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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Santhosha, B.M. Negi, Harendra Singh Balaraju, V. Vekariya, Daxa Patil, Harshal Maranan, Ramya Ballari Institute of Technology and Management Visvesvaraya Technological University Department of Electrical and Electronics Engineering Ballari Karnataka India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Dr. Ntr College of Agricultural Engineering Department of Basic Engineering and Applied Sciences Andhra Pradesh Bapatla522101 India Parul Institute of Engineering and Technology Parul University Department of Computer Science & Engineering Gujarat 391760 India Department of Computer Science and Engineering Maharashtra Pune412115 India Saveetha School of Engineering Simats Department of Research and Innovation Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350361186

The main objective of this work is to model, control and simulate an electrical microgrid. To achieve this objective, models of the subsystems or associated elements (generators, power interfaces, electrical loads, etc.) are built, the control strategies necessary for the integration of each subsystem in the microgrid are implemented, and it is proposed an optimization algorithm that allows the economic dispatch of the system to be carried out under different generation and demand conditions. Finally, after simulating the model, its behavior is validated in a considered operating scenario. The work environment selected to develop this project is Simulink, a tool integrated into MATLAB, due to the ease it offers when modeling and simulating dynamic systems, allowing you to customize the included libraries and export data to MATLAB for subsequent analysis. At the end of this work, it is intended to have a simulated environment to validate the behavior of the proposed microgrid, which allows considering different generation and demand scenarios, and an optimization algorithm that allows carrying out the economic dispatch of the system under the operating conditions. considered, without taking into account the randomness of generation sources and energy demand. © 2024 IEEE.

关键词： Wind power

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Enhancing the Swanepoel method: precise envelope detection of thin-film transmission spectra

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Optics Express 2025年第6期33卷 13376-13400页

作者： Ballester, Manuel Marquez, Almudena P. Lopez-Tapia, Santiago Fernandez, Susana Bass, John Minkov, Dorian Wuersch, Christoph Willomitzer, Florian Marquez, Emilio Katsaggelos, Aggelos K. Computer Science Department Northwestern University EvanstonIL60208 United States Department of Mathematics Faculty of Science University of Cadiz Puerto Real11510 Spain Department of Electrical and Computer Engineering Northwestern University EvanstonIL60208 United States CIEMAT Avenida Complutense 40 Madrid28040 Spain Wyant College of Optical Sciences University of Arizona TucsonAZ85721 United States Sofia1000 Bulgaria Institute for Computational Engineering ICE OST Eastern Switzerland University of Applied Sciences Switzerland Department of Condensed-matter Physics Faculty of Science University of Cadiz Puerto Real11510 Spain

The Swanepoel method is a widely used optical technique for characterizing thin films through normal-incidence transmission measurements. A critical step in this approach involves extracting the upper and lower envelopes of the measured oscillatory spectrum. By analyzing the transmission spectrum and its corresponding envelopes, the Swanepoel procedure enables precise determination of the film refractive index and extinction coefficient. However, even minor inaccuracies in envelope construction can propagate significant errors into the final characterization results. To address this challenge, we present what we believe to be a novel physics-informed optimization algorithm for envelope detection. Our mathematical model reformulates the envelope detection problem as a global optimization task that enforces the physical properties of the Swanepoel envelopes. Extensive validation on fifty randomly generated transmission spectra demonstrates unprecedented accuracy: the method achieves root-mean-square errors (RMSE) below 0.10% for the upper envelope and 0.06% for the lower envelope (more than doubling the accuracy of current state-of-the-art approaches). Furthermore, the proposed model has been assessed with two experimental transmission spectra, demonstrating its robustness and accuracy with real noisy data. We have developed an open-source Python software package (see text below). This software includes not only our innovative envelope construction algorithm but also additional envelope drawing algorithms for comparison and an efficient implementation of the Swanepoel method, enabling complete optical characterization of thin films. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Mean square error

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Trusting Computing as a Service for Blockchain Applications

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IEEE Internet of Things Journal 2023年第13期10卷 11326-11342页

作者： Li, Wen-Wei Meng, Weizhi Yeh, Kuo-Hui Cha, Shi-Cho National Taiwan University of Science and Technology Department of Information Management Taipei10607 Taiwan Technical University of Denmark Department of Applied Mathematics and Computer Science Copenhagen2800 Denmark National Dong Hwa University Department of Information Management Hualien97401 Taiwan National Sun Yat-sen University Department of Computer Science and Engineering Kaohsiung804 Taiwan

