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Advanced Neural Network Models for Optimal Energy Management in Microgrids with Integrated Electric Vehicles

Advanced Neural Network Models for Optimal Energy Management...

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Trends in Material Science and Inventive Materials (ICTMIM), International Conference on

作者： Sharada K A A. Kathiravan Praveen Mannam D Akila B Suresh Kumar Vaibhav Vilas Godase Department of Computer Science and Engineering HKBK College of Engineering Bengaluru Karnataka India Department of EEE Rajiv Gandhi College of Engineering and Technology Puducherry India Department of Electrical and Electronics Engineering KKR&KSR Institute of Technology and Sciences Guntur Andhra Pradesh India Department of Computer Applications Saveetha College of Liberal Arts and Sciences SIMATS Chennai Tamilnadu India Department of EEE Chaitanya Bharathi Institute of Technology Hyderabad Telangana India Department of Electronics and Telecommunication Engineering SKN Sinhgad College Pandharpur India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

EV and BSS are putting a pressure on power networks, and chaotic charging techniques are a major contributor to this. Due to the nonlinear, unpredictable, and energy-intensive character of electric car charging demands, smart microgrids must have optimal energy management. Our regional energy management system optimises operations for electric vehicles and battery storage by orchestrating grid-connected charging using a price-incentive scheme. The system will implement smart microgrids. They hope to achieve this by maximising the profits from battery storage systems and decreasing the costs of charging electric vehicles. To improve search performance, a new method combines GA with NB. Dimensionality reduction, unbalanced dataset classification, and min-max normalisation are all data preprocessing tasks that genetic algorithms are used for. The proposed NBGA hybrid model outperforms previous imputation methods by a whopping 98% in trials where feature selection is not used. In order to make energy management systems better, evolutionary algorithms and naive Bayes classification are used. Through reducing the impact of uncoordinated charging, the suggested method improves the stability and economic feasibility of smart microgrids.

关键词： Dimensionality reduction Uncertainty Computational modeling Feature extraction Imputation Smart grids Naive Bayes methods Batteries Energy management systems Genetic algorithms

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An Effective Forest Fire Prediction Based on Contrastive Vision Transformer with Pool Former

An Effective Forest Fire Prediction Based on Contrastive Vis...

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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： N. Deepa Saef Obad Husain G. Ezhil V. Indhumathi R. Revathi Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Humanities Bannari Amman Institute of Technology Sathyamangalam India Department of Computer Applications Sona College of Arts and Science Salem India Department of Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

Since years, forest fires are a significant concern globally, causing damage to ecosystems and human settlements. For effective prevention and mitigation predicting forest fires is important, different factors contribute to forest fires which includes human activities, topography, climate change and weather conditions. Traditional approaches for predicting forest fires had faced significant challenges which include limited validation of prediction models and lack of standardization. Therefore, this paper proposes Contrastive Vision Transformer (CViT)-Pool Former for predicting forest fire which is employed on Forest Fire Big Data dataset which enabled better comparability. This dataset is preprocessed by using data augmentation, noise reduction and data enhancement techniques. Then, the features are extracted by using CViT which extracted optimal features based on weather conditions and captured contextual relationships by maximizing mutual information. After that, Pool Former light weight attention module is used for predicting fires which gave more accurate predictions by enabling targeted prevention and mitigation, minimized unwanted deployments and resource waste, boosted prediction accuracy by token fusion and enhanced representation learning. The proposed CViT-Pool Former method established better results including accuracy (92.8%), precision (89.4%), recall (91.4%) and F1-score (90.2%) when compared with existing Region Proposed Convolutional Neural Network (R-CNN).

关键词： Accuracy Prevention and mitigation Biological system modeling Data preprocessing Noise reduction Forestry Predictive models Feature extraction Transformers Data mining

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Protein-Ligand Interaction Prediction with Effective Method of Graph Attention Networks and Long Short-Term Memory

Protein-Ligand Interaction Prediction with Effective Method ...

