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4th IEEE International Conference on Quantum Computing and engineering, QCE 2023

作者： Johnson, Hans Zorzetti, Silvia Saniie, Jafar Research Laboratory Department of Electrical and Computer Engineering ChicagoIL United States Fermi National Accelerator Laboratory BataviaIL60510 United States

ISBN: (纸本)9798350343236

This study investigates the optimization of Signal-to-Noise Ratio (SNR) in superconducting quantum computing readout signals through adaptive filtering. Quantum computing technology has the potential to revolutionize various fields by delivering exponential speedup in solving certain computational problems. However, the technology's practical implementation is hindered by the difficulty of extracting clean, reliable signals during the readout phase, with various sources of noise presenting a significant barrier to clean signals. This noise, often present in readout profiles due to imperfect isolation, degrades the system's overall SNR, thus impeding the ability to extract the quantum state accurately. The research leverages the power of adaptive filtering to improve the SNR of quantum computing readout signals. Specifically, an adaptive filter is implemented in a PYNQ overlay on an FPGA, and eventually will be connected to a quantum computing system. The system models the noise with a Least Mean Squares (LMS) adaptive filter, and then subtracts the estimated noise from the received signal to improve the SNR. A Direct Memory Access (DMA) channel is used to handle the signal processing, delivering efficient, high-speed data transfer between the PYNQ system and the hardware. The study explores the benefits of this adaptive filtering technique, potentially providing a significant contribution to practical and fast quantum computing. © 2023 IEEE.

关键词： Digital signal processing

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Multimodal Pain Assessment Βased on Physiological Biosignals: The Impact of Demographic Factors on Perception and Sensitivity 11

Multimodal Pain Assessment Βased on Physiological Biosignal...

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11th International Conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE 2025

作者： Pavlidou, Elisavet Tsiknakis, Manolis Department of Electrical and Computer Engineering Hellenic Mediterranean University Estavromenos Heraklion71410 Greece Computational BioMedicine Laboratory Institute of Computer Science Foundation for Research & Technology-Hellas Vassilika Vouton Heraklion70013 Greece

ISBN: (纸本)9789897587436

Pain is a multidimensional and highly personalized sensation that affects individuals’ physical and emotional state. Visual analog scales, numeric rate indicators, and various questionnaires, all relying on patient-reported outcome measurements, are considered the "gold" standard methods for assessing the severity of pain. Nevertheless, self-report tools require cognitive, linguistic, and social abilities, which may manifest variations in certain populations such as neonates, individuals with intellectual disabilities, and those affected by dementia. The purpose of this study is to automate the process through multimodal physiological-data-driven machine-learning models in order to gain deeper insights into pain sensation. We developed a pipeline using electrocardiogram (ECG), galvanic skin response (GSR), and electromyogram (EMG), along with demographic information from the BioVid dataset. The Pan & Tompkins algorithm was applied for ECG signal processing, while statistical analysis was used for feature extraction across all signals. Our study achieved 82.83% accuracy in the SVM classification task of baseline (BL) vs the highest level of pain (PA4) for females aged 20-35. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Sensory perception

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Power System Transient Security Assessment using Unsupervised Probabilistic Deep Bayesian Neural Network

Power System Transient Security Assessment using Unsupervise...

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2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023

作者： Afrasiabi, Shahabodin Allahmoradi, Sarah Liang, Xiaodong Afrasiabi, Mousa Aghaei, Jamshid Chung, C.Y. University of Saskatchewan Department of Electrical and Computer Engineering Saskatoon Canada Lappeenranta University of Technology School of Energy Systems Department of Electrical Engineering Lappeenranta Finland Central Queensland University School of Engineering and Technology Department of Electrical Engineering Rockhampton Australia The Hing Kong Polytechnic University Department of Electrical Engineering Hong Kong

