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Enhancing Industrial Load Scheduling through Demand Side Management and Advanced Optimization Techniques Using Neuro-Fuzzy and Deep Learning Approaches 4

Enhancing Industrial Load Scheduling through Demand Side Man...

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4th IEEE International Conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Agarwal, Shivani Sharma, Ritu Tamilselvi, M. Sharma, Harsh Mohan Sahu, Dinesh Prasad Singh, Jagendra Ajay Kumar Garg Engineering College Department of Information Technology Ghaziabad India Gyan Ganga Institute of Technology and Science Dept. of Electrical and Electronics Engineering Jabalpur India Roever Engineering College Perambalur Deartment of Computer Science and Engineering Perambalur India Raj Kumar Goel Institute of Technology Department of Management Ghaziabad India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9798350306118

This study aims to investigate whether physical factors like temperature, humidity, and pressure interact with current in Micro-electromechanical systems (MEMS). To study these interactions, a straightforward system with four spin-operated engines was employed. The pressure, humidity, and temperature may all change as a result of changing the motor's output current. The results are evaluated and predicted using LSTM and Neuro-Fuzzy models. Sensors are used to keep an eye on these circumstances. Neurofuzzy models employ fuzzy logic and artificial intelligence to capture interconnections, as opposed to LSTM models, which use their capacity to comprehend data connections to capture long-Term effects. The results demonstrate that the body's capacity to exert energy is enhanced by the effect of body energy on temperature, humidity, and pressure. The research could lead to improvements in temperature control, energy efficiency, and the functionality and design of massive industrial machinery. A straightforward design is used to launch controlled experiments, and the data collected is then used to train and assess the model. By offering helpful suggestions for the improvement of the motor and the operating electric economy, this study contributes to our understanding of the evolution of the electric motor. © 2023 IEEE.

关键词： Electric loads

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Advancing Drug Discovery through a Deep Learning-Based Protein-Ligand Interaction Prediction Tool 3

Advancing Drug Discovery through a Deep Learning-Based Prote...

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3rd International Conference on Intelligent and Innovative Technologies in Computing, electrical and Electronics, IITCEE 2025

作者： Hunge, Sudhir S. Yadao, Aman Rajendra Shewate, Varsha Tekdas Lalsare, Prakash Namdeorao Prasannan, Shiji Ramulu, Kothuri Parashu Chintamani College of Science Pg Dept. of Chemistry Maharashtra Pombhurna India Shriram Arts And Science Junior College Dept. Of Chemistry Maharashtra Ramtek India Chintamani College of Science Dept. of Chemistry Maharashtra Pombhurna India Shri Prabhu Ramchandra Junior College Dept. of Chemistry Nanda Tah Korpana Maharashtra India Faculty of Management Studies Parul Institute of Management and Research Parul University Gujarat Vadodara India Indur Institute Of Engineering And Technology Dept. of Computer Science and Engineering India

ISBN: (纸本)9798331515911

In recent years, deep learning has emerged as a promising approach to advance drug discovery by accurately predicting interactions between proteins and small molecules, or ligands. The identification of these interactions is essential in developing drugs, as it informs us about a molecule's potential therapeutic efficacy. Traditional methods, like molecular docking, often struggle with accuracy and computational efficiency, limiting their application in high-throughput settings. In this paper, we propose a novel deep learning-based tool designed to predict protein-ligand interactions with enhanced precision and scalability. Our model leverages convolutional and graph neural networks to capture the complex, three-dimensional nature of these interactions. Using a large, curated dataset, we train and test the model to optimize binding affinity prediction, employing metrics such as mean squared error and accuracy to validate performance. The results show that our approach outperforms conventional techniques and recent machine learning models, demonstrating a significant improvement in predictive reliability. This tool has potential applications in screening vast compound libraries in early-stage drug discovery, reducing time and costs associated with experimental trials. By providing more accurate insights into protein-ligand affinities, our method represents a step forward in computational drug design, with implications for developing targeted therapies across various disease areas. © 2025 IEEE.

关键词： Graph neural networks

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LSTM Based Model Predictive Control Approach for Energy Management System in PV-Battery Integrated Microgrid Network 10

LSTM Based Model Predictive Control Approach for Energy Mana...

