Edge-Cloud systems provide efficient computation and storage close to data sources, with lower latency, scalability, and application performance for various applications, such as IoT, autonomous vehicles, and real-tim...
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This paper presents a learning-based methodology for developing an optimal lane-changing control policy for a Remote controlled (RC) car using real-time sensor data. The RC car is equipped with sensors including GPS, ...
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ISBN:
(纸本)9798350399462
This paper presents a learning-based methodology for developing an optimal lane-changing control policy for a Remote controlled (RC) car using real-time sensor data. The RC car is equipped with sensors including GPS, IMU devices, and a camera integrated in an Nvidia Jetson AGX Xavier board. By a novel Adaptive Dynamic Programming (ADP) algorithm, our RC car is capable of learning the optimal lane-changing strategies based on the real-time processed measurement from the sensors. The experimental outcomes show that our learning-based control algorithm can be effectively implemented, adapt to parameter changes, and complete the lane changing tasks in a short learning time with satisfactory performance.
Hip assistance with cable-driven devices has been proven to help decrease the metabolic cost of gait. However, most existing devices use heavy actuating modules or provide assistance in only one direction, limiting th...
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ISBN:
(纸本)9798350377712;9798350377705
Hip assistance with cable-driven devices has been proven to help decrease the metabolic cost of gait. However, most existing devices use heavy actuating modules or provide assistance in only one direction, limiting the effectiveness. Cable-driven devices are also difficult to accurately estimate the hip position using only motor encoders, therefore utilizing various auxiliary sensors. This paper introduces a 1.5 kg cable-driven soft wearable hip assist device that can provide assistance in both flexion and extension, using a velocity-dependent delayed output feedback controller (v-DOFC). The device is designed with the consideration of ergonomics and pressure distribution of wearable parts, to increase the anchoring performance and comfort. The controller uses time-delayed feedback proportional to the velocity output state, allowing control without requiring accurate position estimation. Additionally, directional weighting is used to provide different assistance forces for extension and flexion to match different optimal assistance values. Experimental results show that the device can reduce metabolic cost by 13.8 % compared to walking without the device. The soft wearable hip assist device can be applied to help the elderly with weaker muscles to walk longer distances.
With the development of intelligent manufacturing in the context of Industry 4.0, more and more data from industrial sites is urgently required to be collected and analyzed accordingly. However, faced with multiple he...
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ISBN:
(纸本)9798350379860;9798350379877
With the development of intelligent manufacturing in the context of Industry 4.0, more and more data from industrial sites is urgently required to be collected and analyzed accordingly. However, faced with multiple heterogeneous devices and different types of on-site systems, factors such as diverse protocol types, large data volumes, and tight on-site debugging time have caused difficulties in obtaining data from industrial equipment. This article proposes a universal framework for data acquisition of industrial field equipment and systems, and designs a flexible and configurable method for data acquisition based on this framework. To achieve this method, a data acquisition tool called Digital Acquisition Tool(DATool) has been designed and developed. Finally, this article established a simulation experimental environment and implemented data acquisition and processing on devices within Modbus protocols. The experiment proves that this method is convenient and effective in data acquisition.
Currently, the concept of artificial intelligence has become extremely widespread, but there is a difficulty in accurately defining this concept. In general, artificial intelligence is divided into two main categories...
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This paper delineates a meticulous case study centered on the deployment of digital twin technology in Unity3D to augment operations in offshore environments, with a particular emphasis on Floating Liquefied Natural G...
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ISBN:
(纸本)9798350364200;9798350364194
This paper delineates a meticulous case study centered on the deployment of digital twin technology in Unity3D to augment operations in offshore environments, with a particular emphasis on Floating Liquefied Natural Gas operations. It encompasses the integration of real-time meteorological data, the application of a Proportional-Integral-Derivative controller for refined vessel maneuvering, and the incorporation of an advanced voice navigation interface. The primary objective of this research is to substantially elevate the controllability, predictability, and operational efficacy within the realm of offshore asset management. The findings of this study significantly contribute to the enhancement of strategic decision-making frameworks and propel forward-looking innovations in the offshore sector, culminating in heightened operational efficiency and fortified risk management strategies.
The traditional operation and maintenance mode of substations are unable to keep up with the rapid development of the power grid. intelligent operation and maintenance of substations have become an important trend. As...
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The rapid development of 5G and Internet of Things (IoT) has led to a surge in connected wireless devices, creating a significant demand for computation due to the evolution of intelligent applications. Addressing the...
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ISBN:
(纸本)9798350361261;9798350361278
The rapid development of 5G and Internet of Things (IoT) has led to a surge in connected wireless devices, creating a significant demand for computation due to the evolution of intelligent applications. Addressing the computational pressure on Mobile Edge computing (MEC) is now an urgent concern. Effectively offloading intensive tasks via Device-to-Device (D2D) links to the base station (BS) or idle devices improves computation quality and reduces latency. In this paper, we propose a D2D-assisted MEC system to address the scheduling challenges posed by numerous independent computing tasks generated by multiple users. We consider splitting the user's task into multiple independent subtasks and calculating offloading separately to reduce processing delay. We represent this scheduling problem in the form of a task permutation and propose an improved Grey Wolf Optimizer (IGWO) metaheuristic algorithm to search for the optimal scheduling solution. This approach, through improvements to the nonlinear convergence factor and dynamic weighting, enhances the optimization speed and accuracy of the Grey Wolf algorithm, effectively reducing task processing latency. Simulation results indicate that the IGWO metaheuristic algorithm outperforms other benchmark methods in addressing this scheduling problem.
The dense deployment of wireless nodes in the next generation of wireless local area networks (WLANs) poses a potential threat to network performance. Enhancing spatial reuse (SR) in WLANs can effectively address this...
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ISBN:
(纸本)9798350361261;9798350361278
The dense deployment of wireless nodes in the next generation of wireless local area networks (WLANs) poses a potential threat to network performance. Enhancing spatial reuse (SR) in WLANs can effectively address this issue. Dynamic clear channel assessment (CCA) threshold and transmit power control are crucial techniques to improve SR. This paper formulates the SR problem as a multi-armed bandit problem. A Bayesian optimization online learning algorithm with Gaussian process is proposed to optimize CCA thresholds and transmit power jointly. Finally, the proposed algorithm is compared with the default configuration and Thompson sampling algorithm across four performance metrics with the NS-3 simulator. The results demonstrate that our algorithm can significantly diminish cumulative regret, amplify total throughput, reduce the number of nodes in starvation, and improve overall network fairness.
Unmanned Aerial Vehicles (UAVs) significantly deliver safe, sustainable, and autonomous services to intelligent Transportation systems and urban air mobility applications. In this context, image classification tasks h...
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ISBN:
(纸本)9798350399462
Unmanned Aerial Vehicles (UAVs) significantly deliver safe, sustainable, and autonomous services to intelligent Transportation systems and urban air mobility applications. In this context, image classification tasks have an important role in these applications, and Federated Learning (FL) can empower these services with a decentralized approach to machine learning tasks taking advantage of UAV's broad access to distributed data and the edge computing capacity of these devices. However, data can have a heterogeneous distribution, negatively affecting federated training and impacting the accuracy of results. To address this issue, we propose a method called Federated Learning Algorithm with Weight Standardization on Convolutional Layers (FedWS) that locally normalizes the weights of a neural network's convolutional layers. The results show that FedWS provided more smoothness on losses in the training process at the aggregation level, allowed 3% to 6% higher accuracy in different levels of heterogeneity, and reduced the communication cost in a margin of 25% to 50% in a more linear manner compared to others methods on image classification tasks in the EuroSAT dataset.
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