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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Singh, Nisha Joshi, Bhagawati Prasad Gupta, Sachin Singh, Akhilesh Rathee, Sachit Maharaja Surajmal Institute of Technology Department of Electrical & Electronics Engineering Janakpuri New Delhi India Graphic Era Hill University Department of MathematicsComputer Science & Engineering Bhimtal India Uttarakhand Technical University Veer Madho Singh Bhandari Department of Mechanical Engineering India Uttarakhand Technical University Veer Madho Singh Bhandari Department of Electrical Engineering India

ISBN: (纸本)9798350317558

The theory of Ambiguous-Intuitionistic-Fuzzy-Sets (IFS) termed as AIFS is a better way to deal with uncertain/vagueness information as compared to IFS. Considering the novelty of AIFS, this paper presents one of the applications of AIFS to the Renewable-Energy (RE) Systems (RESs). This is achieved by taking the foundation development of Ambiguous-Intuitionistic-Fuzzy-Hybrid-Averaging-Operator referred by the AFHA operator. The incorporation of the AFHA operator into RESs is a viable approach to tackle complex difficulties. This research examines the practical uses, theoretical basis, and operational consequences of implementing AFHA operator in renewable energy systems. It also demonstrates the effectiveness of AFHA operator in addressing various operational situations in RESs. A comparative study is also included in this study to show and observe the validity of the obtained results. © 2024 IEEE.

关键词： Ambiguous Intuitionistic fuzzy set (AIFS) Hybrid-Averaging operator multi-criteria decision-making (MCDM) Renewable energy (RE) systems (RESs)

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Fluorescence Lifetime of NV Centers for Temperature-Insensitive All-Optical Magnetic Field Sensing 26

Fluorescence Lifetime of NV Centers for Temperature-Insensit...

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26th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2024

作者： Horsthemke, Ludwig Pogorzelski, Jens Stiegekotter, Dennis Hoffmann, Frederik Bulter, Ann-Sophie Langguth, Lutz Staacke, Robert Trinschek, Sarah Gregor, Markus Glosekotter, Peter FH Münster - University of Applied Sciences Department of Electrical Engineering and Computer Science Steinfurt48565 Germany Quantum Technologies GmbH Leipzig04103 Germany FH Münster - University of Applied Sciences Department of Engineering Physics Steinfurt48565 Germany

ISBN: (纸本)9798331532833

We investigate the combined temperature and magnetic field dependent behavior of high NV-density microdiamonds in an all-optical frequency-domain based setup, utilizing the change in fluorescence lifetime. We use these frequency-domain data to train neural networks for the prediction of temperatures and magnetic fields. At zero magnetic field, we show the ability to predict temperatures in the range of 0° C to 100°C, with a standard deviation of 1.24 °C. Furthermore, we show the ability to predict applied magnetic fields, while varying the ambient temperature. We achieve a higher accuracy, compared to the approach of observing of the fluorescence intensity at a single excitation frequency. © 2024 IEEE.

关键词： Magnetometry

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Novel Human-Posture Recognition System Based on Advanced Graph Convolutional Network Using Skeletal Data

IEEE Journal of Selected Areas in Sensors

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IEEE Journal of Selected Areas in Sensors 2024年 1卷 224-236页

作者： Liu, Guannan Xie, Rende Fang, Shih-Hau Wu, Hsiao-Chun Yan, Kun San Jose State University Department of Applied Data Science San JoseCA95112-3613 United States National Taiwan Normal University Department of Electrical Engineering Taipei106 Taiwan Yuan Ze University Department of Electrical Engineering Taoyuan320315 Taiwan Louisiana State University School of Electrical Engineering and Computer Science Baton RougeLA70803 United States Guilin University of Electronic Technology School of Information and Communication Guilin541004 China