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10th IEEE International Symposium on Smart Electronic Systems, iSES 2024

作者： Preetha Roselyn, J. Sundaravadivel, Prabha Vignesh Babu, V. Nithya, C. Devaraj, D. SRM Institute of Science and Technology Kattankulathur Dept of Electrical and Electronics Engineering Chennai603203 India The University of Texas Department of Electrical and Computer Engineering TylerTX75701 United States SRM Institute of Science and Technology Kattankulathur Department of Electrical and Electronics Engineering Chennai603203 India Kalasalingam Academy of Research and Education Srivilliputhur India

ISBN: (纸本)9798331533229

This paper proposes an intelligent energy management system in grid-connected microgrid with renewable energy and battery storage systems. The battery charging and discharging strategy is regulated so that the overall energy cost, along with battery degradation cost, is minimized considering the variation in grid tariff, inconsistent renewable power output and changing load demand. The proposed model is developed as an optimization-based problem over a 24-hour horizon using Long Short-Term Memory (LSTM) based forecasting Energy Management-based Model Predictive Control. The proposed model provides information about the renewable power generation and demand for next 24 hours through LSTM based prediction model. The Long Short-Term Memory network is developed for time series forecasting of renewable generation and its performance is compared with other regression models. The Receding Horizon strategy is proposed to reduce the forecasting results of LSTM is fed as inputs to the Model Predictive Control based Energy management system forecasts and enable the implementation of energy management system. The proposed algorithm is developed and implemented in microgrid environment in MATLAB/Simulink 2022b under three case studies and the results claim that the proposed LSTM-MPC control strategy is successful in terms of reducing the grid consumption cost without much degrading the energy storage. The proposed model is also validated in 1kW microgrid hardware setup and promising results are obtained © 2024 IEEE.

关键词： Microgrids

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High Isolation Dual Port MIMO Antenna for Vehicle-to-Vehicle Communication

High Isolation Dual Port MIMO Antenna for Vehicle-to-Vehicle...

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2023 IEEE International Conference on Communications, Information, Electronic and Energy Systems, CIEES 2023

作者： Sharma, Preeti Singh, Prabhakar Tiwari, Rakesh N. Kumar, Pradeep Galgotias University Physics Division School of Basic Sciences Greater Noida India Greater Noida Institute of Technology Dept. of Applied Science Greater Noida India Madanapalle Institute of Technology & Science Dept. of Electronics and Communication Engineering Madanapalle India University of KwaZulu-Natal Dept. of Electrical Electronic and Computer Engineering Durban South Africa

ISBN: (纸本)9798350336917

This research paper reports a very thin 2-port multiple-input multiple-output (MIMO) antenna with a small footprint of 14× 10× 0.58mm3. The proposed design works between the frequencies of 8.73 to 15.86 GHz. A modified hexagonal patch is used along with the defected ground plane to obtain wide bandwidth. A conventional decoupling strip technique is utilized to improve the isolation between the ports and it is found >19dB across the whole operating band. In addition to that, 'H'-shaped slot has been incorporated into the grounding plane to enhance impedance matching. The realized gain and radiation patterns of the designed antenna are calculated and found quite acceptable. Furthermore, the diversity parameters of the antenna are computed with envelope correlation coefficient (ECC) 9.97dB across the entire operating frequency range. Finally, the results of MIMO antenna are compared with the calculated data for the purpose of practical validation. © 2023 IEEE.

关键词： Defects

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A Comparative Analysis between Karatsuba, Toom-Cook and NTT Multiplier for Polynomial Multiplication in NTRU on FPGA

A Comparative Analysis between Karatsuba, Toom-Cook and NTT ...

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IEEE Asian Hardware-Oriented Security and Trust (AsianHOST)

作者： Harish Prasad Allam Suraj Mandal Debapriya Basu Roy Dept. of Electrical Engineering IIT Kanpur India Dept. of Computer Science & Engineering IIT Kanpur India

