Camouflaged object detection (COD) remains a challenging task in computer vision. Existing methods often resort to additional branches for edge supervision, incurring substantial computational costs. To address this, ...
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In this paper, a recently-reported single-phase rectifier with two outputs (RECTO) is further improved to reduce the current stress of the neutral inductor in the rectifier. The reduction is achieved by moving the neu...
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ISBN:
(纸本)9781509007387
In this paper, a recently-reported single-phase rectifier with two outputs (RECTO) is further improved to reduce the current stress of the neutral inductor in the rectifier. The reduction is achieved by moving the neutral inductor away from the path of the grid current. As a result, the inductor only carries the differential current of the dual loads. Since the maximum value of the differential current is much smaller than that of the grid current, the current stress of the neutral inductor can be significantly reduced, and the size of the inductor becomes much smaller, which helps improve the power density of the RECTO. In theory, the current stress can be reduced by at least three times and the inductor size by at least nine times. It is worth noting that the current stress of the switches and the other features of the RECTO, e.g., operation principles, independent DC outputs and unity power factor, are not affected. Comparative experimental results are presented to demonstrate the reduction.
Considering at the issue that the performance of the traditional beamforming algorithm will decrease sharply when the disturbance occurs at the disturbed position and the steering vector mismatch occurs, a new robust ...
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ISBN:
(纸本)9781665468893
Considering at the issue that the performance of the traditional beamforming algorithm will decrease sharply when the disturbance occurs at the disturbed position and the steering vector mismatch occurs, a new robust beamforming method based on covariance matrix reconstruction and Alternating Direction Method of Multipliers (ADMM) is proposed. Firstly, based on the maximum output power criterion of beamformers, an optimization model is designed to solve the optimal steering vector. Then, the covariance matrix is reconstructed by using the defined interference range to widen the nullity and enhance the anti-motion interference ability of the system. Then, for the quadratic inequality constraint problem of the steering vector, this paper uses ADMM to solve the model iteratively, and obtains the specific solution of the steering vector in each iteration. In addition, the complexity of the algorithm is also analyzed. The experimental results show that, compared with the existing beamforming algorithms, the proposed method Widen the width of zero notch in the interference area, improves the anti-interference performance of the beam, and can well correct the mismatched steering vector.
This paper presents general frequency domain criteria for the robust stability of systems with parametric uncertainities. The criteria are applied to the robust stability verification of LTI systems with or without ti...
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This paper presents general frequency domain criteria for the robust stability of systems with parametric uncertainities. The criteria are applied to the robust stability verification of LTI systems with or without time delays and of LTI systems operating under possibly nonlinear passive feedback.
This paper studies a class of strongly monotone games involving non-cooperative agents that optimize their own time-varying cost functions. We assume that the agents can observe other agents' historical actions an...
This paper studies a class of strongly monotone games involving non-cooperative agents that optimize their own time-varying cost functions. We assume that the agents can observe other agents' historical actions and choose actions that best respond to other agents' previous actions; we call this a best response scheme. We start by analyzing the convergence rate of this best response scheme for standard time-invariant games. Specifically, we provide a sufficient condition on the strong monotonicity parameter of the time-invariant games under which the proposed best response algorithm achieves exponential convergence to the static Nash equilibrium. We further illustrate that this best response algorithm may oscillate when the proposed sufficient condition fails to hold, which indicates that this condition is tight. Next, we analyze this best response algorithm for time-varying games where the cost functions of each agent change over time. Under similar conditions as for time-invariant games, we show that the proposed best response algorithm stays asymptotically close to the evolving equilibrium. We do so by analyzing both the equilibrium tracking error and the dynamic regret. Numerical experiments on economic market problems are presented to validate our analysis.
Decentralized deep learning has made significant success since it avoids the single point of failure in centralized solutions. However, the system might deviate from the correct model due to Byzantine attacks. Existin...
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Decentralized deep learning has made significant success since it avoids the single point of failure in centralized solutions. However, the system might deviate from the correct model due to Byzantine attacks. Existing Byzantine-resilient defense models are mainly of a one-step evaluation fashion, making them vulnerable to rigorous topology and sophisticated cyber-attacks due to lack of historical evaluations. This paper proposes a credibility assessment based parameter aggregation rule (CA-PAR) that evaluates each neighboring node by its long-term performance. For each node and its neighbors, two concepts, immediate reward and history information based credibility are firstly proposed to describe the immediate reliability at current iteration and the comprehensive assessment of the reliability respectively. Thereafter, all the received parameters are aggregated in linear combination, in which the adjacent weight is determined by credibility value. Finally, the influences of suspicious nodes can gradually be reduced and eliminated. Experimental results in MNIST and CIFAR-10 datasets indicate the algorithm’s tolerance for five state-of-the-art attack methods against an arbitrary number of faulty nodes. Compared with the previous defense models, the proposed algorithm in this paper outperforms in topology constraints, training accuracy and computation cost. IEEE
Quantum random-number generators (QRNGs) can offer a means to generate information-theoretically provable random numbers, in principle. In practice, unfortunately, the quantum randomness is inevitably mixed with class...
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Quantum random-number generators (QRNGs) can offer a means to generate information-theoretically provable random numbers, in principle. In practice, unfortunately, the quantum randomness is inevitably mixed with classical randomness due to classical noises. To distill this quantum randomness, one needs to quantify the randomness of the source and apply a randomness extractor. Here, we propose a generic framework for evaluating quantum randomness of real-life QRNGs by min-entropy, and apply it to two different existing quantum random-number systems in the literature. Moreover, we provide a guideline of QRNG data postprocessing for which we implement two information-theoretically provable randomness extractors: Toeplitz-hashing extractor and Trevisan's extractor.
The purpose of this paper is to provide a path for designing a tool for decision support to ensure the effectiveness of Quality Management System (QMS). For this, we propose a Fuzzy-Neural Networks (FNN) approach for ...
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The purpose of this paper is to provide a path for designing a tool for decision support to ensure the effectiveness of Quality Management System (QMS). For this, we propose a Fuzzy-Neural Networks (FNN) approach for improving the efficiency of such system. The aim of this approach is to classify the objectives for a real-world case study which presents a major problem for controlling the quality levels of its production lines. This approach provided a significant improvement when the testing data are various or complex.
This paper considers the design problem of the robust optimal state-feedback controllers for the uncertain nonlinear dynamic systems, where the uncertain nonlinear dynamic systems can be represented by the Takagi-Suge...
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This paper considers the design problem of the robust optimal state-feedback controllers for the uncertain nonlinear dynamic systems, where the uncertain nonlinear dynamic systems can be represented by the Takagi-Sugeno (TS) fuzzy-model-based dynamic systems with both elemental parametric uncertainties and norm-bounded approximation error. An integrative method, which complementarily fuses the robust stabilizability condition, the orthogonal-functions approach (OFA) and the hybrid Taguchi-genetic algorithm (HTGA), is presented in this paper to design the robust quadratic-optimal state-feedback controllers, in which the robust stabilizability condition is proposed in terms of linear matrix inequalities (LMIs). A design example is given to demonstrate the applicability of the proposed integrative optimization approach.
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