This article introduces an intelligent trackless laying system for urban rail transit projects. The track laying system mainly consists of mechanical systems, intelligentcontrolsystems, deflection correction systems...
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ISBN:
(纸本)9781510674479
This article introduces an intelligent trackless laying system for urban rail transit projects. The track laying system mainly consists of mechanical systems, intelligentcontrolsystems, deflection correction systems, obstacle avoidance systems, electrical systems, and hydraulic systems. The overall equipment can achieve free span change, lifting and height adjustment, suspension angle adjustment, and trackless movement in circular, horseshoe shaped, rectangular and other tunnels. The lifting platform also has functions such as lifting, traversing, and turning, and can lift various components such as track bed boards, track panels, ash hoppers, and concrete mixing tanks. Under electrical and hydraulic control, the equipment having strong adaptability, and multifunctional integration can achieve stepless speed change and wireless remote control, and also has automatic deviation alarm, obstacle alarm and speed reduction, and Fault self-diagnosis function. The developed equipment has improved the overall construction efficiency of track laying operations, saved project costs, and also solved the safety hazards caused by traditional construction methods.
Multi-robot systems have gained increasing interest across various fields such as medicine, environmental monitoring, and more. Despite the evident advantages, the coordination of the swarm arises significant challeng...
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ISBN:
(纸本)9798350377712;9798350377705
Multi-robot systems have gained increasing interest across various fields such as medicine, environmental monitoring, and more. Despite the evident advantages, the coordination of the swarm arises significant challenges for human operators, particularly concerning the cognitive burden needed for efficiently controlling the robots. In this study, we present a novel approach for enabling a human operator to effectively control the motion of multiple robots. Leveraging a shared control data-driven approach, we enable a single user to control the 9 degrees of freedom related to the pose and shape of a swarm. Our methodology was evaluated through an experimental campaign conducted in simulated 3D environments featuring a narrow cylindrical path, which could represent, e.g., blood vessels, industrial pipes. Subjective measures of cognitive load were assessed using a post-experiment questionnaire, comparing different levels of autonomy of the system. Results show substantial reductions in operator cognitive load when compared to conventional teleoperation techniques, accompanied by enhancements in task performance, including reduced completion times and fewer instances of contact with obstacles. This research underscores the efficacy of our approach in enhancing human-robot interaction and improving operational efficiency in multi-robot systems.
Smartgrids integrate information technologies. Their concept is based on the intelligent management of intermittent renewable energies, using bidirectional communication tools between production and consumption throug...
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The probabilistic safety verification problem of stochastic hybrid systems is very important. In this paper, for a given stochastic hybrid system, an algorithm for generating probabilistic barrier certificates is prop...
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Vehicular Edge computing (VEC) seeks adept task offloading strategies to streamline resource allocation and elevate vehicular application performance. This paper delves into scrutinizing the effectiveness of an Actor-...
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ISBN:
(纸本)9798350361261;9798350361278
Vehicular Edge computing (VEC) seeks adept task offloading strategies to streamline resource allocation and elevate vehicular application performance. This paper delves into scrutinizing the effectiveness of an Actor-Critic-based Multi-Agent Deep Reinforcement Learning (MADRL) framework in orchestrating cost-minimized task offloading within VEC domains. Embracing the MADRL paradigm, this framework empowers adaptive decision-making for task allocation and execution in highly dynamic vehicular environments. By integrating deep reinforcement learning methodologies into multi-agent systems, the study endeavors to bolster the efficiency of task offloading strategies operating within VEC realms. The research emphasizes MADRL's role in fostering agile and responsive task allocation paradigms within VEC, aiming to optimize resource utilization while curtailing costs associated with task execution. Through adaptive decision mechanisms, MADRL facilitates dynamic task allocation, enabling vehicles to seamlessly distribute computational tasks among onboard systems and proximal edge resources. This exploration illuminates the potential of MADRL-based strategies in refining task offloading efficiency, presenting a promising avenue for enhancing VEC's computational resource management and overall performance.
An integration of computer vision algorithms, machine learning, and deep learning to extract information from digital photos is particularly important in dermatology for the early identification and treatment of disea...
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High-performance computing (HPC) has become an essential tool for improving the efficiency and scalability of transaction processing systems, especially as data volumes continue to grow in fields like finance, e-comme...
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Recent years have witnessed a surge in the deployment of Deep Neural Network (DNN)-based services, which drives the development of emerging intelligent transportation systems (ITSs). However, it is still challenging t...
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ISBN:
(纸本)9798350399462
Recent years have witnessed a surge in the deployment of Deep Neural Network (DNN)-based services, which drives the development of emerging intelligent transportation systems (ITSs). However, it is still challenging to enable efficient and reliable DNN inference in Vehicular Edge computing (VEC) environments due to resource constraints and system dynamics. In view of this, this work investigates a DNN inference partition and offloading scenario with environmental uncertainties in VEC, which motivates the necessity to strike a balance between inference delay and the success ratio of receiving the offloading outputs. Then, by considering communication and computation overheads as well as failed offloading conditions in an analytical model, we propose an Adaptive Splitting, Partitioning, and Merging (ASPM) strategy that reduces the inference delay while maintaining a decent offloading success ratio. Specifically, ASPM first splits and partitions the DNN model in a recursive way to find the optimal split blocks with the aim of minimizing inference delay. On this basis, it further merges DNN blocks in a greedy way to reduce the number of blocks to be offloaded, thus, enhancing the offloading success ratio for the whole DNN inference. Finally, we conduct comprehensive performance evaluations to demonstrate the superiority of our design.
The MIPS RISC processor is characterized by its simplicity, pipelined architecture, register-centric design, and a reduced set of instructions optimized for performance. This paper explores the implementation of MIPS ...
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Maximizing the efficiency of multi-robot systems is one of the primary objectives in solving the multi-robot coverage path planning (mCPP) problem. During coverage tasks, unexpected imbalances in the multi-robot syste...
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ISBN:
(纸本)9798331518509;9798331518493
Maximizing the efficiency of multi-robot systems is one of the primary objectives in solving the multi-robot coverage path planning (mCPP) problem. During coverage tasks, unexpected imbalances in the multi-robot systems, such as changes in speed, can lead to suboptimal utilization of the system's capabilities, subsequently reducing the efficiency of task execution. In this paper, we developed a multi-robot dynamic spanning tree coverage (MDSTC) algorithm, an online area-division-based approach that adjusts region allocation among robots based on their coverage status through an exchange-based mechanism to enhance multi-robot system efficiency. To validate the effectiveness of the proposed algorithm, we conducted extensive numerical simulations. The results demonstrate that our algorithm achieves a superior solution in scenarios where robot efficiency remains balanced, closely matching the performance of state-of-the-art mCPP methods. Furthermore, when differences in efficiency arise among the robots during task execution, the proposed algorithm provides a dynamic solution that enhances overall coverage efficiency.
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