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2024 IEEE International Workshop on Metrology for Living Environment, MetroLivEnv 2024

作者： Morioka, Junya Miyamoto, Ryusuke Graduate School of Science and Technology Meiji University Department of Computer Science Kawasaki Japan School of Science and Technology Meiji University Department of Computer Science Kawasaki Japan

ISBN: (纸本)9798350385014

Recent advances in VR devices and technology have increased the demand for technologies that reflect real space in virtual space. In particular, technologies that reflect the indoor environment in a virtual space are used in various fields such as architecture, interior design, and robotics. To reflect a real space in a virtual space, estimating the 3D structure of the real space is significant. Yet, this requires expensive equipment and poses challenges in computational complexity and ease of measurement. In this study, we propose a method of estimating 3D structure of indoor environments using a single image. Existing methods generally use depth estimation, but in this study, we introduce a new approach using semantic segmentation and rectangular fitting. Experimental results with 3D CG show that the proposed method outperforms existing methods in accuracy and competitive results. © 2024 IEEE.

关键词： Semantics

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Personal Identification Based on Joint Motions with Error Correction Using Particle Filter

Personal Identification Based on Joint Motions with Error Co...

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2024 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2024

作者： Aoyagi, Kosuke Yuda, Yuki Ohkoba, Masakazu Adachi, Miho Miyamoto, Ryusuke Department of Computer Science School of Science and Technology Meiji University Kawasaki Japan Department of Computer Science Graduate School of Science and Technology Meiji University Kawasaki Japan

ISBN: (纸本)9798350389210

Gait analysis is useful for personal identification in public spaces. Since the emergence of deep learning that can accurately estimate the joint locations of a human in an image, gait analysis has become feasible in practical applications that have only a visible light camera. This paper proposes a novel method for personal identification based on joint motions that can improve utility of gait analysis. Specifically, the proposed method adopts a particle filter to correct the misclassification of left and right legs using the time-series input of their locations. Experimental results using datasets comprising real-world images showed that the proposed error correction improved the accuracy of personal classification from 84.19% to 85.94% when joint locations were estimated using ViTPose. © 2024 IEEE.

关键词： Error correction

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DiffMat:Latent diffusion models for image-guided material generation

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Visual Informatics 2024年第1期8卷 6-14页

作者： Liang Yuan Dingkun Yan Suguru Saito Issei Fujishiro Graduate School of Science and Technology Keio UniversityKanagawaJapan School of Computing Tokyo Institute of TechnologyTokyoJapan Department of Information and Computer Science Keio UniversityKanagawaJapan

Creating realistic materials is essential in the construction of immersive virtual *** existing techniques for material capture and conditional generation rely on flash-lit photos,they often produce artifacts when the illumination mismatches the training *** this study,we introduce DiffMat,a novel diffusion model that integrates the CLIP image encoder and a multi-layer,crossattention denoising backbone to generate latent materials from images under various *** a pre-trained StyleGAN-based material generator,our method converts these latent materials into high-resolution SVBRDF textures,a process that enables a seamless fit into the standard physically based rendering pipeline,reducing the requirements for vast computational resources and expansive *** surpasses existing generative methods in terms of material quality and variety,and shows adaptability to a broader spectrum of lighting conditions in reference images.

关键词： SVBRDF Diffusion model Generative model Appearance modeling

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sEMG-based Sarcopenia risk classification using empirical mode decomposition and machine learning algorithms

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Mathematical Biosciences and Engineering 2024年第2期21卷 2901-2921页

作者： Kumar, Konki Sravan Lee, Daehyun Jamsrandoj, Ankhzaya Soylu, Necla Nisa Jung, Dawoon Kim, Jinwook Mun, Kyung Ryoul Center for Artificial Intelligence Korea Institute of Science and Technology Seoul Korea Republic of KHU-KIST Department of Converging Science and Technology Graduate School Kyung Hee University Seoul Korea Republic of Department of Human Computer Interface and Robotics Engineering University of Science and Technology Daejeon Korea Republic of Department of Computer Science Ozyegin University Istanbul Turkey

