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Retraction Note: Solar energy harvesting to optimise the power constraints in 5G systems

Optical and Quantum Electronics 2024年第10期56卷 1-2页

作者： Kiran, N. Chitra Senthilkumar, S. Satish, G. Soni, Narendra Ganatra, Amit Al-Ammar, Essam A. Iqbal, Amjad Department of Electronics and Communication Engineering Alliance University Bengaluru India Department of Electronics and Communication Engineering E.G.S. Pillay Engineering College Nagapattinam India Department of Electrical and Electronics Engineering Sree Dattha Institute of Engineering and Science Sheriguda India Department of Electrical Engineering Maharshi Parshuram College of Engineering Velneshwar Velneshwar India Department of Computer Science and Engineering Parul Institute of Engineering and Technology Parul University Limda India Department of Electrical Engineering College of Engineering King Saud University Riyadh Saudi Arabia Department of Materials Technologies Faculty of Materials Engineering Silesian University of Technology Gliwice Poland

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Design and Analysis of a Wide Input Voltage Range Low-Dropout Regulator in TSMC 180nm BCD Technology

Design and Analysis of a Wide Input Voltage Range Low-Dropou...

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electrical, Electronic and Information Sciences (eStream), Open Conference of

作者： Lukas Šalavėjus Vaidotas Barzdėnas Aleksandr Vasjanov Tzung-Je Lee Department of Computer Science and Communications Technologies Vilnius Gediminas Technical University Vilnius Lithuania Department of Electrical Engineering Institute of Integrated Circuit Design National Sun Yat-Sen University Kaohsiung Taiwan

ISBN: (数字)9798331598730

ISBN: (纸本)9798331598747

This paper presents the design and simulation of a low-dropout (LDO) linear voltage regulator intended for integration in high-voltage DC-DC converter systems. Implemented in a 180 nm BCD CMOS process, the proposed LDO supports an input voltage range of 8 V to 60 V and provides a regulated 5 V output with up to 20 mA load current. A custom three-stage error amplifier is introduced, combining low-voltage signal processing with high-voltage interfacing, while a self-biased startup circuit ensures reliable operation across process, voltage, and temperature (PVT) variations. The LDO achieves a worst-case quiescent current of 95.5 µA at 60 V input and 125 °C, and demonstrates excellent line and load regulation of 0.075 mV/V and 0.215 mV/mA, respectively. Power supply rejection reaches –60 dB at 1 kHz under nominal conditions. Compared with recent state-of-the-art designs, this work achieves a favorable balance of wide input range, regulation precision, and power efficiency, making it well suited for analog and digital internal supply rails in automotive and industrial applications.

关键词： Temperature measurement Low voltage Transient response Regulators Power system management High-voltage techniques DC-DC power converters Regulation Voltage control Automotive engineering

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Training Discrete Deep Generative Models via Gapped Straight-Through Estimator

arXiv

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arXiv 2022年

作者： Fan, Ting-Han Chi, Ta-Chung Rudnicky, Alexander I. Ramadge, Peter J. Department of Electrical and Computer Engineering Princeton University PrincetonNJ United States Language Technologies Institute Carnegie Mellon University PittsburghPA United States

While deep generative models have succeeded in image processing, natural language processing, and reinforcement learning, training that involves discrete random variables remains challenging due to the high variance of its gradient estimation process. Monte Carlo is a common solution used in most variance reduction approaches. However, this involves time-consuming resampling and multiple function evaluations. We propose a Gapped Straight-Through (GST) estimator to reduce the variance without incurring resampling overhead. This estimator is inspired by the essential properties of Straight-Through Gumbel-Softmax. We determine these properties and show via an ablation study that they are essential. Experiments demonstrate that the proposed GST estimator enjoys better performance compared to strong baselines on two discrete deep generative modeling tasks, MNIST-VAE and ListOps. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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Effect on Compressive Strength of Portland Pozzolana Cement on Adding Admixtures Using Machine Learning Technique 2nd

Effect on Compressive Strength of Portland Pozzolana Cement ...

