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2024 International Conference on Advancement in Renewable Energy and Intelligent Systems, AREIS 2024

作者： Balaji, V. Ganga, M. Barathi, K. Gouda, Pramod Kumar Prasad, Rajesh Venkatesh, Mammula MAI-NEFHI College of Engineering and Technology Department of Electrical and Computer Engineering Asmara Eritrea Hindusthan College of Engineering and Technology Department of Biomedical Engineering Coimbatore India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Dept. of Eee Tamil Nadu India Aditya Institute of Technology and Management Dept. of Eee Andhra Pradesh Srikakulam India Stella Mary's College of Engineering Dept. of Eee Tamil Nadu India Avanthi's Research and Technological Academy Dept. of Eee A. P Vizianagaram531162 India

ISBN: (纸本)9798350387230

The transition towards Renewable Energy Sources (RES), mainly solar power is crucial in addressing the depletion of fossil fuel-based energy supplies and mitigating environmental concerns. This paper proposes an innovative approach to enhance efficiency of PhotoVoltaic (PV) systems by integrating a two-phase Interleaved Boost (IB) converter with an Adaptive Neuro Fuzzy Inference System (ANFIS) controller. The ANFIS controller optimizes the operation of the IB converter by adjusting the duty cycle based on two key input parameters: solar irradiation and temperature. This adaptive control method effectively transforms solar energy into electrical power, maximizing the PV system's power production. By using the ANFIS mechanism, the suggested system improves total output power, decreases overshoot, and produces a higher voltage output than conventional techniques, all of which lead to increased performance and dependability. The IB converter operates with an efficiency of 96.15% and minimized settling time. © 2024 IEEE.

关键词： Adaptive control systems

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An Improved YOLOv5 Detection Method for Strip Surface Defect 28

An Improved YOLOv5 Detection Method for Strip Surface Defect

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28th International Conference on Automation and Computing, ICAC 2023

作者： Liu, Yefeng Yu, Longfei Zhang, Qichun School of Automation and Electrical Dept. Liaoning Key Laboratory of Information Physical Fusion and Intelligent Cnc Machine Tool Manufacturing Shenyang Institute of Technology Dept. Fushun China Shenyang Ligong University Dept. School of Automation and Electrica Engineering China University of Bradford Bradford Dept. Department of Computer Science United Kingdom

ISBN: (纸本)9798350335859

Traditional steel surface defect detection methods have issues such as low detection accuracy and efficiency. It is suggested a technique based on enhanced YOLOv5 steel surface fault identification. In order to improve the network's capacity to handle feature information, this study suggests adding SE attention mechanisms to the YOLOv5s network's Backbone, Neck, and SPPF and replacing the C3 structure at the end of the Backbone with C3TR. To achieve network model lightweight, it is proposed to replace the Neck part in the YOLOv5s model and the convolution in SPPF with Ghost convolution. Then, to improve network feature fusion, extend the improved SPPF to the Neck part. Introduce the SPD convolution in the Backbone section to reduce the information loss caused by the neural network's convolution. The tested mAP is 75.3%, which is 5.3% higher than the original model, 24.6% higher than the SSD method, and 4.6% higher than the YOLOv6 algorithm after the upgraded model has been trained on the NEU-DET dataset. The observed FPS is 81.3, which is 6.3 more than the relative literature algorithm and 18.0 more than the SSD algorithm. © 2023 IEEE.

关键词： Convolution

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Beyond the ROC curve: classification trees using cost-optimal curves, with application to imbalanced datasets 24

Beyond the ROC curve: classification trees using cost-optima...

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Proceedings of the 41st International Conference on Machine Learning

作者： Magzhan Gabidolla Arman Zharmagambetov Miguel Á. Carreira-Perpiñán Dept. of Computer Science and Engineering University of California Merced Meta AI (FAIR)

Important applications such as fraud or spam detection or churn prediction involve binary classification problems where the datasets are imbalanced and the cost of false positives greatly differs from the cost of false negatives. We focus on classification trees, in particular oblique trees, which subsume both the traditional axis-aligned trees and logistic regression, but are more accurate than both while providing interpretablemodels. Rather than using ROC curves, we advocate a loss based on minimizing the false negatives subject to a maximum false positive rate, which we prove to be equivalent to minimizing a weighted 0/1 loss. This yields a curve of classifiers that provably dominates the ROC curve, but is hard to optimize due to the 0/1 loss. We give the first algorithm that can iteratively update the tree parameters globally so that the weighted 0/1 loss decreases monotonically. Experiments on various datasets with class imbalance or class costs show this indeed dominates ROC-based classifiers and significantly improves over previous approaches to learn trees based on weighted purity criteria or over- or undersampling.

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Career Craft AI: A Personalized Resume Analysis and Job Recommendations System

Career Craft AI: A Personalized Resume Analysis and Job Reco...

