Aiming at the disadvantages of the standard seagull algorithm (SOA), such as discrete distribution of initial population position, low solution accuracy and slow convergence speed, a seagull optimization algorithm (L-...
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In the digital era, data is pivotal across disciplines like business, marketing, engineering, and social sciences. This research proposes a new method of acquiring intraday stock data from the National Stock Exchangea...
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The rapid progression in autonomous driving and vehicle networking technologies has catalyzed the emergence of advanced vehicle applications, aiming to augment traffic safety and the driving experience. This advanceme...
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ISBN:
(纸本)9798350361261;9798350361278
The rapid progression in autonomous driving and vehicle networking technologies has catalyzed the emergence of advanced vehicle applications, aiming to augment traffic safety and the driving experience. This advancement, however, is challenged by the limited computational and storage capacities inherent in on-board vehicle systems. To address this, Vehicle Edge computing (VEC) emerges as a pivotal solution, enhancing vehicular computational capabilities. In this context, we introduce a novel VEC task offloading model utilizing Deep Reinforcement Learning (DRL). This model leverages the otherwise idle computational resources available in vehicles to facilitate efficient edge computing offloading within heterogeneous networks. A key innovation in our approach is the integration of Reinforcement Learning (RL) with Deep Learning (DL), significantly improving the convergence efficiency of the system. We also introduce an enhanced Q-learning algorithm tailored to jointly address the task offloading and processing challenges in VEC. This algorithm is adept at making optimal offloading decisions, aiming to minimize the overall system cost, encompassing both latency and energy consumption. Through rigorous simulation, our results demonstrate that this improved Q-learning approach substantially reduces total system costs while concurrently improving the quality of service in VEC environments. Our study not only offers a robust framework for computation offloading in vehicular networks but also paves the way for future research in AI-driven vehicular technology optimizations.
In recent years the problem of planning paths through complex obstacles while respecting kinematic constraints has seen increased attention. Many have found that respecting curvature constraints is particularly diffic...
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ISBN:
(纸本)9798350395747
In recent years the problem of planning paths through complex obstacles while respecting kinematic constraints has seen increased attention. Many have found that respecting curvature constraints is particularly difficult without substantially slowing convergence speed. Batch Informed Trees (BIT*) is a path-planning algorithm that has been shown to converge rapidly in large environments without considering kinematic constraints. This work proposes an extension to BIT* that employs fillets as motion primitives, enabling the incorporation of curvature constraints into the planning process. Path-length heuristics for fillet-based planning are introduced to accelerate convergence. Comparisons to pre-existing approaches are made with an Unmanned Aerial Vehicle (UAV) simulation modeled off of Manhattan, New York.
The OSP project, which was started to build a digital twin in the marine environment, is a structure in which various objects are modeled and each model object is linked using FMI. This project is a structure where va...
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Federated learning (FL) has been widely used in edge computing that enables artificial intelligence at the network edge as a distributed machine learning paradigm. In contrast to traditional cloud-based distributed tr...
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ISBN:
(纸本)9798350358261;9798350358278
Federated learning (FL) has been widely used in edge computing that enables artificial intelligence at the network edge as a distributed machine learning paradigm. In contrast to traditional cloud-based distributed training, the heterogeneity in edge computing may cause federated learning taking long training time. In this paper, we adapt control parameter (i.e., local epoch size) across devices to minimize wall-clock convergence time with joint consideration of resource heterogeneity and statistical heterogeneity. To analyze the influence of statistical heterogeneity, we derive a convergence upper bound for synchronous FL algorithm and establish the relationship between the number of training rounds and local epoch size under heterogeneous data distribution. Based on the convergence bound, we can solve the non-convex problem of minimizing FL training time with accuracy constraint and obtain near-optimal local epoch size. We develop a scheduling algorithm that estimates the statistical heterogeneity in initial training rounds and subsequently guides adaptive local training across devices. Practically, we evaluate our algorithm in a variety of heterogeneous scenarios. Extensive simulation results demonstrate that our algorithm performs high convergence speed over wall-clock time and spends less time reaching target accuracy compared with benchmark approaches.
For the real-time object detection and tracking, CNN-based YOLO(You Only Look Once) is widely used. However, employing YOLO in a multi-streaming environment poses a challenge due to the increasing demand for computing...
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ISBN:
(纸本)9798350376975;9798350376968
For the real-time object detection and tracking, CNN-based YOLO(You Only Look Once) is widely used. However, employing YOLO in a multi-streaming environment poses a challenge due to the increasing demand for computing resources as the number of streams increases. This paper proposes a system that minimizes FPS degradation without necessitating additional computing resources when using YOLO in a multi-streaming environment. Experimental results show that the proposed system can handle multi-streaming with 8 CCTV cameras without encountering FPS degradation, while maintaining delays at an acceptable level.
This paper provides a new solution of unmanned ship control with unmeasurable yaw angle in complex communication environments. A new adaptive output feedback controller is designed in this paper to ensure that the pos...
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ISBN:
(纸本)9798350379860;9798350379877
This paper provides a new solution of unmanned ship control with unmeasurable yaw angle in complex communication environments. A new adaptive output feedback controller is designed in this paper to ensure that the position error converges to zero asymptotically and meets the prescribed performance requirement. It is revealed that the designed control law can make sure that the tracking deviation convergence is not affected by the yaw angle estimated error. A simulation example is designed and completed to demonstrate the validity of the proposed controller.
Modern deep Reinforcement Learning (RL) techniques are quite good at choosing the best possible rules to maximise rewards. By using rich visual information for policy selection, this method combined with Deep Learning...
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With this flood of multimedia data, protecting it is an absolute necessity. This paper describes a complete method for securing images that combines encryption and watermarking. A technique of the sort suggested furth...
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