Quantum mechanics (QM) forms the basis for quantum computation (QC), a paradigm that provides distinctive problem-solving abilities. Although quantum computing (QC) has demonstrated potential in theoretical physics an...
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Integration of distributed energy resources (DERs) poses significant challenges for the operation and development of power systems, particularly regarding the sustenance of proper voltage levels. Solutions to this pro...
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ISBN:
(纸本)9798331531768;9798331531751
Integration of distributed energy resources (DERs) poses significant challenges for the operation and development of power systems, particularly regarding the sustenance of proper voltage levels. Solutions to this problem can involve network reinforcement, element upgrades, and/or demand -generation coordination. A comprehensive study has been conducted with a focus on voltage deviations caused by photovoltaic (PV) generation. Several strategies for voltage rise/drop mitigation are reviewed and compared, including the installation of shunt reactors, battery energy storage systems, line voltage regulators, transformers with on -load tap changers, and smart inverter control. The smart inverter control emerges as a readily available and effective solution, but its feasibility can be limited by the consumers' penetration into the inverters' operational logic.
Anomaly Detection in Cyber Physical systems (CPS) like Industrial controlsystems (ICS) presents research opportunities in different industries considering intelligentsystems, mainly for Intrusion Detection systems (...
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ISBN:
(纸本)9798331540982;9798331540975
Anomaly Detection in Cyber Physical systems (CPS) like Industrial controlsystems (ICS) presents research opportunities in different industries considering intelligentsystems, mainly for Intrusion Detection systems (IDS). This work surveys the specialized literature focusing on main published testbeds, datasets and methodologies for Anomaly Detection based on industrial process measurement data to develop and evaluate the performance of IDS applied for ICS. In this context, this work proposes a novel Deep Learning approach for an IDS based on a Long Short-Term Memory (LSTM) Neural Network. An experimental evaluation of obtained results for the HIL-based Augmented ICS (HAI) testbed demonstrate that the proposed LSTM-based IDS outperforms state-of-the-art alternative IDS based on other algorithms such as K-Nearest Neighbors (KNN), Decision Tree Classifier (DTC) and Random Forest (RF), considering performance metrics such as Accuracy (0.9996), Precision (0.9978), F1-Score (0.9978) and Recall (0.9978).
The smart distribution grid is a type of electrical supply network that has been widely applied in life. Ensuring efficient and secure communication of information within the smart distribution grid has become one of ...
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This study presents a comprehensive approach for classifying insurance claims and detecting car accidents that makes use of image analysis and machine learning. A Convolutional Neural Network (CNN) trained on a datase...
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Modern autonomous driving systems face substantial challenges when navigating dense intersections due to the high uncertainty introduced by other road users. Due to the complexity of the task, the autonomous vehicle n...
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ISBN:
(纸本)9781665491907
Modern autonomous driving systems face substantial challenges when navigating dense intersections due to the high uncertainty introduced by other road users. Due to the complexity of the task, the autonomous vehicle needs to generate policies at multiple levels of abstraction. However, previous deep imitation learning methods focused on learning control policies while using simple rule-based behavior models. To bridge this gap and achieve human-like driving, we develop a hierarchy of high-level behavior decision and low-level control, where both policies are jointly learned from human demonstrations based on imitation learning. Over 60 hours of driving data from 10 drivers at six intersections was collected. The proposed method is extensively evaluated in challenging intersection scenarios. Empirical results demonstrate the method's superior performance over baselines in terms of task completion and control quality. We demonstrate the importance of learning human-like behavior decisions as well as joint learning of behavior and control policies. The capability of imitating different driving styles is also illustrated.
Approximate computing (AC) has emerged in response to the growing need for energy -efficient solutions. AC can be implemented either in hardware or software and involves introducing approximation at different stages l...
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RNA-binding proteins (RBPs) are crucial for numerous cellular processes, with precise identification of RNA-protein binding sites being fundamental to understanding gene regulation mechanisms. This study introduces a ...
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This work proposes a novel and provably correct method for three-dimensional optimal motion planning in complex environments. Our approach models the 3D motion planning problem by solving streamlines of the potential ...
ISBN:
(纸本)9798350377712;9798350377705
This work proposes a novel and provably correct method for three-dimensional optimal motion planning in complex environments. Our approach models the 3D motion planning problem by solving streamlines of the potential fluid flow, filling a gap in traditional motion planning techniques by guaranteeing a closed-loop, smooth and natural-looking navigation solution. Special emphasis is given to an inherent challenge of artificial potential field (APF) methods, namely establishing proofs of safety and stability over the entire optimization process. A model-based actor-critic reinforcement learning algorithm is introduced to approximate the optimal solution to the Hamilton-Jacobi-Bellman equation and update the controller parameters in a deterministic manner. Through a series of ROS-Gazebo software-in-the-loop simulations the proposed methodology demonstrates robustness and outperforms widely used methods such as the RRT*, highlighting its contribution to the field of 3D optimal motion planning.
In recent years, face recognition technology has experienced significant advancements driven by improvements in computer vision and machine learning. This paper introduces a novel approach to face recognition by lever...
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