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SSRN 2024年

作者： Cheng, Chang Parthiban, Rajendran Kalavally, Vineetha Wang, Xin Department of Mechanical Engineering School of Engineering Monash University Malaysia Subang Jaya47500 Malaysia Department of Electrical and Computer Systems Engineering School of Engineering Monash University Malaysia Subang Jaya47500 Malaysia Department of Electrical and Computer Systems Engineering Faculty of Engineering Monash University Clayton3800 Australia

Unharvested palm fruit bunch (PFB) ripeness detection is crucial for efficient palm oil production as labor costs rise and worker shortages grow. Challenges remain under varying illumination, affecting detection performance. Although the deep learning-based color constancy model shows promise, the limitation lies in the lack of data. This paper addresses these issues by proposing a hybrid color correction method that combines the learning-based and the physical-based color constancy technique, then trains with YOLOv8 to enhance unharvested PFB detection performance. Specifically, the captured images and their corresponding simultaneous spectra are used to generate the ground truth images, then map the images to other ambient spectra to train the color constancy model, and finally, train the YOLOv8 with the corrected images for ripeness detection. Results demonstrate a 1.5% improvement in the mean Average Precision (mAP) @0.5 of YOLOv8, increasing from 88.2% to 89.7% after applying the proposed hybrid color correction method. For each ripeness category, the mAP@0.5 for unripe and ripe bunches increased by 1.7% and 1.8%, respectively. Underripe bunches exhibit the most increment in mAP of 2.1%, while the overripe bunch gives the least increase of 0.4%. The qualitative results show that the model has enhanced the ability to distinguish subtle differences in PFB ripeness. Consequently, it advances the effectiveness and reliability of PFB ripeness detection in practical palm oil production scenarios, especially for unripe to ripe bunches wherein color gives the most information. This research demonstrates potential in object detection tasks that incorporate illumination-based color correction methods. © 2024, The Authors. All rights reserved.

关键词： Wages

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Transient Stability Analysis of Low-Inertia Shipboard electrical Systems

Transient Stability Analysis of Low-Inertia Shipboard Electr...

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Modeling, Simulation & Intelligent Computing (MoSICom), International Conference on

作者： Imran Uddin Arash Safavizadeh Ekamjot Singh Tahim Seyyedmilad Ebrahimi Juri Jatskevich Department of Electrical and Computer Engineering University of British Columbia Vancouver Canada School of Electrical Engineering and Computer Science Washington State University Pullman United States

ISBN: (数字)9798331533311

ISBN: (纸本)9798331533328

Transient stability analysis (TSA) of synchronous generators (SGs) is essential for reliable operation of electrical systems. Conventionally, the equal area criterion (EAC) is used to assess the transient stability of SGs, assuming that they are connected to a bus with a fixed frequency. However, during disturbances in low-inertia systems, e.g., small microgrids and shipboard power systems, the frequency of the main ac bus may noticeably deviate. This paper proposes a new formulation within the EAC method for calculating the critical clearing time (CCT) of SGs in low-inertia electrical systems, considering the effect of variations in system frequency during TSA. To verify the effectiveness of the extended EAC, the CCTs of SGs in a multi-machine shipboard power system are obtained using the proposed formulation and compared with time-domain simulation results. The proposed EAC method estimates the CCTs more accurately when the main ac bus frequency varies following a disturbance.

关键词： Time-frequency analysis Simulation Stability criteria Microgrids Power system stability Synchronous generators Circuit stability Circuit faults Transient analysis Time-domain analysis

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An effective approach for detecting deepfake videos using Long Short-Term Memory and ResNet

An effective approach for detecting deepfake videos using Lo...

