Multiple linear regression (MLR) is one of the most widely used statistical procedures for scholarly and research. The main limitation of MLR is that when being estimated with linear methodologies as ordinary least sq...
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Recently, Facial Emotion Recognition (FER) has been one of the most promising and growing field in computer vision and human-robot interaction. In this work, a deep learning neural network is introduced to address the...
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Recently, Facial Emotion Recognition (FER) has been one of the most promising and growing field in computer vision and human-robot interaction. In this work, a deep learning neural network is introduced to address the problem of facial emotion recognition. In particular, a CNN+RNN architecture has been designed to capture both spatial features and temporal dynamics of facial expressions. Experiments are performed on CK+ dataset. Furthermore, we present a possible application of the proposed Facial Emotion Recognition system in human-robot interaction. A method for dynamically changing ambient light or LED colors, based on recognized emotions is presented. Indeed, it is proven that equipping robots with the ability of perceiving emotions and accordingly reacting by introducing suitable emphatic strategies significantly improves human-robot interaction performances. Possible scenarios of application are education, healthcare and autism therapy where such kind of emphatic strategies play a fundamental role. Copyright for this paper by its authors. Use permitted under Creative.
Abstract: An approach to designing subway traction power supply facilities with an 825-V traction power system is considered in a general digital model. The continuously increasing passenger traffic in metropolitan ci...
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To obtain precise motion control of wafer stages, an adaptive neural network and fractional-order super-twisting control strategy is proposed. Based on sliding mode control (SMC), the proposed controller aims to addre...
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Traditional LiDAR odometry (LO) systems mainly leverage geometric information obtained from the traversed surroundings to register laser scans and estimate LiDAR ego-motion, while it may be unreliable in dynamic or un...
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In this work, we formulate NEWRON: a generalization of the McCulloch-Pitts neuron structure. This new framework aims to explore additional desirable properties of artificial neurons. We show that some specializations ...
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The flow estimation problem consists of predicting missing edge flows in a network (e.g., traffic, power, and water) based on partial observations. These missing flows depend both on the underlying physics (edge featu...
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Rotating machinery is an integral part of many industrial systems. Domain adaptation technique provides a powerful tool to detect faults under different working conditions. However, there is still a challenge: convent...
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Rotating machinery is an integral part of many industrial systems. Domain adaptation technique provides a powerful tool to detect faults under different working conditions. However, there is still a challenge: conventional domain adaptation approach only works under the ‘closed set’ assumption that all test classes are known at training time. In practice, a more realistic situation is ‘open set’, i.e., knowledge is incomplete in the training process, resulting in unknown classes during the testing. In this paper, a sparse autoencoder based adversarial open set domain adaptation (SAOSDA) model is proposed for rotating machinery fault diagnosis under open set scenarios, which can recognize the unknown faults and detect the known faults under different working conditions. This model utilizes adversarial learning to reduce the discrepancies between source samples and known target samples and reject the unknown target samples simultaneously. Experimental results of the actual bearing dataset verify the superiority and effectiveness of this method.
Networked controlsystems typically come with a limited communication bandwidth and thus require special care when designing the underlying control and triggering law. A method that allows to consider hard constraints...
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ISBN:
(数字)9781665467612
ISBN:
(纸本)9781665467629
Networked controlsystems typically come with a limited communication bandwidth and thus require special care when designing the underlying control and triggering law. A method that allows to consider hard constraints on the communication traffic as well as on states and inputs is self-triggered model predictive control (MPC). In this scheme, the optimal length of the sampling interval is determined proactively using predictions of the system behavior. However, previous formulations of self-triggered MPC have neglected the widespread phenomenon of packet loss, such that these approaches might fail in practice. In this paper, we present a novel self-triggered MPC scheme which is robust to bounded packet loss by virtue of a min-max optimization problem. We prove recursive feasibility, constraint satisfaction and convergence to the origin for any possible packet loss realization consistent with the boundedness constraint, and demonstrate the advantages of the proposed scheme in a numerical example.
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