Post-quantum cryptography (PQC) is a rapidly growing field that aims to develop cryptographic algorithms that can resist attacks by quantum computers. Large-degree polynomial multiplication is an essential mathematical operation for lattice-based post-quantum cryptographic algorithms like CRYSTALS-Kyber, Dilithium, and NTRU. Due to the complexity involved in modular arithmetic, large-degree polynomial multiplication is a critical operation that needs to be accelerated for fast execution of the underlying cryptographic algorithms. In this paper, we aim to provide a comparative case study that focuses on analyzing the effectiveness of Karatsuba, Toom-Cook, and Number theoretic transform (NTT) based multipliers for performing polynomial multiplication in NTRU that uses NTT unfriendly quotient q. Our proposed Karatsuba-based implementation is based on the overlap-free version, and for Toom-Cook, we have used a three-way decomposition. Our result shows that for NTRU, the proposed overlap-free Karatsuba architecture provides a better area delay product than the Toom-cook architecture, even though the Toom-cook algorithm is asymptotically faster than the Karatsuba. Additionally, we have implemented NTRU polynomial multiplication using NTT by performing NTT prime lifting, which exhibits a low area and a compact architecture. This comparative study between different multiplier architectures would enable the hardware designer to choose the suitable multiplier depending on their requirement.

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Authorizable Tripartite Entanglement Routing via 3-GHZ State in Quantum Networks

Authorizable Tripartite Entanglement Routing via 3-GHZ State...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Huang, Shao-Min Wei, Ching-Ting Zhan, Kai-Xu Le Touze, Juliette Chou Kuo, Jian-Jhih Wang, Chih-Yu National Chung Cheng University Dept. of Computer Science and Information Engineering Chiayi Taiwan Academia Sinica Research Center for Information Technology Innovation Taipei City Taiwan

ISBN: (纸本)9798350351255

Traditional end-to-end entanglement typically operates between two parties. However, such a setup may fall short when three parties are involved. GHZ states, a multi-qubit entangled state, provide an approach to such a problem. A fusion node is first chosen, and then the paths from all end nodes to the fusion node are fused to create an entangled state between all parties. The selection of the fusion node becomes critical since it highly affects the success probability of the whole process. Thus, in this paper, we explore the promising scenario for multiple requests of authorizable tripartite teleportation and introduce a novel optimization problem to maximize the (expected) total profit for 3-GHZ requests. Via extensive simulation results, we show that our algorithm can outperform existing approaches significantly. © 2024 IEEE.

关键词： Quantum entanglement

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Enhancing Health Product Traceability on the Blockchain: A Novel Approach for Supply Chain Management inspection to AI

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EAI Endorsed Transactions on Pervasive Health and Technology 2024年 10卷

作者： Prashanth, Mallellu Sai Uma Maheswari, V. Aluvalu, Rajinikanth Prasad Kantipudi, M.V.V. Dept. of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India Dept. of Computer Science and Engineering Chaitanya Bharathi Institute of Technology Hyderabad India Dept. of Information Technology Chaitanya Bharathi Institute of Technology Hyderabad India Pune India

INTRODUCTION: Blockchain technology is being investigated as a viable solution due to the industry's growing requirement for accountability and traceability. This study describes a fresh method for tracking down medical products that makes use of a decentralised smart contract network set up on the Ethereum blockchain. In order to enable secure and auditable tracking of health products throughout their lifecycle, the suggested system, named "HealthProductTraceability," makes use of the transparency and immutability of blockchain. OBJECTIVES: The system uses a "Product" struct to hold pertinent data such the product name, batch number, temperature, producer, and distributors. To quickly get product information depending on the batch number, a mapping is used. The use of tools to manufacture items, send them to distributors, and market them is one significant contribution of this *** demanding validation tests, such as verifying that batch numbers are unique and exist before carrying out certain activities, these functions protect the integrity of the traceability system. METHODS: In order to enable interested parties to track the product's travel and temperature changes, the system additionally emits events for product manufacture, distribution, and temperature adjustments. The suggested system is innovative because it can track the temperature of health items from beginning to end on a decentralised, open platform. RESULTS: By utilising blockchain technology, the system lessens reliance on centralised authorities, fosters stakeholder trust, and minimises the likelihood of fraud, forgery, and tampering in the supply chain for health products. The contract's architecture recognises some of the issues with blockchain technology, including scalability and privacy. By investigating solutions like sidechains, off-chain transactions, and enhancements to consensus methods, scalability issues are solved. CONCLUSION: In summary, the suggested HealthProductTraceability

关键词： Smart contract

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Leveraging On-Board UAV Motion Estimation for Lightweight Macroscopic Crowd Identification

Leveraging On-Board UAV Motion Estimation for Lightweight Ma...