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2024 International Conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Keerthana, S. Deepika, N. Pooja, E. Nandhini, I. Shanthalakshmi, M. Khanaghavalle, G.R. Sri Venkateswara College Of Engineering Department Of Computer Science And Engineering Pennalur Tamilnadu Sriperumbudur602117 India Rajalakshmi Institute Of Technology Department Of Computer Science And Business Systems Tamilnadu Kuthambakkam600124 India School Of Electrical And Communication Vel Tech Rangarajan Dr. Sakunthala R&d Institute Of Science And Technology Department Of Ece Avadi India

ISBN: (纸本)9798350352689

Deep learning algorithms are becoming more potent and producing human-synthesized, undifferentiated footage is a simple process thanks to advances in computing power. A method of synthesizing human images called deep fakes is built on neural network techniques like GAN. These technologies use deep learning algorithms to overlay target pictures onto the source films, producing realistic-looking deep-fake photos and videos. In this work, we provide a novel deep-learning approach that can reliably discriminate between actual and AI-generated fake videos. In this work, we test our method on a large number of balanced and mixed datasets created by mixing various publicly available datasets, such as Face-Forensic++, Deepfake detection, Celeb DF, and other publicly available datasets as images and videos, to emulate real-time scenarios and improve model performance with real-time data by using LSTM and ResNet. © 2024 IEEE.

关键词： computer vision

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Bridging Automated Reasoning and Machine Learning for Information Analysis 2

Bridging Automated Reasoning and Machine Learning for Inform...

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2nd IEEE International Conference on Futuristic Technologies, INCOFT 2023

作者： Joshi, Sarita Murugan, R. Balaji, N.V. Chandrakala, P. Deshmukh, Gopal B. Mathur, Mala Maharishi School of Business Management Maharishi University of Information Technology Uttar Pradesh India Bangalore Department of Computer Science and Information Technology Karnataka India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Vivekananda Global University Department of Civil Engineering Jaipur India

ISBN: (纸本)9798350308846

Recent advances in automated reasoning and system studying have appreciably impacted the automation of statistics evaluation. These technologies facilitate understanding the increasingly more complicated volumes of facts in a ramification of approaches. Automatic reasoning helps decide valid solutions based on structured good judgment, while machines can pick out patterns and developments in massive datasets. Bridging those two technologies improves accuracy and efficiency in automated facts evaluation. This paper will overview present-day research on bridging automated reasoning and device-gaining knowledge for such purposes and gift novel approaches to combining the two technologies for more effective data evaluation. Discussion subjects will encompass integrating neural networks and reasoning engines, methods for addressing records incompleteness and uncertainty, and enhancement of reasoning-driven context-conscious systems. Furthermore, the paper will discover the capacity benefits that can be performed via leveraging the strengths of each automated reasoning and gadget gaining knowledge of to optimize answers for a selection of software scenarios. © 2023 IEEE.

关键词： Gaining Optimize Reasoning Selection Uncertainty

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1 What I Learn from Arthur Yaghjian, from Graduate school till Now!

1 What I Learn from Arthur Yaghjian, from Graduate School ti...

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USNC-URSI Radio Science Meeting (Joint with AP-S Symposium)

作者： Weng Cho Chew Elmore Family School of Electrical and Computer Engineering Purdue University West Lafayette IN USA Department of Electrical and Computer Engineering University of Illinois Urbana IL USA

ISBN: (数字)9789463968119

ISBN: (纸本)9798350359497

Arthur Yaghjian is a great scholar in our community. His works have profoundly influenced the works of scholars in our community. I first know of Arthur Yaghjian's works through his work on the dyadic Green's function when I was a graduate student at MIT. He was the first or among the first to realize that the correct treatment of this work needs math-physics rigor, and Arthur was the first to offer this rigor. He was the first to realize that a thorough treatment of this problem needs to be decomposed into principal volume integral. Even though principal value integral was prevalent in classical mathematics like complex variables, and Cauchy integration, it was not well-known and not well understood in electromagnetics.

关键词： Conferences Mathematics Green's function methods Electromagnetics

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The Loss of Field Protection for Synchronous Generators: A Review

The Loss of Field Protection for Synchronous Generators: A R...

