This paper studies the consensus problem for directed positive multiagent systems with nonlinear control input. The directed topology is supposed to be strongly connected. For the case of sector input nonlinearities, ...
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This paper studies the consensus problem for directed positive multiagent systems with nonlinear control input. The directed topology is supposed to be strongly connected. For the case of sector input nonlinearities, the non-negative global consensus conditions are presented by using a novel analysis method which directly considers the nonlinear input, and the feedback matrices are obtained with convex optimization method by solving the quadratic matrix inequalities subject to nonnegative constraints. For the case of saturation-type sector input nonlinearities, the global consensus and local consensus results are analyzed, respectively. With the properties of Metzler matrix, the non-negative consensus results are further extended to the strongly connected and balanced positive multiagent systems subject to (saturation-type) sector input nonlinearities and parameter uncertainties. The results are simplified, and the feedback matrices can be given by solving iterative linear matrix inequality with non-negative constraints. Two simulation examples and a practical electrical circuit model are finally carried out to verify the proposed control schemes.
This paper is concerned with the problem of event-based H-infinity filtering for networked systems with communication delay (or signal transmission delay). We first propose a novel event-triggering scheme upon which t...
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This paper is concerned with the problem of event-based H-infinity filtering for networked systems with communication delay (or signal transmission delay). We first propose a novel event-triggering scheme upon which the sensor data is transmitted only when the specified event condition involving the sampled measurements of the plant is violated. By using delay system approach, a new model of filtering error system with state delay is formulated where the communication delay and event-triggering scheme are dealt with in a unified framework for networked systems. Then, by utilizing the Lyapunov-Krasovskii functional method plus free weighting matrix technique, sufficient conditions for ensuring the exponential stability as well as prescribed H-infinity performance for the filtering error system are derived in the form of linear matrix inequalities (LMIs). Based on these conditions, the explicit expression is given for the desired filter parameters. Finally, an illustrative example is presented to show the advantage of introducing the event-triggering scheme and the effectiveness of the proposed theoretical results. Crown Copyright (C) 2012 Published by Elsevier B.V. All rights reserved.
This paper presents a novel single and multiple open circuit fault detection and identification method for three phase Voltage Source Inverter (VSI) fed vector controlled drives. In healthy condition, d-axis reference...
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This paper presents a novel single and multiple open circuit fault detection and identification method for three phase Voltage Source Inverter (VSI) fed vector controlled drives. In healthy condition, d-axis reference voltage is constant. When fault occurs, it is distorted, which is applied to detect the insulated-gate bipolar transistors (IGBTs) open circuit fault occurrence. The d-axis reference voltage distorted last for an interval time, the fault can be isolated by calculating the instant current vector rotating angles when the d-axis reference voltage gets distorted and recovered to constant, respectively, combined with space position of the current arc trajectory. The proposed method is fair robust to load torque changes and variable speed, and can be embedded into the existing drive software as a subroutine without excessive computational effort. The feasibility of the proposed fault diagnosis algorithm is evaluated by both simulation and experimental results.
Takagi-Sugeno-Kang (TSK) fuzzy systems are flexible and interpretable machine learning models;however, they may not be easily optimized when the data size is large, and/or the data dimensionality is high. This article...
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Takagi-Sugeno-Kang (TSK) fuzzy systems are flexible and interpretable machine learning models;however, they may not be easily optimized when the data size is large, and/or the data dimensionality is high. This article proposes a minibatch gradient descent (MBGD) based algorithm to efficiently and effectively train TSK fuzzy classifiers. It integrates two novel techniques: First, uniform regularization (UR), which forces the rules to have similar average contributions to the output, and hence to increase the generalization performance of the TSK classifier;and, second, batch normalization (BN), which extends BN from deep neural networks to TSK fuzzy classifiers to expedite the convergence and improve the generalization performance. Experiments on 12 UCI datasets from various application domains, with varying size and dimensionality, demonstrated that UR and BN are effective individually, and integrating them can further improve the classification performance.
Spiking neural P systems with weights are a new variant of spiking neural P systems, where the applicability of rules is checked by a given potential threshold instead of the regular expression. There is a considerabl...
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Spiking neural P systems with weights are a new variant of spiking neural P systems, where the applicability of rules is checked by a given potential threshold instead of the regular expression. There is a considerable computational power hidden into the implicit mechanism that spiking neural P systems use to decide whether a given rule can be applied or not. In this work, several applications of spiking neural P systems with weights regarding their capability to solve some classical computer science problems are investigated. Specifically, three spiking neural P systems with weights are constructed, which can provide basic models for simulating a well-known parallel computational device-Boolean circuits. A family of spiking neural P systems with weights is also presented, in which a system of size k can perform the sorting of arbitrary k natural numbers in linear time with respect to the maximally natural number to be sorted. These applications of spiking neural P systems with weights partially show the computational power hidden into the mechanism of using a given potential threshold to check the applicability of rules
Spiking neural P systems are a class of distributed parallel computing models inspired from the way neurons communicate with each other by means of electrical impulses, where there is a synapse between each pair of co...
