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arXiv 2022年

作者： Betz, Johannes Zheng, Hongrui Liniger, Alexander Rosolia, Ugo Karle, Phillip Behl, Madhur Krovi, Venkat Mangharam, Rahul Department of Electrical and Systems Engineering University of Pennsylvania Philadelphia United States Department of Information Technology and Electrical Engineering ETH Zurich Switzerland AMBER Lab California Institute of Technology Pasadena United States Institute of Automotive Technology Technical University of Munich Germany Department of Computer Science University of Virginia Charlottesville United States College of Engineering Computing and Applied Sciences Clemson University Clemson United States

The rising popularity of self-driving cars has led to the emergence of a new research field in the recent years: Autonomous racing. Researchers are developing software and hardware for high performance race vehicles which aim to operate autonomously on the edge of the vehicles limits: High speeds, high accelerations, low reaction times, highly uncertain, dynamic and adversarial environments. This paper represents the first holistic survey that covers the research in the field of autonomous racing. We focus on the field of autonomous racecars only and display the algorithms, methods and approaches that are used in the fields of perception, planning and control as well as end-to-end learning. Further, with an increasing number of autonomous racing competitions, researchers now have access to a range of high performance platforms to test and evaluate their autonomy algorithms. This survey presents a comprehensive overview of the current autonomous racing platforms emphasizing both the software-hardware co-evolution to the current stage. Finally, based on additional discussion with leading researchers in the field we conclude with a summary of open research challenges that will guide future researchers in this field. © 2022, CC BY.

关键词： Motion planning

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Artificial Neural Network Model for Steel Strip Tandem Cold Mill Power Prediction 3rd

Artificial Neural Network Model for Steel Strip Tandem Cold ...

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3rd International Conference on applied Informatics, ICAI 2020

作者： de Oliveira, Danilo G. da Silva, Eliton M. Miranda, Fabiano J. F. Filho, José F. S. Parpinelli, Rafael S. Graduate Program in Applied Computing Santa Catarina State University – UDESC Joinville Brazil Department of Computer Science Santa Catarina State University – UDESC Joinville Brazil ArcelorMittal – Global Research and Development Group São Francisco do Sul Brazil

ISBN: (纸本)9783030617011

In the cold rolling of flat steel strips, electric energy consumption is one of the highest expenses. Predicting the power requirements according to the line and product conditions can significantly impact the energy cost and, thus, on the business’s profitability. This paper proposes predicting the power requirements of a tandem cold rolling mill of steel strips on a coil-to-coil base applying Artificial Neural Networks (ANN) as a uni-variate regression problem. The tests yielded an MSE of 300.39 kW or 2.4% and are better than the acceptable 5% error margin for this project, indicating that the ANN presented satisfactory results. The application of six full-month worth of data in the trained ANN model showed the excellent correlation of the ANN predictions with the measured data, leading to the conclusion that the system is ready for the deployment for daily use for line engineers. Overall, the results obtained show that the steel industry can highly benefit from Industry 4.0 and Artificial Intelligence technologies. © 2020, Springer Nature Switzerland AG.

关键词： Automotive industry

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On the study of multiple wave profiles and hybrid phenomena to the newly developed multi-component Gross-Pitaevskii system and stability analysis

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Ain Shams Engineering Journal 2025年第9期16卷

作者： Muhammad Bilal Shafqat Ur Rehman Yazen M. Alawaideh Ioan-Lucian Popa Bashar M. Al-khamiseh Department of Physics Shanghai University Shanghai 200444 China Department of Mathematics Grand Asian University Sialkot 51310 Pakistan Applied Science Research Center Applied Science Private University Amman Jordan Department of Computing Mathematics and Electronics 1 Decembrie 1918 University of Alba Iulia 510009 Alba Iulia Romania Faculty of Mathematics and Computer Science Transilvania University of Brasov Iuliu Maniu Street 50 500091 Brasov Romania

