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Novel Three-Phase Buck-Boost Inverter With Reduced Input Current Ripple and No Short-Circuit and Open-Circuit Faults

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IEEE Transactions on Power Electronics 2024年第5期39卷 5695-5706页

作者： Khan, Shah Nawaz Khan, Ashraf Ali Jamil, Mohsin Khan, Usman Ali Ahmed, Hafiz Furqan Ahmed, Shehab Memorial University of Newfoundland Department of Electrical and Computer Engineering St. JohnNLA1C 5S7 Canada Yonsei University Department of Electrical and Electronics Engineering Seoul03722 Korea Republic of National Sun Yat-sen University Department of Electrical Engineering Kaohsiung894 Taiwan King Abdullah University of Science and Technology CEMSE Divisions Thuwal23955 Saudi Arabia

This article proposes a new single-stage three-phase buck-boost inverter and control scheme, which remarkably reduces both the low and high-frequency ripple components in the input current. The low-frequency ripple component is eliminated by phase-shifting the output three-phase voltages by 120°. The high-frequency ripple component is decreased by doubling the effective switching frequencies and lowering the voltages of the input inductors. The proposed inverter has buck-boost dc-ac conversion ability and contains only six active switches. It is short-circuiting free and can avoid the reverse recovery losses of the body diodes of the mosfets. The proposed inverter has better spectral performance and has a lower distortion in the output waveforms due to no delays (equivalent to dead-time in the traditional three-phase inverter) in the switching signals. To validate the performance and advantages of the proposed inverter, detailed analytical, simulation, and experimental results are reported. Experiments have been conducted with a 1.5-kW laboratory prototype at a 50 kHz switching frequency for the line-line output voltage of 110-220 Vrms/60. At the end, integration of inductors is also suggested to decrease the magnetic volume. © 1986-2012 IEEE.

关键词： Electric inverters

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Vacancy Modulated Analog Resistive Switching Memory Device Based on Bilayer of Zn@ZnO/ZnO for Neuromorphic Computing 30

Vacancy Modulated Analog Resistive Switching Memory Device B...

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30th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2023

作者： Khan, Muhammad Umair Mohammad, Baker Khalifa University System on Chip Center Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350326499

This work proposed a vacancy-modulated analog resistive random-access memory (ReRAM) for neuromorphic computing, utilizing a structure composed of Ag/Zn@ZnO/ZnO/FTO. The resistive switching behavior can be modulated by the movement of oxygen vacancies under positive or negative voltages. This characteristic leads to the desired resistive switching behavior for neuromorphic computing. The Ag/Zn@ZnO/ZnO/FTO based analog ReRAM exhibits several desirable features, including a low operation current, forming-free operation, and self-compliance. The conductance change rate of the device can be adjusted efficiently by modifying the amplitudes and pulse intervals of spike pulses. The device can be used for short-time memory (STM) and long-term memory (LTM) memory operations with good retention time. These characteristics align with the requirements for next-generation ReRAM. The introduction of the Ag/Zn@ZnO/ZnO/FTO-based device structure paves the way for the future development of neuromorphic computing memory applications. © 2023 IEEE.

关键词： Zinc oxide

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Ensemble R2-based Hypervolume Contribution Approximation

Ensemble R2-based Hypervolume Contribution Approximation

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Wu, Guotong Shu, Tianye Nan, Yang Shang, Ke Ishibuchi, Hisao Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen China

ISBN: (纸本)9781665430654

The hypervolume-based multi-objective evolutionary algorithms (HV-MOEAs) have proven to be highly effective in solving multi-objective optimization problems. However, the computation time of the hypervolume calculation increases significantly as the number of objectives increases. To address this issue, an R2-based hypervolume contribution approximation (R2-HVC) method was proposed. Nevertheless, the original R2-HVC generates a large number of vectors and computes the HVC only once. In this study, we propose an ensemble method based on the R2-HVC method. By using a small number of vectors for repetitive computation and majority voting, the ensemble method can reduce the probability of making incorrect choices. Experimental results show that the proposed method can improve the approximation accuracy while maintaining a similar computation time to the original R2-HVC method. © 2023 IEEE.

关键词： Ensemble Method Hypervolume Contribution Majority Voting Multi-objective Optimization

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Design and Development of a Microcontroller Based Solar PV Cooker 4

Design and Development of a Microcontroller Based Solar PV C...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Morake, Anita Samikannu, Ravi Akila, L. Karpagam, R. Rohini, R. Yahya, Abid Botswana International University of Science and Technology Department of Electrical and Communications Systems Engineering Palapye Botswana Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Ece Chennai India Periyar Maniammai Institute of Science and Technology Department of Electrical and Electronics Engineering Thanjavur India Easwari Engineering College Department of Electrical and Electronics Engineering Ramapuram India Vivekanandha College of Engineering for Women Department of Computer Science and Engineering Tiruchengode India