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Spiking neural P systems are a class of distributed parallel computing models inspired from the way neurons communicate with each other by means of electrical impulses, where there is a synapse between each pair of connected neurons. However, in a biological system, there can be several synapses for each pair of connected neurons. In this study, inspired by this biological observation, synapses in a spiking neural P system are endowed with integer weight denoting the number of synapses for each pair of connected neurons. With the price of weight on synapses, quite small universal spiking neural P systems can be constructed. Specifically, a universal spiking neural P system with standard rules and weight having 38 neurons is produced as device of computing functions;as generator of sets of numbers, we find a universal system with standard rules and weight having 36 neurons.
Emotion recognition is a critical component of affective computing. Training accurate machine learning models for emotion recognition typically requires a large amount of labeled data. Due to the subtleness and comple...
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Emotion recognition is a critical component of affective computing. Training accurate machine learning models for emotion recognition typically requires a large amount of labeled data. Due to the subtleness and complexity of emotions, multiple evaluators are usually needed for each affective sample to obtain its ground-truth label, which is expensive. To save the labeling cost, this paper proposes an inconsistency-based active learning approach for cross-task transfer between emotion classification and estimation. Affective norms are utilized as prior knowledge to connect the label spaces of categorical and dimensional emotions. Then, the prediction inconsistency on the two tasks for the unlabeled samples is used to guide sample selection in active learning for the target task. Experiments on within-corpus and cross-corpus transfers demonstrated that cross-task inconsistency could be a very valuable metric in active learning. To our knowledge, this is the first work that utilizes prior knowledge on affective norms and data in a different task to facilitate active learning for a new task, even the two tasks are from different datasets.
In this paper, we make the first research effort to address the RGB-Thermal (RGB-T) crowd counting problem with the decision -level late fusion manner. Being different from the existing pixel -level or feature -level ...
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In this paper, we make the first research effort to address the RGB-Thermal (RGB-T) crowd counting problem with the decision -level late fusion manner. Being different from the existing pixel -level or feature -level fusion methods, our proposition chooses to fuse the density maps yielded by RGB and thermal counterparts via spatially adaptive weighting with RGB illumination -aware attention. Our key intuition to conduct RGB-T density map fusion lies in 2 main folders. First, compared with the raw RGB-T images or convolutional feature maps, RGB-T density maps contain stronger counting -wise semantic meanings. Secondly, they are also of high spatial resolution for revealing fine local details. To fuse them adaptively, a spatial weighting map for each modality, together with an illumination -related RGB weight is generated. In this way, the issues of RGB illumination awareness and local counting pattern characterization ability are concerned jointly. To the best of our knowledge, we are the first to leverage RGB-T crowd counting concerning these 2 issues in a unified way. Meanwhile, cross -modality feature interaction is conducted between RGB and thermal modalities to facilitate spatial weighting map generation. The experiments on 2 well -established RGB-T crowd counting datasets ( i.e. , RGBT-CC and DroneRGBT) verify the superiority of our proposition.
Talking head animation transforms a source anime image to a target pose, where the transformation includes the change of facial expression and head movement. In contrast to existing approaches that operate on the low-...
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Talking head animation transforms a source anime image to a target pose, where the transformation includes the change of facial expression and head movement. In contrast to existing approaches that operate on the low-resolution image (256 x 256), we study this task at a higher resolution, e.g., 512 x 512 . High-resolution talking head animation, however, raises two major challenges: i) how to achieve smooth global transformation while maintaining rich details of anime characters under large-displacement pose variations;ii) how to address the shortage of data, because no related dataset is publicly available. In this paper, we present a Hierarchical Feature Warping and Blending (HFWB) model, which tackles talking head animation hierarchically. Specifically, we use low-level features to control global transformation and high-level features to determine the details of anime characters, under the guidance of feature flow fields. These features are then blended by selective fusion units, outputting transformed anime images. In addition, we construct an anime pose dataset-AniTalk-2K, aiming to alleviate the shortage of data. It contains around 2000 anime characters with thousands of different face/head poses at a resolution of 512 x 512 . Extensive experiments on AniTalk-2K demonstrate the superiority of our approach in generating high-quality anime talking heads over state-of-the-art methods.
The authors review the recent notion of multiwavelets and describe the use of the discrete multiwavelet transform (DMWT) in image fusion processing. Multiwavelets are extensions from scalar wavelets, and have several ...
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The authors review the recent notion of multiwavelets and describe the use of the discrete multiwavelet transform (DMWT) in image fusion processing. Multiwavelets are extensions from scalar wavelets, and have several advantages in comparison with scalar wavelets. Multiwavelet analysis can offer more precise image analysis than wavelet multiresolution analysis. A novel fusion algorithm is presented for multisensor images based on the discrete multiwavelet transform that can be performed at pixel level. After the registering of source images, a pyramid for each source image can be obtained by applying decomposition with multiwavelets in each level. The multiwavelet decomposition coefficients of the input images are appropriately merged and a new fused image is obtained by reconstructing the fused multiwavelet coefficients. This image fusion algorithm may be used to combine images from multisensors to obtain a single composite with extended information content. The results of experiments indicate that this image fusion algorithm can provide a more satisfactory fusion outcome.
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