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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Anitha, C. Chitra, K. Vijayalakshmi, G. Kumaran, G. Vijetha, T. Ormila, K. Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Cmr Institute of Technology Department of Electrical and Electronics Engineering Karnataka Bengaluru India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Cse Tamilnadu Chennai India Mlr Institute of Technology Department of Ece Hyderabad Dundigal India A.M.K. Technological Polytechnic College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350349900

The electric vehicle charging station is a game-changer that supplants the older kind of charging stations. Our futuristic electric vehicle charging station may be installed virtually anywhere, takes minimal space, and operates independently of the government's power source, in contrast to the current practice of reserving these stations for major urban areas. Among the many global industries that rely on fossil fuels, transportation ranks high. The development of electric vehicles (EVs) and other forms of transportation electrification are thus essential in the fight against carbon-dioxide emissions and the over-reliance on traditional energy sources that are harmful to the environment. At the same time, electricity distribution, use, and supply have been accompanied by massive amounts of data due to the fast digitization of electrical networks and EVs. These datasets may be used to inform choices and insights by using artificial intelligence (AI) algorithms. Despite a number of recent studies in this area, no one has explicitly examined the usefulness of AI for charge-demand profiling, data augmentation, demand forecasting, explainability of demand, and charge optimization of the EVI. For the purpose of efficiently delivering the fast charging function, this study suggested a novel logic called Grid Distributed Fast EV Charging (GDFEVC). To evaluate the proposed model's efficiency, it was cross-validated with the standard Fast EV Charging Model (FEVCM). This AI framework has been empirically tested on a real-world EVI case study, and the results show that it helps with the new problems with dispersed energy supplies that are coming up with EV adoption. © 2024 IEEE.

关键词： Charging stations

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Enhancing Track Management Systems with Vehicle-To-Vehicle Enabled Sensor Fusion

arXiv

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arXiv 2024年

作者： Billington, Thomas Gwash, Ansh Kothari, Aadi Izquierdo, Lucas Talty, Timothy Department of Computer Science Virginia Polytechnic Institute State University BlacksburgVA United States Department of Electrical and Computer Engineering Virginia Polytechnic Institute State University BlacksburgVA United States

In the rapidly advancing landscape of connected and automated vehicles (CAV), the integration of Vehicle-to-Everything (V2X) communication in traditional fusion systems presents a promising avenue for enhancing vehicle perception. Addressing current limitations with vehicle sensing, this paper proposes a novel Vehicle-to-Vehicle (V2V) enabled track management system that leverages the synergy between V2V signals and detections from radar and camera sensors. The core innovation lies in the creation of independent priority track lists, consisting of fused detections validated through V2V communication. This approach enables more flexible and resilient thresholds for track management, particularly in scenarios with numerous occlusions where the tracked objects move outside the field of view of the perception sensors. The proposed system considers the implications of falsification of V2X signals which is combated through an initial vehicle identification process using detection from perception sensors. Presented are the fusion algorithm, simulated environments, and validation mechanisms. Experimental results demonstrate the improved accuracy and robustness of the proposed system in common driving scenarios, highlighting its potential to advance the reliability and efficiency of autonomous vehicles. Copyright © 2024, The Authors. All rights reserved.

关键词： Vehicle to Everything

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Non-Invasive Pig Weight Prediction using SAM-Enhanced Deep Learning and Depth Imaging

Non-Invasive Pig Weight Prediction using SAM-Enhanced Deep L...

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Bharadwaj, Sanjana Ranjan, Pranjal Pei, Yingqi Yim, Jay Bi, Ye Ha, Dong Morota, Gota Shin, Sook School of Animal Sciences Virginia Polytechnic and State University Bradley Department of Electrical and Computer Engineering BlacksburgVA United States

ISBN: (纸本)9798331504847

Non-invasive pig weight prediction is crucial for efficient pork production and animal welfare monitoring. This study introduces a novel approach combining Segment Anything Model (SAM)-enhanced image preprocessing with deep learning techniques for accurate pig weight estimation using depth images. We compare traditional statistical methods with advanced deep learning models, focusing on minimizing preprocessing requirements and improving accuracy. Our custom image preprocessing pipeline, leveraging SAM for precise segmentation, enables efficient feature extraction and data cleaning. We evaluate various models, including Convolutional Neural Networks (CNNs), Multi-input CNNs, ResNets, and XceptionNet, against statistical methods such as linear regression, random forest, and support vector regression. Results demonstrate that deep learning models, particularly XceptionNet, significantly outperform statistical approaches. XceptionNet achieves a mean absolute error of 2.82 kg and a mean absolute percentage error of 7.42%, compared to the best statistical model (support vector regression) with 4.51 kg and 15.56%, respectively. This study highlights the potential of integrating state-of-the-art segmentation techniques like SAM with deep learning for enhanced accuracy in livestock monitoring, paving the way for real-time, non-invasive weight prediction systems in modern pig farming. © 2025 IEEE.

