in industry: stable-failures refinement is used in several commercial Model-Driven Engineering tool suites, whereas the ioco conformance relation is used in Model-Based Testing tools. Refinement-based Model-Driven Engineering approaches promise to generate executable code from high-level models, thus guaranteeing that the code upholds specified behavioural contracts. Manual testing, however, is still required to gain confidence that the model-to-code transformation and the execution platform do not lead to unexpected contract violations. We identify conditions under which also this last step in the design methodology can be automated using the ioco conformance relation and the associated tools. © 2023 Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing. All rights reserved.

关键词： Model checking

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Online Prediction Threshold Optimization Under Semi-deferred Labelling

Online Prediction Threshold Optimization Under Semi-deferred...

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Workshops of the EDBT/ICDT 2024 Joint Conference, EDBT/ICDT-WS 2024

作者： Spenrath, Yorick Hassani, Marwan van Dongen, Boudewijn F. Process Analytics Group Faculty of Mathematics and Computer Science Eindhoven University of Technology Netherlands

In supermarket loyalty campaigns, shoppers collect stamps to redeem limited-time luxury products. Having an accurate prediction of which shoppers will eventually redeem is crucial to effective execution. With the ultimate goal of changing shopper behavior, it is important to ensure an adequate number of rewards and to be able to steer promising shoppers into joining the campaign and redeeming a reward. If information from previous campaigns is available, a prediction model can be built to predict the redemption probability, possibly also adapting the prediction threshold to determine predicted the label. During a running campaign, we only know a subset of the labels of the positive class (the so-far redeemers), and have no access to the labels of any example of the negative class (non-redeemers at the end of the campaign). The majority of the examples during the campaign do not have a label yet (shoppers that could still redeem but have not done so yet). This is a semi-deferred labelling setting and our goal is to improve the prediction quality using this limited information. Existing work on predicting (semi-deferred) labels either focuses on positive-unlabelled learning, which does not use existing models, or updates models after the prediction is made by assigning expected labels using unsupervised learning models. In this paper we present a framework for Online Prediction threshold optimization Under Semi-deferred labelling (OPUS). Our framework does not change the existing model, but instead adapts the prediction threshold that decides which probability is required for a positive label, based on the semi-deferred labels we already know. We apply OPUS to two real-world datasets: a supermarket with two campaigns and over 160 000 shoppers. © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Forecasting

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