ISBN: (纸本)9798331529635

This study proposes an innovative approach to improve solar cooking systems by combining microcontroller technology and deep-learning techniques. The Solar-PV Microcontroller-based Cooker concept aims to provide a sustainable and eco-friendly cooking method by harnessing the sun's power. This innovative approach addresses global energy conservation and access to clean cooking options, particularly in areas where conventional methods contribute to deforestation and indoor air pollution. The system includes inputs for cooking duration and temperature monitoring, enhancing solar energy efficiency and user experience. The study investigates how deep learning algorithms can be incorporated into the microcontroller-based solar cooker system to enhance functionality and versatility. Cooking settings can be automatically adjusted to maximize energy consumption and cooking results by analyzing real-time data from sensors and human inputs. The hybrid approach combines the best features of deep learning with microcontroller technology, demonstrating the significance of renewable energy in a cleaner, more efficient future. The Solar-Pv cooker demonstrates the possibilities of fusing culinary technology with computer algorithms, showcasing the potential of renewable energy in a cleaner, more efficient future. © 2024 IEEE.

关键词： Indoor air pollution

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Cyber Attack Detection with Encrypted Network Connection Analysis 12th

Cyber Attack Detection with Encrypted Network Connection Ana...

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12th International Symposium on Intelligent Manufacturing and Service Systems, IMSS 2023

作者： Gonen, Serkan Karacayilmaz, Gokce Artuner, Harun Bariskan, Mehmet Ali Yilmaz, Ercan Nurcan Software Engineering Department Istanbul Gelisim University Istanbul Turkey Hacettepe University Institute of Science Ankara Turkey Computer Engineering Department Hacattepe University Ankara Turkey Computer Engineering Department Istanbul Gelisim University Istanbul Turkey Electrical – Electronic Engineering Department Gazi University Ankara Turkey

ISBN: (纸本)9789819960613

The evolution of science and technology has led to increasingly complex cyber security threats, with advanced evasion techniques and encrypted communication channels making attacks harder to detect. While encryption has improved privacy and confidentiality for users, it has also provided a new avenue for attackers to exploit. Traditional intrusion detection systems, which transitioned from signature-based to behavior-based approaches, have struggled to keep up with these challenges. To address this issue, researchers have turned to continuous system monitoring and network traffic packet analysis. However, this method can be resource-intensive and time-consuming, particularly when analyzing encrypted packets. In this study, the JA3 fingerprint infrastructure was examined as a potential solution for quickly detecting attacks conducted over encrypted sessions while minimizing system downtime and damage. The results demonstrated that the JA3 infrastructure effectively detected attacks carried out via encrypted channels. Although Windows 10 and Kali 2020.4 operating systems were used as the victim and attacker systems respectively, the methodology can be applied to other operating systems and network hardware by following the outlined steps. This research is expected to make a significant contribution to the field of encryption-based attack prevention. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cybersecurity

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Physics-Informed Neural Networks for Quantum Wavefunctions

Physics-Informed Neural Networks for Quantum Wavefunctions

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2024 International Conference on computer and Applications, ICCA 2024

作者： Mahmud, Istiak Asthana, Ayush Hoffmann, Mark Abdelhadi, Ahmeb School of Electrical Engineering & Computer Science University of North Dakota ND United States University of North Dakota Department of Chemistry ND United States

ISBN: (纸本)9798350367560

The wave functions and permitted energy levels of quantum systems, which characterize the probability distributions and particle behaviors, are able to be determined via the time-independent Schrodinger equation. Our research study introduces an experimental method of solving the time-independent Schrodinger equation by utilizing Physics-Informed Neural Networks (PINNs) with a customized loss function designed for output waveshape prediction. The technique makes use of PINNs' capacity to directly integrate physical rules into the learning process, assuring that the solutions follow the guiding principles of quantum mechanics. We forecast the associated wavefunctions for different energy levels (n states) and effectively solve the Schrodinger equation for distinct quantum states by using PINNs and a customized loss function. Our findings show that PINNs are a reliable and accurate way to represent the fine features of quantum wavefunctions, and they present a viable substitute for more conventional numerical techniques. This methodology not only improves computer performance but also offers more profound understanding of the behavior of systems in quantum mechanics, which may have implications for material science, nanotechnology, and quantum computing. © 2024 IEEE.

关键词： Schrodinger equation

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A Digital Twin Based Framework for Real-Time Machine Condition Monitoring 19

A Digital Twin Based Framework for Real-Time Machine Conditi...

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19th IEEE International Conference on Automation science and engineering, CASE 2023

作者： Chen, Zixiao Choudhury, Madhurjya Dev Blincoe, Kelly Dhupia, Jaspreet Singh University of Auckland Department of Mechanical and Mechatronics Engineering New Zealand School of Engineering and Computer Science Victoria University of Wellington New Zealand University of Auckland Department of Electrical Computer and Software Engineering New Zealand

ISBN: (纸本)9798350320695

Condition Monitoring (CM) is an important approach to extending the life of complex equipment by forecasting the outcome of an event before catastrophic failure occurs. Recent advancements in digital twins (DT) offer additional benefits to machine condition monitoring. In this study, a framework based on DT for real-time condition monitoring of industrial machines is proposed. The multi-layer DT framework consists of a physical entity (PE), virtual equipment (VE), edge device, fidelity service and digital twin services. The virtual equipment is a replica of the physical entity or the monitored machine. It also contains a cloud platform to store data online and an application to interface with the cloud enabling users to check the data remotely. The fidelity service ensures conformity between the PE and the VE. The digital service provides optimal operation and maintenance schedules based on the data from both physical and virtual spaces. The integration of the edge layer enables real-time handling of high-frequency machine data for effective health monitoring. The validity of the proposed framework is demonstrated with a case study based on monitoring a critical component of an industrial drivetrain test rig. The features of the framework allow end-users to visualize the component's real-time health status remotely. © 2023 IEEE.

