In the field of machine manufacturing, the effective use of CNC machining technology and monitoring technology is of great significance, because it can realize the scientificity and perfection of machine manufacturing...
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Efficient operation and maintenance of wastewater treatment plants (WWTPs) are essential for safeguarding public health and the environment. The emergence of mechanical faults within complex systems can lead to disrup...
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ISBN:
(纸本)9798350364309;9798350364293
Efficient operation and maintenance of wastewater treatment plants (WWTPs) are essential for safeguarding public health and the environment. The emergence of mechanical faults within complex systems can lead to disruptions, increased operational costs, and environmental risks. As the world moves towards a digitally connected and sustainable future, the development of Deep Learning (DL) tools for fault detection and isolation (FDI) in wastewater treatment processes is expected to become paramount. Therefore, in this study, we developed two neural models, a Feedforward Neural Network (FFNN) and a Long Short-Term Memory (LSTM), to address the detection of mechanical faults such as bias, stuck, spikes, and precision degradation of the Dissolved Oxygen (DO) sensor. The classification results showed remarkable accuracy performances during testing: for Dataset 1, FFNN achieved 96.56%, while LSTM reached 99.36%;and for Dataset 2, FFNN achieved 99.36%, and LSTM reached 99.57%.
In this paper we extend our previous research on coherent observer-based pole placement approach to study the synthesis of robust decoherence-free (DF) modes for linear quantum passive systems, which is aimed at prese...
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Managing parking efficiently has become a critical challenge in today's densely populated urban areas, such as Tirana, Albania, where increasing vehicle congestion exacerbates traffic and environmental concerns. V...
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The PIM (Processing in Memory) architecture, performing MAC operations inside memory, garners attention as a next-gen deep learning processor by eliminating memory movement between memory and computation units, unlike...
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ISBN:
(纸本)9798331517939;9788993215380
The PIM (Processing in Memory) architecture, performing MAC operations inside memory, garners attention as a next-gen deep learning processor by eliminating memory movement between memory and computation units, unlike NPUs and GPUs. However, applying conventional network pruning for the same purpose faces challenges due to small memory and analog-based MAC operations in PIM. This paper proposes techniques for effective network pruning, demonstrating how weight pruning based on temperature/humidity modeling can mitigate inference noise in PIM. Additionally, it introduces a grouping-based importance metric for channel pruning applicable to any hardware. Both approaches quantitatively enhance performance in simulation, proving their efficacy.
The amalgamation of Cloud computing and Internet-of-Things (IoT), i.e., Cloud-of-Things (CoT), has emerged as one of the indispensable technologies in the IT and business world. The success of CoT depends on the effic...
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To address the challenge of developing effective control methods for governing the entire driving process of both connected and automated vehicles (CAVs) and human-driven vehicles (HDVs) in mixed traffic scenario, thi...
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ISBN:
(纸本)9798350399462
To address the challenge of developing effective control methods for governing the entire driving process of both connected and automated vehicles (CAVs) and human-driven vehicles (HDVs) in mixed traffic scenario, this paper proposes a parallel control framework with calibrating module and pricing control module. Based on parallel learning theory, this framework enables intelligent agents to take over both HDVs and CAVs and engage in right-of-way (RoW) negotiations in the mixed traffic network. Game theory is employed to model RoW transactions and incentivize transactions through utility compensation. Empirical datasets are used to showcase the performance of the proposed framework in a lane-changing scenario. Our findings demonstrate that the introduction of incentives can increase the RoW trading probability of drivers significantly. To the best of our knowledge, this is the first parallel control framework based on pricing mechanism allowing both CAVs and HDVs to negotiate based on intelligent agents.
Detection and control of congestion in Mobile ad-hoc networks (MANET) is a challenging task. Congestion hurts performance and reduces the throughput of the system. Thus it is imperative to avoid or control congestion ...
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In this paper, we propose a hierarchical framework for multi-agent systems to enhance cooperative tasks in dynamic environments. Accomplishing cooperative tasks can be challenging in dynamic environments. Reinforcemen...
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ISBN:
(纸本)9798350364200;9798350364194
In this paper, we propose a hierarchical framework for multi-agent systems to enhance cooperative tasks in dynamic environments. Accomplishing cooperative tasks can be challenging in dynamic environments. Reinforcement learning is a popular approach in this field, enabling agents to make real-time decisions. However, large state and action spaces often lead to poor performance, such as slow convergence and suboptimal policies. To address this issue, we utilize a hierarchical framework. Long-horizon and complicated tasks are decomposed into multiple subtasks. At the low-level, each subtask has a corresponding decision-making model, trained using the Soft Actor-Critic reinforcement learning algorithm. Additionally, a high-level component is introduced to determine which subtask to tackle at any given time. We discuss our method in the context of the popular hunting problem involving pursuers and an evader. Simulation demonstrates the efficacy and feasibility of our method in the hunting problem environment setting.
Visual servoing, the method of controlling robot motion through feedback from visual sensors, has seen significant advancements with the integration of optical flow-based methods. However, its application remains limi...
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ISBN:
(纸本)9798350377712;9798350377705
Visual servoing, the method of controlling robot motion through feedback from visual sensors, has seen significant advancements with the integration of optical flow-based methods. However, its application remains limited by inherent challenges, such as the necessity for a target image at test time, the requirement of substantial overlap between initial and target images, and the reliance on feedback from a single camera. This paper introduces Imagine2Servo(dagger), an innovative approach leveraging diffusion-based image editing techniques to enhance visual servoing algorithms by generating intermediate goal images. This methodology allows for the extension of visual servoing applications beyond traditional constraints, enabling tasks like long-range navigation and manipulation without predefined goal images. We propose a pipeline that synthesizes subgoal images grounded in the task at hand, facilitating servoing in scenarios with minimal initial and target image overlap and integrating multi-camera feedback for comprehensive task execution. Our contributions demonstrate a novel application of image generation to robotic control, significantly broadening the capabilities of visual servoing systems. Real-world experiments validate the effectiveness and versatility of the Imagine2Servo framework in accomplishing a variety of tasks, marking a notable advancement in the field of visual servoing.
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