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共 7880 条记录，以下是531-540 订阅

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Federated Learning Methodology for Indoor Location systems in Multi-Layer Architecture

Federated Learning Methodology for Indoor Location Systems i...

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international joint conference on Neural Networks (IJCNN)

作者： José Luis López Ruiz Ángeles Verdejo Espinosa Macarena Espinilla Estévez Computer Science Department University of Jaén Jaén Spain Electrical Engineering Department University of Jaén Jaén Spain

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Recent research has shown that federated learning offers numerous benefits, including improved privacy, reduced communication costs, and enhanced scalability. Our proposed architecture and methodology for indoor Bluetooth localization systems leverages these advantages by utilizing federated learning in the fog layer, leading to increased accuracy in classifier models. To do so, we propose an architecture for indoor Bluetooth localization systems based on edge, fog, and cloud computing layers with temporal windows, utilizing federated learning in the fog layer of the architecture. Additionally, we present a methodology in which data is collected and aggregated on the edge layer, the localization model is trained and deployed on fog nodes, and a conflict resolution method is proposed in the cloud. A localisation case study is evaluated to determine the optimal parameters of the methodology, and to check the consistency and speed of a model generated with the methodology. According to the experiment performed, increasing the time window for data aggregation in both the edge and fog layers results in higher classifier accuracy.

关键词： Location awareness Cloud computing Privacy Bluetooth Accuracy Federated learning Computational modeling

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Reliability Evaluation of Automated Tape Library systems

Reliability Evaluation of Automated Tape Library Systems

引用

international Symposium on modeling, Analysis and Simulation of computer and Telecommunication systems (MASCOTS)

作者： Ilias Iliadis Mark Lantz IBM Research Europe Zurich Switzerland

ISBN: (数字)9798331531300

ISBN: (纸本)9798331531317

Magnetic tape is a digital data storage technology that has evolved continuously over the last seven decades. It provides a cost-effective way to retain the rapidly increasing volumes of data being created in recent years. The low cost per terabyte combined with tape’s low energy consumption make it an appealing option for storing infrequently accessed data and has resulted in a resurgence in use of the technology. Power and operational failures may damage tapes and lead to data loss. To protect stored data against loss and achieve high data reliability, an erasure coding scheme is employed. A theoretical model capturing the effect of tape failures and latent errors on system reliability is developed. Closed-form expressions are derived for the the Mean Time to Data Loss (MTTDL) and the Expected Annual Fraction of Effective Data Loss (EAFEDL) reliability metric, which assesses losses at the file, object, or block, level. The results obtained demonstrate that, for realistic values of bit error rates, reliability is unaffected by the presence of latent errors. The theoretical results derived can be used to dimension and provision tape libraries to provide desired levels of durability.

关键词： measurement Analytical models Closed-form solutions Computational modeling Bit error rate Termination of employment Reliability theory Libraries Encoding Telecommunications

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Convolutional Neural Network with CBAM Module for Fitness Activity Recognition Using Wearable IMU Sensors

Convolutional Neural Network with CBAM Module for Fitness Ac...

引用

international ECTI Northern Section conference on Electrical, Electronics, computer and Telecommunications Engineering (ECTI-NCON)

作者： Sakorn Mekruksavanich Ponnipa Jantawong Wikanda Phaphan Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Department of Applied Statistics Faculty of Applied Science Research Group in Statistical Learning and Inference King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Mathematics Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut’s University of Technology North Bangkok Bangkok Thailand

Wearable motion sensors are gaining traction for fitness activity tracking due to their utility in assisted living, wellness tracking, and fitness training applications. Such sensors typically incorporate accelerometers and gyroscopes to monitor and log physical activities. This work proposes a deep-learning approach for identifying fitness activities using wearable inertial measurement units (IMUs) signals. Specifically, a convolutional neural network (CNN) integrated with a convolutional block attention module (CBAM) provides the core framework. The CBAM module applies channel and spatial attention mechanisms to enhance CNN’s capacity to discriminate salient features. Multichannel IMU time-series data, comprising accelerometer and gyroscope readings as subjects perform various exercises, supply input to the model. By learning via an attention-enhanced architecture, the model can extract robust and informative representations to categorize activities accurately. Experiments on a public IMU dataset with five exercise types across over twenty participants evaluated the approach. Results showed over 99.58% subject-independent activity recognition accuracy when augmenting the CNN with CBAM, constituting significant performance gains. The research findings indicate that an efficient deep-learning solution with attention modeling can recognize fitness activities reliably from on-body sensors. The proposed methodology holds substantial promise for applications in sports analytics and personalized health tracking.

