Data security and cyberattack have become critical issues in the distributed power system where adversaries can swap the source information of sensors or even spoof and alter measurements. However, the cyber security ...
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ISBN:
(数字)9798350313604
ISBN:
(纸本)9798350313611
Data security and cyberattack have become critical issues in the distributed power system where adversaries can swap the source information of sensors or even spoof and alter measurements. However, the cyber security of the power system is challenged by the unpredictability and stealth of the spoofing attacks. To protect the data security at the grid edge, this paper developed a synchrophasor data spoofing attack detection framework based on the time-frequency feature extraction techniques including the short-time Fourier transform (STFT) and object detection network for real-time synchrophasor data categorization and spoofing attack localization. The proposed approach outperforms earlier work in terms of spoofing attack detection and offers a vital localization function employing distributed synchrophasor sensors.
This paper reports latest developments in event-triggered and self-triggered control of uncertain nonholonomic systems in the perturbed chained *** order to tackle the effects of drift uncertain nonlinearities,nonholo...
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This paper reports latest developments in event-triggered and self-triggered control of uncertain nonholonomic systems in the perturbed chained *** order to tackle the effects of drift uncertain nonlinearities,nonholonomic constraints and nonsmooth aperiodic sampling in eventbased control,a novel systematic design scheme is proposed by integrating set-valued maps with stateseparation and state-scaling *** stability analysis of the closed-loop event-triggered control system is based on the cyclic-small-gain techniques that overcome the limitation of Lyapunov theory in the construction of Lyapunov functions for nonsmooth dynamical systems and enjoy inherent robustness properties due to the use of gain-based characterization of robust *** specifically,the closed-loop event-triggered control system is transformed into an interconnection of multiple input-tostate stable systems,to which the cyclic-small-gain theorem is applied for robust stability *** self-triggered mechanisms are also developed as natural extensions of the event-triggered control *** proposed event-based control design approach is new and original even when the system model is reduced to the ideal unperturbed chained ***,the proposed methodology is also applicable to a broader class of nonholonomic systems subject to state and input-dependent *** efficacy of the obtained event-triggered controllers is validated by a benchmark example of mobile robots subject to parametric uncertainties and a measurement noise such as bias in the orientation.
This study addresses the affine formation maneuver control of cooperative multi-agent systems (MAS) having periodic inter-agent communication for both static and dynamic leader cases. Here, we focus on the leader-foll...
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ISBN:
(数字)9798331531812
ISBN:
(纸本)9798331531829
This study addresses the affine formation maneuver control of cooperative multi-agent systems (MAS) having periodic inter-agent communication for both static and dynamic leader cases. Here, we focus on the leader-follower MASs. The primary aim of the control system is to steer the entire collection of agents to produce required patterns (geometric) along with any required maneuver through the direct control of only few a selected agents referred to as leaders. Most of the existing works are constrained to either the individual agents communicate with each other in continuous-time or the sample-data scenario where the leaders are stationary or have constant acceleration or velocities. Here, we consider the scenarios where the velocities of the leaders can be time-varying or constant. Here, different cases are addressed and some control laws are proposed. Conditions are established to help guarantee the overall stability of the systems. A simulation study is employed for the illustration of our proposed laws.
In this work, we propose a distributed learning framework that classifies users' transportation modes by leveraging heterogeneous networks (HetNets) architecture and employing a federated learning (FL) algorithm. ...
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ISBN:
(数字)9798350387414
ISBN:
(纸本)9798350387421
In this work, we propose a distributed learning framework that classifies users' transportation modes by leveraging heterogeneous networks (HetNets) architecture and employing a federated learning (FL) algorithm. The ultra-densification of small cells and the dynamic mobility of users impact performance by triggering unnecessary handovers. Therefore, information on user mobility allows the network to perform handover management more intelligently and efficiently. The proposed machine learning framework adopts distributed learning using a federated learning algorithm to detect transportation modes, including driving a car, riding a bicycle, walking, and running. In the proposed framework, local models are trained at small cells using user history information inherently distributed on the network side. A macro cell aggregates the local models to get a global model for classifying the transportation modes of users. Training the local models by small cells over user history information addresses critical FL issues, such as non-independent and identically distributed data and system heterogeneity. Simulation results demonstrate that the proposed framework achieves an accuracy of 98.85% in classifying transportation modes, utilizing input features extracted from user history information.
Iron chalcogenides superconductors, such as Fe(Te,Se) have recently garnered significant attention due to their simple crystal structure with a relatively easy synthesis process, high-temperature superconductivity, in...
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We consider a communication system consisting of a server that tracks and publishes updates about a time-varying data source or event, and a gossip network of users interested in closely tracking the event. The timeli...
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ISBN:
(数字)9798350351255
ISBN:
(纸本)9798350351262
We consider a communication system consisting of a server that tracks and publishes updates about a time-varying data source or event, and a gossip network of users interested in closely tracking the event. The timeliness of the information is measured through the version age of information. The users wish to have their expected version ages remain below a threshold, and have the option to either rely on gossip from their neighbors or subscribe to the server directly to follow updates about the event if the former option does not meet the timeliness requirements. The server wishes to maximize its profit by increasing the number of subscribers and reducing costs associated with the frequent sampling of the event. We model the problem setup as a Stackelberg game between the server and the users, where the server commits to a frequency of sampling the event, and the users make decisions on whether to subscribe or not. As an initial work, we focus on directed networks with unidirectional flow of information and obtain the optimal equilibrium strategies for all the players. We provide simulation results to confirm the theoretical findings and provide additional insights.
This research explores the integration of language embeddings for active learning in autonomous driving datasets, with a focus on novelty detection. Novelty arises from unexpected scenarios that autonomous vehicles st...
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This research explores the integration of language embeddings for active learning in autonomous driving datasets, with a focus on novelty detection. Novelty arises from unexpected scenarios that autonomous vehicles struggle to navigate, necessitating higher-level reasoning abilities. Our proposed method employs language-based representations to identify novel scenes, emphasizing the dual purpose of safety takeover responses and active learning. The research presents a clustering experiment using Contrastive Language-Image Pretrained (CLIP) embeddings to organize datasets and detect novelties. We find that the proposed algorithm effectively isolates novel scenes from a collection of subsets derived from two real-world driving datasets, one vehicle-mounted and one infrastructure-mounted. From the generated clusters, we further present methods for generating textual explanations of elements which differentiate scenes classified as novel from other scenes in the data pool, presenting qualitative examples from the clustered results. Our results demonstrate the effectiveness of language-driven embeddings in identifying novel elements and generating explanations of data, and we further discuss potential applications in safe takeovers, data curation, and multi-task active learning. Note to Practitioners—The processes of data collection, curation, and annotation are important in building massive but learning-efficient datasets towards a variety of applications in autonomous driving. Using the diversity-based sampling techniques presented in this research at the curation stage of data management can help in identifying unique samples to be annotated or analyzed, potentially saving arduous hours of fine-grained human labelling. Accordingly, such curation steps, especially with the explainability feature highlighted in this research, can indicate areas where data may be lacking in the current set, offering ideas for fleet management to fill gaps in the data collection process.
We derive the coding capacity for duplication-correcting codes capable of correcting any number of duplications. We do so both for reverse-complement duplications, as well as palindromic (reverse) duplications. We sho...
In GNSS-R (Global Navigation Satellite System Reflectometry) land applications, bistatic scattering occurs in the vicinity of the specular direction, and the scattered waves can include both coherent and incoherent co...
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