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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Zerrad, Raoua Diaz, Kamar Mechkouri, Meriem Hayani Sefiani, Naoufal Department of Mechanical Engineering Research Laboratory in Engineering Innovation and Management of Industrial Systems Faculty of Sciences and Techniques Abdelmalek Essaadi University Tangier Morocco Department of Industrial Engineering and Maintenance Computer Engineering Production and Maintenance Laboratory University of Lorraine Metz France

This paper presents a comprehensive framework for optimal coordination of production, logistics, and maintenance in multi-site stamping plants within the context of Industry 4.0. By integrating key technologies such as the Internet of Things, cloud computing and big data analytics, we developed a system that would enable real-time data sharing, predictive analytics, and intelligent decision-making. This framework aims to enhance operational efficiency, reduce downtime, and improve service rate. Additionally, our approach aims to optimize spare parts inventory management across different sites, minimizing inventory costs and lead times by ensuring timely availability and efficient sourcing of spare parts. © 2024 The Authors. Published by Elsevier B.V.

关键词： Inventory control

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A flexible capacitive photoreceptor for the biomimetic retina

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Light(Science & Applications) 2022年第1期11卷 38-49页

作者： Mani Teja Vijjapu Mohammed E.Fouda Agamyrat Agambayev Chun Hong Kang Chun-Ho Lin Boon S.Ooi Jr-Hau He Ahmed M.Eltawil Khaled N.Salama Sensors lab Advanced Membranes and Porous Materials CenterComputerElectrical and Mathematical Science and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Kingdom of Saudi Arabia Communication and Computing Systems Lab ComputerElectrical and Mathematical Science and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Kingdom of Saudi Arabia Department of Electrical Engineering and Computer Science University of California-IrvineIrvineCA 92612USA Department of Electrical Computer and Energy EngineeringArizona State UniversityTempeAZUSA Computer Electrical and Mathematical Science and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Kingdom of Saudi Arabia Department of Materials Science and Engineering City University of Hong KongHong Kong SARChina

Neuromorphic vision sensors have been extremely beneficial in developing energy-efficient intelligent systems for robotics and privacy-preserving security *** is a dire need for devices to mimic the retina's photoreceptors that encode the light illumination into a sequence of spikes to develop such ***,we develop a hybrid perovskite-based flexible photoreceptor whose capacitance changes proportionally to the light intensity mimicking the retina's rod cells,paving the way for developing an efficient artificial retina *** proposed device constitutes a hybrid nanocomposite of perovskites(methyl-ammonium lead bromide)and the ferroelectric terpolymer(polyvinylidene fluoride trifluoroethylene-chlorofluoroethylene).A metal-insulator-metal type capacitor with the prepared composite exhibits the unique and photosensitive capacitive behavior at various light intensities in the visible light *** proposed photoreceptor mimics the spectral sensitivity curve of human photopic *** hybrid nanocomposite is stable in ambient air for 129 weeks,with no observable degradation of the composite due to the encapsulation of hybrid perovskites in the hydrophobic *** functionality of the proposed photoreceptor to recognize handwritten digits(MNIST)dataset using an unsupervised trained spiking neural network with 72.05%recognition accuracy is *** demonstration proves the potential of the proposed sensor for neuromorphic vision applications.

关键词： perovskite illumination preserving

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Towards Accurate RPM Detection Using Primary Ignition Pulses 49

Towards Accurate RPM Detection Using Primary Ignition Pulses

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

作者： Wijayaraja, Jaliya L. Abhayasinghe, Nimsiri Wellalage, Sasini Sliit Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka Sliit Faculty of Engineering Department of Electrical and Electronic Engineering Malabe Sri Lanka