ISBN: (纸本)9781665471640

This paper introduces an unsupervised deep Bayesian network, built upon normalizing flow and deep Bayesian network principles, to precisely evaluate the transient security condition of a power system. The proposed approach can capture locational and temporal features using an imbalanced dataset, is noise-model-free, and can handle unlabeled data. It can learn interdependencies between different signals and understand high-dimensional signals in power systems. To validate its effectiveness, the proposed method is studied using the New England power system and shows accuracy and reliability in comparison with state-of-the-art deep networks (convolutional neural network (CNN) and long short-term memory (LSTM)) and shallow networks (support vector machine (SVM) and artificial neural network (ANN)). © 2023 IEEE.

关键词： Bayesian network imbalanced dataset noise-model free normalizing flow transient security assessment unsupervised deep learning

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New Skills and Knowledge for Digital Entrepreneurs in the age of Artificial Intelligence 19

New Skills and Knowledge for Digital Entrepreneurs in the ag...

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19th European Conference on Innovation and Entrepreneurship, ECIE 2024

作者： Orozova, Daniela Angelova, Nadezhda Zlatev, Zlatin Department of Informatics and Mathematics Faculty of Economics Trakia University Stara Zagora Bulgaria Department of Electrical Engineering Automation Computer Systems and Communications Faculty of Technics and Technologies Trakia University Yambol Bulgaria

ISBN: (纸本)9781917204149

Digital technologies are becoming increasingly complex and integrated, leading to significant transformations in society and the economy. The article aims to explore and summarize the new opportunities and potential risks of the widespread use of artificial intelligence (AI) in all aspects of life, to define the new skills and necessary knowledge of digital entrepreneurs and to highlight the need for transformation in modern education. Recognizing that the relationship between technology and business is two-way and becoming stronger, revealing that well-prepared employees are a guarantee of success and prosperity of companies in various fields, we try to focus on the main groups of qualities, skills and basic knowledge of students in the age of artificial intelligence. The development of the Internet, expansion of connectivity through social networks, the advent of AI, 3D printing, and immersive technologies like Augmented Reality, and Virtual Reality, require new knowledge and skills, leading to new challenges in education. Qualified personnel in this modern world must have solid professional training and systemic thinking (knowledge, skills, accumulated information), developed cognitive abilities, and personal skills based on collecting and analyzing large amounts of diverse information from heterogeneous sources. Questions arise: how can multiple information sources be combined effectively, and how can the fusion of multiple sources provide additional information to support decision-making processes? Combining information obtained from the real world makes the results heterogeneous and more informative. It follows the need to develop machine learning methods to extract relevant information from increasingly complex data sets. The goal is to improve the accuracy of the applied classification algorithms by combining predictions from multiple models, as well as obtaining a more stable final classification evaluation, effective handling of noisy data, adaptation to c

关键词： Adversarial machine learning

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Resilient Operation of DC Standalone Microgrids: Experimental Validation of Hybrid RNN-PI Control for Pulse Load Management

Resilient Operation of DC Standalone Microgrids: Experimenta...

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IEEE Green Technologies Conference (IEEE-Green)

作者： Ahmed Aghmadi Osama A. Mohammed Department of Electrical and Computer Engineering Energy Systems Research Laboratory Florida International University Miami FL USA

ISBN: (数字)9798331520182

ISBN: (纸本)9798331520199

Effective energy storage system (ESS) management is critical for enhancing the performance of standalone DC microgrids, particularly when integrating renewable energy sources such as photovoltaic (PV) or wind systems. This research describes a new hybrid control technique to improve DC microgrid operations' efficiency and resiliency, especially when handling critical loads. This research presents a hybrid closed-loop control technique that uses Recurrent Neural Networks (RNNs) and Proportional-Integral (PI) controllers to address the complex issues of dynamic load situations and transient disturbances. The RNN evaluates voltage errors to generate accurate reference currents, which the PI controllers then adjust to improve the performance of the bidirectional converter. Unlike standard PI-PI controls, our hybrid approach is particularly effective at regulating pulsed power loads (PPLs) and enhancing system response to transitory situations. The experimental results show that the RNN-PI control scheme significantly improves voltage regulation and transient responsiveness, exceeding conventional performance and resilience techniques. This approach provides a convincing solution for enhancing microgrid systems' stability and efficiency in various real-world applications.