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Research on Real-Time Processing and Efficient Sharing Program of Power measurement Data

Research on Real-Time Processing and Efficient Sharing Progr...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE international conference on

作者： Cuicui Zhang Jing Zhang Jiali Sun Qi Sun Information and Telecommunication Branch State Grid Anhui Electric Power Co. Ltd Hefei Anhui China

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

In modern power systems, power measurement data is a key basis for monitoring, analyzing, and making decisions regarding operational status. This paper proposes a solution to improve the performance of power measurement data processing and sharing in response to the high requirements of the power system. By integrating a real-time database, the real-time data processing module preprocesses the measurement data based on key information. Parallel computing architecture and ADMM algorithm are used to realize information sharing and collaboration between data streams. The accuracy of state variable estimation is improved by introducing a weighted M-P generalized inverse matrix using iterative methods and linearization. In terms of data processing speed, the model in this paper, in terms of data sharing efficiency, the average response time of the optimized sharing scheme is shortened to 0.5 seconds, and the data transfer rate is increased to 1000 Mbps. the average response time of the optimized sharing scheme is shortened from 5 seconds to 0.5 seconds, and the data transfer rate is increased from 100 Mbps to 1000 Mbps. in terms of user experience, the optimized power measurement In terms of user experience, the optimized Power measurement solution has an average satisfaction score of more than 90, and the user operation steps have been simplified to three. It not only improves the data processing capability of the power measurement system, but also enhances the system sharing level.

关键词： Analytical models Power measurement Accuracy Computational modeling computer architecture Real-time systems Data models User experience Power systems Time factors

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A Deep Learning Approach for Knee Osteoarthritis Classification Using Convolutional Neural Networks

A Deep Learning Approach for Knee Osteoarthritis Classificat...

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Information Technology, Electronics and Intelligent Communication systems (ICITEICS), IEEE international conference on

作者： Rahul Singh Neha Sharma Deepak Upadhyay Swati Devliyal Akira Singh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382709

Knee osteoarthritis (OA) is a prevalent degenerative joint disorder that has a substantial worldwide ramification, especially among the geriatric population. It is distinguished by the gradual deterioration of cartilage in the knee joint, resulting in manifestations such as stiffness, discomfort, and diminished range of motion. Knee OA, as reported by the World Health Organization, is positioned within the leading ten causes of disability in developed nations. Classification and early detection of knee OA are essential for its effective management. Standard diagnostic techniques, including radiography and clinical examinations, are susceptible to subjectivity and may fail to identify OA in its early stages. In response, a novel Convolutional Neural Network (CNN) method for knee OA classification is introduced in this study. The CNN model is designed to derive detailed features from knee joint images using a dataset of 8381 images from the Osteoarthritis Initiative (OAI), that is divided into training (5923 images) and testing (2458 images) sets. As evidenced by evaluation metrics such as accuracy, precision, recall, and F1 score, the model effectively classifies different phases of knee OA. This strategy exhibits potential in enhancing clinical decision-making and tailoring treatment regimens for patients diagnosed with knee osteoarthritis.

关键词： measurement Training Accuracy Standards organizations Sociology Convolutional neural networks Osteoarthritis

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Evaluating the Implementation of a Modified Method for Calculating Symmetrical Components Based on Synchronous measurements of Instantaneous Currents in Three Phases in Operating Power Supply systems

Evaluating the Implementation of a Modified Method for Calcu...

引用

international Ural conference on measurements (UralCon)

作者： A. A. Pesterev S. S. Kostinskiy Department “Power Supply and Electric Drive” South-Russian State Polytechnic University (NPI) of the M.I. Platov Novocherkassk Russia

ISBN: (数字)9798350386028

ISBN: (纸本)9798350386035

The trend of increasing the number and installed capacity of single-phase non-linear power consumers leads to deteriorated power quality. Unbalance in zero and negative sequences is a criterion for assessing the power quality. Moreover, the zero sequence unbalance factor is determined by the zero sequence current magnitude. The study is aimed at empirically justifying the advantages of implementing a method based on measuring the instantaneous currents synchronously in three phases over a single period to evaluate the zero sequence unbalance factor compared to the symmetrical component method using the phase factor for a more accurate assessment of current and voltage unbalance in distribution power grids. To achieve this goal, a test bench and a computer model of the power supply system were developed and built. The developed bench and computer model allowed for obtaining an array of synchronously measured instantaneous currents and voltages for each phase and in the neutral conductor with non-sinusoidal load magnitude andtypevarying independently for each phase. Thereat, the criterion for assessing the accuracy of calculating the zero sequence unbalance factor was the magnitude of the current in the neutral conductor. The use of the symmetrical component method for determining the zero sequence current in the case of amplitude-phase unbalance with active and inductive sinusoidal and non-sinusoidal loads givesan unacceptable error. To reduce errors in calculating the zero sequence current, the implementation of a method based on measuring the instantaneous currents synchronously in three phases over a single period is advisable.

关键词： Reactive power Phase measurement Voltage measurement Power measurement Current measurement Computational modeling measurement uncertainty Power quality Conductors Load modeling

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Detection of Malnutrition in Children Under 5 Years of Old Using Deep Learning

Detection of Malnutrition in Children Under 5 Years of Old U...