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IEEE International Conference on Pervasive Computing and Communications (PerCom)

作者： Khizar Anjum Tahmeed Chowdhury Sreeram Mandava Benedetto Piccoli Dario Pompili Dept. of Electrical and Computer Engineering Dept. of of Mathematical Sciences Rutgers University NJ USA

This paper introduces a novel, lightweight, on-board approach to crowd pattern identification, ingeniously using the processes of existing video compression standards, particularly H.264 or MPEG-4. Piggy-backing on the H.264 video-encoding algorithm, we propose real-time crowd pattern recognition and identification methodologies that can identify macroscopic patterns in as low as 2 milliseconds on NVIDIA TX2, resulting in around 45 x execution time reduction compared to existing approaches. Furthermore, we introduce a temporally aware approach to pinpoint and adapt to crowd movement patterns, continuously recalibrating as a drone's Point Of View (POV) varies or observed motions diverge. Evaluating our method against publicly available datasets, we emphasize our system's performance and computational advantages, especially when faced with real-time observational shifts. In conclusion, our approach elegantly bridges the gap between crowd safety imperatives and the challenges of UAV monitoring, heralding a new era of real-time drone-centric crowd management intelligence.

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Performance study of an optimized lead-free Cs3Sb2I9-based perovskite solar cell using SCAPS-1D

Performance study of an optimized lead-free Cs3Sb2I9-based p...

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2022 IEEE Green Technologies Conference, GreenTech 2022

作者： Datta, Shamik Hossain, Md Maruf Nuzhat, Samiha Debnath, Ananya Huq, Hasina University Of Texas Rio Grande Valley Dept. Of Electrical And Computer Engineering EdinburgTX United States Shahjalal University Of Science And Technology Dept. Of Electrical And Electronic Engineering Sylhet Bangladesh Chittagong University Of Engineering And Technology Dept. Of Electronics And Telecommunication Engineering Chittagong Bangladesh

ISBN: (纸本)9781665467681

Commercialization of perovskite solar cells as clean source of energy can augment reduction of carbon footprint. Nonetheless, lead incorporation in the conventional lead cation based perovskites poses an environmental threat. Physical proximity of living organisms to lead point sources exhibits a wide spectrum of negative consequences. Antimony(Sb) is widely considered as one of the promising lead alternatives for its suitable electronic configuration and non-toxic nature. In this study, we have explored the photovoltaic performance of Cs3Sb2I9 as absorber for the first time using SCAPS-1D software. A planar, regular architecture with device configuration of FTO/TiO2/Cs3Sb2I9 /NiOx/Au has been proposed and numerically simulated. Two key material parameters of the absorber layer: thickness and bandgap have been modified to inspect their effect on device performance. Also, the effect of temperature change on photovoltaic performance has been studied. The best optimized device yielded a maximum power conversion efficiency of 17.49%, open-circuit voltage Voc of 1.4 V, short-circuit current density Jsc of 15.33 mA/cm2 and fill factor FF of 80.95%. © 2022 IEEE.

关键词： Perovskite

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A technical Concept for enhancing the Student Experience in Hybrid Lecture Scenarios 26

A technical Concept for enhancing the Student Experience in ...

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26th International Symposium on Multimedia, ISM 2024

作者： Schimanke, Florian Mertens, Robert Prankel, Felix Hsw University of Applied Sciences Dept. of Computer Science and Engineering Hameln Germany Sennheiser Electronic Se and Co. Kg Vocational Training Kg Wedemark Germany

ISBN: (纸本)9798331511111

During the COVID-19 pandemic, learning had to be shifted from in-person settings to entirely online or hybrid teaching scenarios. Schools and universities in Germany were forced to quickly deploy solutions which often provided lackluster quality. This shift was also implemented indiscriminately across subjects so that screen intensive courses (such as programming) were impacted in the same way as practical courses like hardware engineering. In many cases, this left a negative impression of such hybrid scenarios on participants. However, with steady improvements in technology, hybrid meetings have established themselves in corporate environments and steadily gained popularity in parts of higher education programs. Especially in the case of dual programs, this is also applicable to lectures and seminars. This paper explores using products from Sennheiser electronic SE & Co. KG with microphone arrays and audio-over-IP technology in combination with network cameras as an example, this concept aims to create an environment for seamlessly integrated hybrid lectures with about 30 participants in presence. © 2024 IEEE.

关键词： Curricula

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