This research investigates a dynamical analysis of the newly developed Gross-Pitaevskii equations, focusing on finding exact soliton solutions. By uncovering these solutions, this research aims to understand the complicated dynamics underlying superfluidity, superconductivity, and associated nonlinear phenomena, thereby providing valuable insights into the behavior of these systems. The considered model is analytically examined using advanced integration tools, such as the new extended hyperbolic function and Kumar-Malik methods. Diverse wave solutions are extracted in hyperbolic, periodic, Jacobi elliptic, exponential functions, and complex multiple soliton structures. We meticulously simulate and verify all reported solutions using the Wolfram Mathematica package. The achieved outcomes have significant implications for several scientific disciplines, spanning optical fiber, plasma physics, mathematical physics, condensed matter physics, and fluid dynamics. Additionally, we display different wave profiles with the appropriate parametric values and plot the gain spectrum for the modulation instability analysis. This visualization strategy increases our comprehension of the derived solutions and expedites an in-depth exploration of their potential real-world applications. The aforementioned techniques provide a robust foundation for examining the nonlinear phenomena encountered in numerous physical domains. The key novelty of this study lies in its pioneering application of two effective analytical methods to a previously unexplored model, thereby filling a significant knowledge gap in the field.

关键词： Exact solutions Newly developed Gross-Pitaevskii system Efficient analytical techniques Condensed matter physics Modulation instability analysis

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A new method of modeling the multi-stage decision-making process of CRT using machine learning with uncertainty quantification

arXiv

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arXiv 2023年

作者： Larsen, Kristoffer Zhao, Chen Keyak, Joyce Sha, Qiuying Paez, Diana Zhang, Xinwei Hung, Guang-Uei Zou, Jiangang Peix, Amalia Zhou, Weihua Department of Mathematical Sciences Michigan Technological University HoughtonMI United States Department of Computer Science Kennesaw State University MariettaGA United States Department of Radiological Sciences Department of Biomedical Engineering Department of Mechanical and Aerospace Engineering University of California IrvineCA United States Nuclear Medicine and Diagnostic Imaging Section Division of Human Health Department of Nuclear Sciences and Applications International Atomic Energy Agency Vienna Austria Department of Cardiology The First Affiliated Hospital of Nanjing Medical University Jiangsu Nanjing China Department of Nuclear Medicine Chang Bing Show Chwan Memorial Hospital Changhua Taiwan Nuclear Medicine Department Institute of Cardiology La Habana Cuba Department of Applied Computing Michigan Technological University HoughtonMI United States Center for Biocomputing and Digital Health Institute of Computing and Cybersystems Health Research Institute Michigan Technological University HoughtonMI United States

Aims. Current machine learning-based (ML) models usually attempt to utilize all available patient data to predict patient outcomes while ignoring the associated cost and time for data acquisition. The purpose of this study is to create a multi-stage machine learning model to predict cardiac resynchronization therapy (CRT) response for heart failure (HF) patients. This model exploits uncertainty quantification to recommend additional collection of single-photon emission computed tomography myocardial perfusion imaging (SPECT MPI) variables if baseline clinical variables and features from electrocardiogram (ECG) are not sufficient. Methods. 218 patients who underwent rest-gated SPECT MPI were enrolled in this study. CRT response was defined as an increase in left ventricular ejection fraction (LVEF) > 5% at a 6±1 month follow-up. A multi-stage ML model was created by combining two ensemble models: Ensemble 1 was trained with clinical variables and ECG;Ensemble 2 included Ensemble 1 plus SPECT MPI features. Uncertainty quantification from Ensemble 1 allowed for multi-stage decision-making to determine if the acquisition of SPECT data for a patient is necessary. The performance of the multi-stage model was compared with that of Ensemble models 1 and 2. Results. The response rate for CRT was 55.5% (n = 121) with overall male gender 61.0% (n = 133), an average age of 62.0±11.8, and LVEF of 27.7±11.0. The multi-stage model performed similarly to Ensemble 2 (which utilized the additional SPECT data) with AUC of 0.75 vs. 0.77, accuracy of 0.71 vs. 0.69, sensitivity of 0.70 vs. 0.72, and specificity 0.72 vs. 0.65, respectively. However, the multi-stage model only required SPECT MPI data for 52.7% of the patients across all folds. Conclusions. By using rule-based logic stemming from uncertainty quantification, the multi-stage model was able to reduce the need for additional SPECT MPI data acquisition without sacrificing performance. © 2023, CC BY.

