The surge in Internet of Things (IoT) usage has precipitated the need to replace classical internet network prediction and congestion control methods with a more efficient and reliable learning-based approach. Many ma...
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A search and rescue localization algorithm is proposed using an unscented Kalman filter (UKF). The UKF provides superior performance to other filtering options such as the extended Kalman filter or the particle filter...
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ISBN:
(纸本)9798350374278;9798350374261
A search and rescue localization algorithm is proposed using an unscented Kalman filter (UKF). The UKF provides superior performance to other filtering options such as the extended Kalman filter or the particle filter and is computationally less expensive than neural network localization methods. Each search agent obtains asynchronous measurements of its range and/or bearing relative to the victim. It is assumed that the victim's pose (position and orientation) and speed estimates and the associated covariance matrices are available to all agents simultaneously. Each agent maintains its own UKF to update these estimates and its pose estimate when a new measurement is received. Simulation results demonstrate that the agents can track the victim's location and orientation with minimal steady-state error despite significant measurement noise and large initiate estimation error.
Many controlsystems show deteriorated performance because of hard nonlinearities that are common in different applications. Fractional-order control can find new results to compensate nonlinearities, but control desi...
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Many controlsystems show deteriorated performance because of hard nonlinearities that are common in different applications. Fractional-order control can find new results to compensate nonlinearities, but control design methods are still at infancy. This paper addresses the issue of limit cycles determined by saturation elements in nonlinear control loops including plant servo systems. The fractional-order controller consists of two lead networks in series, one shifted with respect to the other on the frequency axis, and both of non-integer order. They are designed in the frequency domain to provide a high phase lead at the resulting gain crossover frequency and a Bode plot of the loop transfer function with phase varying slowly around that frequency. Robust prevention of the limit cycle is obtained such that the Nyquist plot of the linear elements in the loop does not intersect the negative inverse describing function plot. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)
In the present paper, we investigate the impact of imperfections (non-idealities) of the energy storage system (e.g., batteries, capacitors, or supercapacitors) on the energy performance of green IoT nodes. In particu...
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ISBN:
(纸本)9798331531317;9798331531300
In the present paper, we investigate the impact of imperfections (non-idealities) of the energy storage system (e.g., batteries, capacitors, or supercapacitors) on the energy performance of green IoT nodes. In particular, we investigate the impact of energy leakage from the Energy Storage System (ESS) on important energy performance metrics, such as service outage probability, the density of the lifetime of the node, and the time-dependent mean number of energy packets (EPs) in the ESS. Also, we explore various strategies that can be employed to compensate for the impact of energy losses due to energy leakage on the energy performance metrics. Specifically, we examine two potential strategies for improving the energy performance of the IoT nodes: (i) through increasing the energy generation rate of the energy harvesters (e.g., by adding additional solar panels or replacing the existing solar panels with more efficient ones that can produce more energy), and (ii) through reducing the energy consumption rate of the IoT node (e.g., by configuring ESS energy thresholds below which the nodes are forced to operate in low energy consumption states).
The purpose of this research is to suggest the use of neural network techniques to analyze a mathematical model that is associated with a biomass power plant. Four subsystems are connected in series to form the system...
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This paper focuses on network congestion control in a disaggregated storage system. In such a system, the supporting network requires low latency and is extremely sensitive to network congestion. The existing congesti...
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ISBN:
(纸本)9781665457194
This paper focuses on network congestion control in a disaggregated storage system. In such a system, the supporting network requires low latency and is extremely sensitive to network congestion. The existing congestion control algorithms for data center networks do not work well in our target system because of the unique characteristics of the network topology and the storage I/O workload. Motivated by the existing issues, we develop a new solution, DIRS, which dynamically sets the initial sending rate for each flow. Our scheme helps improve the effectiveness of the congestion control protocols, especially under heavy I/O traffic. It chooses an appropriate initial rate for a flow and mitigates the congestion from the beginning while not degrading the flow's networkperformance.
HVAC systems represent the most significant energy consumers in buildings, constituting over 60% of total energy usage. This research endeavors to enhance energy efficiency and thermal comfort within buildings, partic...
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Botnets are collections of compromised devices manipulated by malicious entities. To safeguard against their varied and constantly evolving threats, it is essential to have sophisticated detection techniques in place....
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ISBN:
(数字)9798350375480
ISBN:
(纸本)9798350375480;9798350375497
Botnets are collections of compromised devices manipulated by malicious entities. To safeguard against their varied and constantly evolving threats, it is essential to have sophisticated detection techniques in place. In this work, we investigate the utilization of machine learning methodologies for identifying botnets using CTU-13, a large repository that contains a wide range of botnet examples. By extracting features from the packet payloads and the header data, we are able to distinguish between botnet and harmless network traffic. We utilize a range of supervised machine learning techniques, including a Convolutional Neural network (CNN), to identify botnet behavior. With rigorous evaluation, we see the nuanced performance of various machine learning models. In particular, we find that the naive Bayes classifier is very effective in detecting botnets, while CNN shows remarkable accuracy, especially when it is asked to classify botnet data converted to images. We also explore preprocessing techniques that improve the quality of textual data. This helps to improve feature extraction as well as model performance, emphasizing the importance of proper data preparation for cybersecurity analyses. These insights not only shed light on how effective machine learning can be in detecting botnets but also provide actionable recommendations for improving cyber security strategies.
For automated driving, predicting the future trajectories of other road users in complex traffic situations is a hard problem. Modern neural networks use the past trajectories of traffic participants as well as map da...
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ISBN:
(纸本)9798350399462
For automated driving, predicting the future trajectories of other road users in complex traffic situations is a hard problem. Modern neural networks use the past trajectories of traffic participants as well as map data to gather hints about the possible driver intention and likely maneuvers. With increasing connectivity between cars and other traffic actors, cooperative information is another source of data that can be used as inputs for trajectory prediction algorithms. Connected actors might transmit their intended path or even complete planned trajectories to other actors, which simplifies the prediction problem due to the imposed constraints. In this work, we outline the benefits of using this source of data for trajectory prediction and propose a graph-based neural network architecture that can leverage this additional data. We show that the networkperformance increases substantially if cooperative data is present. Also, our proposed training scheme improves the network's performance even for cases where no cooperative information is available. We also show that the network can deal with inaccurate cooperative data, which allows it to be used in real automated driving environments.
The demand for efficient renewable energy systems, particularly solar photovoltaic (PV) systems, has surged in recent years, driven by the need for sustainable and environmentally friendly power generation solutions. ...
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