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8th International Conference on Intelligent Technologies, ICIT 2023

作者： Kyaw, Nwe Ni Mitra, Pabitra Sinha, G.R. Department of Computer Science and Engineering Indian Institute of Technology Kharagpur West Bengal721302 India Indian Knowledge System Lab International Institute of Information Technology Bangalore Karnataka Bengaluru560100 India

ISBN: (纸本)9789819738588

The proposed system for recognizing Myanmar sign language between individuals who are deaf. The aim of this study is to develop deep learning models for the purpose of accurately identifying dynamic hand gesture images depicting 14 classes of Myanmar words and numbers. The present system utilizes a new dataset comprising 7834 images for 14 classes obtained through Webcam. The objective of this study is to enhance the robustness of the new model. Next, we proceed to evaluate the proposed model by comparing them to those of existing convolutional neural network models. These models are generally recognized as effective deep-learning approaches for recognition tasks. The proposed model exhibits a performance level of 96% for both validation and testing accuracies. Furthermore, the experimental finding demonstrates that the model, convolutional layers, achieved an accuracy of 100% in recognizing Myanmar Sign Language (MSL). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Beam-line parameter image processing optimization and maintenance for beam alignment in the National Ignition Facility 7

Beam-line parameter image processing optimization and mainte...

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High Power Lasers for Fusion Research VII 2023

作者： Awwal, Abdul A.S. Leach, Richard Lowe-Webb, Roger Kamm, Vicki Miller Integrated Computer Control System National Ignition Facility Computational Engineering Division Lawrence Livermore National Laboratory LivermoreCA94551 United States

ISBN: (纸本)9781510659070

The National Ignition Facility (NIF) employs 192 laser beams to achieve inertial confinement fusion by irradiating a mm scale fusion target. Automatic alignment (AA) image processing algorithms are used to align 192 beams to the NIF target chamber center. Cameras placed along the beam path supply the images that are analyzed by AA algorithms to provide beam location and alignment information. NIF has the capability of using beam-specific database parameters. This allows beam line images to be processed using optimized algorithms tailored to individual beam alignment needs. For a given segment of alignment in the NIF beam path, 192 different versions of the algorithm can be run simply by changing data base parameters. This capability is vital to alignment precision in a system as complex and mature as NIF. Since optical components and devices age, laser parameters and beam alignment quality can and do change. Constant beam-by-beam monitoring of alignment performance is needed in order to mitigate any issues caused by such changes. The objective of this work is to evaluate how periodic AA algorithm beam parameter changes might better maintain alignment requirements over time in the NIF facility. We show examples from final optics assembly (FOA) and harmonic generator (THG, SHG) loops. © 2023 SPIE.

关键词： Alignment

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Multi-view 3D reconstruction based on speckle correlation 8

Multi-view 3D reconstruction based on speckle correlation

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8th International Conference on Speckle Metrology, Speckle 2023

作者： Zeng, Xiaoxian Jing, Yu Tang, Qijian Liu, Xiaoli Peng, Xiang Key Laboratory of Optoelectronic Devices System of Ministry of Education and Guangdong Province College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen518060 China School of Electrical Computer and Telecommunications Engineering University of Wollongong WollongongNSW2522 Australia

ISBN: (数字)9781510674592

ISBN: (纸本)9781510674585

Three-dimensional (3D) reconstruction based on digital speckle correlation has gained widespread usage in dynamic scenarios due to its ability to achieve single-frame reconstruction. Traditional binocular speckle structured light systems often face challenges such as insufficient surface texture richness and excessive curvature, leading to limitations in the accuracy of corresponding point localization during the matching process. Furthermore, partial point cloud loss caused by varying viewpoints can impact the precision and completeness of 3D reconstruction. This paper introduces a Multiview speckle correlation-based approach for 3D reconstruction. By establishing multi-view stereo camera epipolar rectification, complex speckle matching issues are transformed into one-dimensional searches along unified epipolar lines, simplifying the matching process and enhancing efficiency. Additionally, a digital speckle correlation computational model and subpixel interpolation algorithm based on Newton-Raphson iteration are employed to realize multi-view subpixel-level corresponding point localization. Ultimately, the reconstruction point cloud data is obtained through the intersection of three-ray least squares method. Experimental results demonstrate that this method achieves higher accuracy and better model completeness in 3D data reconstruction. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Speckle