关键词： Machine learning

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Correction to: Optimal Location Identification of Solar PV Systems in Distributed Generators Based on Prediction of Load Flow and Factor Using Rule Based Deep Learning Algorithm (Journal of Electrical Engineering & Technology, (2025), 20, 3, (1269-1281), 10.1007/s42835-024-02063-8)

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Journal of Electrical Engineering and Technology 2025年 1-1页

作者： Kolanchinathan, V.P. Aswini, K.R.N. Rajendran, Narayanasamy Chinthamani, B. Bhuvana, S. Deepa, S.N. Mohan, S.B. Shanker, N.R. Gayathri, J. Department of Electronics and Communication Engineering Veltech Hightech Dr. Rangarajan Dr. Sagunthula Engineering College Avadi Chennai India Department of Electronics and Communication Chinmaya Vishwa Vidyapeeth Kerala Ernakulam India College of Computing and Information Sciences University of Technology and Applied Sciences Nizwa United Arab Emirates Department of Electronics and Instrumentation Engineering Easwari Engineering College Ramapuram Chennai India Dr. MGR Educational and Research Institute Chennai India Department of Electrical and Electronics Engineering NIT Calicut Kerala Kozhikode India Department of Electronics and Communication Engineering S.A. Engineering College Chennai India Department of Computer Science and Engineering Alim Muhammed Salegh College of Engineering Chennai India Department of Electronics and Communication Engineering Veltech Multitech DR.RR & SR Engineering College Avadi Chennai India

In this article the affiliation details for Author J. Gayathri were incorrectly given as both affiliations 1 and 9 but should have been only affiliation 9 " department of Electronics and Communication Engineering, Veltech Multitech ***& SR Engineering College, Avadi, Chennai, India". The original article has been corrected. © The Author(s) under exclusive licence to The Korean Institute of Electrical Engineers 2025.

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Monitoring System-Based Flying IoT in Public Health and Sports Using Ant-Enabled Energy-Aware Routing

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Journal of Healthcare Engineering 2021年 2021卷 1686946页

作者： Khan, Inam Ullah Hassan, Muhammad Abul Alshehri, Mohammad Dahman Ikram, Mohammed Abdulaziz Alyamani, Hasan J. Alturki, Ryan Hoang, Vinh Truong Department of Electronic Engineering School of Engineering and Applied Sciences Isra University Islamabad Pakistan Department of Computing and Technology Abasyn University Peshawar25000 Pakistan Department of Computer Science College of Computers and Information Technology Taif University Taif21944 Saudi Arabia Computer Science Department University College in Al-Jamoum Umm Al-Qura University Makkah Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology in Rabigh King Abdulaziz University Jeddah Saudi Arabia Department of Information Science College of Computer and Information Systems Umm Al-Qura University Makkah Saudi Arabia Department of Information Technology Specialization Fpt University Hoa Lac High Tech Park Hanoi Viet Nam

In recent decades, the Internet of flying networks has made significant progress. Several aerial vehicles communicate with one another to form flying ad hoc networks. Unmanned aerial vehicles perform a wide range of tasks that make life easier for humans. However, due to the high frequency of mobile flying vehicles, network problems such as packet loss, latency, and perhaps disrupted channel links arise, affecting data delivery. The use of UAV-enabled IoT in sports has changed the dynamics of tracking and working on player safety. WBAN can be merged with aerial vehicles to collect data regarding health and transfer it to a base station. Furthermore, the unbalanced energy usage of flying things will result in earlier mission failure and a rapid decline in network lifespan. This study describes the use of each UAV's residual energy level to ensure a high level of safety using an ant-based routing technique called AntHocNet. In health care, the use of IoT-Assisted aerial vehicles would increase operational performance, surveillance, and automation optimization to provide a smart application of flying IoT. Apart from that, aerial vehicles can be used in remote communication for treatment, medical equipment distribution, and telementoring. While comparing routing algorithms, simulation findings indicate that the proposed ant-based routing protocol is optimal. © 2021 Inam Ullah Khan et al.