Blockchain and smart contracts have become one of the most popular technologies to establish decentralized applications. Due to public visibility of data stored in blockchain, the information processed by a smart contract is visible to every party in a blockchain. To improve data confidentiality, this study proposes a trusted computing as a service (TCaaS) framework based on the blockchain. The framework includes a trustworthy node to perform trusted computing. Using the proposed framework, users of blockchain systems can integrate trustworthy node into blockchain systems. Users can also upload general-purposed programs and associated parameters and discover trustworthy node for execution via related smart contracts. The programs and parameters are encrypted so that only selected trustworthy node can decrypt the data. The execution environment returns computed results to the corresponding blockchain system. We evaluate our concept with the ESP32 microcontroller with the ATECC508A security chip and the Quorum blockchain system. Therefore, the study contributes to construct a trusted computing environment for blockchain system and preserve confidentiality at the same time. © 2014 IEEE.

关键词： Smart contract

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Physics-informed neural networks for modeling water flows in a river channel

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1001-1015页

作者： Nazari, Luis Fernando Camponogara, Eduardo Seman, Laio Oriel Department of Mathematics Federal Institute of Education Science and Technology Catarinense Rio do Sul89160-202 Brazil Department of Automation and Systems Engineering Federal University of Santa Catarina Florianopolis88040-900 Brazil Program in Applied Computer Science Universidade Do Vale Do Itajai Itajai88302-901 Brazil

The impacts incurred by floods regularly affect the planets population, inflicting social and economic problems. Optimal control strategies based on reservoir management may aid in controlling floods and mitigating the resulting damage. To this end, an accurate dynamic representation of water systems is needed. In practice, flood control strategies rely on hydrological forecasting models obtained fromconceptual or data-drivenmethods. Encouraged by recent works, this research proposes a novel surrogate model for water flow in a river channel based on physics-informed neural networks (PINNs). This approach achieved promising results regarding the assimilation of real-data measurements and the parameter identification of differential equations that govern the underlying dynamics. This article investigates PINN performance in a simulated environment built directly from a configuration of the Saint-Venant equations. The objective is to create a suitable model with high prediction accuracy and scientifically consistent behavior for use in real-Time applications. The experiments revealed promising results for hydrological modeling and presented alternatives to solve the main challenges found in conventional methods while assisting in synthesizing real-world representations. Impact Statement-The research seeks to contribute to the hydrological modeling area with a surrogate model based on physicsinformed neural networks (PINNs) to water flow in a watershed. In practice, thesemodels use conceptual or *** models to reach the precision provided by themethodology use large numbers of physical parameters. These parameters can demand deep knowledge about the environment and are possibly hard to identify in a complex basin. On the other hand, while data-driven methods do not require such knowledge about the dynamic system, they depend on a reliable and useful database to guarantee the accuracy of system *** introduce PINNs as a viable solution for

关键词： Floods

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Drug Consumption Detection by Eye Using Image Processing Techniques

Drug Consumption Detection by Eye Using Image Processing Tec...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Pyingkodi, M. Thenmozhi, K. Wilfred Blessing, N.R. Archana, K. Arun Hiruthik, X. Dinesh Kumar, V. Kongu Engineering College Department of Computer Applications Erode India Kristu Jayanti College Department of Computer Science Bengaluru India University of Technology and Applied Sciences-Ibri Department of IT Oman Kongu Engineering College Computer Applications Erode India

ISBN: (纸本)9798350348217

Drug usage is a significant global economic issue that results in countless losses. This study suggests a convolutional neural network-based image processing method for identifying drugged eyes. The front-end packages already in use for this article collect information from eye images using the CNN algorithm. However, it can take some time. As a result, the suggested approach may be used to swiftly and automatically identify drugged eyes. The acquisition of the input image, picture pre-processing, and image processing are the main processes in the suggested *** the reddish regions, emphasising the afflicted regions, confirming the training set, and presenting the findings. Some eyes may go unnoticed, such those that have been socially poisoned. This technique was tried on drugged eyes at different phases, both with and without drug consumption. The technique was used to find a white region of the eye in a given input picture. Images of both drugged and unaffected eyes were presented for training purposes. In order to choose the optimum colour model for this strategy, photos were first transformed to colour models. The Support Erosion technique and the Local Binary Pattern were both used in the model construction for feature extraction. This technique can detect drugged eyes with an average accuracy of 95%. © 2023 IEEE.