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Modulated Model Predictive Control for a Four-Leg Indirect Matrix Converter

Modulated Model Predictive Control for a Four-Leg Indirect M...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Hosseinzadeh, Mohammad Ali Sarebanzadeh, Maryam Kennel, Ralph Babaei, Ebrahim Rivera, Marco Wheeler, Patrick Technical University of Munich High-Power Converters System Munich Germany University of Tabriz Faculty of Electrical and Computer Engineering Tabriz Iran Research Group Department of Electrical and Electronic Engineering Faculty of Engineering Nottingham United Kingdom Vicerrectoría Académica Talca Chile

ISBN: (纸本)9798350376067

Model Predictive Control (MPC) has been increasingly adopted by various converters due to its numerous advantages, including fast dynamic response, multi-objective control, and ease of implementation. However, MPC's variable switching frequency can degrade waveform quality compared to conventional control techniques that utilize proper modulation methods. To address this issue, combining MPC with a PWM technique allows the converter to operate at a fixed switching frequency, resulting in what is known as modulated model predictive control (M2PC). This study proposes an M2PC for a four-leg converter that is coupled with an indirect matrix converter at the input. The proposed M2PC enhances the output waveform quality using a PWM technique for the four-leg converter while maintaining a low fixed switching frequency. The performance of this approach is demonstrated through the implementation of the proposed M2PC technique in the MATLAB/Simulink environment, showing improvements in waveform quality. © 2024 IEEE.

关键词： Predictive control systems

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Enhancing Ocean Scene Video Captioning with Multimodal Pre-Training and Video-Swin-Transformer 49

Enhancing Ocean Scene Video Captioning with Multimodal Pre-T...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Chen, Xinyu Zhao, Meng Shi, Fan Zhang, Meng'en He, Yu Chen, Shengyong School of Computer Science and Engineering Tianjin University of Technology Laboratory of Computer Vision and System of Ministry of Education Tianjin300384 China Technology and Engineering Center for Space Utilization Chinese Academy of Sciences Key Laboratory of Space Utilization Beijing100190 China

ISBN: (纸本)9798350331820

With the success of multimodal pre-training models in the video-language field and various downstream tasks, previous multimodal models used 3DCNN networks as video feature extractors, which have limitations in interacting and fusing with text features. This paper proposes a multimodal pre-training model that utilizes a Video-Swin-Transformer-based network to encode both video and text data, to achieve better performance in video understanding. The model consists of four modules: video encoder, text encoder, interact encoder, and caption decoder to accomplish the task of ocean scene video captioning. A dataset of ocean scene videos, including various content types such as sea surfaces and shores, is also constructed. The training process is divided into two stages: pre-training and fine-tuning. Pre-training is performed on the Howto100m dataset to allow the model to learn video captions in natural scenes and complete video-language matching tasks. The fine-tuning stage is then performed on the ocean1000 dataset to better understand the events and content in ocean scene videos and generate captions that conform to ocean scene video descriptions. The model achieves satisfying results on both the public dataset YouCook2 and the proprietary dataset Ocean1000, demonstrating its ability in video-text information fusion and interaction. © 2023 IEEE.

关键词： Signal encoding

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Design and Evaluation of a Low-profile Haptic Interface Based on Surface Electrical Nerve Stimulation 1

Design and Evaluation of a Low-profile Haptic Interface Base...

