The sixth generation (6G) mobile communication system is expected to meet the different service needs of modern communication scenarios. In this paper, the spectrum sharing problem in the 6G HetNets is addressed by co...
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Applications in the Internet of Things often make use of large networks of independent sensor nodes that generate streams of volatile data. A major challenge in these decentralized networks is to efficiently discover ...
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Fault detection in photovoltaic (PV) arrays is of paramount importance, because the output power is maintained at its highest value and the efficiency of the system is prolonged. In the past, there were issues of outp...
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Deep neural networks have shown remarkable capabilities in computer vision applications. However, their complex architectures can pose challenges for efficient real-time deployment on edge devices, as they require sig...
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ISBN:
(纸本)9798350393613
Deep neural networks have shown remarkable capabilities in computer vision applications. However, their complex architectures can pose challenges for efficient real-time deployment on edge devices, as they require significant computational resources and energy costs. To overcome these challenges, TensorRT has been developed to optimize neural network models trained on major frameworks to speed up inference and minimize latency. It enables inference optimization using techniques such as model quantization which reduces computations by lowering the precision of the data type. The focus of our paper is to evaluate the effectiveness of TensorRT for model quantization. We conduct a comprehensive assessment of the accuracy, inference time, and throughput of TensorRT quantized models on an edge device. Our findings indicate that the quantization in TensorRT significantly enhances the efficiency of inference metrics while maintaining a high level of inference accuracy. Additionally, we explore various workflows for implementing quantization using TensorRT and discuss their advantages and disadvantages. Based on our analysis of these workflows, we provide recommendations for selecting an appropriate workflow for different application scenarios.
The proceedings contain 126 papers. The topics discussed include: tree based diagnosis enhanced with meta knowledge applied to dynamic systems;making systems fail-aware: a semi-supervised machine learning approach for...
The proceedings contain 126 papers. The topics discussed include: tree based diagnosis enhanced with meta knowledge applied to dynamic systems;making systems fail-aware: a semi-supervised machine learning approach for identifying failures by learning the correct behavior of a system;diagnosis driven anomaly detection for cyber-physical systems;a multiple sensor fault diagnosis scheme for autonomous surface vessels;active thruster fault diagnosis for an overactuated autonomous surface vessel;sensor set decomposition for enhanced distributed sensor fault isolability of marine propulsion systems;a survey on data-driven fault diagnostic techniques for marine diesel engines;actuator fault tolerant control for a remotely operated vehicle based on adaptive extended Kalman filter;and nodal hydraulic head estimation through unscented Kalman filter for data-driven leak localization in water networks.
Wireless power transfer (WPT) is a rapidly developing field, and strong and efficient systems are essential, especially for bi-directional applications. In this study, the performance of a CLLLC resonant network in a ...
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In today's increasingly dynamic business environment, optimizing Customer Relationship Management (CRM) platforms is essential to increase operational efficiency and provide the best possible user experience. The ...
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Raft is known as a replicating state machine that tolerates crash faults, and its use enables various distributedsystems, including distributed transaction processing systems. The original Raft protocol creates an in...
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Human-machine collaborative driving is poised to become a cornerstone of intelligent transportation systems, where accurate recognition of driver behavior and intent is crucial for vehicle decision-making and real-tim...
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A large-scale volunteer computing system is a type of distributed system in which contributors volunteer their computing resources, such as personal computers or mobile devices, to contribute to a larger computing eff...
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A large-scale volunteer computing system is a type of distributed system in which contributors volunteer their computing resources, such as personal computers or mobile devices, to contribute to a larger computing effort. Volunteer resources are connected over the Internet and together forma powerful computing system capable of providing a service without depending on a service provider. Volunteer network resource allocation is the process of assigning computing tasks or services to a network of volunteer resources. The allocation process includes identifying the needed resources, selecting appropriate volunteers, and assigning tasks or services based on their capabilities. Volunteer computing systems consist of a large number of heterogeneous resources- in terms of processing power, storage, and availability- belonging to different authorities- users or organizations- and exhibiting uncertain behavior in terms of connection, disconnection, capacity, and failure. All of this makes resource allocation a challenging task in terms of ensuring a minimum quality of service, requiring complex algorithms and optimization techniques to ensure that services are efficiently allocated while respecting the constraints of the available resource. This paper introduces the Network-Aware Resource Allocation mechanism, which leverages the location, connectivity, and network latency of volunteer nodes to minimize the time a service runs with degraded quality of service and aims to deal with the energy consumption resulting from data replication requirements. This resource allocation mechanism applies to both the initial deployment of the service in the network and to the reallocation of nodes in the event that one of the allocated nodes fails or becomes unavailable. Our method has been validated in a simulation environment of a realistic volunteer system. The analysis of the results shows how our mechanism meets the quality requirements of users while minimizing the synchronization a
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