关键词： Condition monitoring

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A Knowledge Graph-Based Deep Learning Framework for Efficient Content Similarity Search of Sustainable Development Goals Data

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Data Intelligence 2023年第3期5卷 663-684页

作者： Irene Kilanioti George A.Papadopoulos School of Electrical and Computer Engineering National Technical University of Athens9 Heroon Polytechneiou St.Zografou Campus Athens15780Greece Department of Computer Science University of Cyprus1 University AvenueAglantziaCY-2109NicosiaCyprus

Sustainable development denotes the enhancement ofliving standards in the present without compromising future generations'*** Development Goals(SDGs)quantify the accomplishment of sustainable development and pave the way for a world worth living in for future *** can contribute to the achievement of the SDGs by guiding the actions of practitioners based on the analysis of SDG data,as intended by this *** propose a framework of algorithms based on dimensionality reduction methods with the use of Hilbert Space Filling Curves(HSFCs)in order to semantically cluster new uncategorised SDG data and novel indicators,and efficiently place them in the environment of a distributed knowledge graph ***,a framework of algorithms for insertion of new indicators and projection on the HSFC curve based on their transformer-based similarity assessment,for retrieval of indicators and loadbalancing along with an approach for data classification of entrant-indicators is ***,a thorough case study in a distributed knowledge graph environment experimentally evaluates our *** results are presented and discussed in light of theory along with the actual impact that can have for practitioners analysing SDG data,including intergovernmental organizations,government agencies and social welfare *** approach empowers SDG knowledge graphs for causal analysis,inference,and manifold interpretations of the societal implications of SDG-related actions,as data are accessed in reduced retrieval *** facilitates quicker measurement of influence of users and communities on specific goals and serves for faster distributed knowledge matching,as semantic cohesion of data is preserved.

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Universally Instance-Optimal Mechanisms for Private Statistical Estimation 37

Universally Instance-Optimal Mechanisms for Private Statisti...

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37th Annual Conference on Learning Theory, COLT 2024

作者： Asi, Hilal Duchi, John C. Haque, Saminul Li, Zewei Ruan, Feng Apple United States Departments of Statistics and Electrical Engineering Stanford University United States Department of Computer Science Stanford University United States Department of Statistics and Data Science Northwestern University United States

We consider the problem of instance-optimal statistical estimation under the constraint of differential privacy where mechanisms must adapt to the difficulty of the input dataset. We prove a new instance specific lower bound using a new divergence and show it characterizes the local minimax optimal rates for private statistical estimation. We propose two new mechanisms that are universally instance-optimal for general estimation problems up to logarithmic factors. Our first mechanism, the total variation mechanism, builds on the exponential mechanism with stable approximations of the total variation distance, and is universally instance-optimal in the high privacy regime Ε ≤ 1/√n. Our second mechanism, the T-mechanism, is based on the T-estimator framework (Birgé, 2006) using the clipped log likelihood ratio as a stable test: it attains instance-optimal rates for any Ε ≤ 1 up to logarithmic factors. Finally, we study the implications of our results to robust statistical estimation, and show that our algorithms are universally optimal for this problem, characterizing the optimal minimax rates for robust statistical estimation. © 2024 H. Asi, J.C. Duchi, S. Haque, Z. Li & F. Ruan.

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Effects of Initialization Methods on the Performance of Multi-Objective Evolutionary Algorithms

Effects of Initialization Methods on the Performance of Mult...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Gong, Cheng Pang, Lie Meng Nan, Yang Ishibuchi, Hisao Zhang, Qingfu City University of Hong Kong Department of Computer Science Kowloon Tong Hong Kong Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen518055 China

ISBN: (纸本)9798350337020

Population initialization is always needed in evolutionary multi-objective optimization (EMO) algorithms. Intuitively, a well-designed initialization method can help facilitate the evolutionary process and improve the performance of EMO algorithms. However, very few studies have investigated the effects of initialization methods on the performance of EMO algorithms. Many existing EMO algorithms randomly generate an initial population to start the evolutionary process. To fill this research gap and attract more attention from EMO researchers to this important yet under-explored issue, in this paper, we examine the effects of various initialization methods that may become promising alternatives to the commonly-used random initialization method. Each initialization method is evaluated through computational experiments on test problems of various sizes with 5-1000 decision variables. Experimental results clearly demonstrate the advantage of well-designed initialization methods over the random initialization method. This study provides useful insights into EMO algorithm design and motivates further research on population initialization. © 2023 IEEE.

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