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2nd International Conference on Emerging Trends in Expert Applications and Security, ICETEAS 2023

作者： Negi, Prafful Balmiki, Vinod Chandramauli, Awadhesh Joshi, Kapil Bijalwan, Anchit Depatment of Civil Engineering UIT Uttaranchal University Uttarakhand Dehradun India Department of Cyber security School of Computing and Innovative Technologies British University Vietnam Hanoi Viet Nam Adjunct Prof Faculty of Electrical and Computer Engineering Arba Minch University Arba Minch Ethiopia Department of CSE Uttaranchal Institute of Technology Uttaranchal University Uttarakhand Dehradun India

ISBN: (纸本)9789819919086

Although concrete is one of the most common building materials used today, determining the precise concrete compressive strength is still difficult due to the extremely complicated interplay between its composition. The paper shows designing of M40 concrete and then finds the change in compressive strength of concrete on adding admixtures. First admixture used by us is, ground granulated base furnace. We replace cement with GGBS with 5, 20, and 35% of cement. We make 6 cubes sample of each case and then test 3 cubes sample of each case on 7 day and other 3 cubes samples on 28 days. Second admixture used by us is sugar. Here we didn’t replace cement with sugar but taken certain percentage of total weight of cement. We have taken.01,.05,.08, and.1% of weight of cement. In this also we had made 6 cube samples of each case and then test 3 cubes samples of each case for 7 days and remaining cubes are tested on 28 days. Finally, we compared normal concert compressive strength, concrete formed by using GGBS and then concrete made using sugar admixture. In order to determine the concrete workability and determine the impact of the admixture on the workability of the concrete, we again undertake slump tests in both circumstances. In between, we also discussed the problem of input accuracy and output relationship during the processing of data using machine learning techniques. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Compressive strength

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Optimal Sizing of Security Constrained Unit Commitment Problem Integrated with Renewable Energy Sources and PEVs 15

Optimal Sizing of Security Constrained Unit Commitment Probl...

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15th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2023

作者： Dhawale, Pravin G. Kumar, Vikram Bath, S.K. Verma, Chaman Simona Raboaca, Maria Filote, Constantin Kumar, Deepak School of Electrical and Electronics Engineering Lovely Professional University Phagwara India University of Calgary Calgary Canada Maharaja Ranjit Singh Punjab Technical University Department of Electrical Engineering Bhatinda India Eötvös Loránd University Faculty of Informatics Department of Media and Educational Informatics Budapest Hungary National Research and Development Institute for Cryogenics and Isotopic Technologies Icsi Energy Department Ramnicu Valcea240050 Romania Stefan Cel Mare University of Suceava Universitatii Street Faculty of Electrical Engineering and Computer Science Suceava720229 Romania Apex Institute of Technology Chandigarh Univerity Punjab Mohali India

ISBN: (纸本)9798350321388

Optimal sizing of SCUCP with solar energy and PEVs is crucial for electric power system operation and planning. This paper presents a Chaotic Arithmetic Optimization Algorithm to minimize cost while optimizing the sizing of solar energy and PEV systems. The proposed framework integrates probabilistic forecasting models for solar power generation and realistic EV charging profiles based on travel patterns and infrastructure characteristics. It aims to find optimal sizes for Solar power plants and EV charging infrastructure, considering system demand, cost, emissions, and reliability. The Chaotic Arithmetic Optimization Algorithm efficiently solves the optimization problem by accounting for the complexities of SCUCP. The research evaluates the proposed approach using benchmark test systems of varying dimensions and power systems. The results enable informed decision-making by identifying optimal capacities for solar power plants and charging infrastructure, leading to improved system reliability, reduced emissions, and minimized costs. © 2023 IEEE.

关键词： Solar energy

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Predicting Early Dropouts of an Active and Healthy Ageing App

arXiv

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arXiv 2023年

作者： Perifanis, Vasileios Michailidi, Ioanna Stamatelatos, Giorgos Drosatos, George Efraimidis, Pavlos S. Department of Electrical and Computer Engineering Democritus University of Thrace Xanthi67100 Greece Institute for Language and Speech Processing Athena Research Center Xanthi67100 Greece

In this work, we present a machine learning approach for predicting early dropouts of an active and healthy ageing app. The presented algorithms have been submitted to the IFMBE Scientific Challenge 2022 (***), part of IUPESM WC 2022. We have processed the given database and generated seven datasets. We used pre-processing techniques to construct classification models that predict the adherence of users using dynamic and static features. We submitted 11 official runs and our results show that machine learning algorithms can provide high-quality adherence predictions. Based on the results, the dynamic features positively influence a model’s classification performance. Due to the imbalanced nature of the dataset, we employed oversampling methods such as SMOTE and ADASYN to improve the classification performance. The oversampling approaches led to a remarkable improvement of 10%. Our methods won first place in the IFMBE Scientific Challenge 2022. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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IoT Cloud Computing Middleware for crowd monitoring and evacuation

