Blockchain-as-a-Service is an emerging blockchain-based platform service that can potentially contribute to the advancement of contemporary power and energy systems in cyber-physical environment, such as battery energ...
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Suppose we are given a matrix that is formed by adding an unknown sparse matrix to an unknown low-rank matrix. Our goal is to decompose the given matrix into its sparse and low-rank components. Such a problem arises i...
Suppose we are given a matrix that is formed by adding an unknown sparse matrix to an unknown low-rank matrix. Our goal is to decompose the given matrix into its sparse and low-rank components. Such a problem arises in a number of applications in model and system identification, but obtaining an exact solution is NP-hard in general. In this paper we consider a convex optimization formulation to splitting the specified matrix into its components; in fact our approach reduces to solving a semidefinite program. We provide sufficient conditions that guarantee exact recovery of the components by solving the semidefinite program. We also show that when the sparse and low-rank matrices are drawn from certain natural random ensembles, these sufficient conditions are satisfied with high probability. We conclude with simulation results on synthetic matrix decomposition problems.
The present paper proposes an assistive system to help a visually impaired user find available seats that are not occupied by other persons. The system is composed of a white cane, a Microsoft Kinect sensor, a paramet...
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Cloud service providers are provisioning resources including a variety of virtual machine instances to support customers that migrate their services to the cloud. From the customers’ perspective, selecting the approp...
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In this paper, a dynamic droop mechanism is proposed to damp the synchronizing oscillation that exists in a virtual synchronous generator based distributed generator system without varying the steady-state active-powe...
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The research on Variational Quantum Algorithms (VQAs) has gained significant momentum because of their promising practicality in the noisy intermediate-scale quantum (NISQ) era. Recent studies highlight the potential ...
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ISBN:
(数字)9798331530471
ISBN:
(纸本)9798331530488
The research on Variational Quantum Algorithms (VQAs) has gained significant momentum because of their promising practicality in the noisy intermediate-scale quantum (NISQ) era. Recent studies highlight the potential of VQAs in reinforcement learning (RL) by replacing classical components with parameterized quantum circuits (PQCs). Building on this, we propose a quantum Q-learning model for the Capacitated Vehicle Routing Problem (CVRP), combining a PQC with an RL training process. We present a PQC for embedding both static and dynamic states in RL environments. The proposed PQC approximates the Q-value function by using the expectation values of different observables. Experimental results demonstrate that the proposed PQC outperforms the existing PQC for the CVRP with fewer quantum resources. Furthermore, our method shows competitive performance compared to the classical counterpart.
There is a growing need for firms, organizations, and industries to prioritize requirements, which emphasizes the need for an efficient method to satisfy customers. When combined with the Vertical Binary Search approa...
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A technique for the calibration of an active camera system is presented. The calibration of manipulator, camera-to-manipulator, camera, and base-to-world is treated in a unified and elegant way. In this approach, the ...
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Learning the sparse structure of a general Markov network is a hard computational problem. One of the main difficulties is the computation of the generally intractable partition function. To circumvent this difficulty...
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Content-based image retrieval (CBIR) has been an active research topic in the last decade. Multiple feature extraction and representation is one of the most important issues in the CBIR. In this paper, we propose a ne...
Content-based image retrieval (CBIR) has been an active research topic in the last decade. Multiple feature extraction and representation is one of the most important issues in the CBIR. In this paper, we propose a new CBIR method based on an efficient integration of texture and shape features. The texture features are extracted on the decomposed images processed by the optimal non-subsampled shearlet transform (NSST), and are represented by the high-frequency sub-band coefficients, which can be modeled by Bessel K Form (BKF) distribution; the shape features are represented by low-order quaternion polar harmonic transforms (QPHTs). The two kinds of features are then integrated by a weighted distance measurement, where Kullback-Leibler distance (KLD) and Euclidean distance (ED) are used for texture and shape features respectively. The integration of shape and texture information provides a robust feature set for image retrieval. Experimental results on standard benchmarks show significant improvements on retrieval performance using the proposed method compared with previous state-of-the-art methods.
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