Nowadays production companies are in a difficult situation since batch sizes are decreasing, the number of product variants is growing, and the demand is difficult to forecast. New technologies enable to design more c...
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Modeling of cross-medium vehicles with complex shapes still requires a thorough investigation. This paper proposes a multi-method combination modeling approach to tackle such a problem. First-principle model is derive...
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This paper addresses the path planning problem for unmanned aerial vehicle (UAV), where a UAV serves as a messenger to periodically traverse over road-constrained ground vehicles (GVs) to relay information. The GVs ma...
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It has long posed a challenging task to optimally deploy multiagent systems (MASs) to cooperatively coverage poriferous environments in real cooperative detection applications. In response to this challenge, this arti...
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In realistic distributed optimization scenarios, individual nodes possess only partial information and communicate over bandwidth constrained channels. For this reason, the development of efficient distributed algorit...
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The manufacturing industry is gradually reforming in the direction of intelligent digitalization. The traditional automationcontrol technology and virtual simulation technology cannot intuitively reflect the operatio...
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In this research, the Human Following Robot (HFR) prototype has been designed and implemented using YOLO v3-Tiny and Tensor Flow Lite on Raspberry Pi hardware named Rewang. The HFR Rewang is designed to assist aircraf...
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Model-Agnostic Meta-Learning (MAML) and its variants have shown remarkable performance in scenarios characterized by a scarcity of labeled data during the training phase of machine learning models. Despite these succe...
Model-Agnostic Meta-Learning (MAML) and its variants have shown remarkable performance in scenarios characterized by a scarcity of labeled data during the training phase of machine learning models. Despite these successes, MAML-based approaches encounter significant challenges when there is a substantial discrepancy in the distribution of training and testing tasks, resulting in inefficient learning and limited generalization across domains. Inspired by classical proportional-integral-derivative (PID) control theory, this study introduces a Layer-Adaptive PID (LA-PID) Optimizer, a MAML-based optimizer that employs efficient parameter optimization methods to dynamically adjust task-specific PID control gains at each layer of the network, conducting a first-principles analysis of optimal convergence conditions. A series of experiments conducted on four standard benchmark datasets demonstrate the efficacy of the LA-PID optimizer, indicating that LA-PID achieves state-of-the-art performance in few-shot classification and cross-domain tasks, accomplishing these objectives with fewer training steps. Code is available on https://***/yuguopin/LA-PID. Copyright 2024 by the author(s)
Electro-hydraulic actuator (EHA) system is a key component of the ship's electro-hydraulic control system, but the internal failure mechanism of EHA is extremely complex, and it is limited by insufficient detectio...
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The failure of electric valves represents a significant safety hazard in industrial systems. Traditional manual detection and regular replacement strategies are insufficient to address this issue. This study proposes ...
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