97 research outputs found
Chinese Organization Name Recognition Using Chunk Analysis
PACLIC 20 / Wuhan, China / 1-3 November, 200
Initial Value Problem Enhanced Sampling for Closed-Loop Optimal Control Design with Deep Neural Networks
Closed-loop optimal control design for high-dimensional nonlinear systems has
been a long-standing challenge. Traditional methods, such as solving the
associated Hamilton-Jacobi-Bellman equation, suffer from the curse of
dimensionality. Recent literature proposed a new promising approach based on
supervised learning, by leveraging powerful open-loop optimal control solvers
to generate training data and neural networks as efficient high-dimensional
function approximators to fit the closed-loop optimal control. This approach
successfully handles certain high-dimensional optimal control problems but
still performs poorly on more challenging problems. One of the crucial reasons
for the failure is the so-called distribution mismatch phenomenon brought by
the controlled dynamics. In this paper, we investigate this phenomenon and
propose the initial value problem enhanced sampling method to mitigate this
problem. We theoretically prove that this sampling strategy improves over the
vanilla strategy on the classical linear-quadratic regulator by a factor
proportional to the total time duration. We further numerically demonstrate
that the proposed sampling strategy significantly improves the performance on
tested control problems, including the optimal landing problem of a quadrotor
and the optimal reaching problem of a 7 DoF manipulator
Representation Disparity-aware Distillation for 3D Object Detection
In this paper, we focus on developing knowledge distillation (KD) for compact
3D detectors. We observe that off-the-shelf KD methods manifest their efficacy
only when the teacher model and student counterpart share similar intermediate
feature representations. This might explain why they are less effective in
building extreme-compact 3D detectors where significant representation
disparity arises due primarily to the intrinsic sparsity and irregularity in 3D
point clouds. This paper presents a novel representation disparity-aware
distillation (RDD) method to address the representation disparity issue and
reduce performance gap between compact students and over-parameterized
teachers. This is accomplished by building our RDD from an innovative
perspective of information bottleneck (IB), which can effectively minimize the
disparity of proposal region pairs from student and teacher in features and
logits. Extensive experiments are performed to demonstrate the superiority of
our RDD over existing KD methods. For example, our RDD increases mAP of
CP-Voxel-S to 57.1% on nuScenes dataset, which even surpasses teacher
performance while taking up only 42% FLOPs.Comment: Accepted by ICCV2023. arXiv admin note: text overlap with
arXiv:2205.15156 by other author
Positional multi-length and mutual-attention network for epileptic seizure classification
The automatic classification of epilepsy electroencephalogram (EEG) signals plays a crucial role in diagnosing neurological diseases. Although promising results have been achieved by deep learning methods in this task, capturing the minute abnormal characteristics, contextual information, and long dependencies of EEG signals remains a challenge. To address this challenge, a positional multi-length and mutual-attention (PMM) network is proposed for the automatic classification of epilepsy EEG signals. The PMM network incorporates a positional feature encoding process that extracts minute abnormal characteristics from the EEG signal and utilizes a multi-length feature learning process with a hierarchy residual dilated LSTM (RDLSTM) to capture long contextual dependencies. Furthermore, a mutual-attention feature reinforcement process is employed to learn the global and relative feature dependencies and enhance the discriminative abilities of the network. To validate the effectiveness PMM network, we conduct extensive experiments on the public dataset and the experimental results demonstrate the superior performance of the PMM network compared to state-of-the-art methods
Hydrogen jet diffusion modeling by using physics-informed graph neural network and sparsely-distributed sensor data
Efficient modeling of jet diffusion during accidental release is critical for
operation and maintenance management of hydrogen facilities. Deep learning has
proven effective for concentration prediction in gas jet diffusion scenarios.
Nonetheless, its reliance on extensive simulations as training data and its
potential disregard for physical laws limit its applicability to unseen
accidental scenarios. Recently, physics-informed neural networks (PINNs) have
emerged to reconstruct spatial information by using data from
sparsely-distributed sensors which are easily collected in real-world
applications. However, prevailing approaches use the fully-connected neural
network as the backbone without considering the spatial dependency of sensor
data, which reduces the accuracy of concentration prediction. This study
introduces the physics-informed graph deep learning approach (Physic_GNN) for
efficient and accurate hydrogen jet diffusion prediction by using
sparsely-distributed sensor data. Graph neural network (GNN) is used to model
the spatial dependency of such sensor data by using graph nodes at which
governing equations describing the physical law of hydrogen jet diffusion are
immediately solved. The computed residuals are then applied to constrain the
training process. Public experimental data of hydrogen jet is used to compare
the accuracy and efficiency between our proposed approach Physic_GNN and
state-of-the-art PINN. The results demonstrate our Physic_GNN exhibits higher
accuracy and physical consistency of centerline concentration prediction given
sparse concentration compared to PINN and more efficient compared to OpenFOAM.
The proposed approach enables accurate and robust real-time spatial consequence
reconstruction and underlying physical mechanisms analysis by using sparse
sensor data
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