Automatic human-posture or human-activity recognition is a very important research problem nowadays. In this work, we propose a novel human-posture recognition approach using the 3-D skeletal data acquired by the Kinect V2 sensor. The acquired skeletal data are first segmented using our recently proposed automatic-segmentation technique and each segment can be labeled with a particular kind of human-posture. We propose four different types of node feature matrices extracted from the segmented skeletal data, which can serve as the input features to the advanced graph convolutional network for multiclassification. The realworld experimental results demonstrate that our proposed novel human-posture recognition system can reach a very high average classification-accuracy of 91.56%. In addition, the ablation study of the effect of skeletal-graph variations on the recognition performance is also presented. The average classification-accuracy further reaches up to 92.33% when four confusing joint-nodes are removed from the skeletal graph. Our proposed novel human-posture recognition approach can be very useful for practical applications, such as human-computer interface, intelligent healthcare, robotics, etc. © 2024 IEEE.

关键词： Sensor nodes

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A SENSITIVITY ANALYSIS ON THE EFFECT OF HYPERPARAMETERS IN DEEP NEURAL OPERATORS applied TO SOUND PROPAGATION 10

A SENSITIVITY ANALYSIS ON THE EFFECT OF HYPERPARAMETERS IN D...

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10th Convention of the European Acoustics Association, EAA 2023

作者： Borrel-Jensen, Nikolas Engsig-Karup, Allan P. Jeong, Cheol-Ho Technical University of Denmark Dep. of Electrical and Photonics Engineering Denmark Technical University of Denmark Dep. of Applied Mathematics and Computer Science Denmark

ISBN: (纸本)9788888942674

Deep neural operators have seen much attention in the scientific machine learning community over the last couple of years due to their capability of efficiently learning the nonlinear operators mapping from input function spaces to output function spaces showing good generalization properties. This work will show how to set up a per-formant DeepONet architecture in acoustics for predicting 2-D sound fields with parameterized moving sources for real-time applications. A sensitivity analysis is carried out with a focus on the choice of network architectures, activation functions, Fourier feature expansions, and data fidelity to gain insight into how to tune these models. Specifically, a default feed-forward neural network (FNN), a modified FNN, and a convolutional neural network will be compared. This work will de-mystify the DeepONet and provide helpful knowledge from an acoustical point of view. © 2023 Nikolas Borrel-Jensen et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 Unported License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

关键词： Sensitivity analysis

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End-to-end dynamic residual focal transformer network for multimodal medical image fusion

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Neural Computing and Applications 2024年第19期36卷 11579-11601页

作者： Zhang, Weihao Yu, Lei Wang, Huiqi Pedrycz, Witold College of Computer and Information Science Chongqing Normal University Chongqing401331 China College of Mathematics and Statistics Chongqing University Chongqing401331 China Department of Electrical and Computer Engineering University of Alberta EdmontonT6G 2V4 Canada

Multimodal medical image fusion aims to improve the clinical practicability of medical images by integrating complementary information from multiple medical images. However, in traditional fusion methods, the fusion rules based on prior knowledge or logic usually cannot match the feature representation perfectly, which results in partial information loss. Furthermore, most deep learning-based fusion methods depend on convolutional operations, which only focus on local features and have limited retention of context information. To address the above issues, we propose an end-to-end dynamic residual focal transformer network for multimodal medical image fusion, termed DRFT. The DRFT framework is an end-to-end network with no need to manually design fusion rules. Firstly, the context-gated convolution is introduced to construct the context dynamic extraction module (CDEM) to extract the key semantic information more accurately from multimodal medical images. Then, a new residual transformer fusion module (RTFM) is designed by incorporating the focal transformer into the residual mechanism, which can not only extract the deep semantic features, but also adaptively learn the optimal fusion scheme. Finally, the nest architecture is employed to extract multiscale features. In addition, a new objective function consisting of global detail loss and fusion enhancement loss is designed to enrich the modal information in the fused image. Notably, the proposed network does not require the two-stage training strategy as opposed to the traditional encoder–decoder fusion structure. Extensive experimental results on mainstream datasets show that, compared with the state-of-the-art methods, the proposed DRFT delivers better performance in both qualitative and quantitative evaluation. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Convolution

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Adaptive Prescribed Time Path Following Controller for Underactuated Surface Vessels 13

Adaptive Prescribed Time Path Following Controller for Under...