ISBN: (纸本)9798350331820

Enhancing the performance and extending the life-cycle of older vehicles through retrofitting with improved systems presents an alluring opportunity. However, achieving accurate sensing of crucial parameters, such as engine revolutions per minute (RPM), poses challenges due to the need for mechanical manipulation and some designs, which may result in diminished accuracy in adverse settings. This study proposes using the primary ignition pulse signal to accurately determine engine speed without mechanical manipulation. We investigate the effectiveness of signal processing methods, including the RC filter, 2nd and 6th order Butterworth filter, to improve the accuracy of the readings. Our proposed method successfully collects data from conventional internal combustion engines, with simulated signal processing methods producing accurate detection of RPM and reduced fluctuations. The proposed method eliminated the noise, leading to accurate RPM measurements, while the maximum standard deviation observed for the RC filter implementation was 38.74. By accurately sensing key parameters, older vehicles can be retrofitted with improved systems, leading to enhanced performance and an extended lifecycle. The introduced methods can be practically implemented using custom electronics or embedded systems and can contribute to future research on automotive retrofitting. © 2023 IEEE.

关键词： Life cycle

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electrical Motor Fault Detection System using AI's Random Forest Classifier Technique 6

Electrical Motor Fault Detection System using AI's Random Fo...

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6th IEEE International Conference on Advanced systems and Emergent Technologies, IC_ASET 2023

作者： Yaqub, Raziq Ali, Hassan Bin Abd Wahab, Mohd Helmy Alabama A&m University Departmnet of Electrical Engineering and Computer Science HuntsvilleAL United States Qatar Department of Sustainable Systems Engineering Doha Qatar Departmnet of Computer Engineering Johor Malaysia

ISBN: (纸本)9798350321029

Induction motors are very commonly used to create traction for electric vehicles. In electric vehicles and other rotating machine systems, faults develop during regular operation, which, if unaddressed, can accelerate the damage to machines. Therefore, early detection and prediction of faults hold the key importance in predictive and preventive maintenance. This work focuses on collecting sounds and vibrations data of an electric motor, which are the critical indicators of fault initiation in electric vehicle systems. We use artificial intelligence (AI) to predict and classify the types of faults associated with different vibration frequencies and sound levels. We explore random forest classifier technique for faults prediction and derive promising results for different fault conditions. Results show that we correctly predicted and classified different types of manually induced faults associated with different vibration frequencies and sound levels with a 92% accuracy and 86 to 91 % precision level. © 2023 IEEE.

关键词： Fault detection

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Bandwidth Provisioning for Network Slices with Per User QoS Guarantees 36

Bandwidth Provisioning for Network Slices with Per User QoS ...

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36th IEEE/IFIP Network Operations and Management Symposium, NOMS 2023

作者： Nikolaidis, Panagiotis Zoulkarni, Asim Baras, John University of Maryland Institute for Systems Research Department of Electrical & Computer Engineering College ParkMD20742 United States

ISBN: (纸本)9781665477161

A Network Slice (NS) is a set of network resources deployed to deliver premium service to a group of users as described in a Service Level Agreement (SLA). We consider that premium service entails per user Quality of Service (QoS) requirements instead of aggregate NS metrics as in the majority of the literature. Since the users in a NS may run applications with different QoS requirements, aggregate metrics are inadequate. Their inefficiency is even more pronounced in Radio Access Network Slices (RANSs). In this case, even if all users run the same application, provisioning bandwidth based on aggregate metrics may violate the QoS requirements of users experiencing poorer channel conditions. To resolve this issue, we propose a method to compute the bandwidth required to deliver per user average packet delay guarantees in enhanced Mobile Broadband (eMBB) RANSs by dimensioning a multiclass queueing system. Our method considers a varying number of NS users over time and allows the online adaptation of the NS bandwidth to its current traffic load, which significantly limits bandwidth overprovisioning. We also describe in detail how an SLA is reached between the tenant and the Network Operator (NO) based on the cdf of the required NS bandwidth. We conduct simulations to show the need for per user guarantees and the efficiency of our method. © 2023 IEEE.

关键词： Bandwidth

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Developing a Smart Drip Infusion System to Elevate Precision in Healthcare 4

Developing a Smart Drip Infusion System to Elevate Precision...