Early detection of the risk of sarcopenia at younger ages is crucial for implementing preventive strategies, fostering healthy muscle development, and minimizing the negative impact of sarcopenia on health and aging. In this study, we propose a novel sarcopenia risk detection technique that combines surface electromyography (sEMG) signals and empirical mode decomposition (EMD) with machine learning algorithms. First, we recorded and preprocessed sEMG data from both healthy and at-risk individuals during various physical activities, including normal walking, fast walking, performing a standard squat, and performing a wide squat. Next, electromyography (EMG) features were extracted from a normalized EMG and its intrinsic mode functions (IMFs) were obtained through EMD. Subsequently, a minimum redundancy maximum relevance (mRMR) feature selection method was employed to identify the most influential subset of features. Finally, the performances of state-of-the-art machine learning (ML) classifiers were evaluated using a leave-one-subject-out cross-validation technique, and the effectiveness of the classifiers for sarcopenia risk classification was assessed through various performance metrics. The proposed method shows a high accuracy, with accuracy rates of 0.88 for normal walking, 0.89 for fast walking, 0.81 for a standard squat, and 0.80 for a wide squat, providing reliable identification of sarcopenia risk during physical activities. Beyond early sarcopenia risk detection, this sEMG-EMD-ML system offers practical values for assessing muscle function, muscle health monitoring, and managing muscle quality for an improved daily life and well-being. © 2024 American Institute of Mathematical sciences. All rights reserved.

关键词： Empirical mode decomposition

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Multi-Scale Time Series Segmentation Network Based on Eddy Current Testing for Detecting Surface Metal Defects

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 528-538页

作者： Xiaorui Li Xiaojuan Ban Haoran Qiao Zhaolin Yuan Hong-Ning Dai Chao Yao Yu Guo Mohammad S.Obaidat George Q.Huang the School of Intelligence Science and Technology University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering the Key Laboratory of Intelligent Bionic Unmanned Systems and the Institute of Materials Intelligent Technology Liaoning Academy of Materials IEEE the Department of Computer Science Hong Kong Baptist University the School of Computer and Communication Engineering Key Laboratory of Advanced Materials and Devices for Post-Moore Chips Ministry of Education University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing the School of Computer and Communication Engineering University of Science and Technology Beijing the King Abdullah Ⅱ School of Information Technology The University of Jordan the Department of Computational Intelligence the School of Computing SRM University the School of Engineering The Amity University The Hong Kong Polytechnic University

In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network(MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant's heat transfer tube dataset, it captures 90% of defect instances with75% middle localization F1 score.

关键词： Eddy current testing nondestructive testing semantic segmentation time series analysis

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Applying Burn-in Strategy to Deep Reinforcement Learning with Spiking Neural Networks 13

Applying Burn-in Strategy to Deep Reinforcement Learning wit...

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Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Iwata, Takahiro Yoshioka, Seankein Miki, Daisuke Graduate School of Computer Science Chiba Institute of Technology Chiba Japan Chiba Institute of Technology Department of Computer Science Chiba Japan

ISBN: (纸本)9798350373332

In recent years, neural networks in deep reinforce-ment learning (D RL) have achieved success in various tasks, owing to their high expressiveness and flexibility. Neural networks are expected to be increasingly used for robot control. However, large neural networks consume a significant amount of power during training and operation. Given that the power supply that can be installed in robots is limited, challenges remain in the practical application of robot control using DRL, which includes large-scale neural networks. Conversely, spiking neural networks (SNNs) have high power efficiency, which is achieved through dedicated computational elements. Previous studies reported that SNNs can be applied to DRL to learn a robot's walking motion in a virtual environment and reduce energy consumption. Although previous studies have applied SNNs to DRL, they have the drawback of recursive computation, causing output delays. In this study, we propose a method that combines SNNs with a structure that addresses the issues of previous studies using a twin-delayed deep deterministic policy gradient (TD3). In addition, a burn-in strategy was introduced to estimate the action values using the internal states of the SNNs. Experiments in a virtual environment showed that the proposed method achieved higher rewards than the previous methods. © 2024 IEEE.