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13th International Conference on Intelligent Control and Information Processing, ICICIP 2025

作者： Ghommam, Jawhar Saad, Maarouf Zhu, Quanmin Rahman, Mohammed H. Dept. of Electrical and Computer Engineering Sultan Qaboos University Muscat Oman Dept. of Electrical Engineering Ecole de Technologie Superieure Montreal Canada Dept. of Engineering Design and Mathematics University of the West of England Bristol United Kingdom Mechanical Engineering Department University of Wisconsin-Milwaukee Wisconsin United States

ISBN: (纸本)9798331516147

Heading control of marine vehicles is an important issue in navigation and control engineering. This paper investigates the design of a prescribed-time robust adaptive path following control problem of an underactuated surface vessel with parametric uncertainties, unknown disturbances and control input constraints. The establishment of prescribed-time property consists of a prescribed-time regulation of the steering control systems and novel prescribed-time Line of Sight (LOS) guidance control, both of which satisfy the prescribed-time requirement. The feasibility of separate independent control design for the steering and the guidance control systems is guaranteed through the development of new results on nonlinear prescribed-time cascade systems with non-vanishing uncertainties. The Lyapunov method is employed to show that the equilibrium point of the whole system is prescribed-time input to state stable. Numerical simulations are carried out to validate the performance of the proposed control strategy. © 2025 IEEE.

关键词： Steering

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Performance Comparison of the Invasive Weed Optimization K-Means Algorithm With Particle Swarm Optimization K-Means for Banking Data 6

Performance Comparison of the Invasive Weed Optimization K-M...

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Suwirmayanti, Ni Luh Gede Pivin Putra, I. Ketut Gede Darma Sudarma, Made Sukarsa, I. Made Setyaningsih, Emy Department of Computer Systems Faculty of Informatics and Computer Institut Teknologi dan Bisnis STIKOM Bali Bali Indonesia Department of Information Technology Faculty of Engineering Udayana University Jimbaran Hill Bali Indonesia Department of Electrical Engineering Faculty of Engineering Udayana University Jimbaran Hill Bali Indonesia Department of Computer Systems Engineering Faculty of Applied Science Universitas AKPRIND Indonesia Yogyakarta Indonesia

ISBN: (纸本)9798331539603

Banking produces extensive and diverse data, so a clustering process is needed to understand customer behavior patterns and transactions more effectively. This clustering has been widely utilized with the K-Means algorithm due to its simplicity and efficiency. However, this algorithm is limited in determining the optimal cluster center, affecting the accuracy of the clustering results. This study proposes applying two metaheuristic algorithms, Invasive Weed Optimization (IWO) and Particle Swarm Optimization (PSO), as additional solutions combined with K-Means. These two algorithms were chosen because of their ability to find global solutions and convergence speed to handle large and complex banking data. The results show the superiority of IWOKM compared to PSOKM in terms of SSE and DBI. From the SSE value, IWOKM produces a lower value (3029.77), which indicates that this method is better at minimizing clustering errors and producing more accurate clustering than PSOKM (80007.09). From the DBI value, IWOKM also produces a better value (1.6684) than PSOKM (1.6782), which shows that the clusters produced by IWOKM are more compact and more separated. However, in terms of computation time, PSOKM is more efficient, with a faster average computation time. Although IWOKM produces better clustering quality, PSOKM offers advantages in terms of processing speed. This finding confirms that the selection of the IWO algorithm is more appropriate for use on datasets with characteristics such as the German Credit Dataset. © 2024 IEEE.

关键词： Invasive weed optimization

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Kernel Regression Method for Stochastic Real-time State Estimation in Power Grid 4

Kernel Regression Method for Stochastic Real-time State Esti...

