Discrete cosine transform (DCT) is an effective method to extract proper features for face recognition. Discrete cosine transform can only map the resource data to another data field instead of compress data. How to s...
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Discrete cosine transform (DCT) is an effective method to extract proper features for face recognition. Discrete cosine transform can only map the resource data to another data field instead of compress data. How to select the DCT coefficients that are most effective for classification is an important problem. This paper proposes a novel method to search the best discriminant combination of DCT coefficients. A feature selection algorithm according to the separability criterion is used to preselect the DCT coefficients, and then follows a search algorithm based on binary particle swarm optimization and support vector machine to find an optimal combination of the DCT coefficient. The performance of the algorithm is assessed by computing the recognition rate and the number of selected features on ORL database and Cropped Yale database.
Dynamics DNA nanotechnology is currently the most common method for designing molecular circuits, referred to as DNA circuits, which can process complex information by utilizing DNA strands as information molecules. H...
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This paper proposes global feedback control for nonlinear affine systems defined on (noncontractible) manifolds parametetrized by time-varying parameter. The proposed global feedback control consists of a coordinate t...
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In this paper, a synchronous control strategy based on super-twisting sliding mode algorithm is proposed to enhance the tracking accuracy and robustness of H-type linear motor systems. Such systems are widely utilized...
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This study introduces a novel method for integrating Stratified Sampling for Density-Based Spatial Clustering of Applications with Noise (SS-DBSCAN) clustering with the human-in-the-loop approach to semi-supervised da...
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This paper presents a novel finite-and-quantized output feedback asymptotic tracking control method for a general class of continuous-time linear time-invariant ***,we construct a finite quantizer with time-varying th...
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This paper presents a novel finite-and-quantized output feedback asymptotic tracking control method for a general class of continuous-time linear time-invariant ***,we construct a finite quantizer with time-varying thresholds and design a pole placement control law that exclusively utilizes the finite-and-quantized output signal and an external reference ***,we establish the boundedness of all closed-loop signals and prove the asymptotic convergence of the output tracking error to *** proposed method combines the advantages of classical pole placement control technique and finite quantization feedback *** not only reduces the requirement for feedback information compared with existing tracking control methods but also effectively handles unstable poles and zeros in controlled systems,thereby achieving asymptotic output ***,we provide a representative example to validate the effec-tiveness and new features of our proposed method.
In recent years, with the continuous progress of China's high-speed railway construction, railways play an important role in the country's economic development and provide a strong driving force for people to ...
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The application of artificial intelligence makes the collaboration between machine and human closer in the future. How to build the architecture of human and machine collaboration and evaluate the profit of human and ...
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This paper develops distributed algorithms for solving Sylvester *** authors transform solving Sylvester equations into a distributed optimization problem,unifying all eight standard distributed matrix *** the authors...
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This paper develops distributed algorithms for solving Sylvester *** authors transform solving Sylvester equations into a distributed optimization problem,unifying all eight standard distributed matrix *** the authors propose a distributed algorithm to find the least squares solution and achieve an explicit linear convergence *** results are obtained by carefully choosing the step-size of the algorithm,which requires particular information of data and Laplacian *** avoid these centralized quantities,the authors further develop a distributed scaling technique by using local information *** a result,the proposed distributed algorithm along with the distributed scaling design yields a universal method for solving Sylvester equations over a multi-agent network with the constant step-size freely chosen from configurable ***,the authors provide three examples to illustrate the effectiveness of the proposed algorithms.
The vertical gradient freeze crystal growth process is the main technique for the production of high quality compound semiconductors that are vital for today's electronic applications. A simplified model of this p...
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The vertical gradient freeze crystal growth process is the main technique for the production of high quality compound semiconductors that are vital for today's electronic applications. A simplified model of this process consists of two 1D diffusion equations with free boundaries for the temperatures in crystal and melt. Both phases are coupled via an ordinary differential equation that describes the evolution of the moving solid/liquid interface. The control of the resulting two-phase Stefan problem is the focus of this contribution. A flatness-based feedforward design is combined with a multi-step backstepping approach to obtain a controller that tracks a reference trajectory for the position of the phase boundary. Specifically, based on some preliminary transformations to map the model into a time-variant PDE-ODE system, consecutive transformations are shown to yield a stable closed loop. The tracking controller is validated in a simulation that considers the actual growth of a Gallium arsenide single crystal.
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