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4th International Conference on Ubiquitous Computing and Intelligent Information systems, ICUIS 2024

作者： Molefinyane, Sefemo Yahya, Abid Hamsadhwani, V. Rohini, R. Samikannu, Ravi Begam, K.Maruliya Botswana International University of Science and Technology Department of Electrical and Communications Systems Engineering Palapye Botswana Periyar Maniammai Institute of Science and Technology Department of Electrical and Electronics Engineering Thanjavur India Vivekanandha College of Engineering for Women Department of Computer Science and Engineering Tiruchengode India Saveetha School of Engineering Saveetha Institute of Medical and Technology Science Department of Computer Science and Communications Engineering Tiruchengode India

ISBN: (纸本)9798331529635

Intravenous (IV) therapy is a critical component of modern healthcare, yet traditional IV systems are prone to human errors, manual adjustments, and limited monitoring capabilities, which can jeopardize patient safety and hinder healthcare efficiency. This paper presents the development of the Smartdrip IV Infusion System, a sophisticated and automated solution that integrates advanced sensors, control algorithms, and intuitive user interfaces to address these limitations. The Smartdrip system minimizes human intervention, provides real-time monitoring, and automates fluid administration with enhanced precision and safety features. The system demonstrates high reliability through simulation and prototype testing in detecting anomalies, issuing alerts, and ensuring accurate infusion rates, thereby improving patient outcomes and reducing healthcare provider workload. The results validate the Smartdrip system as a transformative approach to optimizing IV therapy in diverse clinical settings. © 2024 IEEE.

关键词： Automated Infusion System Drip Rate Monitoring Healthcare Automation Intravenous Therapy Patient

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Intent-Guided Trajectory Prediction for E-Scooter Riders and Bicyclists 27

Intent-Guided Trajectory Prediction for E-Scooter Riders and...

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27th IEEE International Conference on Intelligent Transportation systems, ITSC 2024

作者： Zhang, Zhengming Ding, Zhengming Chen, Yaobin Chien, Stanley Li, Lingxi Sheroy, Rini Domeyer, Joshua Tian, Renran School of Industrial Engineering Purdue University United States Tulane University Department of Computer Science United States School of Electrical and Computer Engineering Purdue University United States United States North Carolina State University Department of Industrial and Systems Engineering United States

ISBN: (纸本)9798331505929

The rapidly increasing popularity of micromobility devices, such as e-scooters and bicycles, in urban environments underscores the critical need for advanced trajectory prediction models to enhance road safety and facilitate seamless integration into intelligent transportation systems. This study introduces a novel approach to predicting the movements of bicyclists and e-scooter riders by leveraging a unique dataset that combines bird's-eye and egocentric views, collected from diverse urban settings across the United States. Employing state-of-the-art deep learning techniques, our model significantly outperforms traditional linear and polynomial regression models in terms of Average Displacement Error (ADE) and Final Displacement Error (FDE), demonstrating superior accuracy in forecasting the trajectories of vulnerable road users. Furthermore, our approach incorporates cognitive annotations to predict crossing intentions, enriching the model's predictive capabilities. The findings highlight the potential of our method to contribute to the development of proactive safety measures and collision avoidance systems, ultimately fostering safer urban mobility landscapes for all road users. © 2024 IEEE.

关键词： Prediction models

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Iterative Reconstruction for Multimodal Neutron Tomography

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IEEE Transactions on Nuclear Science 2025年第5期72卷 1686-1697页

作者： Gregor, Jens Heath, Matthew R. Deller, Timothy Blackston, Matthew A. Hausladen, Paul The University of Tennessee Department of Electrical Engineering and Computer Science KnoxvilleTN37996 United States Oak Ridge National Laboratory Oak RidgeTN37831 United States Leidos Oak RidgeTN37830 United States

