430 research outputs found
Lateral Control of Brain-Controlled Vehicle Based on SVM Probability Output Model
The non-stationary characteristics of EEG signal and the individual
differences of brain-computer interfaces (BCIs) lead to poor performance in the
control process of the brain-controlled vehicles (BCVs). In this paper, by
combining steady-state visual evoked potential (SSVEP) interactive interface,
brain instructions generation module and vehicle lateral control module, a
probabilistic output model based on support vector machine (SVM) is proposed
for BCV lateral control to improve the driving performance. Firstly, a filter
bank common spatial pattern (FBCSP) algorithm is introduced into the brain
instructions generation module, which can improve the off-line decoding
performance. Secondly, a sigmod-fitting SVM (SF-SVM) is trained based on the
sigmod-fitting method and the lateral control module is developed, which can
produce all commands in the form of probability instead of specific single
command. Finally, a pre-experiment and two road-keeping experiments are
conducted. In the pre-experiment, the experiment results show that, the average
highest off-line accuracy among subjects is 95.64\%, while for those in the
online stage, the average accuracy is only 84.44\%. In the road-keeping
experiments, the task completion rate in the two designed scenes increased by
25.6\% and 20\%, respectively
Hydrodynamic of a Novel Liquid-Solid Circulating Fluidized Bed Operating Below Particle Terminal Velocity
A novel type of circulating fluidized bed operating below the particle terminal velocity known as conventional circulating fluidized bed (CCFB) was proposed and tested for the first time in this study. The experiments were carried out in a liquid-solid circulating fluidized bed system, where both liquid and solid flew upwards in the riser and solids exiting the top of the riser were separated from liquid and then returned to the bottom of the riser via an accompanying downer. The system was essentially operated in the conventional fluidization regime but with continuously feeding of particles into riser bottom and particles moving up the riser to achieve solids circulation or circulating fluidization. The hydrodynamic of the CCFB was investigated at various operating conditions with two types of particles. The solids holdup of the conventional circulating fluidization was clearly higher when compared to conventional fluidization. Particles with a higher terminal velocity have higher solids holdup
SYNLOCO: Synthesizing Central Pattern Generator and Reinforcement Learning for Quadruped Locomotion
The Central Pattern Generator (CPG) is adept at generating rhythmic gait
patterns characterized by consistent timing and adequate foot clearance. Yet,
its open-loop configuration often compromises the system's control performance
in response to environmental variations. On the other hand, Reinforcement
Learning (RL), celebrated for its model-free properties, has gained significant
traction in robotics due to its inherent adaptability and robustness. However,
initiating traditional RL approaches from the ground up presents computational
challenges and a heightened risk of converging to suboptimal local minima. In
this paper, we propose an innovative quadruped locomotion framework, SYNLOCO,
by synthesizing CPG and RL that can ingeniously integrate the strengths of both
methods, enabling the development of a locomotion controller that is both
stable and natural. Furthermore, we introduce a set of performance-driven
reward metrics that augment the learning of locomotion control. To optimize the
learning trajectory of SYNLOCO, a two-phased training strategy is presented.
Our empirical evaluation, conducted on a Unitree GO1 robot under varied
conditions--including distinct velocities, terrains, and payload
capacities--showcases SYNLOCO's ability to produce consistent and clear-footed
gaits across diverse scenarios. The developed controller exhibits resilience
against substantial parameter variations, underscoring its potential for robust
real-world applications.Comment: 7 Page
TCBERT: A Technical Report for Chinese Topic Classification BERT
Bidirectional Encoder Representations from Transformers or
BERT~\cite{devlin-etal-2019-bert} has been one of the base models for various
NLP tasks due to its remarkable performance. Variants customized for different
languages and tasks are proposed to further improve the performance. In this
work, we investigate supervised continued
pre-training~\cite{gururangan-etal-2020-dont} on BERT for Chinese topic
classification task. Specifically, we incorporate prompt-based learning and
contrastive learning into the pre-training. To adapt to the task of Chinese
topic classification, we collect around 2.1M Chinese data spanning various
topics. The pre-trained Chinese Topic Classification BERTs (TCBERTs) with
different parameter sizes are open-sourced at
\url{https://huggingface.co/IDEA-CCNL}
Evaluation and comparison of the processing methods of airborne gravimetry concerning the errors effects on downward continuation results: Case studies in Louisiana (USA) and the Tibetan Plateau (China)
Gravity data gaps in mountainous areas are nowadays often filled in with the data from airborne gravity surveys. Because of the errors caused by the airborne gravimeter sensors, and because of rough flight conditions, such errors cannot be completely eliminated. The precision of the gravity disturbances generated by the airborne gravimetry is around 3–5 mgal. A major obstacle in using airborne gravimetry are the errors caused by the downward continuation. In order to improve the results the external high-accuracy gravity information e.g., from the surface data can be used for high frequency correction, while satellite information can be applying for low frequency correction. Surface data may be used to reduce the systematic errors, while regularization methods can reduce the random errors in downward continuation. Airborne gravity surveys are sometimes conducted in mountainous areas and the most extreme area of the world for this type of survey is the Tibetan Plateau. Since there are no high-accuracy surface gravity data available for this area, the above error minimization method involving the external gravity data cannot be used. We propose a semi-parametric downward continuation method in combination with regularization to suppress the systematic error effect and the random error effect in the Tibetan Plateau; i.e., without the use of the external high-accuracy gravity data. We use a Louisiana airborne gravity dataset from the USA National Oceanic and Atmospheric Administration (NOAA) to demonstrate that the new method works effectively. Furthermore, and for the Tibetan Plateau we show that the numerical experiment is also successfully conducted using the synthetic Earth Gravitational Model 2008 (EGM08)-derived gravity data contaminated with the synthetic errors. The estimated systematic errors generated by the method are close to the simulated values. In addition, we study the relationship between the downward continuation altitudes and the error effect. The analysis results show that the proposed semi-parametric method combined with regularization is efficient to address such modelling problems
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