905 research outputs found
RNN Controller for Lane-Keeping Systems with Robustness and Safety Verification
This paper proposes a Recurrent Neural Network (RNN) controller for
lane-keeping systems, effectively handling model uncertainties and
disturbances. First, quadratic constraints cover the nonlinearities brought by
the RNN controller, and the linear fractional transformation method models the
dynamics of system uncertainties. Second, we prove the robust stability of the
lane-keeping system in the presence of uncertain vehicle speed using a linear
matrix inequality. Then, we define a reachable set for the lane-keeping system.
Finally, to confirm the safety of the lane-keeping system with tracking error
bound, we formulate semidefinite programming to approximate the outer set of
the reachable set. Numerical experiments demonstrate that this approach
confirms the stabilizing RNN controller and validates the safety with an
untrained dataset with untrained varying road curvatures.Comment: 7 pages, 6 figure
Uncertainty Quantification of Autoencoder-based Koopman Operator
This paper proposes a method for uncertainty quantification of an
autoencoder-based Koopman operator. The main challenge of using the Koopman
operator is to design the basis functions for lifting the state. To this end,
this paper builds an autoencoder to automatically search the optimal lifting
basis functions with a given loss function. We approximate the Koopman operator
in a finite-dimensional space with the autoencoder, while the approximated
Koopman has an approximation uncertainty. To resolve the problem, we compute a
robust positively invariant set for the approximated Koopman operator to
consider the approximation error. Then, the decoder of the autoencoder is
analyzed by robustness certification against approximation error using the
Lipschitz constant in the reconstruction phase. The forced Van der Pol model is
used to show the validity of the proposed method. From the numerical simulation
results, we confirmed that the trajectory of the true state stays in the
uncertainty set centered by the reconstructed state.Comment: 6 pages, 3 figure
Dynamic Extension Algorithm Based Tracking Control of STATCOM Via Port-Controlled Hamiltonian System
Sequence-to-Sequence Prediction of Vehicle Trajectory via LSTM Encoder-Decoder Architecture
In this paper, we propose a deep learning based vehicle trajectory prediction
technique which can generate the future trajectory sequence of surrounding
vehicles in real time. We employ the encoder-decoder architecture which
analyzes the pattern underlying in the past trajectory using the long
short-term memory (LSTM) based encoder and generates the future trajectory
sequence using the LSTM based decoder. This structure produces the most
likely trajectory candidates over occupancy grid map by employing the beam
search technique which keeps the locally best candidates from the decoder
output. The experiments conducted on highway traffic scenarios show that the
prediction accuracy of the proposed method is significantly higher than the
conventional trajectory prediction techniques
Improving Scene Text Recognition for Character-Level Long-Tailed Distribution
Despite the recent remarkable improvements in scene text recognition (STR),
the majority of the studies focused mainly on the English language, which only
includes few number of characters. However, STR models show a large performance
degradation on languages with a numerous number of characters (e.g., Chinese
and Korean), especially on characters that rarely appear due to the long-tailed
distribution of characters in such languages. To address such an issue, we
conducted an empirical analysis using synthetic datasets with different
character-level distributions (e.g., balanced and long-tailed distributions).
While increasing a substantial number of tail classes without considering the
context helps the model to correctly recognize characters individually,
training with such a synthetic dataset interferes the model with learning the
contextual information (i.e., relation among characters), which is also
important for predicting the whole word. Based on this motivation, we propose a
novel Context-Aware and Free Experts Network (CAFE-Net) using two experts: 1)
context-aware expert learns the contextual representation trained with a
long-tailed dataset composed of common words used in everyday life and 2)
context-free expert focuses on correctly predicting individual characters by
utilizing a dataset with a balanced number of characters. By training two
experts to focus on learning contextual and visual representations,
respectively, we propose a novel confidence ensemble method to compensate the
limitation of each expert. Through the experiments, we demonstrate that
CAFE-Net improves the STR performance on languages containing numerous number
of characters. Moreover, we show that CAFE-Net is easily applicable to various
STR models.Comment: 17 page
Compensative microstepping based position control with passive nonlinear adaptive observer for permanent magnet stepper motors
This paper presents a compensative microstepping based position control with passive nonlinear adaptive observer for permanent magnet stepper motor. Due to the resistance uncertainties, a position error exists in the steady-state, and a ripple of position error appears during operation. The compensative microstepping is proposed to remedy this problem. The nonlinear controller guarantees the desired currents. The passive nonlinear adaptive observer is designed to estimate the phase resistances and the velocity. The closed-loop stability is proven using input to state stability. Simulation results show that the position error in the steady-state is removed by the proposed method if the persistent excitation conditions are satisfied. Furthermore, the position ripple is reduced, and the Lissajou curve of the phase currents is a circle
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