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International Journal of Circuits, Systems and Signal Processing 2021年 15卷 1790-1802页

作者： Gazis, Alexandros Katsiri, Eleftheria Democritus University of Thrace Department of Electrical and Computer Engineering Xanthi67100 Greece Institute for the Management of Information Systems Athena Research & Innovation Center in Information Communication & Knowledge Technologies Marousi15125 Greece

Map-Reduce is a programming model and an associated implementation for processing and generating large data sets. This model has a single point of failure: the master, who coordinates the work in a cluster. On the contrary, wireless sensor networks (WSNs) are distributed systems that scale and feature large numbers of small, computationally limited, low-power, unreliable nodes. In this article, we provide a top-down approach explaining the architecture, implementation and rationale of a distributed fault-tolerant IoT middleware. Specifically, this middleware consists of multiple mini-computing devices (Raspberry Pi) connected in a WSN which implement the Map-Reduce algorithm. First, we explain the tools used to develop this system. Second, we focus on the Map-Reduce algorithm implemented to overcome common network connectivity issues, as well as to enhance operation availability and reliability. Lastly, we provide benchmarks for our middleware as a crowd tracking application for a preserved building in Greece (i.e., M. Hatzidakis’ residence). The results of this study show that IoT middleware with low-power and low-cost components are viable solutions for medium-sized cloud computing distributed and parallel computing centres. Potential uses of this middleware apply for monitoring buildings and indoor structures, in addition to crowd tracking to prevent the spread of COVID-19. © 2021, North Atlantic University Union NAUN. All rights reserved.

关键词： Middleware

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The applied principles of EEG analysis methods in neuroscience and clinical neurology

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Military Medical Research 2024年第6期11卷 907-941页

作者： Hao Zhang Qing-Qi Zhou He Chen Xiao-Qing Hu Wei-Guang Li Yang Bai Jun-Xia Han Yao Wang Zhen-Hu Liang Dan Chen Feng-Yu Cong Jia-Qing Yan Xiao-Li Li School of Systems Science Beijing Normal UniversityBeijing 100875China College of Electrical and Control Engineering North China University of TechnologyBeijing 100041China School of Automation Science and Engineering South China University of TechnologyGuangzhou 510641China Department of Psychology the State Key Laboratory of Brain and Cognitive Sciencesthe University of Hong KongHong Kong SAR 999077China HKU-Shenzhen Institute of Research and Innovation Shenzhen 518057GuangdongChina Department of Health Technology and Informatics the Hong Kong Polytechnic UniversityHong Kong SAR 999077China Department of Rehabilitation Medicine the First Afliated Hospital of Nanchang UniversityNanchang 330006China Rehabilitation Medicine Clinical Research Center of Jiangxi Province Nanchang 330006China Beijing Key Laboratory of Learning and Cognition School of PsychologyCapital Normal UniversityBeijing 100048China School of Communication Science Beijing Language and Culture UniversityBeijing 100083China Institute of Electrical Engineering Yanshan UniversityQinhuangdao 066004HebeiChina School of Computer Science Wuhan UniversityWuhan 430072China School of Biomedical Engineering Faculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalian 116081LiaoningChina Guangdong Artifcial Intelligence and Digital Economy Laboratory(Guangzhou) Guangzhou 510335China.

Electroencephalography(EEG)is a non-invasive measurement method for brain *** to its safety,high resolution,and hypersensitivity to dynamic changes in brain neural signals,EEG has aroused much interest in scientific research and medical *** article reviews the types of EEG signals,multiple EEG signal analysis methods,and the application of relevant methods in the neuroscience feld and for diagnosing neurological ***,3 types of EEG signals,including time-invariant EEG,accurate event-related EEG,and random event-related EEG,are ***,5 main directions for the methods of EEG analysis,including power spectrum analysis,time-frequency analysis,connectivity analysis,source localization methods,and machine learning methods,are described in the main section,along with diferent sub-methods and effect evaluations for solving the same ***,the application scenarios of different EEG analysis methods are emphasized,and the advantages and disadvantages of similar methods are *** article is expected to assist researchers in selecting suitable EEG analysis methods based on their research objectives,provide references for subsequent research,and summarize current issues and prospects for the future.