关键词： Renewable energy sources Recurrent neural networks PI control Voltage fluctuations Microgrids Robustness Voltage control Transient analysis Standards Resilience

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Radiative cooling for passive thermal management towards sustainable carbon neutrality

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National Science Review 2023年第1期10卷 87-97页

作者： Jun Liang Jiawei Wu Jun Guo Huagen Li Xianjun Zhou Sheng Liang Cheng-Wei Qiu Guangming Tao Wuhan National Laboratory for Optoelectronics and Optics Valley Laboratory Huazhong University of Science and Technology Department of Electrical and Computer Engineering National University of Singapore MOE Key Laboratory of Thermo-Fluid Science and Engineering School of Energy and Power Engineering Xi'an Jiaotong University Key Laboratory of Education Ministry on Luminescence and Optical Information Technology National Physical Experiment Teaching Demonstration CenterDepartment of PhysicsSchool of Physical Science and Engineering Beijing Jiaotong University State Key Laboratory of Material Processing and Die &Mould Technology School of Materials Science and Engineering Huazhong University of Science and Technology

Photonic structures at the wavelength scale offer innovative energy solutions for a wide range of applications,from high-efficiency photovoltaics to passive cooling,thus reshaping the global energy *** cooling based on structural and material design presents new opportunities for sustainable carbon neutrality as a zero-energy,ecologically friendly cooling *** this review,in addition to introducing the fundamentals of the basic theory of radiative cooling technology,typical radiative cooling materials alongside their cooling effects over recent years are summarized and the current research status of radiative cooling materials is outlined and ***,technical challenges and potential advancements for radiative cooling are forecast with an outline of future application scenarios and development *** the future,radiative cooling is expected to make a significant contribution to global energy saving and emission reduction.

关键词： photonic structures radiative cooling energy saving and emission reduction carbon neutrality

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Mechanical Design of a Feline Robot for Dynamic Scaling Testing 13th

Mechanical Design of a Feline Robot for Dynamic Scaling Tes...

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13th International Conference on Biomimetic and Biohybrid systems, Living Machines 2024

作者： Budzichowski, Amy Riddle, Shane Batchu, Rucha Chen, Tina Jackson, Clayton B. Nourse, William R. P. Quinn, Roger D. Department of Mechanical and Aerospace Engineering Case Western Reserve University ClevelandOH44106-7222 United States Department of Electrical Computer and Systems Engineering Case Western Reserve University ClevelandOH44106-7222 United States

ISBN: (纸本)9783031725968

Animal size impacts locomotion, due to its effect on the influence of gravity, inertia, and an animal’s internal elasticity and damping. We are developing a cat robot to further explore the impact of these four characteristics on animal locomotion. This paper details the mechanical design of this cat robot’s rear limbs, as well as the simulated validation of this design. In future work, we plan to design forelimbs with a floating scapula and test the robot in split-belt treadmill experiments similar to those done in mammalian experiments as we develop its synthetic nervous system controller. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Mammals

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Chemical environment adaptive learning for optical band gap prediction of doped graphitic carbon nitride nanosheets

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Neural Computing and Applications 2025年第5期37卷 3287-3301页

作者： Chen, Chen Xu, Enze Yang, Defu Yan, Chenggang Wei, Tao Chen, Hanning Wei, Yong Chen, Minghan Intelligent Information Processing Laboratory Hangzhou Dianzi University Hangzhou China Department of Computer Science Wake Forest University Winston-SalemNC United States Department of Chemical Engineering Howard University WashingtonDC United States Texas Advanced Computing Center University of Texas at Austin AustinTX United States Department of Computer Science & Information Systems University of North Georgia DahlonegaGA United States

