Advancements in deep learning and computer vision have greatly enhanced object detection, playing a vital role in applications such as autonomous driving and surveillance. This study explores the trade-offs between tw...
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This paper presents a modified Particle Swarm Optimization (PSO) algorithm designed to enhance computational efficiency without compromising solution quality. Two approaches are proposed: the first emphasizes the adva...
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This paper investigates reinforcement learning (RL) as a practical framework for achieving optimal adaptive control across several simple dynamical system models. All experiments were conducted using the Proximal Poli...
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This study investigates optimal control problems described by fractional differential equations, with the control vector components subject to algebraic constraints. Two case studies are analyzed: an illustrative exam...
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This paper evaluates two common methods for trajectory estimation: the Extended Kalman Filter (EKF), the Unscented Kalman Filter (UKF). The EKF and UKF are well-established recursive filtering techniques commonly used...
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Academic writing is a significant challenge for many learners striving for proficiency. Adaptive scaffolding techniques and AI tools in education have proven effective in addressing this challenge and supporting learn...
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In this paper, an integral sliding mode (ISM) control scheme is developed to tackle platooning control issues of vehicle systems under both an improved constant-Time headway (CTH) spacing strategy and a coding-decodin...
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Advancements in deep learning and computer vision have greatly enhanced object detection, playing a vital role in applications such as autonomous driving and surveillance. This study explores the trade-offs between tw...
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ISBN:
(数字)9798331517649
ISBN:
(纸本)9798331517656
Advancements in deep learning and computer vision have greatly enhanced object detection, playing a vital role in applications such as autonomous driving and surveillance. This study explores the trade-offs between two distinct YOLO model architectures: the vision-only models, referred to as pre-YOLO-World, and a vision-language model known as YOLO-World. While the pre-YOLO-World models are renowned for their speed and accuracy, the YOLO-World model integrates large language models to enhance contextual comprehension, enabling open-vocabulary object detection. This paper provides a qualitative analysis of their strengths and limitations, focusing on speed, accuracy, adaptability, computational requirements, and application scenarios. By contrasting these architectures, we aim to offer insights into their potential roles in evolving object detection paradigms and to guide practitioners in selecting models suited to specific use cases.
Rerouting drivers from selfish route choices to system-optimal traffic patterns has the potential to improve the performance of existing infrastructure. Previous research has looked into assessing the potential of rer...
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Rerouting drivers from selfish route choices to system-optimal traffic patterns has the potential to improve the performance of existing infrastructure. Previous research has looked into assessing the potential of rerouting through the empirical price of anarchy, a measure of network efficiency. However, studies using real-world measurements have been limited by methodological accuracy and network size. Also, they have lacked understanding of the spatial distribution of benefits from rerouting and the relationship with marginal external cost road charges that can be used for implementation. In this article, we create an accurate data-driven traffic assignment model of England's Strategic Road Network. We use it to calculate the national price of anarchy, which is found to be almost 1 implying gains from rerouting at the national scale are minimal and smaller than in other studies. The results show the distribution of rerouting benefits varies strongly with different network zones and demand profiles. This did not match the distribution of marginal external cost charges. Some zones have noticeable benefits from rerouting although the overall network benefit is small, however, these zones do not coincide with where the largest road charges have to be applied for system-optimal rerouting. These results have implications for rerouting implementation.
Quantum computing is progressing at a fast rate and there is a real threat that classical cryptographic methods can be compromised and therefore impact the security of blockchain networks. All of the ways used to secu...
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