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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： N. Deepa Ramy AlFatlawy Shashidhara K S R. Revathi M. Reka Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of Computer Applications Sona College of Arts and Science Salem India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

Nowadays, the drug discovery field of molecules in protein-ligand interactions developing effective therapeutics for identifying potential drug candidates with spatial features to predict binding affinity. However, the existing Deep Learning (DL) based prediction approaches failed to interact and predict the binding affinity of protein-ligand precisely for drug design with binding affinity. To overcome this problem, a Graph Attention Networks with Long Short-Term Memory (GATN-LSTM) method is proposed with spatiotemporal forecasting for interaction between protein and ligand in binding affinity prediction for drug design. The proposed GATN-LSTM method is employed on DUD-E dataset consists of enhanced the prediction by capturing relationship (interaction) between protein and ligand in molecules for drug discovery using PubChem for prediction. Then, the preprocessed data of protein-ligand interaction is extracted using (CNN-GNN) for better generalization of both local and global features for predicting drug discovery. Finally, GATN-LSTM is employed for interaction of molecules with binding affinity which enhanced the drug design and also improved the prediction results. Experimental results of the proposed GATN-LSTM method attained accuracy (0.9992), F1-score (0.9985) are higher than existing prediction approaches such as Convolutional Neural Network (CNN) and Mutual Information-based Feature Selection (MIFS).

关键词： Proteins Drugs Adaptation models Accuracy Predictive models Feature extraction Drug discovery Spatiotemporal phenomena Convolutional neural networks Long short term memory

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The Food Preservation using a Solar Dryer Monitoring System 4

The Food Preservation using a Solar Dryer Monitoring System

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4th IEEE International Conference on Smart Technologies in Computing, electrical and Electronics, ICSTCEE 2023

作者： Sugumaran, S. Sivasankaran, V. Vijayakumar, S. Nithya, A. Chitra, M.G. Department Of Ece Sreenivasa Institute Of Technology And Management Studies Andhra Pradesh Chittoor India School Of Electrical And Electronics Engineering Vit Bhopal University M.P Sehore India Department Of Ece Sreeenivasa Institute Of Technology And Management Studies Andhra Pradesh Chittoor India Department Of Ece Mrk Institute Of Technology Tamil Nadu Kattumannarkoil India Department Of Computer Science Mmes Women's Arts And Science College Ranipet Dist. Tamilnadu Melvisharam India

ISBN: (纸本)9798350384628

High-moisture foods like mango, chili, meat etc., are not able to use long days because they are easily decomposed due to changing atmospheres. So the primitive peoples followed different methods to preserve the food and used it for a long time without poisoning. The sun dryer is the usual method to dry the food, but it will take more than one day. The food drying process is done with fossil fuels, but the increasing population is increasing the cost of the fuels. So, the food drying process needs an alternative method to dry the food. In this paper, the food is dried and preserved by solar energy. The overall processes are continuously monitored and preservation of the food by Arduino microcontroller with different sensors such as moisture sensor, LDR, Temperature sensor and cooling fans. The result of this work is better than other existing work and dried food at a low cost. © 2023 IEEE.

关键词： Fossil fuels

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VRMN-bD: A Multi-modal Natural Behavior Dataset of Immersive Human Fear Responses in VR Stand-up Interactive Games

VRMN-bD: A Multi-modal Natural Behavior Dataset of Immersive...

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IEEE Annual International Symposium Virtual Reality

作者： He Zhang Xinyang Li Yuanxi Sun Xinyi Fu Christine Qiu John M. Carroll The Future Laboratory Tsinghua University College of Information Sciences and Technology Penn State University Academy of Arts & Design Tsinghua University School of Computer and Cyber Sciences Communication University of China The Future Laboratory Tsinghua University School of Electrical Engineering and Computer Science The KTH Royal Institute of Technology College of Information Sciences and Technology Penn State University

Understanding and recognizing emotions are important and challenging issues in the metaverse era. Understanding, identifying, and predicting fear, which is one of the fundamental human emotions, in virtual reality (VR) environments plays an essential role in immersive game development, scene development, and next-generation virtual human-computer interaction applications. In this article, we used VR horror games as a medium to analyze fear emotions by collecting multi-modal data (posture, audio, and physiological signals) from 23 players. We used an LSTM-based model to predict fear with accuracies of 65.31% and 90.47% under 6-level classification (no fear and five different levels of fear) and 2-level classification (no fear and fear), respectively. We constructed a multi-modal natural behavior dataset of immersive human fear responses (VRMN-bD) and compared it with existing relevant advanced datasets. The results show that our dataset has fewer limitations in terms of collection method, data scale and audience scope. We are unique and advanced in targeting multi-modal datasets of fear and behavior in VR stand-up interactive environments. Moreover, we discussed the implications of this work for communities and applications. The dataset and pre-trained model are available at https://***/KindOPSTAR/VRMN-bD.

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On Real-time Failure Localization via Instance Correlation in Optical Transport Networks

On Real-time Failure Localization via Instance Correlation i...