We describe a unified framework for model-based iterative 3-D reconstruction of multimodal neutron transmission, hydrogen-scatter, and induced-fission images from low resolution data recorded using 14.1-MeV neutrons and the associated-particle imaging (API) technique. The framework, which was developed to facilitate use in challenging field-deployment scenarios, is centered around physics-based system models and a total variation (TV) constrained implementation of the simultaneous iterative reconstruction technique (SIRT). Modified to solve a statistically weighted least squares (WLS) problem, the SIRT algorithm is accelerated using ordered subsets and Nesterov’s momentum for which we derive a near-optimal value of the governing Lipschitz constant. The approach enables the reconstruction of images that are high resolution compared to the acquired data and is robust to both limited statistics and a limited number of projection angles. Moreover, the framework is fast enough to be practical. Example images are provided that demonstrate both the ability to perform fast-neutron imaging of high-atomic-number materials with low radiation dose and the benefit of multimodal neutron imaging to identify key materials. © 1963-2012 IEEE.

关键词： Constrained optimization

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Work-in-Progress: A Hybrid Collaborative Multi Agent Decision Making Algorithm With Factored-Value Max-Plus

Work-in-Progress: A Hybrid Collaborative Multi Agent Decisio...

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2023 IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2023

作者： Cotae, Paul Reindorf, Nii Emil Alexander Kang, Myong Velazquez, Alexander The University of the District of Columbia Department of Electrical and Computer Engineering WashingtonDC United States Center for High Assurance Computer Systems U. S. Naval Research Laboratory WashingtonDC United States

ISBN: (纸本)9798350337822

We focus on a real-time multi agent decision-making algorithm that combines a centralized algorithm and a distributed algorithm. A network segmentation is unavoidable in a dynamic environment. In such cases, it is necessary for each agent to continually make the most urgent real-time decisions in both centralized and decentralized ways. In this paper, we present a Hybrid Factored-Value Max-Plus algorithm with cost which has online, anytime, and scalable properties despite network segmentation. We also study the performance of centralized and distributed algorithms to understand the performance characteristics of a hybrid algorithm. © 2023 IEEE.

关键词： Decision making

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Decoupling General and Personalized Knowledge in Federated Learning via Additive and Low-rank Decomposition 24

Decoupling General and Personalized Knowledge in Federated L...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Wu, Xinghao Liu, Xuefeng Niu, Jianwei Wang, Haolin Tang, Shaojie Zhu, Guogang Su, Hao State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Zhongguancun Laboratory Beijing China Department of Management Science and Systems University at Buffalo BuffaloNY United States School of Computer Science and Engineering Beihang University Beijing China

ISBN: (纸本)9798400706868

To address data heterogeneity, the key strategy of Personalized Federated Learning (PFL) is to decouple general knowledge (shared among clients) and client-specific knowledge, as the latter can have a negative impact on collaboration if not removed. Existing PFL methods primarily adopt a parameter partitioning approach, where the parameters of a model are designated as one of two types: parameters shared with other clients to extract general knowledge and parameters retained locally to learn client-specific knowledge. However, as these two types of parameters are put together like a jigsaw puzzle into a single model during the training process, each parameter may simultaneously absorb both general and client-specific knowledge, thus struggling to separate the two types of knowledge effectively. In this paper, we introduce FedDecomp, a simple but effective PFL paradigm that employs parameter additive decomposition to address this issue. Instead of assigning each parameter of a model as either a shared or personalized one, FedDecomp decomposes each parameter into the sum of two parameters: a shared one and a personalized one, thus achieving a more thorough decoupling of shared and personalized knowledge compared to the parameter partitioning method. In addition, as we find that retaining local knowledge of specific clients requires much lower model capacity compared with general knowledge across all clients, we let the matrix containing personalized parameters be low rank during the training process. Moreover, a new alternating training strategy is proposed to further improve the performance. Experimental results across multiple datasets and varying degrees of data heterogeneity demonstrate that FedDecomp outperforms state-of-the-art methods up to 4.9%. The code is available at https://***/XinghaoWu/FedDecomp © 2024 ACM.

关键词： Federated learning

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