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1st IEEE Conference on Telepresence, Telepresence 2024

作者： Jakes, Rachel S. Mesias, Luis Santos, Veronica J. Fu, Michael J. Tyler, Dustin J. Cwru Dept. of Biomedical Engineering United States Dept. of Mechanical and Aerospace Engineering United States United States Cwru Dept. of Electrical Computer and Systems Engineering United States The MetroHealth System Dept. of Physical Medicine & Rehabilitation United States Advanced Platform Technology Center Dept. of Va Northeast Ohio Healthcare System United States

ISBN: (纸本)9798331528249

Haptic feedback in telepresence applications is vital for remote task performance and object manipulation. To maximize telepresence capabilities, haptic systems need to be integrated with immersive telepresence interfaces such as virtual reality, which offer benefits such as improved spatial awareness and operator mobility. Compatibility with the mobility of these systems and their built-in optical hand tracking require low-profile wearable interfaces. Further, for full functional benefits, haptic systems must be able to elicit spatially congruent sensation without impeding operator movement and physical object interactions. In this paper, we describe the design of a lightweight, compact, and adaptable surface electrical nerve stimulation-based haptic interface. We demonstrate its capacity to consistently generate spatially congruent sensation on the all five fingertips without occlusion, as well as its compatibility with headset-based optical hand tracking. © 2024 IEEE.

关键词： Haptic interfaces

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Output Linearization of Single-Input Single-Output Fuzzy system to Improve Accuracy and Performance

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computers, Materials & Continua 2023年第5期75卷 2413-2427页

作者： Salah-ud-din Khokhar QinKe Peng Muhammad Yasir Noor Department of Automation Science and Technology(System Engineering Institute) School of Electronics and Information EngineeringXi’an Jiaotong UniversityXi’anShaanxi710049China Department of Computer Science G.C.UniversityLahore54000PunjabPakistan Department of Physics(Electronics) G.C.UniversityLahore54000PunjabPakistan

For fuzzy systems to be implemented effectively,the fuzzy membership function(MF)is essential.A fuzzy system(FS)that implements precise input and output MFs is presented to enhance the performance and accuracy of single-input single-output(SISO)FSs and introduce the most applicable input and output MFs protocol to linearize the fuzzy system’s *** a variety of non-linear techniques,a SISO FS is *** results of FS experiments conducted in comparable conditions are then *** simulated results and the results of the experimental setup agree fairly *** findings of the suggested model demonstrate that the relative error is abated to a sufficient range(≤±10%)and that the mean absolute percentage error(MPAE)is reduced by around 66.2%.The proposed strategy to reduceMAPE using an FS improves the system’s performance and control *** using the best input and output MFs protocol,the energy and financial efficiency of every SISO FS can be improved with very little tuning of *** proposed fuzzy system performed far better than other modern days approaches available in the literature.

关键词： Mean absolute percentage error membership functions relative error fuzzy system

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Reward-Driven Clustered Federated Learning for DRL Based UAV-RIS Networks 8

Reward-Driven Clustered Federated Learning for DRL Based UAV...

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8th IEEE International Conference on Recent Advances and Innovations in engineering, ICRAIE 2023

作者： Wong, Yi Jie Tham, Mau-Luen Kwan, Ban-Hoe Iqbal, Amjad Lee, Ying Loong Chang, Yoong Choon Universiti Tunku Abdul Rahman Department of Electrical and Electronic Engineering Kajang Malaysia Universiti Tunku Abdul Rahman Department of Mechatronics and Biomedical Engineering Kajang Malaysia Carleton University Department of System and Computer Engineering Canada

ISBN: (纸本)9798350315516

Deep reinforcement learning (DRL) has empowered numerous reconfigurable intelligent surface (RIS) aided millimeter-wave unmanned aerial vehicle (UAV) communications. However, most existing works often employ single-Task DRL, that could only handle one specific environment (or task). This hurdles the large-scale adoption of DRL-based UAV-RIS networks, since the environment has infinite variations. In this paper, we aim to solve diverse UAV-RIS tasks with a minimum number of DRLs required. Specifically, we proposed a reward-driven clustering technique to autonomously cluster all 50 simulated tasks into 10 clusters. For each cluster, we train a multi-Task DRL using Federated Learning to handle all tasks in the cluster. Simulation shows that given 50 unique tasks, the proposed method could reduce the total agents required from 50 to 10, which is a 5-fold reduction. © 2023 IEEE.

