1,076 research outputs found
Measurement Axis Searching Model for Terrestrial Laser Scans Registration
Nowadays, terrestrial Lidar scans can cover rather a large area; the point densities are strongly varied because of the line-of-sight measurement principle in potential overlaps with scans taken from different viewpoints. Most of the traditional methods focus on registration algorithm and ignore searching model. Sometimes the traditional methods are directly used to align two point clouds; a large critically unsolved problem of the large biases will be created in areas distant from the overlaps while the local overlaps are often aligned well. So a novel measurement axis searching model (MASM) has been proposed in this paper. The method includes four steps: (1) the principal axis fitting, (2) the measurement axis generation, (3) low-high-precision search, and (4) result generation. The principal axis gives an orientation to the point cloud; the search scope is limited by the measurement axis. The point cloud orientation can be adjusted gradually until the achievement of the global optimum using low- and high-precision search. We perform some experiments with simulated point clouds and real terrestrial laser scans. The results of simulated point clouds have shown the processing steps of our method, and the results of real terrestrial laser scans have shown the sensitivity of the approach with respect to the indoor and outdoor scenes
A study of energy correction for the electron beam data in the BGO ECAL of the DAMPE
The DArk Matter Particle Explorer (DAMPE) is an orbital experiment aiming at
searching for dark matter indirectly by measuring the spectra of photons,
electrons and positrons originating from deep space. The BGO electromagnetic
calorimeter is one of the key sub-detectors of the DAMPE, which is designed for
high energy measurement with a large dynamic range from 5 GeV to 10 TeV. In
this paper, some methods for energy correction are discussed and tried, in
order to reconstruct the primary energy of the incident electrons. Different
methods are chosen for the appropriate energy ranges. The results of Geant4
simulation and beam test data (at CERN) are presented
Spectrum Focused Frequency Adversarial Attacks for Automatic Modulation Classification
Artificial intelligence (AI) technology has provided a potential solution for
automatic modulation recognition (AMC). Unfortunately, AI-based AMC models are
vulnerable to adversarial examples, which seriously threatens the efficient,
secure and trusted application of AI in AMC. This issue has attracted the
attention of researchers. Various studies on adversarial attacks and defenses
evolve in a spiral. However, the existing adversarial attack methods are all
designed in the time domain. They introduce more high-frequency components in
the frequency domain, due to abrupt updates in the time domain. For this issue,
from the perspective of frequency domain, we propose a spectrum focused
frequency adversarial attacks (SFFAA) for AMC model, and further draw on the
idea of meta-learning, propose a Meta-SFFAA algorithm to improve the
transferability in the black-box attacks. Extensive experiments, qualitative
and quantitative metrics demonstrate that the proposed algorithm can
concentrate the adversarial energy on the spectrum where the signal is located,
significantly improve the adversarial attack performance while maintaining the
concealment in the frequency domain.Comment: 6 pages, 9 figure
A Systematic Evaluation of Federated Learning on Biomedical Natural Language Processing
Language models (LMs) like BERT and GPT have revolutionized natural language
processing (NLP). However, privacy-sensitive domains, particularly the medical
field, face challenges to train LMs due to limited data access and privacy
constraints imposed by regulations like the Health Insurance Portability and
Accountability Act (HIPPA) and the General Data Protection Regulation (GDPR).
Federated learning (FL) offers a decentralized solution that enables
collaborative learning while ensuring the preservation of data privacy. In this
study, we systematically evaluate FL in medicine across biomedical NLP
tasks using LMs encompassing corpora. Our results showed that: 1) FL
models consistently outperform LMs trained on individual client's data and
sometimes match the model trained with polled data; 2) With the fixed number of
total data, LMs trained using FL with more clients exhibit inferior
performance, but pre-trained transformer-based models exhibited greater
resilience. 3) LMs trained using FL perform nearly on par with the model
trained with pooled data when clients' data are IID distributed while
exhibiting visible gaps with non-IID data. Our code is available at:
https://github.com/PL97/FedNLPComment: Accepted by KDD 2023 Workshop FL4Data-Minin
A Dexterous Tip-extending Robot with Variable-length Shape-locking
Soft, tip-extending "vine" robots offer a unique mode of inspection and
manipulation in highly constrained environments. For practicality, it is
desirable that the distal end of the robot can be manipulated freely, while the
body remains stationary. However, in previous vine robots, either the shape of
the body was fixed after growth with no ability to manipulate the distal end,
or the whole body moved together with the tip. Here, we present a concept for
shape-locking that enables a vine robot to move only its distal tip, while the
body is locked in place. This is achieved using two inextensible, pressurized,
tip-extending, chambers that "grow" along the sides of the robot body,
preserving curvature in the section where they have been deployed. The length
of the locked and free sections can be varied by controlling the extension and
retraction of these chambers. We present models describing this shape-locking
mechanism and workspace of the robot in both free and constrained environments.
We experimentally validate these models, showing an increased dexterous
workspace compared to previous vine robots. Our shape-locking concept allows
improved performance for vine robots, advancing the field of soft robotics for
inspection and manipulation in highly constrained environments.Comment: 7 pages,10 figures. Accepted to IEEE International Conference on
Rootics and Automation (ICRA) 202
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