102 research outputs found
Safety-Guaranteed Trajectory Tracking Control for the Underactuated Hovercraft with State and Input Constraints
Design, Modelling, and Control of a Reconfigurable Rotary Series Elastic Actuator with Nonlinear Stiffness for Assistive Robots
In assistive robots, compliant actuator is a key component in establishing
safe and satisfactory physical human-robot interaction (pHRI). The performance
of compliant actuators largely depends on the stiffness of the elastic element.
Generally, low stiffness is desirable to achieve low impedance, high fidelity
of force control and safe pHRI, while high stiffness is required to ensure
sufficient force bandwidth and output force. These requirements, however, are
contradictory and often vary according to different tasks and conditions. In
order to address the contradiction of stiffness selection and improve
adaptability to different applications, we develop a reconfigurable rotary
series elastic actuator with nonlinear stiffness (RRSEAns) for assistive
robots. In this paper, an accurate model of the reconfigurable rotary series
elastic element (RSEE) is presented and the adjusting principles are
investigated, followed by detailed analysis and experimental validation. The
RRSEAns can provide a wide range of stiffness from 0.095 Nm/deg to 2.33 Nm/deg,
and different stiffness profiles can be yielded with respect to different
configuration of the reconfigurable RSEE. The overall performance of the
RRSEAns is verified by experiments on frequency response, torque control and
pHRI, which is adequate for most applications in assistive robots.
Specifically, the root-mean-square (RMS) error of the interaction torque
results as low as 0.07 Nm in transparent/human-in-charge mode, demonstrating
the advantages of the RRSEAns in pHRI
Low-rank Adaptation Method for Wav2vec2-based Fake Audio Detection
Self-supervised speech models are a rapidly developing research topic in fake
audio detection. Many pre-trained models can serve as feature extractors,
learning richer and higher-level speech features. However,when fine-tuning
pre-trained models, there is often a challenge of excessively long training
times and high memory consumption, and complete fine-tuning is also very
expensive. To alleviate this problem, we apply low-rank adaptation(LoRA) to the
wav2vec2 model, freezing the pre-trained model weights and injecting a
trainable rank-decomposition matrix into each layer of the transformer
architecture, greatly reducing the number of trainable parameters for
downstream tasks. Compared with fine-tuning with Adam on the wav2vec2 model
containing 317M training parameters, LoRA achieved similar performance by
reducing the number of trainable parameters by 198 times.Comment: 6page
System Fingerprint Recognition for Deepfake Audio: An Initial Dataset and Investigation
The malicious use of deep speech synthesis models may pose significant threat
to society. Therefore, many studies have emerged to detect the so-called
``deepfake audio". However, these studies focus on the binary detection of real
audio and fake audio. For some realistic application scenarios, it is needed to
know what tool or model generated the deepfake audio. This raises a question:
Can we recognize the system fingerprints of deepfake audio? Therefore, in this
paper, we propose a deepfake audio dataset for system fingerprint recognition
(SFR) and conduct an initial investigation. We collected the dataset from five
speech synthesis systems using the latest state-of-the-art deep learning
technologies, including both clean and compressed sets. In addition, to
facilitate the further development of system fingerprint recognition methods,
we give researchers some benchmarks that can be compared, and research
findings. The dataset will be publicly available.Comment: 12 pages, 3 figures. arXiv admin note: text overlap with
arXiv:2208.0964
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