关键词： Electroencephalogram analysis methods Applied principles Neuroscience Diagnosis Neurological diseases

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Loss Distillation via Gradient Matching for Point Cloud Completion with Weighted Chamfer Distance

Loss Distillation via Gradient Matching for Point Cloud Comp...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Fangzhou Lin Haotian Liu Haoying Zhou Songlin Hou Kazunori D Yamada Gregory S. Fischer Yanhua Li Haichong K. Zhang Ziming Zhang Department of Robotics Engineering Worcester Polytechnic Institute Worcester MA USA Graduate School of Information Sciences Tohoku University Sendai Japan Dell Technologies Hopkinton MA USA Department of Computer Science Worcester Polytechnic Institute Worcester MA USA Department of Electrical & Computer Engineering Worcester Polytechnic Institute Worcester MA USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

3D point clouds enhanced the robot’s ability to perceive the geometrical information of the environments, making it possible for many downstream tasks such as grasp pose detection and scene understanding. The performance of these tasks, though, heavily relies on the quality of data input, as incomplete can lead to poor results and failure cases. Recent training loss functions designed for deep learning-based point cloud completion, such as Chamfer distance (CD) and its variants (e.g. HyperCD [1]), imply a good gradient weighting scheme can significantly boost performance. However, these CD-based loss functions usually require data-related parameter tuning, which can be time-consuming for data-extensive tasks. To address this issue, we aim to find a family of weighted training losses (weighted CD) that requires no parameter tuning. To this end, we propose a search scheme, Loss Distillation via Gradient Matching, to find good candidate loss functions by mimicking the learning behavior in backpropagation between HyperCD and weighted CD. Once this is done, we propose a novel bilevel optimization formula to train the backbone network based on the weighted CD loss. We observe that: (1) with proper weighted functions, the weighted CD can always achieve similar performance to HyperCD, and (2) the Landau weighted CD, namely Landau CD, can outperform HyperCD for point cloud completion and lead to new state-of-the-art results on several benchmark datasets. Our demo code is available at https://***/Zhang-VISLab/IROS2024-LossDistillationWeightedCD.

关键词： Point cloud compression Training Three-dimensional displays Laser radar Benchmark testing Iterative algorithms Tuning Optimization Intelligent robots Convergence

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Extinguishing Real Fires by Fully Autonomous Multirotor UAVs in the MBZIRC 2020 Competition

Field Robotics

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Field Robotics 2025年 2卷 406-436页

作者： Viktor Walter Vojtěch Spurný Matěj Petrlík Tomáš Báča David Žaitlík Lyubomyr Demkiv Martin Saska Department of Cybernetics Faculty of Electrical Engineering Czech Technical University in Prague http://mrs.felk.cvut.cz Robotics Group of SoftServe Inc. and with the Institute of Computer Science and Information Technologies Lviv Polytechnic National University Ukraine

In this paper, we describe a system for combating real fires with sprayed liquid extinguishing agent using a team of multirotor UAVs. The system design relies on onboard sensors and operates without the need for human intervention throughout its complex mission, from its takeoff to landing. The core UAV platform can estimate its state, self-localize, navigate and locate and extinguishing fires. Thermal and RGB cameras are used, each with a specialized computer vision subsystem and are combined with planar LIDAR for 3D localization of fires on multistory building facades. The system conducts aerial firefighting with a software stack that addresses flight dynamics and sensor limitations and a liquid-spraying subsystem incorporating a rigidly-attached water nozzle. The approach presented in this paper was motivated by the Mohamed Bin Zayed International Robotics Challenge (MBZIRC 2020) firefighting scenario, which focused on coordinated multi-UAV teams that can autonomously combat high-rise building fires. The MBZIRC series places particular emphasis on fast and reliable deployment of robots in realistic conditions. These contests promote development of real-world applications that are greatly needed by society, but which still exceed State-of-the-Art in the robotics community. To our knowledge, our system was the only MBZIRC 2020 contender to extinguish a facade fire successfully in autonomous mode without using an RTK-GNSS system. Our approach contributed to victory in the overall competition and we have now adapted it into an industrial prototype for a firefighting UAV. A video attachment to this paper is available at http://***/fr2020firechallenge-facadefires.

关键词： Robots Buildings Autonomous aerial vehicles Location awareness Robot kinematics Inspection Navigation Cameras Monitoring Sensor systems

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