Smart street light solution utilizing Long Range Wide Area Network (LoRaWAN) is one of the highly interesting topics in the field of Internet of Things (IoT) automation engineering. It is seen that many prototypes and...
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The Control Barrier Function (CBF) proves to be an effective method for enhancing the safety of robot control systems. However, addressing safety-critical control in contact tasks is challenging due to the influence o...
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ISBN:
(纸本)9798350385731;9798350385724
The Control Barrier Function (CBF) proves to be an effective method for enhancing the safety of robot control systems. However, addressing safety-critical control in contact tasks is challenging due to the influence of switching contact dynamics, uncertain environmental stiffness, and uncertain contact position. In this letter, we propose a two-layer framework for adaptive safety-critical control in contact tasks. To address the challenge of abrupt changes in the control affine model caused by switching contact dynamics, we introduce a virtual proxy to replace the actual robot model. Additionally, we introduce the concept of adaptive CBF (ACBF) with an adaptive algorithm for the variable safety set based on an event-triggering mechanism. The algorithm can dynamically adjust the safety set boundary to realize the trade-off between task performance and the safety. To validate the proposed framework, we conduct two sets of experiments. The results demonstrate the effectiveness of the framework when compared to the classical CBF method with fixed safety set.
In this paper we propose a solution to the problem of tracking quasi-periodically varying systems based on the local basis function (LBF) approach. Within this framework, parameter trajectories are locally approximate...
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ISBN:
(纸本)9789464593617;9798331519773
In this paper we propose a solution to the problem of tracking quasi-periodically varying systems based on the local basis function (LBF) approach. Within this framework, parameter trajectories are locally approximated using linear combinations of specific functions of time known as basis functions. We derive both bias and variance characteristics of LBF estimators. Additionally, we demonstrate that the computational burden associated with LBF estimation algorithms can be significantly reduced, without sacrificing high estimation accuracy, by employing the computationally fast, approximate version of the LBF scheme.
Group testing, a problem with applications in various fields, traditionally assumes independent node states. Recent research, however, focuses on real-world scenarios that often involve correlations among nodes, chall...
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ISBN:
(纸本)9798350382853;9798350382846
Group testing, a problem with applications in various fields, traditionally assumes independent node states. Recent research, however, focuses on real-world scenarios that often involve correlations among nodes, challenging the simplifying assumptions made in existing models. In this work, we consider a comprehensive model for arbitrary statistical correlation among node states. To capture and leverage these correlations effectively, we model the problem by hypergraphs inspired by [1]. We establish that arbitrary correlations among nodes can be represented as a hypergraph with a probability distribution over its edges, and design a novel greedy adaptive algorithm capable of conducting informative tests and dynamically updating the distribution. We analyze its performance and give theoretical guarantees on the number of tests that depend solely on the entropy of the underlying probability distribution and the average number of infections.
In the Graph Reconstruction (GR) problem, a player initially only knows the vertex set V of an input graph G = (V, E) and is required to learn its set of edges E. To this end, the player submits queries to an oracle a...
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ISBN:
(纸本)9783959773614
In the Graph Reconstruction (GR) problem, a player initially only knows the vertex set V of an input graph G = (V, E) and is required to learn its set of edges E. To this end, the player submits queries to an oracle and must deduce E from the oracle’s answers. Angluin and Chen [Journal of Computer and System Sciences, 2008] resolved the number of Independent Set (IS) queries necessary and sufficient for GR on m-edge graphs. In this setting, each query consists of a subset of vertices U ⊆ V, and the oracle responds with a boolean, indicating whether U is an independent set in G. They gave algorithms that use O(m · log n) IS queries, which is best possible. In this paper, we initiate the study of GR via Maximal Independent Set (MIS) queries, a more powerful variant of IS queries. Given a query U ⊆ V, the oracle responds with any, potentially adversarially chosen, maximal independent set I ⊆ U in the induced subgraph G[U]. We show that, for GR, MIS queries are strictly more powerful than IS queries when parametrized by the maximum degree ∆ of the input graph. We give tight (up to poly-logarithmic factors) upper and lower bounds for this problem: 1. We observe that the simple strategy of taking uniform independent random samples of V and submitting those to the oracle yields a non-adaptive randomized algorithm that executes O(∆2 · log n) queries and succeeds with high probability. This should be contrasted with the fact that Ω(∆ · n · log(∆n)) IS queries are required for such graphs, which shows that MIS queries are strictly more powerful than IS queries. Interestingly, combining the strategy of taking uniform random samples of V with the probabilistic method, we show the existence of a deterministic non-adaptive algorithm that executes O(∆3 · log(∆n)) queries. 2. Regarding lower bounds, we prove that the additional ∆ factor when going from randomized non-adaptive algorithms to deterministic non-adaptive algorithms is necessary. We show that every non-adaptive determini
Artificial Intelligence algorithms are gaining significant importance in autonomous systems and control techniques due to their potential to learn from data, thus enhancing the autonomy and adaptability of nonlinear s...
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ISBN:
(纸本)9781624107115
Artificial Intelligence algorithms are gaining significant importance in autonomous systems and control techniques due to their potential to learn from data, thus enhancing the autonomy and adaptability of nonlinear systems to unknown conditions. However, the validation and verification of such algorithms still pose a significant challenge given their intricate complex nature, which makes it a challenge to validate and verify. In an effort expand the state of the art of this area, this paper presents an on-going research on the design, validation, and verification of AI-based algorithms by implementing three attitude controllers on a spacecraft testbed platform. The controllers include a deep-reinforcement learning adaptive controller, a fuzzy-logic based controller, and a nonlinear dynamic inversion controller augmented with an artificial immune system strategy. To validate and verify the adaptive algorithms and analyze their capabilities, numerical simulations using a high-fidelity model and experimental tests are conducted. Furthermore, different failure scenarios are considered to validate and assess the robustness and performance of the AI-based attitude controllers.
The computational resources and energy carried by Autonomous Undersea Vehicle (AUV) are limited, failing to meet the requirements of real-time operation of the adaptive algorithm in the sonar system. To address these ...
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The non-contact respiration sensing technology based on RF holds significant potential for monitoring human life states. In real-world scenarios, targets not only undergo their own motion but are also surrounded by mo...
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In the Generalized Mastermind problem, there is an unknown subset H of the hypercube {0, 1}d containing n points. The goal is to learn H by making a few queries to an oracle which given a point q in {0, 1}d, returns t...
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In the Generalized Mastermind problem, there is an unknown subset H of the hypercube {0, 1}d containing n points. The goal is to learn H by making a few queries to an oracle which given a point q in {0, 1}d, returns the point in H nearest to q. We give a two-round adaptive algorithm for this problem that learns H while making at most exp(Oe(√d log n)) queries. Furthermore, we show that any r-round adaptive randomized algorithm that learns H with constant probability must make exp(Ω(d3−(r−1) )) queries even when the input has poly(d) points;thus, any poly(d) query algorithm must necessarily use Ω(log log d) rounds of adaptivity. We give optimal query complexity bounds for the variant of the problem where queries are allowed to be from {0, 1, 2}d. We also study a continuous variant of the problem in which H is a subset of unit vectors in Rd, and one can query unit vectors in Rd. For this setting, we give an O(n⌊d/2⌋) query deterministic algorithm to learn the hidden set of points. Copyright 2024 by the author(s)
Image reconstruction is at the core of improving Computed Tomography (CT), enabling high-quality imaging while addressing challenges like noise, limited data, and radiation exposure. Recent innovations have introduced...
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