The paper includes a report on the preliminary results of six-phase induction motor tests. Using a power supply with more than three phases requires a slightly different approach in the motor design process. In theory...
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Weighted vertex cover(WVC)is one of the most important combinatorial optimization *** this paper,we provide a new game optimization to achieve efficiency and time of solutions for the WVC problem of weighted *** first...
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Weighted vertex cover(WVC)is one of the most important combinatorial optimization *** this paper,we provide a new game optimization to achieve efficiency and time of solutions for the WVC problem of weighted *** first model the WVC problem as a general game on weighted *** the framework of a game,we newly define several cover states to describe the WVC ***,we reveal the relationship among these cover states of the weighted network and the strict Nash equilibriums(SNEs)of the ***,we propose a game-based asynchronous algorithm(GAA),which can theoretically guarantee that all cover states of vertices converging in an SNE with polynomial ***,we improve the GAA by adding 2-hop and 3-hop adjustment mechanisms,termed the improved game-based asynchronous algorithm(IGAA),in which we prove that it can obtain a better solution to the WVC problem than using a the ***,numerical simulations demonstrate that the proposed IGAA can obtain a better approximate solution in promising computation time compared with the existing representative algorithms.
This paper presents the (Formula presented.) State-Feedback control for Continuous Semi-Markov Jump Linear Systems where the transition rates are given by the ratio of polynomials of the sojourn time. We show that, fo...
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1 1 Work supported by the National Resilience and Recovery Plan (PNRR) through the National Center for HPC, Big Data and Quantum *** Learning Accelerators (DLA) are pervasive hardware units in modern applications, inc...
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ISBN:
(数字)9781665477635
ISBN:
(纸本)9781665477642
1 1 Work supported by the National Resilience and Recovery Plan (PNRR) through the National Center for HPC, Big Data and Quantum *** Learning Accelerators (DLA) are pervasive hardware units in modern applications, including safety critical systems such as Automotive, Aerospace, Robotics, and health monitoring systems. Therefore, it is crucial to guarantee their reliability during the mission operation of any of these applications, as a failure can produce catastrophic results (e.g., loss of human lives). In fact, modern semiconductor technologies used to implement DLAs can be affected by faults due to several phenomena, such as aging, process variation, or manufacturing defects. Periodic testing and functional testing strategies have demonstrated their utility and effectiveness on DLA accelerators in spotting faults arising during the in-field operation of the system. Nonetheless, these approaches can have significant testing times and memory footprint overheads, making them hard to apply during the online operation of large-size accelerators. This work proposes an effective strategy for generating efficient functional test patterns for computational units in DLA accelerators by reducing the required testing time (43×) and memory footprints (3.3×) compared with literature solutions. Moreover, our strategy provides diagnostic capabilities for identifying defective units (e.g., multipliers).
This paper addresses the parameter design problem of magnetic couplers and proposes a multi-objective optimization design method based on the Metamodel of Optimal Prognosis (MOP). The method involves mathematically fi...
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Convolutional Neural Networks (CNNs) have shown exceptional effectiveness in complex and data-intensive domains such as image and video processing, conversational systems, and healthcare. Moreover, sectors like High-P...
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ISBN:
(数字)9781665477635
ISBN:
(纸本)9781665477642
Convolutional Neural Networks (CNNs) have shown exceptional effectiveness in complex and data-intensive domains such as image and video processing, conversational systems, and healthcare. Moreover, sectors like High-Performance Computing and safety-critical applications, including automotive, aerospace, and autonomous robotics, impose stringent requirements on energy efficiency, performance, and robustness. However, modern semiconductor technologies are increasingly vulnerable to faults, which can degrade CNN performance and potentially result in catastrophic failures. This work explores the impact of regularization techniques (dropout layer) in enhancing the inference robustness of CNN models against soft errors. We analyzed soft error impacts on five widely adopted CNN architectures, each trained with ten different dropout rates. Our experimental results reveal that optimizing the dropout rate during training can improve the in-field robustness of CNN models by up to 12% compared to baseline configurations under soft error conditions. Additionally, fine-tuning this architectural parameter can lead to accurary improvements of up to 10%.
In the past two decades, object tracking has progressively advanced in computer vision and image processing. Tracking is a collection of algorithms that detect and track objects in a video sequence. This has resulted ...
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In the shallow sea environment, array of small array elements may not be able to accurately locate targets due to undersampling. To address this issue, the paper proposed a method called sound field extension based on...
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The method [K]control of Adaptive Multiple-timescale Systems (KAMS) has been used as a method of adaptive control for systems with states that evolve at vastly different rates and with uncertain parameters. Prior rese...
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With the continuous development of industrialization, warehouse storage is becoming more and more intelligent. The storage state of goods in the warehouse is one of the very important components of the warehouse manag...
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