134 research outputs found
A LightGBM-Based EEG Analysis Method for Driver Mental States Classification
Fatigue driving can easily lead to road traffic accidents and bring great harm to individuals and families. Recently, electroencephalography-
(EEG-) based physiological and brain activities for fatigue detection have been increasingly investigated.
However, how to find an effective method or model to timely and efficiently detect the mental states of drivers still remains a
challenge. In this paper, we combine common spatial pattern (CSP) and propose a light-weighted classifier, LightFD, which is
based on gradient boosting framework for EEG mental states identification. ,e comparable results with traditional classifiers,
such as support vector machine (SVM), convolutional neural network (CNN), gated recurrent unit (GRU), and large margin
nearest neighbor (LMNN), show that the proposed model could achieve better classification performance, as well as the decision
efficiency. Furthermore, we also test and validate that LightFD has better transfer learning performance in EEG classification of
driver mental states. In summary, our proposed LightFD classifier has better performance in real-time EEG mental state
prediction, and it is expected to have broad application prospects in practical brain-computer interaction (BCI)
Randomly Projected Convex Clustering Model: Motivation, Realization, and Cluster Recovery Guarantees
In this paper, we propose a randomly projected convex clustering model for
clustering a collection of high dimensional data points in
with hidden clusters. Compared to the convex clustering model for
clustering original data with dimension , we prove that, under some mild
conditions, the perfect recovery of the cluster membership assignments of the
convex clustering model, if exists, can be preserved by the randomly projected
convex clustering model with embedding dimension ,
where is some given parameter. We further prove that the
embedding dimension can be improved to be , which is
independent of the number of data points. Extensive numerical experiment
results will be presented in this paper to demonstrate the robustness and
superior performance of the randomly projected convex clustering model. The
numerical results presented in this paper also demonstrate that the randomly
projected convex clustering model can outperform the randomly projected K-means
model in practice
A Novel Uncalibrated Visual Servoing Controller Baesd on Model-Free Adaptive Control Method with Neural Network
Nowadays, with the continuous expansion of application scenarios of robotic
arms, there are more and more scenarios where nonspecialist come into contact
with robotic arms. However, in terms of robotic arm visual servoing,
traditional Position-based Visual Servoing (PBVS) requires a lot of calibration
work, which is challenging for the nonspecialist to cope with. To cope with
this situation, Uncalibrated Image-Based Visual Servoing (UIBVS) frees people
from tedious calibration work. This work applied a model-free adaptive control
(MFAC) method which means that the parameters of controller are updated in real
time, bringing better ability of suppression changes of system and environment.
An artificial intelligent neural network is applied in designs of controller
and estimator for hand-eye relationship. The neural network is updated with the
knowledge of the system input and output information in MFAC method. Inspired
by "predictive model" and "receding-horizon" in Model Predictive Control (MPC)
method and introducing similar structures into our algorithm, we realizes the
uncalibrated visual servoing for both stationary targets and moving
trajectories. Simulated experiments with a robotic manipulator will be carried
out to validate the proposed algorithm.Comment: 16 pages, 8 figure
Adaptive Finite-Time Model Estimation and Control for Manipulator Visual Servoing using Sliding Mode Control and Neural Networks
The image-based visual servoing without models of system is challenging since
it is hard to fetch an accurate estimation of hand-eye relationship via merely
visual measurement. Whereas, the accuracy of estimated hand-eye relationship
expressed in local linear format with Jacobian matrix is important to whole
system's performance. In this article, we proposed a finite-time controller as
well as a Jacobian matrix estimator in a combination of online and offline way.
The local linear formulation is formulated first. Then, we use a combination of
online and offline method to boost the estimation of the highly coupled and
nonlinear hand-eye relationship with data collected via depth camera. A neural
network (NN) is pre-trained to give a relative reasonable initial estimation of
Jacobian matrix. Then, an online updating method is carried out to modify the
offline trained NN for a more accurate estimation. Moreover, sliding mode
control algorithm is introduced to realize a finite-time controller. Compared
with previous methods, our algorithm possesses better convergence speed. The
proposed estimator possesses excellent performance in the accuracy of initial
estimation and powerful tracking capabilities for time-varying estimation for
Jacobian matrix compared with other data-driven estimators. The proposed scheme
acquires the combination of neural network and finite-time control effect which
drives a faster convergence speed compared with the exponentially converge
ones. Another main feature of our algorithm is that the state signals in system
is proved to be semi-global practical finite-time stable. Several experiments
are carried out to validate proposed algorithm's performance.Comment: 24 pages, 10 figure
kNN-CTC: Enhancing ASR via Retrieval of CTC Pseudo Labels
The success of retrieval-augmented language models in various natural
language processing (NLP) tasks has been constrained in automatic speech
recognition (ASR) applications due to challenges in constructing fine-grained
audio-text datastores. This paper presents kNN-CTC, a novel approach that
overcomes these challenges by leveraging Connectionist Temporal Classification
(CTC) pseudo labels to establish frame-level audio-text key-value pairs,
circumventing the need for precise ground truth alignments. We further
introduce a skip-blank strategy, which strategically ignores CTC blank frames,
to reduce datastore size. kNN-CTC incorporates a k-nearest neighbors retrieval
mechanism into pre-trained CTC ASR systems, achieving significant improvements
in performance. By incorporating a k-nearest neighbors retrieval mechanism into
pre-trained CTC ASR systems and leveraging a fine-grained, pruned datastore,
kNN-CTC consistently achieves substantial improvements in performance under
various experimental settings. Our code is available at
https://github.com/NKU-HLT/KNN-CTC.Comment: Accepted by ICASSP 202
Block Hankel Tensor ARIMA for Multiple Short Time Series Forecasting
This work proposes a novel approach for multiple time series forecasting. At
first, multi-way delay embedding transform (MDT) is employed to represent time
series as low-rank block Hankel tensors (BHT). Then, the higher-order tensors
are projected to compressed core tensors by applying Tucker decomposition. At
the same time, the generalized tensor Autoregressive Integrated Moving Average
(ARIMA) is explicitly used on consecutive core tensors to predict future
samples. In this manner, the proposed approach tactically incorporates the
unique advantages of MDT tensorization (to exploit mutual correlations) and
tensor ARIMA coupled with low-rank Tucker decomposition into a unified
framework. This framework exploits the low-rank structure of block Hankel
tensors in the embedded space and captures the intrinsic correlations among
multiple TS, which thus can improve the forecasting results, especially for
multiple short time series. Experiments conducted on three public datasets and
two industrial datasets verify that the proposed BHT-ARIMA effectively improves
forecasting accuracy and reduces computational cost compared with the
state-of-the-art methods.Comment: Accepted by AAAI 202
IPDreamer: Appearance-Controllable 3D Object Generation with Image Prompts
Recent advances in text-to-3D generation have been remarkable, with methods
such as DreamFusion leveraging large-scale text-to-image diffusion-based models
to supervise 3D generation. These methods, including the variational score
distillation proposed by ProlificDreamer, enable the synthesis of detailed and
photorealistic textured meshes. However, the appearance of 3D objects generated
by these methods is often random and uncontrollable, posing a challenge in
achieving appearance-controllable 3D objects. To address this challenge, we
introduce IPDreamer, a novel approach that incorporates image prompts to
provide specific and comprehensive appearance information for 3D object
generation. Our results demonstrate that IPDreamer effectively generates
high-quality 3D objects that are consistent with both the provided text and
image prompts, demonstrating its promising capability in
appearance-controllable 3D object generation.Comment: 11 pages, 7 figure
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