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IEEE Industry Applications Society Annual Meeting (IAS)

作者： Abbas Hasani Xiaodong Liang Farhad Haghjoo Moien Abedini Majid Sanaye-Pasand Claus Leth Bak Department of Electrical and Computer Engineering University of Saskatchewan Saskatoon SK Canada Department of Electrical and Computer Engineering Shahid Beheshti University Tehran Iran University of Tehran School of Electrical and Computer Engineering Tehran Iran Department of Energy Technology Aalborg University Aalborg Denmark

ISBN: (数字)9798350372717

ISBN: (纸本)9798350372724

The loss of field (LOF) is a common phenomenon that occurs in the excitation system of a synchronous generator, and the LOF relay (ANSI code 40) offers crucial LOF protections for synchronous generators in electrical power plants. Conventional LOF relays employ one or two offset Mho elements and exploit measured impedance in the generator terminal to detect LOF events. Although widely used by industry, these relays have a low speed in LOF detections, and inadequate security during power swings. During the last decades, LOF protection schemes in synchronous generators have been improved with a higher detection speed and enhanced security. This paper firstly investigates synchronous generator models for LOF studies, and then reviews different LOF protection schemes. Future research directions in this area are recommended in the paper.

关键词： Reviews Microgrids Systems modeling Synchronous generators Generators Security Protection Relays Integrated circuit modeling Power generation

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Enhancing Sovereign Allocation of Resources in Cloud Milieus Using a Causal Dilated Geometric Algebra Approach for Dynamic Scalability 2

Enhancing Sovereign Allocation of Resources in Cloud Milieus...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Katru, Chandrasekhar Rao Srinivasan, Sathish Kumar, Mohan Yadwad, Sunita Satish, G. Maranan, Ramya Lead Software Engineer Indian LandSC29707 United States Machine Learning & AI Development Oracle Cloud Infrastructure Division San FranciscoCA United States Nitte Meenakshi Institute of Technology Department of Information Science and Engineering Karnataka Bengaluru India Parul institute of Engineering and Technology Parul University Computer science and Engineering Faculty of Engineering and Technology Gujarat India Sree Dattha Institute of Engineering and Science Department of Electrical and Electronics Engineering Telangana Sheriguda India Saveetha School of Engineering SIMATS Department of Research and Innovation Tamil Nadu Chennai India

ISBN: (纸本)9798331505745

Cloud environments utilize availability and usage of resources as crucial components in order to produce scalability, reliability and low cost. Such resource capabilities require autonomous systems that can handle these resources dynamically in spite of fluctuating workloads and service delivery requirements. Static models of resource management suffer from scalability and inability to react on the fly and accurately predict the demand thus causing Service Level Agreement (SLA) breaches and affecting the Quality of Service (QoS) as well as energy consumption profile. Advanced and elaborative cloud workloads require an intelligent system that can analyse the temporal and spatial patterns without impacting the overall system's performance significantly. Inspired by these challenges, this research proposes a Causal Dilated Geometric Algebra Convolutional Transformer Network (CATNet) along with the Prairie-Dog Optimizer (P-DogO), referred to as CAT-DogNet to learn and predict the dynamic scalability. CATNet integrates Causal Dilated Convolutional Networks in time domain for temporal structure capturing and Geometric Algebra Transformer for spatial resource coding. P-DogO adjusts weight parameters and losses for resource allocation, enhancing them depending on a work environment. A set of comprehensive assessments demonstrate a 0.85% SLA violation rate, average response time of less than 10ms, and overall 12.3% less energy consumption than baseline methods. The system had favourable predictability rates over 99.4% for the resource demand estimate. CAT-DogN et presents a sound and agnostic architecture for autonomous resource allocation in cloud environment, which indeed extend the state of art by considering flexibility, power consumption and QoS compliance. © 2025 IEEE.

关键词： Infrastructure as a service (IaaS)

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A Novel Preference Scale Function Based on Poincaré Metric for Decision-Making

A Novel Preference Scale Function Based on Poincaré Metric ...

