This study addresses two algorithms for set stabilisation of Boolean control networks (BCNs). Based on the semi-tensor product tool, the dynamics of BCNs can be characterised by its labelled digraph, which derived an ...
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This study addresses two algorithms for set stabilisation of Boolean control networks (BCNs). Based on the semi-tensor product tool, the dynamics of BCNs can be characterised by its labelled digraph, which derived an graphical expression for the set stabilisation of BCNs. Then, two tree-search algorithms, namely, generalised breadth-first search and generalised depth-first search, are proposed for the first time to decide the controllers for the set stabilisation of BCNs. In addition, some properties concerning the treesearch algorithm are proposed. Finally, an example is employed to show the application of the presented algorithms.
We adopt the following measures of clustering based on simple edge counts in an undirected loop-free graph. Let S be a subset of the points of the graph. The compactness of S is measured by the number of edges connect...
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We adopt the following measures of clustering based on simple edge counts in an undirected loop-free graph. Let S be a subset of the points of the graph. The compactness of S is measured by the number of edges connecting points in S to other points in S. The isolation or separation of S is measured by the negative of the number of edges connecting points in S to points not in S. The subset S is a cluster if it is compact and isolated. We study the cluster search problem: find a subset S which maximizes a linear combination of the compactness and (negative) isolation edge counts. We show that a closely related decision problem is NP-complete. We develop a pruned searchtree algorithm which is much faster than complete search, especially for graphs which are derived from points embedded in a space of low dimensionality.
The emerging paradigm of Quantum computing has the potential to transform the established way-of-working in several scientific and industrial fields if the open challenges of applying quantum computing systems for rea...
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ISBN:
(纸本)9783031492686;9783031492693
The emerging paradigm of Quantum computing has the potential to transform the established way-of-working in several scientific and industrial fields if the open challenges of applying quantum computing systems for real-world applications are addressed. One of the major challenges is that the quantum computing systems accessible for industrial and commercial users have very few qubits. Several research initiatives are being proposed to work around this constraint. We investigate the amenable scope and limits of a hybrid platform where classical computing works in tandem with quantum computing to address practical problems. Instead of focusing on quantum supremacy or specialized academic problems, this paper proposes a framework where generalized industrial applications can be solved using hybrid computing systems with limited qubit capacity using a decomposition technique that can be modified to any decision-support procedure.
This paper focuses on underwater target tracking based on a multi-static sonar network composed of passive sonobuoys and an active ping. In the multi-static sonar network, the location of the target can be estimated u...
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This paper focuses on underwater target tracking based on a multi-static sonar network composed of passive sonobuoys and an active ping. In the multi-static sonar network, the location of the target can be estimated using TDOA (Time Difference of Arrival) measurements. However, since the sensor network may obtain insufficient and inaccurate TDOA measurements due to ambient noise and other harsh underwater conditions, target tracking performance can be significantly degraded. We propose a robust target tracking algorithm designed to operate in such a scenario. First, track management with track splitting is applied to reduce performance degradation caused by insufficient measurements. Second, a target location is estimated by a fusion of multiple TDOA measurements using a Gaussian Mixture Model (GMM). In addition, the target trajectory is refined by conducting a stack-based data association method based on multiple-frames measurements in order to more accurately estimate target trajectory. The effectiveness of the proposed method is verified through simulations.
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