关键词： Deep reinforcement learning

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A Neural Computing-Based Access Control Protocol for AI-Driven Intelligent Flying Vehicles in Industry 5.0-Assisted Consumer Electronics

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 3573-3581页

作者： Mahmood, Khalid Tariq, Tayyaba Sangaiah, Arun Kumar Ghaffar, Zahid Saleem, Muhammad Asad Shamshad, Salman National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Douliu64002 Taiwan National Yunlin University of Science and Technology International Graduate School of AI Douliu64002 Taiwan Lebanese American University Department of Electrical and Computer Engineering Byblos1102 2801 Lebanon University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China The University of Lahore Department of Software Engineering Lahore54590 Pakistan

The continuous revolution in Artificial Intelligence (AI) has played a significant role in the development of key consumer applications, including Industry 5.0, autonomous decision-making, fault diagnosis, etc. In practice, autonomous and intelligent robots and flying vehicles (i.e., drones) are also becoming the integral part of Industry 5.0. These vehicles are attaining significant importance nowadays because of their applications in consumer electronics. However, there may be numerous privacy issues in such an infrastructure. Nevertheless, neural computing-driven security solutions have played a significant role in overcoming the security and privacy issues of AI-driven intelligent flying vehicles. Motivated by these facts, this article presents a neural computing-based access control protocol for AI-driven flying vehicles to prevent all possible security threats. A detailed security evaluation of the devised protocol is done formally using the extensively recognized Real-Or-Random (ROR) oracle model. Similarly, the informal security analysis ensures the resistance of our scheme against various potential attacks under the assumptions of CK and DY adversarial models. Moreover, detailed performance scrutiny of the devised protocol with various existing schemes shows the robustness and supremacy of our scheme in terms of computation, communication overhead, and security attributes. © 1975-2011 IEEE.

关键词： Authentication

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Alphabet-Level Indian Sign Language Translation to Text Using Hybrid-AO Thresholding with CNN

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Intelligent Automation & Soft Computing 2023年第9期37卷 2567-2582页

作者： Seema Sabharwal Priti Singla Department of Computer Science and Engineering Baba Mastnath UniversityRohtak124001India Department of Computer Science Government Post Graduate College for WomenPanchkula134109India

Sign language is used as a communication medium in the field of trade,defence,and in deaf-mute communities *** the last few decades,research in the domain of translation of sign language has grown and become more *** necessitates the development of a Sign Language Translation System(SLTS)to provide effective communication in different research *** this paper,novel Hybrid Adaptive Gaussian Thresholding with Otsu Algorithm(Hybrid-AO)for image segmentation is proposed for the translation of alphabet-level Indian Sign Language(ISLTS)with a 5-layer Convolution Neural Network(CNN).The focus of this paper is to analyze various image segmentation(Canny Edge Detection,Simple Thresholding,and Hybrid-AO),pooling approaches(Max,Average,and Global Average Pooling),and activation functions(ReLU,Leaky ReLU,and ELU).5-layer CNN with Max pooling,Leaky ReLU activation function,and Hybrid-AO(5MXLR-HAO)have outperformed other *** open-access dataset of ISL alphabets with approx.31 K images of 26 classes have been used to train and test the *** proposed framework has been developed for translating alphabet-level Indian Sign Language into *** proposed framework attains 98.95%training accuracy,98.05%validation accuracy,and 0.0721 training loss and 0.1021 validation loss and the perfor-mance of the proposed system outperforms other existing systems.