关键词： Unmanned aerial vehicles (UAV)

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Deep learning for breast cancer diagnosis: A bibliometric analysis and future research directions

Computational and Structural Biotechnology Reports

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Computational and Structural Biotechnology Reports 2024年 1卷

作者： Oladosu Oyebisi Oladimeji Ian Mcloughlin Saritha Unnikrishnan Centre for Mathematical Modelling and Intelligent Systems for Health and Environment (MISHE) Atlantic Technological University Sligo Ireland Department of Computing and Electronics Atlantic Technological University Sligo Ireland Department of Computer Science and Applied Physics Atlantic Technological University Galway Ireland

Breast cancer remains the leading cause of cancer-related deaths among women globally. Early detection of breast cancer significantly reduces the death rate and increases the chances of targeted treatment and recovery. Deep learning is one of the techniques used in breast cancer diagnosis to reduce the assessment period and false positives. Over the years, research on breast cancer diagnosis using deep learning has grown significantly. However, due to the large volumes of research publications in this area, it is challenging for interested researchers or clinical practitioners to understand thoroughly and objectively. Hence, to gain more clear insights into deep learning-assisted breast cancer diagnosis studies, a comprehensive bibliometric analysis has been conducted. The analysis of 4797 articles published between 2019 and 2023 including the subject areas, relevant outlets, institutions, countries, and keywords. Based on the review findings, there is continuous growth in deep learning-based breast cancer diagnosis research in China, the United States, and India which are the countries with the most research and strongest research collaboration on this subject matter. Additionally, a future research agenda to propel the advancement of deep learning in breast cancer diagnosis is suggested. This review serves as a valuable guide for researchers interested in the potential of deep learning in breast cancer diagnosis.

关键词： Breast cancer research Deep learning Bibliometric analysis Breast cancer diagnosis

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High-Rate Four Photon Subtraction from Squeezed Vacuum: Preparing Cat State for Optical Quantum Computation

arXiv

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arXiv 2025年

作者： Endo, Mamoru Nomura, Takefumi Sonoyama, Tatsuki Takahashi, Kazuma Takasu, Sachiko Fukuda, Daiji Kashiwazaki, Takahiro Inoue, Asuka Umeki, Takeshi Nehra, Rajveer Marek, Petr Filip, Radim Takase, Kan Asavanant, Warit Furusawa, Akira Department of Applied Physics School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo Tokyo113-8656 Japan Optical Quantum Computing Research Team RIKEN Center for Quantum Computing 2-1 Hirosawa Saitama Wako351-0198 Japan National Institute of Advanced Industrial Science and Technology 1-1-1 Umezono Ibaraki Tsukuba305-8563 Japan AIST-UTokyo Advanced Operando-Measurement Technology Open Innovation Laboratory 1-1-1 Umezono Ibaraki Tsukuba305-8563 Japan NTT Device Technology Labs NTT Corporation 3-1 Morinosato Wakamiya Kanagawa Atsugi243-0198 Japan Department of Electrical and Computer Engineering University of Massachusetts Amherst 100 Natural Resources Rd AmherstMA01003 United States Department of Physics University of Massachusetts Amherst 100 Natural Resources Rd AmherstMA01003 United States College of Information and Computer Science University of Massachusetts Amherst 100 Natural Resources Rd AmherstMA01003 United States Department of Optics Palacky University 17. listopadu 1192/12 Olomouc77146 Czech Republic