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IFIP Networking Conference

作者： Yan Jiao Pin-Han Ho Xiangzhu Lu Kairan Liang Yuren You János Tapolcai Bingbing Li Limei Peng Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada Huawei Ottawa Research and Development Centre Ottawa Canada Department of Telecommunications and Media Informatics Budapest University of Technology and Economics Hungary School of Computer Science Hubei University of Technology Wuhan China School of Computer Science and Engineering Kyungpook National University Daegu South Korea

Failure localization serves as a key to an effective fault management plane in the Internet backbone. This paper investigates a novel failure localization approach, namely Instance Correlation based Fault Diagnosis (IC-FD), for achieving efficient fault management in Optical Transport Networks (OTN). The IC-FD is aimed at real-time localization of failed components in the optical layer of OTN through correlation of alarms and status changes of network devices (referred to as instances) via a learned binary classifier. The outcome of IC-FD is one or multiple instance correlation trees (ICT) where the instances corresponding to the faulty network devices are taken as the tree roots. Notably, the proposed binary classifier is characterized by an intelligent feature extraction of historical instance correlation in dimensions of time, board/alarm attribute, network topology, and traffic distribution. Extensive case studies are conducted to demonstrate the advantages gained by IC-FD in terms of its high precision and low computation complexity, as well as analysis of its performance due to various environmental turbulence such as network topology, traffic diversity and noise alarms.

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A Low-profile Broadband Dual-polarized Magneto-electric Dipole Antenna for 5G Applications

A Low-profile Broadband Dual-polarized Magneto-electric Dipo...

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World Congress on Computing and Communication Technologies (WCCCT)

作者： Yihuang Yang Botao Feng Xiao Ding Li Deng Wenzhe Gu Kwok L. Chung College of Electronics and Information Engineering Shenzhen University Shenzhen China Faculty of Humanities and Arts Macau University of Science and Technology Macau China College of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China School of Computer Science and Engineering Huizhou University Huizhou China School of Intelligent Manufacturing and Electrical Engineering Guangzhou Institute of Science and Technology Guangdong China

ISBN: (数字)9798331512620

ISBN: (纸本)9798331512637

This study proposes a compact wideband dual-polarized (DP) magneto-electric (ME) dipole antenna with cross-differential feeding, designed for 5G/6G millimeter-wave (mm-Wave) systems. The antenna features a geometrically symmetric configuration, integrating radiating elements and feeding networks into a single metallic layer, achieving a low-profile structure. By leveraging ME coupling mechanisms, the antenna supports dual-orthogonal polarization operation, offering a broad impedance bandwidth of 39.4% (26.3-39.1 GHz) under differential port excitation. Experimental results demonstrate excellent radiation performance, including highly symmetric radiation patterns, cross-polarization suppression below -30 dB, a gain range of 2.6-6.2 dBi, and an axial ratio (AR) below 2 dB across the operational band. The unified feed-radiator co-design effectively mitigates mm-Wave challenges such as narrow bandwidth and integration constraints commonly found in conventional dual-polarized antennas, which makes the proposed design a promising solution for compact MIMO arrays in the 28/39 GHz 5G NR bands.

关键词： Wireless communication Dipole antennas Magnetoelectric effects Microstrip antennas Directive antennas Antenna feeds Broadband communication Broadband antennas Wideband Gain

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Dual-Stage Interleaved On-board Charger for Electric Vehicle using Snow Geese Algorithm

Dual-Stage Interleaved On-board Charger for Electric Vehicle...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： A Chandrashekhar B Mallikarjuna Thiyagesan M L. Umasankar G. Ezhil Rukmani Devi Department of Mechanical Engineering Faculty of Science and Technology ICFAI Foundation for Higher Education Hyderabad Telangana India Department of Electrical and Electronics Engineering RNS Institute of Technology Bengaluru Karnataka India Department of Electrical and Electronics Engineering R.M.K Engineering College Chennai Tamil Nadu India Department of Electrical and Electronics Engineering S. A. Engineering College Chennai Tamil Nadu India Department of English Bannari Amman Institute of technology Sathyamangalam Tamil Nadu India Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Chennai Tamil Nadu India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

