The distributed nonconvex optimization problem of minimizing a global cost function formed by a sum of n local cost functions by using local information exchange is considered. This problem is an important component o...
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Decomposition offers the potential to reduce the complexity of model-based optimization, prediction, control and diagnosis by accounting for the structure and sparsity of the describing model. Motivated by this fact, ...
ISBN:
(数字)9781728121437
ISBN:
(纸本)9781728121444
Decomposition offers the potential to reduce the complexity of model-based optimization, prediction, control and diagnosis by accounting for the structure and sparsity of the describing model. Motivated by this fact, a rich and powerful collection of decomposition methods are available for model based diagnosis of large-scale complex dynamic systems, too. At the same time, one usually does not have enough information about a large-scale complex dynamic system to construct its precise enough model, so a kind of qualitative dynamic model is often used for the diagnosis. Two structural decomposition based qualitative diagnostic methods are presented in this lecture, together with their component-driven system decomposition techniques.
The photonic spin Hall effect is a manifestation of the spin-orbit interaction of light and can be measured by a transverse shift δ of photons with opposite spins. The precise measurement of transverse shifts can ena...
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The photonic spin Hall effect is a manifestation of the spin-orbit interaction of light and can be measured by a transverse shift δ of photons with opposite spins. The precise measurement of transverse shifts can enable many spin-related applications, such as precise metrology and optical sensing. However, this transverse shift is generally small (i.e., δ/λ<10−1, where λ is the wavelength), which impedes its precise measurement. To date, proposals to generate a giant spin Hall effect (namely, with δ/λ>102) have severe limitations, particularly its occurrence only over a narrow angular cone (with a width of Δθ<1∘). Here we propose a universal scheme to realize the wide-angle giant photonic spin Hall effect with Δθ>70∘ by exploiting the interface between free space and uniaxial epsilon-near-zero media. The underlying mechanism is ascribed to the almost-perfect polarization splitting between s and p polarized waves at the designed interface. Remarkably, this almost-perfect polarization splitting does not resort to the interference effect and is insensitive to the incident angle, which then gives rise to the wide-angle giant photonic spin Hall effect.
In this paper, we propose a data-adaptive non-parametric kernel learning framework in margin based kernel methods. In model formulation, given an initial kernel matrix, a data-adaptive matrix with two constraints is i...
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In this paper, we propose a data-adaptive non-parametric kernel learning framework in margin based kernel methods. In model formulation, given an initial kernel matrix, a data-adaptive matrix with two constraints is imposed in an entry-wise scheme. Learning this data-adaptive matrix in a formulation-free strategy enlarges the margin between classes and thus improves the model flexibility. The introduced two constraints are imposed either exactly (on small data sets) or approximately (on large data sets) in our model, which provides a controllable trade-off between model flexibility and complexity with theoretical demonstration. In algorithm optimization, the objective function of our learning framework is proven to be gradient-Lipschitz continuous. Thereby, kernel and classifier/regressor learning can be efficiently optimized in a unified framework via Nesterov's acceleration. For the scalability issue, we study a decomposition-based approach to our model in the large sample case. The effectiveness of this approximation is illustrated by both empirical studies and theoretical guarantees. Experimental results on various classification and regression benchmark data sets demonstrate that our non-parametric kernel learning framework achieves good performance when compared with other representative kernel learning based algorithms.
Active metamaterials are engineered structures that possess novel properties that can be changed after the point of manufacture. Their novel properties arise predominantly from their physical structure, as opposed to ...
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— In this paper, we consider the problem of learning a predictive model for population cell growth dynamics as a function of the media conditions. We first introduce a generic data-driven framework for training opera...
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With the rapid development of measurwement technology, LiDAR and depth cameras are widely used in the perception of the 3D environment. Recent learning based methods for robot perception most focus on the image or vid...
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Convolutional neural network-based broad learning with efficient incremental reconstruction model (CNNBL) is proposed to recognize emotions in human-robot interaction. It aims to extract deep and abstract features fro...
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Convolutional neural network-based broad learning with efficient incremental reconstruction model (CNNBL) is proposed to recognize emotions in human-robot interaction. It aims to extract deep and abstract features from facial emotional images, and reduce the influence of the complex structure and slow network updates on facial emotion recognition in deep learning. Feature extraction is carried out by convolution and maximum pooling, and then the ridge regression algorithm is used for emotion recognition. When the network needs to expand, the network is dynamically updated by incremental learning algorithm. We verified the experimental performance through k -fold cross validation. According to the recognition results, the accuracy on JAFFE database of our proposal is greater than that of the state of the art, such as the Local Binary Patterns with Softmax and Deep Attentive Multi-path convolutional neural network.
Most controlsystems used for cable-driven parallel manipulators employ a simple inner control loop for controlling the driving force of the actuators. By this means the effects of some nonlinear uncertainties of the ...
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ISBN:
(数字)9781728158150
ISBN:
(纸本)9781728158167
Most controlsystems used for cable-driven parallel manipulators employ a simple inner control loop for controlling the driving force of the actuators. By this means the effects of some nonlinear uncertainties of the actuator and power transmission systems are significantly reduced, and in turn, this helps the outer user-specified position control loop to perform more accurately. However, this positive impact on performance relies on non achievable assumptions that the inner control loop is fast and accurate enough, and its dynamics can be totally ignored. The main contribution of this paper is to analyze the stability of the system as a whole, considering both inner and outer loop controllers. The outer loop controller proposed in here is a finite time robust sliding mode whose stability is analyzed through the Lyapunov direct method. A deployable cable-driven parallel robot, as the case study, is also considered in this paper, which is characterized by several intrinsic kinematic and dynamic uncertainties. Finally, the performance and effectiveness of the proposed robust controller is evaluated through some simulations and experiments in order to verify the effectiveness and the characteristics of the cascade controller structure in practice.
This paper presents an adaptive narrative game system that focuses on sequential logic design. The system adapts a random forest machine learning model to estimate a student's current level of domain knowledge rel...
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ISBN:
(数字)9781728169040
ISBN:
(纸本)9781728169057
This paper presents an adaptive narrative game system that focuses on sequential logic design. The system adapts a random forest machine learning model to estimate a student's current level of domain knowledge relative to the problem presented to him through his game-playing behavior data, such as time taken to find solutions, errors in solutions, and emotional indicators. Hints, prompts, and/or individualized lessons are then offered to the player to guide their learning in a positive and productive direction. Our preliminary pilot study demonstrates that the model can make accurate classifications, from which proper assistance can then be provided to individual students as they play.
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