关键词： Local binary pattern

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Analysis of Artificial Intelligence Techniques on Sustainable Employee Performance Management

Analysis of Artificial Intelligence Techniques on Sustainabl...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Siluvainathan, Panneerselvam Padmakala, S. Selvakumar, A. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Applied Machine Learning Chennai India

ISBN: (纸本)9798331510022

Sustainable employee performance management (EPM) models are key to achieving the SDGs such as Promoting decent work and economic growth, reducing inequality, ensuring quality education, promoting gender equality, fostering innovation, sustainable practices like Green Human Resource Management, and Building Partnerships in work environments. Such performance management models would require data-driven insights to arrive at the right decisions in recruiting, assessing, rewarding, ensuring good employee health, and retaining employees for strategic advantages to institutions and countries alike. AI technologies could be employed for effective employee performance management with checks and balances as they have immense learning and predictive potential to avoid human errors and biases. This work presents EPM elements, an analysis of the EPM framework, EPM measurement techniques using AI techniques, Metrics for EPM, and finally, the future direction of research. © 2024 IEEE.

关键词： AI Employee Performance Management System Performance Metrics

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Impact Assessment and Mitigation Strategies for Plug-in Electric Vehicles on Distribution Networks: A Probabilistic Approach 7

Impact Assessment and Mitigation Strategies for Plug-in Elec...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Raj, Vijilius Helena Reddy, M. Siva Swetha Thethi, H. Pal Ramesh, G. Mohammad, Q. Bhavana, Munugapati New Horizon College of Engineering Department of Applied Sciences Bangalore India Institute of Aeronautical Engineering Telangana Hyderabad50043 India Lovely Professional University Phagwara India Griet Department of Computer Science and Engineering Bachupally Hyderabad India Hilla University College Babylon Iraq Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798350350067

The rapid depletion of fossil fuel reserves coupled with the ever-increasing demand for electrical power has prompted the search for new energy sources. Electric vehicles (EVs) are one potential answer because of the technology that allows for more efficient transportation while simultaneously reducing fossil fuel usage and emissions of greenhouse chemicals. Depending on the power system's capability, structure, and architecture, several studies have shown that the distribution network may be negatively affected by the widespread interconnection of electric vehicles. Consequently, it is important to determine the effects of this additional load on the network so that measures can be taken to prevent threats to the system's stability, dependability, and security, especially because the Indian federal and state governments have introduced incentives to increase sales of this type of vehicle. © 2024 IEEE.

关键词： Power distribution networks

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Heart Disease Prediction Using Novel Ensemble and Blending Based Cardiovascular Disease Detection Networks: EnsCVDD-Net and BlCVDD-Net

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IEEE Access 2024年 12卷 109230-109254页

作者： Khan, Hira Javaid, Nadeem Bashir, Tariq Akbar, Mariam Alrajeh, Nabil Aslam, Sheraz Department of Electrical and Computer Engineering COMSATS University Islamabad Islamabad44000 Pakistan Department of Computer Science COMSATS University Islamabad Islamabad44000 Pakistan International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Yunlin Douliu64002 Taiwan Department of Biomedical Technology College of Applied Medical Sciences King Saud University Riyadh11633 Saudi Arabia Cyprus University of Technology Limassol3036 Cyprus