关键词： Reinforcement learning

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Sigma-Mixed Unscented Kalman Filter-Based Fault Detection for Traction systems in High-Speed Trains

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Chinese Journal of Electronics 2023年第5期32卷 982-991页

作者： CHENG Chao WANG Weijun MENG Xiangxi SHAO Haidong CHEN Hongtian Department of Computer Science and Technology Changchun University of Technology Department of Mathematics and Statistics Changchun University of Technology Institute of System Research China Industrial Control Systems Cyber Emergency Response Team Department of Mechanical and Vehicle Engineering Hunan University Department of Chemical and Materials Engineering University of Alberta

Fault detection(FD) for traction systems is one of the active topics in the railway and academia because it is the initial step for the running reliability and safety of high-speed trains. Heterogeneity of data and complexity of systems have brought new challenges to the traditional FD methods. For addressing these challenges, this paper designs an FD algorithm based on the improved unscented Kalman filter(UKF) with consideration of performance degradation. It is derived by incorporating a degradation process into the state-space *** network topology of traction systems is taken into consideration for improving the performance of state estimation. We first obtain the mixture distribution by the mixture of sigma points in UKF. Then, the Lévy process with jump points is introduced to construct the degradation model. Finally, the moving average interstate standard deviation(MAISD) is designed for detecting *** the proposed methods via a traction systems in a certain type of trains obtains satisfactory results.

关键词： Degradation Network topology Fault detection Rail transportation State-space methods Safety Kalman filters

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A novel U-net model for brain tumor segmentation from MRI images

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Alexandria engineering Journal 2025年 126卷 220-230页

作者： Obayya, Marwa Alshuhail, Asma Mahmood, Khalid Alanazi, Meshari H. Alqahtani, Mohammed Aljehane, Nojood O. Almansour, Hamad Al-Hagery, Mohammed Abdullah Department of Biomedical Engineering College of Engineering Princess Nourah bint Abdulrahman University PO Box 84428 Riyadh11671 Saudi Arabia Department of Information Systems College of Computer Sciences & Information Technology King Faisal university Saudi Arabia Department of Information Systems Applied College at Mahayil King Khalid University Saudi Arabia Department of Computer Science College of Sciences Northern Border University Arar Saudi Arabia Department of Information System and Cyber Security College of Computing and Information Technology University of Bisha Bisha61922 Saudi Arabia Department of Computer Science Faculty of Computers and Information Technology University of Tabuk Tabuk Saudi Arabia Applied Collage Najran University Najran Saudi Arabia Department of Computer Science College of Computer Qassim University Saudi Arabia

Segmentation of brain tumors aids in diagnosing the disease early, planning treatment, and monitoring its progression in medical image analysis. Automation is necessary to eliminate the time and variability associated with traditional segmentation methods. Convolutional neural networks (CNNs) and U-Net architectures have demonstrated their efficiency and effectiveness in segmenting brain tumors from MRI images using deep learning techniques. The paper presents an improved U-Net-based segmentation algorithm that integrates nested skip paths to improve encoder-decoder feature fusion. The performance of segmentation was optimized by utilizing a variety of activation functions and loss functions, including Dice Loss and Intersection over Union (IoU). A high level of accuracy was demonstrated in the proposed model when it was evaluated using the LGG Segmentation Dataset. The proposed approach for segmenting medical images has been shown to be both robust and efficient in a comparative analysis. © 2025 The Authors

关键词： Convolutional neural networks

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