关键词： Convolutional neural networks

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Approximately Gaussian Replicator Flows: Nonconvex Optimization as a Nash-Convergent Evolutionary Game

arXiv

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arXiv 2024年

作者： Anderson, Brendon G. Pfrommer, Samuel Sojoudi, Somayeh Department of Mechanical Engineering California Polytechnic State University United States Department of Electrical Engineering and Computer Sciences University of California Berkeley United States

This work leverages tools from evolutionary game theory to solve unconstrained nonconvex optimization problems. Specifically, we lift such a problem to an optimization over probability measures, whose minimizers exactly correspond to the Nash equilibria of a particular population game. To algorithmically solve for such Nash equilibria, we introduce approximately Gaussian replicator flows (AGRFs) as a tractable alternative to simulating the corresponding infinite-dimensional replicator dynamics. Our proposed AGRF dynamics can be integrated using off-the-shelf ODE solvers when considering objectives with closed-form integrals against a Gaussian measure. We theoretically analyze AGRF dynamics by explicitly characterizing their trajectories and stability on quadratic objective functions, in addition to analyzing their descent properties. Our methods are supported by illustrative experiments on a range of canonical nonconvex optimization benchmark functions. Copyright © 2024, The Authors. All rights reserved.

关键词： Nash equilibrium

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Smart Cost-based Charging Profile Management and Optimisation for Multiple Electric Vehicles in Power Grids

Smart Cost-based Charging Profile Management and Optimisatio...

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International Conference on Power Electronics Systems and Applications (ICPESA)

作者： Muhamad Danish Karlin Bin Mohamed Isa Huajun Wang Rui Liang Tenghao Ji Yajie Jiang Kaiwen Chen School of Electrical and Electronic Engineering Nanyang Technological University Singapore Department of Electrical and Electronic Engineering Hong Kong Polytechnic University Hong Kong China Department of Electrical and Computer Engineering University of Michigan Ann Arbor USA

ISBN: (数字)9798350365801

ISBN: (纸本)9798350365818

With the advent of the Internet-of-Things (IoT) and smart grid, further optimisation potential can be explored from the load side in the field of power management. it opens more opportunities for load management to be done in a more granular fashion. In this work, one method for smart load management of electric vehicle (EV) charging profile is proposed. More specifically, this is done by scheduling the charging sessions during periods of lower electricity spot prices. This results in lower EV charging costs. The resilience of the power grid is also strengthened by shifting the EV charging loads to a period of lower electricity demand. This optimised charging scheme is applied in a multi-EV system in MATLAB. The optimisation results are provided to verify the efficacy of the proposed charging scheme.

关键词： Costs Electricity Power system management Load management Electric vehicle charging Power electronics Smart grids

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Experimental Validation of Dynamics for Air Cushion Vehicles

Experimental Validation of Dynamics for Air Cushion Vehicles

引用

OCEANS

作者： Shivani Sakri Matthew Dim Stefano Brizzolara Department of Aerospace and Ocean Engineering Virginia Polytechnic Institute and State University Blacksburg VA USA Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State University Blacksburg VA USA

ISBN: (数字)9798331540081

ISBN: (纸本)9798331540098

The research study conducts experimental tests to validate the theoretical mathematical model of hovercraft dynamics. Static and dynamic tests have been conducted to understand vehicle response and cushion dynamics. The study allows us to use experimental data to calibrate the theoretical model to understand the vehicle and fluid physics better.

关键词： Fans Fluids Shape Simulation Atmospheric modeling Oceans Geometric modeling Data models Vehicle dynamics Physics

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Impact of the Antenna on the Sub-Terahertz Indoor Channel Characteristics: An Experimental Approach

arXiv

引用

arXiv 2024年

作者： Sen, Priyangshu Badran, Sherif Petrov, Vitaly Singh, Arjun Jornet, Josep M. Department of Engineering SUNY Polytechnic Institute UticaNY United States Department of Electrical and Computer Engineering Northeastern University BostonMA United States

Terahertz-band (100 GHz-10 THz) communication is a promising radio technology envisioned to enable ultrahigh data rate, reliable and low-latency wireless connectivity in next-generation wireless systems. However, the low transmission power of THz transmitters, the need for high gain directional antennas, and the complex interaction of THz radiation with common objects along the propagation path make crucial the understanding of the THz channel. In this paper, we conduct an extensive channel measurement campaign in an indoor setting (i.e., a conference room) through a channel sounder with 0.1 ns time resolution and 20 GHz bandwidth at 140 GHz. Particularly, the impact of different antenna directivities (and, thus, beam widths) on the channel characteristics is extensively studied. The experimentally obtained dataset is processed to develop the path loss model and, subsequently, derive key channel metrics such as the path loss exponent, delay spread, and K-factor. The results highlight the multi-faceted impact of the antenna gain on the channel and, by extension, the wireless system and, thus, show that an antenna-agnostic channel model cannot capture the propagation characteristics of the THz channel. © 2024, CC BY.