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Innovative Sustainable Technologies for Energy, Mechatronics, and Smart Systems (ISTEMS), International Conference on

作者： Akshay Rajput Akanksha Dubey Rishabh Thakur Deepak Singh Upendra Pratap Singh Dept. of Computer Science and Engineering Graphic Era Deeemed to be University Dehradun India

ISBN: (数字)9798350384246

ISBN: (纸本)9798350384253

In the today's era of endless career options, the challenge to get that one ideal job that aligns best with individual's skills and interests is absent due to lack of personalized recommendations and lack of guidance about resume making it becomes challenging to find best jobs and get shortlisted for the hiring process of a company. This paper introduces a novel approach to address these challenges by the development of a Career Crafter AI - A Resume analysis and Job Recommendation System. Knowing the limitations of existing recommendations systems that primarily rely on individuals provided interests, our system, implemented on the e-learning platform for skill building, Level Up, goes beyond the traditional approach of recommendation system. Using Vector Machine algorithm and the cosine similarity our system classifies the users based on their skills and profile mentioned on the platform and Natural Language Processing enables to provide the tips and recommendation to improve our resume and get the insights about the user for better recommendation. The recommendation engine utilizes these insights to provide accurate and best fit jobs to the users and as well as provide potential candidate for job roles to the recruiters This research paper aims to provide a one stop destination for learners to improve their resume for getting an edge during shortlisting and interviews and to transform and optimize the job recommendation systems by offering a more personalized and effective approach, empowering individuals to navigate their career paths with confidence and precision.

关键词： Support vector machines Electronic learning engineering profession Processor scheduling Resumes Transforms Vectors

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Basic Study on Transmission Power Enhancement in Dynamic Wireless Power Transfer Systems using Circular–Solenoid Multi-coil

Basic Study on Transmission Power Enhancement in Dynamic Wir...

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Wireless Power Week (WPW)

作者： Harutaka Suzuki Ryosuke Ota Dept. of Electrical Engineering and Computer Science Tokyo Metropolitan University Tokyo Japan

ISBN: (数字)9798350349139

ISBN: (纸本)9798350349146

This paper proposes a method for stable power transmission in dynamic wireless power transfer (DWPT) systems with a coil array installed on the ground. The proposed method adopts a Circular-Solenoid type multi-coil on the electric vehicle (EV) side to utilize both vertical and horizontal magnetic fields, achieving stable power transmission all over the DWPT lane. In the experiments, a prototype was constructed, and the magnetic coupling characteristics of the ground and EV side coils were clarified. The power transmission characteristics were then derived, and a guideline was provided for efficiently and stably transmitting 3 kW of power to EVs all over the DWPT lane.

关键词： Coils Couplings Wireless communication Magnetic field measurement Power transmission Prototypes Wireless power transfer

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Synergizing Insights for Precise Rice Leaf Disease Diagnosis Via Multi-Modal Fusion

Synergizing Insights for Precise Rice Leaf Disease Diagnosis...

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： Shelly Chelsy Pally Jenefa J Dept. of Computer Science and Engineering CHRIST (Deemed to be University) engaluru Karnataka India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

Rice holds a significant position in India, especially in the southern part of the country, where people tend to eat some rice at least once a day. Farmers are facing a huge loss due to diseases in leaf, which is the main problem of agriculture. By using techniques like machine learning, main problems detection can be done. This review, discusses common plant diseases that affect the leaf. Some include Leaf Spots, Rusts, Fusarium Wilt, Early Blight, Powdery Mildew and Downey Mildew. Our research found that machine learning techniques on rice plants make finding diseases on leaves easier. Finally, we concluded that the most accurate method is the Enhanced VGG16, with an accuracy of 99.60% because it is really good at spotting diseases on rice leaves because it's great at recognizing the small details and patterns in leaf pictures. This helps it to tell the diseases apart more accurately and make fewer mistakes in identifying them.

关键词： Productivity Accuracy Reviews Prevention and mitigation Computational modeling Crops Prediction algorithms Medical diagnosis Reliability Diseases

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Modular Deep Reinforcement Learning: Enhancing Flexibility through Agent Splitting

Modular Deep Reinforcement Learning: Enhancing Flexibility t...

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IEEE International Conference on Big Data

作者： Chong Tian Danda B. Rawat Dept. of Electrical Engineering & Computer Science Howard University Washington DC USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Deep reinforcement learning (DRL) has shown remarkable success in tackling complex tasks by learning representations directly from raw data. However, as DRL agents become increasingly sophisticated, they often face challenges in adapting to new tasks or environments due to their monolithic structure. In this paper, we prove that by decomposing the agent into modular components, we enable more efficient adaptation as well as more efficient than that of the singular DRL agent on tackling complex tasks. Overall, our work contributes to advancing the field of DRL by introducing a flexible and scalable framework for agent decomposition. By enabling the creation of modular agents, we empower DRL systems to efficiently adapt to changing environments, learn new tasks with minimal intervention, and achieve higher levels of performance and versatility.

关键词： Big Data Deep reinforcement learning Faces

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A New Holistic Approach for Prosthesis Polymeric Aortic Valve Design

A New Holistic Approach for Prosthesis Polymeric Aortic Valv...