Generating logical qubits, essential for error detection and correction in quantum computation, remains a critical challenge in continuous-variable (CV) optical quantum information processing. The Gottesman-Kitaev-Preskill (GKP) code is a leading candidate for logical qubits, and its generation requires large-amplitude coherent state superpositions—Schrödinger cat states. However, experimentally producing these resource states has been hindered in the optical domain by technical challenges. The photon subtraction method, a standard approach for generating cat states using a squeezed vacuum and a photon number-resolving detector, has proven difficult to scale to multi-photon operations. While the amplitude of the generated cat states increases with the number of subtracted photons, limitations in the generation rate have restricted the maximum photon subtraction to n = 3 for over a decade. In this work, we demonstrate high-rate photon subtraction of up to four photons from a squeezed vacuum with picosecond wavepackets generated by a broadband optical parametric amplifier. Using a Ti-Au superconducting-transition-edge sensor, we achieve high-speed, high-resolution photon number discrimination. The resulting states exhibit Wigner function negativity without loss correction, and their quantum coherence is verified through off-diagonal density matrix elements in CV representation. These results overcome long-standing limitations in multi-photon operations, providing a critical foundation for generating quantum resources essential for fault-tolerant quantum computing and advancing ultrafast optical quantum processors. Copyright © 2025, The Authors. All rights reserved.

关键词： Parametric amplifiers

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Phase-dependent Andreev molecules and superconducting gap closing in coherently coupled Josephson junctions

arXiv

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arXiv 2023年

作者： Matsuo, Sadashige Imoto, Takaya Yokoyama, Tomohiro Sato, Yosuke Lindemann, Tyler Gronin, Sergei Gardner, Geoffrey C. Nakosai, Sho Tanaka, Yukio Manfra, Michael J. Tarucha, Seigo Center for Emergent Matter Science RIKEN Saitama351-0198 Japan Department of Physics Tokyo University of Science Tokyo162-8601 Japan Department of Materials Engineering Science Osaka University Osaka560-8531 Japan Birck Nanotechnology Center Purdue University West LafayetteIN47907 United States Department of Physics and Astronomy Purdue University West LafayetteIN47907 United States Department of Applied Physics Nagoya University Nagoya464-8603 Japan School of Materials Engineering Purdue University West LafayetteIN47907 United States Elmore Family School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States RIKEN Center for Quantum Computing RIKEN Saitama351-0198 Japan

The Josephson junction (JJ) is an essential element of superconducting (SC) devices for both fundamental and applied physics. The short-range coherent coupling of two adjacent JJs forms the Andreev molecule states (AMSs), which will provide a new ingredient to engineer the SC transport in JJs and control the Andreev qubits. However, no experimental evidence of the AMSs in the coupled JJs has been reported. Here we provide the tunnel spectroscopic results of electrically controllable two planar JJs sharing one SC electrode. We discover that the coupled JJ results are highly modulated from the single JJ results, due to formation of the phase-dependent AMSs, meaning that the two JJs are coherently coupled. In addition, the superconducting gap closing due to the AMS formation is observed. Our results would help in understanding the microscopic mechanism of the coherent coupling and promoting the AMS physics to apply for research of the topological superconductivity and quantum information technology. © 2023, CC BY.

关键词： Molecules

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A Hybrid Adaptive Filter for Head Tracking in Augmented Reality (AR)-based Flight Simulators

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IEEE Transactions on computers 2025年

作者： Nwobodo, Onyeka J. Kuaban, Godlove Suila Nkemeni, Valery Wereszczynski, Kamil Cyran, Krzysztof A. Silesian University of Technology Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Gliwice Poland Polish Academy of Sciences Institute of Theoretical and Applied Informatics Poland University of Buea Laboratory of Electrical Engineering and Computing Faculty of Engineering and Technology Buea Cameroon

This paper presents a novel approach for head tracking in augmented reality (AR) flight simulators using an adaptive fusion of Kalman and particle filters. This fusion dynamically balances the strengths of both algorithms, leveraging Kalman filters for computational efficiency and particle filters for handling non-linearities based on real-time factors such as sensor noise and head movement patterns. Our method demonstrates superior tracking precision and reduced latency, making it particularly effective for immersive pilot training in AR-based flight simulations. While focused on flight simulation, our approach holds high potential for broader applications in other AR and virtual reality (VR) environments where precise, real-time head tracking is crucial. These results provide actionable design guidance for developers optimizing tracking systems in environments that require fast response times and high accuracy. Future extensions of this research could explore the generalization of our approach across diverse AR applications, including human-computer interaction (HCI), medical simulations, and gaming. © 1968-2012 IEEE.

关键词： Flight simulators

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