关键词： Directive antennas

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Signal Processing in the Synchronization System of Communication Channel with OFDM 4

Signal Processing in the Synchronization System of Communica...

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4th IEEE International Conference on Advanced Trends in Information Theory, ATIT 2022

作者： Boiko, Juliy Pyatin, Ilya Eromenko, Oleksander Khmelnytskyi National University Department of Telecommunications Media and Intelligent Technologies Khmelnytskyi Ukraine Khmelnytskyi Polytechnic Professional College Lviv Polytechnic National University Department of Computer Engineering Khmelnytskyi Ukraine Khmelnytskyi National University Department of Physics and Electrical Engineering Khmelnytskyi Ukraine

ISBN: (纸本)9798350332629

This paper is devoted to the study of the signal synchronization conditions in the 5G downlink. A model of the physical layer of a downlink communication channel is considered. The structure of the OFDM signal frame is investigated, the noise immunity data transmission is determined, and the error vector magnitude is estimated. A technique for ensuring signal synchronization while minimizing the error in a channel with OFDM signals is considered. Mechanisms have been developed for solving the problem ensuring the orthogonality subcarriers of OFDM signals in the condition's interference. © 2022 IEEE.

关键词： Synchronization

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Data-Driven Approaches for Distribution Transformer Health Monitoring: A Review

Data-Driven Approaches for Distribution Transformer Health M...

引用

Canadian Conference on electrical and computer engineering (CCECE)

作者： Aman Samson Mogos Xiaodong Liang C. Y. Chung Department of Electrical and Computer Engineering University of Saskatchewan Saskatoon Canada Department of Electrical Engineering The Hong Kong Polytechnic University Hong Kong China

Distribution transformers are the key components in distribution systems to maintain reliability of the system operation and reduce power outages. In this paper, a literature review is conducted on data-driven methods of the distribution transformer health monitoring by classifying the research streams and emphasizing advancements in machine learning, artificial intelligence and hybrid approaches in this area. The significance of data-driven methods is highlighted, demonstrating their ability to overcome traditional analytic limitations by providing real-time monitoring, prediction, and adaptability. As the distribution system continues to expand due to the increasing penetration of distributed energy resources (DERs) and electric vehicles (EVs), data-driven techniques emerge as a dependable and adaptable option for the effective transformer health monitoring.

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An Authentication Model Using Brainwave Panic Region Classifications from Electroencephalography 7th

An Authentication Model Using Brainwave Panic Region Classif...

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7th International Conference on Communication, Device and Networking, ICCDN 2024

作者： Abiodun, Opeyemi Anuoluwa Oyinloye, Oghenerukevwe E. Thompson, Aderonke F. Olowoyo, Paul Imoize, Agbotiname Lucky Sur, Samarendra Nath Computer Science Department The Federal Polytechnic Ekiti State Ado-Ekiti360211 Nigeria Computer Science Department Ekiti State University Ekiti State Ado-Ekiti360211 Nigeria Cyber Security Department Federal University of Technology Ondo State Akure350106 Nigeria Federal Teaching Hospital Ekiti State Ido-Ekiti Nigeria Department of Electrical and Electronics Engineering Faculty of Engineering University of Lagos Akoka Lagos 100213 Nigeria Department of Electronics and Communication Engineering Sikkim Manipal Institute of Technology Sikkim Manipal University Sikkim India

ISBN: (纸本)9789819764648

Biometric authentication scheme has been widely adopted for authentication purpose due to its uniqueness, universality, and distinctiveness. However, research has shown that these schemes are not necessarily more secured, especially with issues of coercion proliferating the cyberspace. Also, available techniques on repudiation of biometric features for authentication have not adequately explored this exciting topic. Integrating emotional state into the authentication scheme helps to mitigate coercion. This paper presents a framework for emotion as a way of biometric authentication scheme. An emotion classification model was developed by training an emotion classifier brainwave signal from eight subjects under normal state and duress using KNN machine learning algorithm. The authentication scheme grants access to users who pass both verification phases. The emotion classification model achieved an accuracy of 93.6% on the full feature set, while the reduced-feature set, as a result of feature selection, produced an improved accuracy of 94.15%. Hence, the emotion classification model can be integrated into existing biometric authentication system for improved security of critical user information. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Biometrics

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