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Thermal, Mechanical and Multiphysics Simulation and Experiments in Micro-Electronics and Micro-Systems EuroSimE

作者： Müge Yaren Yaşartürk Emirhan Yığıt Reza Daryanı Emre Cenk Ersan Mustafa Serdar Çelebı Dept. of Computational Science and Engineering Istanbul Technical University Istanbul Turkey Dept. of Computer Engineering Istanbul Beykent University Istanbul Turkey Dept. of Mechanical Engineering Istanbul Technical University Istanbul Turkiye Dept. of Computational Science and Engineering Istanbul Technical University Istanbul Turkiye

ISBN: (数字)9798350393002

ISBN: (纸本)9798350393019

Aortic valve insufficiency is a life-threatening condition. The primary treatment approach is the replacement of the native valve with prosthetic valves in severe cases. However, current prostheses often lead to complications such as durability issues, the need for lifelong anticoagulation medicine usage, and tissue rejection. As a result, multiple surgical interventions are frequently required for prosthesis replacement. This study highlights the importance of a comprehensive prosthetic design process by encompassing patient-specific design, careful material selection, and advanced three-dimensional fabrication, which is essential for addressing these challenges. To enhance the mechanical durability of polymeric heart valve prostheses, we introduce a framework that combines fluid-structure interaction (FSI) simulations to obtain hemodynamic properties prior to advanced structural analyses. While our methodology is based on determining hemodynamic loads with FSI simulations and transferring them to an additional finite element model (FEM) that captures the detailed mechanical properties of valve tissue, the results presented in this study are obtained using more traditional loading conditions in FEM to ensure the validity and comparability of our findings. These results serve as a foundation for future studies incorporating full FSI-driven structural simulations. These analyses lead to the creation of a geometrically optimized design, fabricated with a digital light processing-based (DLP) method. Subsequently, mechanical testing was conducted to evaluate the effects of this manufacturing technique on material behavior, producing more realistic material model coefficients for use in numerical simulations. Consequently, different ratios of material rigidity in fiber-reinforced models are investigated with FEM analysis. An important novelty of this approach is to be able to detect stress accumulations by taking hemodynamic indices into account to optimize the stress di

关键词： Analytical models Valves Finite element analysis Hemodynamics Numerical models Polymers Heart valves Stress Prosthetics Load modeling

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Unsupervised Insider Threat Detection Using Multi-Head Self-Attention Mechanisms

Unsupervised Insider Threat Detection Using Multi-Head Self-...

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Intelligent Cybersecurity Conference (ICSC)

作者： Simon Bertrand Pascal Germain Nadia Tawbi dept. of Computer Science & Software Engineering Laval University Québec Canada

ISBN: (数字)9798350354775

ISBN: (纸本)9798350354782

We propose an unsupervised insider threat detection system that learns normal user behaviors through audit data using neural networks equipped with multi-head self-attention mechanisms. The attention mechanisms learn precise normal user behaviors using large event windows. The key idea is to consider a sequence abnormal when it exhibits some events that are not likely to happen given the preceding events sequence. In addition, our method provides insights into detected threats through an event-based scoring system to facilitate threat understanding by security experts. Furthermore, the proposed solution does not require intensive audit logs pre-processing, such as manual domain knowledge-rich features extraction and data balancing. Thus, the proposed approach is easy to implement and use across organizations regardless of their expertise level. The proposed solution also provides the ability to detect threats in real-time. On the benchmark dataset CERT version 4.2, our solution, combined with hyperparameter search by Bayesian optimization, outperforms the previous state-of-the-art approaches with an area under the ROC curve of 97.23%, a recall of 91.51%, an accuracy of 93.13% and a false positive rate of 6.8%.

关键词： Neural networks Manuals Feature extraction Vectors Threat assessment Data models Real-time systems Bayes methods Optimization Tuning

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Compressive Recovery of Signals Defined on Perturbed Graphs

arXiv

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arXiv 2024年

作者： Ghosh, Sabyasachi Rajwade, Ajit Dept. of Computer Science and Engineering Indian Institute of Technology Bombay Mumbai India

Recovery of signals with elements defined on the nodes of a graph, from compressive measurements is an important problem, which can arise in various domains such as sensor networks, image reconstruction and group testing. In some scenarios, the graph may not be accurately known, and there may exist a few edge additions or deletions relative to a ground truth graph. Such perturbations, even if small in number, significantly affect the Graph Fourier Transform (GFT). This impedes recovery of signals which may have sparse representations in the GFT bases of the ground truth graph. We present an algorithm which simultaneously recovers the signal from the compressive measurements and also corrects the graph perturbations. We analyze some important theoretical properties of the algorithm. Our approach to correction for graph perturbations is based on model selection techniques such as cross-validation in compressed sensing. We validate our algorithm on signals which have a sparse representation in the GFT bases of many commonly used graphs in the network science literature. An application to compressive image reconstruction is also presented, where graph perturbations are modeled as undesirable graph edges linking pixels with significant intensity difference. In all experiments, our algorithm clearly outperforms baseline techniques which either ignore the perturbations or use first order approximations to the perturbations in the GFT bases. Copyright © 2024, The Authors. All rights reserved.

关键词： Recovery

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