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2023 International Conference on Machine Learning and Cybernetics, ICMLC 2023

作者： Yang, Youpeng Lee, Sanghyuk Zhang, Haolan Pedrycz, Witold Kim, Kyeong Soo School of Advanced Technology Xi'an Jiaotong Liverpool University Suzhou China School of Engineering New Uzbekistan University Tashkent Uzbekistan Scdm Center Zhejiang University Ningbo China University of Alberta Department of Electrical & Computer Engineering Edmonton Canada

ISBN: (纸本)9798350303780

This paper proposes a novel preference scale function based on the Poincare metric for decision-making with Intuitionistic Fuzzy Sets (IFSs). We first introduce a pair of two-dimensional vectors expressing the IFSs in multi-criteria decision-making problems, which satisfy the Poincare metric, and then construct a preference scale function based on it. The proposed scale function can address, without resorting to the accuracy function, the issue of the existing score function for IFSs returning zero scores when membership and non-membership degrees of an element are the same. The advantages of the proposed scale function over the existing ones are demonstrated through illustrative examples. © 2023 IEEE.

关键词： Decision making

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Bilateral Multi-Subarray Padded Coprime Array Configuration for DOA Estimation in MIMO Radar

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IEEE Transactions on Vehicular Technology 2025年

作者： Chen, Hua Li, Jiajie Yang, Songjie Liu, Wei Yuen, Chau So, Hing Cheung Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu610054 China The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Kowloon Hong Kong Nanyang Technological University School of Electrical and Electronics Engineering 639798 Singapore Department of Electrical Engineering City University of Hong Kong Hong Kong

Simultaneous consideration of uniform degrees of freedom (uDOF) and mutual coupling is a key focus in sparse array design. To reduce mutual coupling and increase degrees of freedom (DOF), a bilateral multi-subarray coprime array (BMSCA) is proposed with its specific hole locations in the difference co-array (DCA). To address the holes in the central DCA, an additional subarray is incorporated, resulting in the bilateral multi-subarray padded coprime array (BMSPCA), which substantially increases both DOF and uDOF. Additionally, the first three weight functions and the optimal structure are determined for BMSPCA. As an application, BMSPCA is applied to multiple-input multiple-output (MIMO) radar with sum-difference co-array, where the holes in DCA and virtual sparse sensors can be divided into similar layers and exhibit symmetry;specific sparse arrays that exhibit similar symmetrical properties are then identified, which can fundamentally enhance the uDOF of MIMO radar. Simulation results demonstrate the superior performance of both the proposed array and the sparse MIMO array in scenarios with strong mutual coupling. © 1967-2012 IEEE.

关键词： Direction of arrival

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ANIMC: A Soft Approach for Autoweighted Noisy and Incomplete Multiview Clustering

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2022年第2期3卷 192-206页

作者： Fang, Xiang Hu, Yuchong Zhou, Pan Wu, Dapeng Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering The School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Information Storage System Ministry of Education of China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States

Multiview clustering has wide real-world applications because it can process data from multiple sources. However, these data often contain missing instances and noises, which are ignored by most multiview clustering methods. Missing instances may make these methods difficult to use directly, and noises will lead to unreliable clustering results. In this article, we propose a novel autoweighted noisy and incomplete multiview clustering (ANIMC) approach via a soft autoweighted strategy and a doubly soft regular regression model. First, by designing adaptive semiregularized nonnegative matrix factorization, the soft autoweighted strategy assigns a proper weight to each view and adds a soft boundary to balance the influence of noises and incompleteness. Second, by proposing θ-norm, the doubly soft regularized regression model adjusts the sparsity of our model by choosing different θ. Compared with previous methods, ANIMC has three unique advantages: 1) it is a soft algorithm to adjust our approach in different scenarios, thereby improving its generalization ability;2) it automatically learns a proper weight for each view, thereby reducing the influence of noises;3) it performs doubly soft regularized regression that aligns the same instances in different views, thereby decreasing the impact of missing instances. Extensive experimental results show its superior advantages over other state-of-the-art works. © 2020 IEEE.

关键词： Clustering algorithms

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