94 research outputs found
A Generalized Recurrent Neural Architecture for Text Classification with Multi-Task Learning
Multi-task learning leverages potential correlations among related tasks to
extract common features and yield performance gains. However, most previous
works only consider simple or weak interactions, thereby failing to model
complex correlations among three or more tasks. In this paper, we propose a
multi-task learning architecture with four types of recurrent neural layers to
fuse information across multiple related tasks. The architecture is
structurally flexible and considers various interactions among tasks, which can
be regarded as a generalized case of many previous works. Extensive experiments
on five benchmark datasets for text classification show that our model can
significantly improve performances of related tasks with additional information
from others
A Novel Method of Robust Trajectory Linearization Control Based on Disturbance Rejection
A novel method of robust trajectory linearization control for a class of nonlinear systems with uncertainties based on disturbance rejection is proposed. Firstly, on the basis of trajectory linearization control (TLC) method, a feedback linearization based control law is designed to transform the original tracking error dynamics to the canonical integral-chain form. To address the issue of reducing the influence made by uncertainties, with tracking error as input, linear extended state observer (LESO) is constructed to estimate the tracking error vector, as well as the uncertainties in an integrated manner. Meanwhile, the boundedness of the estimated error is investigated by theoretical analysis. In addition, decoupled controller (which has the characteristic of well-tuning and simple form) based on LESO is synthesized to realize the output tracking for closed-loop system. The closed-loop stability of the system under the proposed LESO-based control structure is established. Also, simulation results are presented to illustrate the effectiveness of the control strategy
Antidisturbance Vibration Suppression of the Aerial Refueling Hose during the Coupling Process
In autonomous aerial refueling (AAR), the vibration of the flexible refueling hose caused by the receiver aircraft’s excessive closure speed should be suppressed once it appears. This paper proposed an active control strategy based on the permanent magnet synchronous motor (PMSM) angular control for the timely and accurate vibration suppression of the flexible refueling hose. A nonsingular fast terminal sliding-mode (NFTSM) control scheme with adaptive extended state observer (AESO) is proposed for PMSM take-up system under multiple disturbances. The states and the “total disturbance” of the PMSM system are firstly reconstituted using the AESO under the uncertainties and measurement noise. Then, a faster sliding variable with tracking error exponential term is proposed together with a special designed reaching law to enhance the global convergence speed and precision of the controller. The proposed control scheme provides a more comprehensive solution to rapidly suppress the flexible refueling hose vibration in AAR. Compared to other methods, the scheme can suppress the flexible hose vibration more fleetly and accurately even when the system is exposed to multiple disturbances and measurement noise. Simulation results show that the proposed scheme is competitive in accuracy, global rapidity, and robustness
VFHQ: A High-Quality Dataset and Benchmark for Video Face Super-Resolution
Most of the existing video face super-resolution (VFSR) methods are trained
and evaluated on VoxCeleb1, which is designed specifically for speaker
identification and the frames in this dataset are of low quality. As a
consequence, the VFSR models trained on this dataset can not output
visual-pleasing results. In this paper, we develop an automatic and scalable
pipeline to collect a high-quality video face dataset (VFHQ), which contains
over high-fidelity clips of diverse interview scenarios. To verify the
necessity of VFHQ, we further conduct experiments and demonstrate that VFSR
models trained on our VFHQ dataset can generate results with sharper edges and
finer textures than those trained on VoxCeleb1. In addition, we show that the
temporal information plays a pivotal role in eliminating video consistency
issues as well as further improving visual performance. Based on VFHQ, by
analyzing the benchmarking study of several state-of-the-art algorithms under
bicubic and blind settings. See our project page:
https://liangbinxie.github.io/projects/vfhqComment: Project webpage available at
https://liangbinxie.github.io/projects/vfh
Potential of performance improvement of concentrated solar power plants by optimizing the parabolic trough receiver
This paper proposes a comprehensive thermodynamic and economic model to predict and compare the performance of concentrated solar power plants with traditional and novel receivers with different configurations involving operating temperatures and locations. The simulation results reveal that power plants with novel receivers exhibit a superior thermodynamic and economic performance compared with traditional receivers. The annual electricity productions of power plants with novel receivers in Phoenix, Sevilla, and Tuotuohe are 8.5%, 10.5%, and 14.4% higher than those with traditional receivers at the outlet temperature of 550°C. The levelized cost of electricity of power plants with double-selective-coated receivers can be decreased by 6.9%, 8.5%, and 11.6%. In Phoenix, the optimal operating temperature of the power plants is improved from 500°C to 560°C by employing a novel receiver. Furthermore, the sensitivity analysis of the receiver heat loss, solar absorption, and freeze protection temperature is also conducted to analyze the general rule of influence of the receiver performance on power plants performance. Solar absorption has a positive contribution to annual electricity productions, whereas heat loss and freeze protection temperature have a negative effect on electricity outputs. The results indicate that the novel receiver coupled with low melting temperature molten salt is the best configuration for improving the overall performance of the power plants
Adaptive Neural Network Dynamic Inversion with Prescribed Performance for Aircraft Flight Control
An adaptive neural network dynamic inversion with prescribed performance method is proposed for aircraft flight control. The aircraft nonlinear attitude angle model is analyzed. And we propose a new attitude angle controller design method based on prescribed performance which describes the convergence rate and overshoot of the tracking error. Then the model error is compensated by the adaptive neural network. Subsequently, the system stability is analyzed in detail. Finally, the proposed method is applied to the aircraft attitude tracking control system. The nonlinear simulation demonstrates that this method can guarantee the stability and tracking performance in the transient and steady behavior
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