Cardiovascular Diseases (CVDs) have emerged as a significant physiological condition, being a primary contributor to mortality. Timely and precise diagnosis of heart disease is crucial to safeguard patients from additional harm. Recent studies show that the usage of data driven approaches, such as Deep Learning (DL) and Machine Learning (ML) techniques, in the field of medical science is highly useful in accurately diagnosing heart disease in less time. However, statistical learning and traditional ML approaches require feature engineering to generate robust and effective features from data, which are then used in the prediction models. In the case of large complex data, both processes pose many challenges. Whereas, DL techniques are capable of learning features automatically from the data and are effective at handling large and intricate datasets while outperforming the ML models. This study focuses on the accurate prediction of CVDs, considering the patient’s health and socio-economic conditions while mitigating the challenges presented by imbalanced data. The Adaptive Synthetic Sampling Technique is used for data balancing, while the Point Biserial Correlation Coefficient is used as a feature selection technique. In this study, two DL models, Ensemble based Cardiovascular Disease Detection Network (EnsCVDD-Net) and Blending based Cardiovascular Disease Detection Network (BlCVDD-Net), are proposed for accurate prediction and classification of CVDs. EnsCVDD-Net is made by applying an ensemble technique to LeNet and Gated Recurrent Unit (GRU), and BlCVDD-Net is made by blending LeNet, GRU and Multilayer Perceptron. SHapley Additive exPlanations is used to provide a clear understanding of the influence different factors have on CVD diagnosis. The network’s performance is evaluated on the basis of various performance metrics. The results indicate that the EnsCVDD-Net outperforms all base models with 88% accuracy, 88% F1-score, 91% precision, 85% recall, and 777s execu

关键词： Heart

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Constructing the ChatGPT for PDF Files with Langchain - AI 7

Constructing the ChatGPT for PDF Files with Langchain - AI

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Prem Jacob, T. Bizotto, Beatriz Lucia Salvador Sathiyanarayanan, Mithileysh Sathyabama Institute of Science And Technology Department of Computer Science And Engineering Chennai India University Center Unifacvest Department of Social And Applied Sciences Lages Brazil Research & Innovation MIT Square London United Kingdom

ISBN: (纸本)9798350359299

Queries in PDFs can be time-consuming and labor-intensive because of the unstructured nature of the PDF document type and the need for accurate and relevant search results. By applying cutting-edge algorithms for natural language processing to examine PDF documents and extract relevant data, LangChain solves these difficulties. It makes use of an easy search interface, adjustable filters, and efficient indexing and retrieval mechanisms to enhance the search experience. To efficiently retrieve relevant information from PDF documents, users can annotate critical portions, store queries, and create bookmarks with LangChain. The characteristics of LangChain improve overall productivity and greatly simplify PDF querying. Semantic search, driven by the latest Transformer language models, represents a significant evolution in information retrieval systems. This research work explores the capabilities of semantic search to efficiently retrieve documents from large collections in response to natural language queries. Unlike traditional keyword-based approaches, semantic search connects the power of Transformer models to discern meaning, providing users with more contextually relevant and accurate results within seconds. This technology not only enhances the user experience by delivering superior matches from document collections but also lays the foundation for tackling more intricate tasks, like text summarization and question-answering. The research investigates the impact of semantic search on information retrieval efficiency and accuracy, comparing its performance with conventional methods. The findings presented herein not only showcase the immediate benefits of semantic search but also open paths for future research and development in natural language processing and its applications. © 2024 IEEE.

关键词： Information retrieval systems

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Multiview Orthonormalized Partial Least Squares: Regularizations and Deep Extensions

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IEEE Transactions on Neural Networks and Learning Systems 2023年第8期34卷 4371-4385页

作者： Wang, Li Li, Ren-Cang Lin, Wen-Wei The University of Texas at Arlington Department of Mathematics ArlingtonTX76019 United States The University of Texas at Arlington Department of Computer Science and Engineering ArlingtonTX76019 United States National Chiao Tung University Department of Applied Mathematics Hsinchu300 Taiwan

In this article, we establish a family of subspace-based learning methods for multiview learning using least squares as the fundamental basis. Specifically, we propose a novel unified multiview learning framework called multiview orthonormalized partial least squares (MvOPLSs) to learn a classifier over a common latent space shared by all views. The regularization technique is further leveraged to unleash the power of the proposed framework by providing three types of regularizers on its basic ingredients, including model parameters, decision values, and latent projected points. With a set of regularizers derived from various priors, we not only recast most existing multiview learning methods into the proposed framework with properly chosen regularizers but also propose two novel models. To further improve the performance of the proposed framework, we propose to learn nonlinear transformations parameterized by deep networks. Extensive experiments are conducted on multiview datasets in terms of both feature extraction and cross-modal retrieval. Results show that the subspace-based learning for a common latent space is effective and its nonlinear extension can further boost performance, and more importantly, one of two proposed methods with nonlinear extension can achieve better results than all compared methods. © 2012 IEEE.

关键词： mathematical transformations

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