引用

international ECTI Northern Section conference on Electrical, Electronics, computer and Telecommunications Engineering (ECTI-NCON)

作者： Cliver Aguilar Joel Tucta José Santisteban Information Systems Engineering Universidad Peruana de Ciencias Aplicadas Lima Perú

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

This study evaluates the performance of Convolutional Neural Networks (CNNs) for the early detection of childhood malnutrition. The architectures analyzed include ResNet-50, EfficientNet-B4, VGG16, and AlexNet, selected for their demonstrated effectiveness in image classification tasks. Images were categorized into three classes—normal, at risk of malnutrition, and severely malnourished—and underwent preprocessing steps such as resizing, flipping, zooming, and face detection using MTCNN. The evaluation employed metrics including accuracy, precision, recall, and F1-score, with ResNet-50 emerging as the most effective model, achieving an accuracy of 92%. Based on these findings, this study explores the potential application of ResNet-50 in mobile solutions to provide accessible and practical tools for malnutrition detection in resource-limited settings.

关键词： Deep learning measurement Pediatrics Accuracy Mobile applications Convolutional neural networks Reliability Artificial intelligence Public healthcare Monitoring

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Proceedings - 2022 IEEE international conference on Energy Internet, ICEI 2022

Proceedings - 2022 IEEE International Conference on Energy I...

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6th IEEE international conference on Energy Internet, ICEI 2022

ISBN: (纸本)9781665493277

The proceedings contain 31 papers. The topics discussed include: large-signal modeling for full-bridge LLC resonant converter using extended hyperbolic tangent function;portfolio strategy of power producer considering energy storage in spot market, ancillary market and option market;multi-period two-stage stochastic der aggregation for power flexibility reserve;distributed photovoltaic-storage system optimization planning considering flexible resources in smart distribution network;echo state network based noise detection in energy internet orienting justice blockchain data;joint bidding strategy of onsite energy storage subject in multi-market collaborated with wind power plants;intelligent control technology of air source heat pump for mango drying;a bidirectional converter stabilization control strategy for novel power systems;and analysis of carbon emission reduction potential of regional multi-energy system considering the influencing factors of carbon emissions.

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Brain-Inspired Diagnostic Model for Lung Nodules

Brain-Inspired Diagnostic Model for Lung Nodules

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IEEE WIC ACM international conference on Web Intelligence (WI)

作者： Huanan Bao Guoyin Wang Qun Liu Pengcheng Xu Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing China National Center for Applied Mathematics in Chongqing Chongqing Normal University Chongqing China

ISBN: (数字)9798331504946

ISBN: (纸本)9798331504953

In recent years, artificial intelligence (AI) has attained or even surpassed the level of the human brain in fields such as machine translation and computer vision. However, AI systems still face limitations due to challenges in effectively managing uncertain information and a lack of interpretability. Recent developments in brain science and discoveries in cognitive mechanisms offer potential solutions to address these issues. Inspired by the brain's dual-pathway uncertainty-processing mechanism, we designed a lung nodule diagnosis model that integrates convolutional neural networks (CNNs) and transformers, simulating the regulatory effects of dopamine and kainate receptors on uncertainty. We introduce two regularization terms to reduce diagnostic uncertainty for lung nodules. Experimental results on the LIDC-IDRI and LNDb datasets indicate that this model not only performs well but also provides visual explanations that are more consistent with domain knowledge.

关键词： Visualization Uncertainty Lungs Computational modeling Feature extraction Brain modeling Transformers computer aided diagnosis Convolutional neural networks Noise measurement

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Deep Phase Mapping 3D based on Single-Frame Digital Moiré Fringe

Deep Phase Mapping 3D based on Single-Frame Digital Moiré F...

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Electronic Information Engineering and computer Communication (EIECC), international conference on

作者： Rui Di Hailing Hu Meili Hu Baoan Song Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Key Laboratory of Photoelectric Detection Materials and Devices of Zhejiang Province Ningbo University Ningbo China Engineering Research Center for Advanced Infrared Photoelectric Materials and Devices of Zhejiang Province Ningbo China

ISBN: (数字)9798331534622

ISBN: (纸本)9798331534639

To enhance three-dimensional(3D) measurement precision with minimal fringes, the deep phase mapping 3D based on single-frame digital moiré fringe method (DDM) is proposed. By projecting just one high-frequency fringe and combining wrapped phase with fringe order to calculate continuous phase, it achieves 3D reconstruction through training on three process variables, thereby enhancing measurement precision. Experimental DDM measurements of 3D contours for fox and complex face models surpassed traditional single-frame methods in accuracy. Further evaluation on standard planes confirmed DDM's precision through lower RMSE (root mean square error) and MAE (mean absolute error) values, underlining its superior reconstruction accuracy. It holds great promise for its potential applications in real-time industrial measurements.

关键词： Training Solid modeling Three-dimensional displays Phase measurement Accuracy Real-time systems Wrapping Root mean square Standards Faces

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