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4th Annual IEEE Kansas Power and Energy Conference, KPEC 2023

作者： Barati, Masoud Ensaf, Mohammad Hosseini, Bamdad University of Pittsburgh Dept. Electrical and Computer Engineering Pittsburgh United States University of Washington Dept. of Applied Mathematics Seattle United States

ISBN: (纸本)9798350335408

The paper presents a physics-informed Gaussian/kernel process regression method for real-time state estimation and forecasting of the phase angle, angular speed, and mechanical wind power in a three-generator power grid system using sparse measurements. The method treats unknown terms in the swing equations, such as mechanical wind power, as random processes, which transforms these equations into stochastic differential equations. The proposed method accurately forecasts and estimates both observed and unobserved states, providing a forecast comparable to the standard data-driven GP/kernel for observed states. The method also demonstrates improved accuracy with increased observation frequency and reduced measurement errors. © 2023 IEEE.

关键词： State estimation

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Marine Predators Algorithm with Deep Learning-Based Leukemia Cancer Classification on Medical Images

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computer Modeling in engineering & Sciences 2024年第10期141卷 893-916页

作者： Sonali Das Saroja Kumar Rout Sujit Kumar Panda Pradyumna Kumar Mohapatra Abdulaziz S.Almazyad Muhammed Basheer Jasser Guojiang Xiong Ali Wagdy Mohamed Department of Computer Science&Engineering Gandhi Institute for TechnologyBPUTBhubaneswar752054India Department of Information Technology Vardhaman College of Engineering(Autonomous)Hyderabad501218TelanganaIndia Department of Electronic&Communication Engineering Vedang Institute of TechnologyBhubaneswar752010India Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computing and Information Systems School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Research Centre for Human-Machine Collaboration(HUMAC) School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11931Jordan

In blood or bone marrow,leukemia is a form of cancer.A person with leukemia has an expansion of white blood cells(WBCs).It primarily affects children and rarely affects *** depends on the type of leukemia and the extent to which cancer has established throughout the *** leukemia in the initial stage is vital to providing timely patient *** image-analysis-related approaches grant safer,quicker,and less costly solutions while ignoring the difficulties of these invasive *** can be simple to generalize computer vision(CV)-based and image-processing techniques and eradicate human *** researchers have implemented computer-aided diagnosticmethods andmachine learning(ML)for laboratory image analysis,hopefully overcoming the limitations of late leukemia detection and determining its *** study establishes a Marine Predators Algorithm with Deep Learning Leukemia Cancer Classification(MPADL-LCC)algorithm onMedical *** projectedMPADL-LCC system uses a bilateral filtering(BF)technique to pre-process medical *** MPADL-LCC system uses Faster SqueezeNet withMarine Predators Algorithm(MPA)as a hyperparameter optimizer for feature ***,the denoising autoencoder(DAE)methodology can be executed to accurately detect and classify leukemia *** hyperparameter tuning process using MPA helps enhance leukemia cancer classification *** results are compared with other recent approaches concerning various measurements and the MPADL-LCC algorithm exhibits the best results over other recent approaches.

关键词： Leukemia cancer medical imaging image classification deep learning marine predators algorithm

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Guest Editorial: Special Issue on Evolutionary Computation for Games

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IEEE Transactions on Games 2023年第1期15卷 1-4页

作者： Schrum, Jacob Liu, Jialin Browne, Cameron Ekart, Aniko Gallagher, Marcus Southwestern University Department of Mathematics and Computer Science GeorgetownTX United States Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen China Maastricht University Department of Data Science and Knowledge Engineering Maastricht Netherlands Aston University Department of Computer Science United Kingdom The University of Queensland School of Information Technology and Electrical Engineering Australia

The eight papers in this special section focus on applications of evolutionary computation to games to demonstrate several ways in which evolution can push boundaries and explore new areas of what is possible in the realm of games research, with a focus on game-playing, automatic agent parameter tuning, automatic game testing, and procedural content generation.

关键词： Special issues and sections Evolutionary computation Games

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