The growing demand for electric vehicles (EVs) has highlighted the need for efficient and reliable on-board chargers (OBCs) to reduce charging time and enhance battery life. Nevertheless, there are some issues in classic OBC designs: harmonic distortion may become too large, electromagnetic interference increases, and thermal management is not proper; all of which directly affects other elements of the system. As an optimization approach to enhance the performance of the dual-stage interleaved on-board charger (OBC) for electric vehicles (EVs), this work introduces the Snow Geese Algorithm (SGA). The objective of the proposed method is to enhance charging efficiency and minimize cost. The proposed SGA is used to optimize the dual-stage interleaved converter, focusing on reliable performance and effective operation within EV charging systems. The suggested approach is then put into practice on the MATLAB platform and contrasted with a number of current techniques, including Artificial Bee Colony (ABC), Simulated Annealing (SA), Multi-objective Genetic Algorithm (MOGA), and Particle Swarm Optimization (PSO). The proposed SGO method for dual-stage interleaved EV chargers achieves a cost of $1,050.80 and an efficiency of 97.2 %, demonstrating its effectiveness in reducing expenses while maximizing optimization performance in charging systems.

关键词： Sentiment analysis Costs Heuristic algorithms Snow Electromagnetic interference Simulated annealing Thermal management Harmonic distortion Reliability Vehicle dynamics

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Research on Art Style Rendering Algorithm Based on computer Vision

Research on Art Style Rendering Algorithm Based on Computer ...

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2023 International Conference on Internet of Things, Robotics and Distributed Computing, ICIRDC 2023

作者： Wang, Heng Zhou, Yanwei Zhang, Zihao School of Design and Art Tianjin Academy of Fine Arts Tianjin300141 China School of Architecture and Art Design Hebei University of Technology Tianjin300132 China Center for Urban and Rural Renewal and Architectural Heritage Conservation Hebei University of Technology Tianjin300132 China Hebei Key Laboratory of Healthy Living Environment Tianjin300132 China Samueli Electrical and Computer Engineering University of California Los Angeles90024 United States

ISBN: (纸本)9798350375275

For a long time, the art of painting has been mysterious to most ordinary people. Since ancient times, countless artists have devoted their lives to painting works, and many masters have formed their own unique painting style. With the rapid development of computer technology, we can extract and imitate the painting styles of various famous artists, and even ordinary people can pry into the mysteries of artistic style, so as to make works with master artistic style. This style rendering technology is called style style transfer. With the continuous development of stereoscopic display technology, people's demand for more diversified stereoscopic display effects is also increasing. Chinese ink art, as one of the typical representatives of Oriental painting art, has positive significance and far-reaching influence by combining it with three-dimensional display technology. Through the study of Chinese traditional landscape painting and a large number of research on the simulation results of ink style at home and abroad, this paper focuses on the freehand ink style rendering of three-dimensional terrain, and designs a ink style rendering method based on program gray coloring, which can easily observe the effect of ink change when the terrain changes. © 2023 IEEE.

关键词： computer vision

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Optimized Cascaded Deep Capsule Cell Attention Network for Efficient Music Genre Classification

Optimized Cascaded Deep Capsule Cell Attention Network for E...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： Chennaiah Kate R. Sangeethapriya M Revathy J. Mahil A. Anto Spiritus Kingsly S. Rukmani Devi Department of Computer Science and Engineering St. Peter's Engineering College Hyderabad Telangana India Department of Information Technology Sona College of Technology Salem Tamil Nadu India Department of Electronics and Communication Engineering PSNA College of Engineering and Technology Dindigul Tamil Nadu India Department of Electrical and Electronics Engineering K. Ramakrishnan College of Technology Trichy Tamil Nadu India Department of Electronics and Communication Engineering Oasys Institute of Technology Trichy Tamil Nadu India Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Chennai Tamil Nadu India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

This research work develops a new approach towards music genre prediction by fusing high-end signal processing and machine learning methodologies that will improve prediction quality and resilience. The system commences by signal preprocessing comprising Discrete Wavelet Transformation (DWT) in combination with Pre-Gaussian removal of noise. Feature extraction is done with the Mellin Transform that extracts the important features, which immune to changes in pitch and variations in the general characteristics of the different genre of a particular artist or singer. Hierarchical classification is realized by the proposed Cascaded Capsule Cell Attention Network (CCCAN), which utilizes the capsule networks’ advantage of dynamic routing and the spatial hierarchy to capture and identify the intricate structures of audio patterns. The parameter of the network is optimized with Boosted Sooty Tern Optimization so that convergence speed and classification accuracy is above par. Experimental results presented in this study reveal that the proposed model provides around 99.5% of accuracy, precision, recall, and F1-score improved in comparison to the previous method which provides reliable classification of music genres.

关键词： Accuracy Noise Transforms Feature extraction Routing Multiple signal classification Discrete wavelet transforms Reliability Optimization Resilience

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