关键词： Sign language translation CNN thresholding Indian sign language

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An Efficient Three-Party Authenticated Key Exchange Procedure Using Chebyshev Chaotic Maps with Client Anonymity

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computers, Materials & Continua 2023年第6期75卷 5337-5353页

作者： Akshaykumar Meshram Monia Hadj Alouane-Turki N.M.Wazalwar Chandrashekhar Meshram Department of Applied Mathematics Yeshwantrao Chavan College of EngineeringNagpur441110MaharashtraIndia Department of Statistics Rashtrasant Tukadoji Maharaj Nagpur UniversityNagpur440033MaharashtraIndia Department of Computer Science College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Post Graduate Studies and Research in Mathematics Jaywanti Haksar Government Post-Graduate CollegeCollege of Chhindwara UniversityBetul460001Madhya PradeshIndia

Internet of Things(IoT)applications can be found in various industry areas,including critical infrastructure and healthcare,and IoT is one of several technological *** a result,tens of billions or possibly hundreds of billions of devices will be linked *** smart devices will be able to gather data,process it,and even come to decisions on their *** is the most essential thing in these *** IoT infrastructure,authenticated key exchange systems are crucial for preserving client and data privacy and guaranteeing the security of data-in-transit(e.g.,via client identification and provision of secure communication).It is still challenging to create secure,authenticated key exchange *** majority of the early authenticated key agreement procedure depended on computationally expensive and resource-intensive pairing,hashing,or modular exponentiation *** focus of this paper is to propose an efficient three-party authenticated key exchange procedure(AKEP)using Chebyshev chaotic maps with client anonymity that solves all the problems mentioned *** proposed three-party AKEP is protected from several *** proposed three-party AKEP can be used in practice for mobile communications and pervasive computing applications,according to statistical experiments and low processing *** protect client identification when transferring data over an insecure public network,our three-party AKEP may also offer client ***,the presented procedure offers better security features than the procedures currently available in the literature.

关键词： Client anonymity Chebyshev chaotic maps authenticated key exchange statistical experiment Galois fields

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Effect of aluminium ferrite nanofillers on carboxy methyl cellulose /poly(vinyl alcohol) biopolymer films: improved structural, electrical, optical, and dielectric properties

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Optical and Quantum Electronics 2024年第12期56卷 1-25页

作者： N, Seshan T. Cyriac, Vipin Demappa, T. Ismayil Department of Post-Graduate Studies and Research in Polymer Science University of Mysore Sir M.Visvesvaraya Post Graduate Center Karnataka Tubinakere Mandya571402 India Department of Physics Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

This study investigated the effect of aluminum ferrite (AlFe2O4) nanofillers on the structural, optical, and dielectric characteristics of a polymer blend composed of sodium carboxymethyl cellulose (NaCMC) and poly(vinyl alcohol) (PVA). Hybrid AlFe2O4 nanofillers were synthesized using a co-precipitation method. The synthesized nanofillers were incorporated into the NaCMC/PVA blend at various nanoparticle concentrations (1, 2, 3, 4, and 5 wt%). using the solution-casting method. The effects of the nanofiller loading on the characteristic vibrational bands of the polymer blend were examined using Fourier transform infrared (FTIR) spectroscopy. XRD analysis was employed to assess changes in the crystallinity of the polymer nanocomposite films. The compositions and component percentages of the various polymer nanocomposite films were confirmed by Energy Dispersive X-ray (EDS) analysis, and the surface morphologies of the blends were examined using scanning electron microscopy (SEM).The optical properties of the polymer nanocomposite films were analyzed using a UV-visible spectrometer, revealing a decrease in the direct bandgap from 5.76 eV to 5.50 eV and the indirect bandgap from 5.13 eV to 4.74 eV. TGA studies indicated a increase in the thermal stability of the nanocomposite films, whereas DSC analysis showed a decline in the glass transition temperature with increasing amounts of AlFe2O4 nanoparticles. Impedance spectroscopy was used to determine the electrical conductivities of the nanocomposites. The electrical and dielectric properties of the prepared samples were examined to determine their viability for electrical applications. These findings indicate that the sample containing 3 wt. % of nanoparticles exhibited high direct current (dc) conductivity. The dielectric loss () and dielectric constant () displayed significant values at low frequencies, whereas reduced values were recorded at high frequencies. Additionally, the and values decreased with increasing Al

关键词： Fourier transform infrared spectroscopy

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