This study presents a new machine learning algorithm, named Chemical Environment Graph Neural Network (ChemGNN), designed to accelerate materials property prediction and advance new materials discovery. Graphitic carbon nitride (g-C3N4) and its doped variants have gained significant interest for their potential as optical materials. Accurate prediction of their band gaps is crucial for practical applications;however, traditional quantum simulation methods are computationally expensive and challenging to explore the vast space of possible doped molecular structures. The proposed ChemGNN leverages the learning ability of current graph neural networks (GNNs) to satisfactorily capture the characteristics of atoms' chemical environment underlying complex molecular structures. Our experimental results demonstrate more than 100% improvement in band gap prediction accuracy over existing GNNs on g-C3N4. Furthermore, the general ChemGNN model can precisely foresee band gaps of various doped g-C3N4 structures, making it a valuable tool for performing high-throughput prediction in materials design and development. © The Author(s) 2024.

关键词： Graphitic Carbon Nitride

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Proposed Flight Recorder Recommendations for Unmanned Aircraft systems Integration into the National Airspace System 43

Proposed Flight Recorder Recommendations for Unmanned Aircra...

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43rd AIAA DATC/IEEE Digital Avionics systems Conference, DASC 2024

作者： Ogirimah, Olayemi Raza, Waleed Bawa, Gurvir Renkhoff, Justus Song, Houbing Stansbury, Richard S. Electrical Engineering and Computer Science Embry-Riddle Aeronautical University Daytona Beach United States University of Maryland - Baltimore County Department of Information Systems Baltimore United States

ISBN: (纸本)9798350349610

The increasing integration of Unmanned Aircraft systems (UAS) into the National Airspace System necessitates the development of flight data recorder (FDR) recommendations tailored specifically for these systems. Traditional FDRs used in manned aviation are inadequate for capturing the flight data required for UAS operations. This study involved a detailed review of existing guidelines and regulations for manned aircraft FDRs, adapting and extending them to meet the unique needs of UAS operations. By drawing upon industry best practices and aca-demic research, a holistic framework for UAS FDRrecommendations is proposed. This framework out-lines the essential data parameters and recommended recording intervals required to facilitate detailed in-cident/accident investigations and continuous safety enhancements. Implementing UAS-specific FDR stan-dards is crucial for realizing the full potential of unmanned aviation while upholding stringent safety and operational standards. © 2024 IEEE.

关键词： Aircraft accidents

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Controllable Structure Planning for Energy Internet 1

Controllable Structure Planning for Energy Internet

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1st International Conference on Cyber-Energy systems and Intelligent Energy, ICCSIE 2022

作者： Li, Yushuai Li, Tianyi Gui, Yonghao Gao, David Wenzhong Zhang, Yan University of Oslo Department of Informatics Oslo Norway Aalborg University Department of Computer Science Aalborg Denmark Aalborg University Department of Electronic Systems Aalborg Denmark University of Denver Department of Electrical and Computer Engineering Denver United States

ISBN: (纸本)9781665452151

Secure system operations rely on reliable network structures. The loss of controllability may be the main reason to cause cascaded failures for complex network, e.g., Energy Internet (EI). However, the existing studies do not consider the network controllability to guide the system reconfiguration. To address this issue, the paper proposes a new structuring planning method for EI with consideration of controllability and economy. Firstly, the structure planning problem is modeled as a dynamic optimization problem with the tradeoff objectives of maximum social welfare and minimum driven nodes for long-term period. Then, a mixed maximum matching and deep deterministic policy gradient method is presented to obtain the approximate optimal planning solution with strong adaptability. Finally, simulation results demonstrate the effectiveness of the proposed method. © 2023 IEEE.

关键词： Controllability

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