247 research outputs found
The development of an interface instrument for collecting elec-tromyography data and controlling continuous passive motion machine
There is a lack of research in using electromyography (EMG) signals to control a continuous passive motion (CPM) machine. This study aimed to develop an interface instrument for digitalising EMG signals and controlling a CPM machine. Methods: The proposed device was designed with the following: (1) a signal processing unit which converted the EMGs from analogue to digital for the controller; (2) a personal computer which stored and displayed the EMG signals; (3) an LCD device to display the running angle of the CPM; and (4) a microcontroller unit to control the input/output signals and process the algorithm, driving the CPM. To validate the reliability of the proposed system, a total of 600 EMG trials were collected from 10 healthy subjects by using the proposed device via the Delsys® TringoTM EMG system and simultaneously using the Vicon® motion capture system. Result: This proposed device was able to digitalise and process EMG signals from eight channels of muscles, and the signals were able to drive a CPM. The validated results showed that the digitalised EMG signals by the proposed device were statistically similar to and correlated with the signals by the Vicon system with a median correlation coefficient of 0.81, with the 25% and 75% range being 0.56–0.92 with all pairs (300 pairs of EMG trials) (p < 0.001). Conclusions: This study confirmed that the developed device can digitalise EMG signals and drive a CPM as an applicable prototype that can work as an interface between EMG and CPM devices with high reliability.<br/
Sub Coulomb barrier d+Pb scattering in the time-dependent basis function approach
We employ the ab initio non-perturbative time-dependent basis function (tBF)
approach to study the scattering of the deuteron on Pb below the
Coulomb barrier. We obtain the bound and discretized scattering states of the
projectile, which form the basis representation of the tBF approach, by
diagonalizing a realistic Hamiltonian in a large harmonic oscillator basis. We
find that the higher-order inelastic scattering effects are noticeable for sub
barrier scatterings with the tBF method. We have successfully reproduced
experimental sub Coulomb barrier elastic cross section ratios with the tBF
approach by considering only the electric dipole (E1) component of the Coulomb
interaction between the projectile and the target during scatterings. We find
that the correction of the polarization potential to the Rutherford trajectory
is dominant in reproducing the data at very low bombarding energies, whereas
the role of internal transitions of the deuteron projectile induced by the E1
interaction during the scattering becomes increasingly dominant at higher
bombarding energies.Comment: 9 pages, 4 figure
Two-dimensional simulation of large-scale wind field via a joint wavenumber-frequency power spectrum
The demand of large span spatial structures is booming in developing countries as their urbanization processes keep steady pace. These structures are characterized with long-span roofs, which probably will suffer wind-induced damages in their lifecycles because of their flexible natures and harmful aerodynamic actions caused by complicated forms of roof surface and stochastic wind field around them. Relevant studies are yet relatively rare in history. The recently proposed wavenumberfrequency joint PSD based spectral representation method (WN-SRM) has greatly reduced the computational burden of traditional wind field simulation methods, which makes possible for performing stochastic wind field simulation for large size structures. This paper presents a two-dimensional, homogeneous wind field simulation for a long-span, unevenly curved roof structure. The results show that the improved spectral representation method, i.e. WN-SRM works well in wind field simulation for flexible, long-span roof structure in terms of efficiency and effectiveness
Adaptive output regulation for a class of nonlinear systems with guaranteed transient performance
This paper is dedicated to adaptive output regulation for a class of nonlinear systems with asymptotic output tracking and guarantee of prescribed transient performance. With the employment of internal model principle, we first transform this problem into a specific adaptive stabilization problem with output constraints. Then, by integrating the time-varying Barrier Lyapunov Function (BLF) technique together with the high gain feedback method, we develop an output-based control law to solve the constrained stabilization problem and consequently confine the output tracking error to a predefined arbitrary region. The output-based control law enables adaptive output regulation in the sense that, under unknown exosystem dynamics, all the closed-loop system signals are bounded whilst the controlled output constraints are not violated. Finally, efficacy of the proposed design is illustrated through a simulation example
Effect of wheelchair configurations on shoulder movements, push rim kinetics and upper limb kinematics while negotiating a speed bump
Practice with Graph-based ANN Algorithms on Sparse Data: Chi-square Two-tower model, HNSW, Sign Cauchy Projections
Sparse data are common. The traditional ``handcrafted'' features are often
sparse. Embedding vectors from trained models can also be very sparse, for
example, embeddings trained via the ``ReLu'' activation function. In this
paper, we report our exploration of efficient search in sparse data with
graph-based ANN algorithms (e.g., HNSW, or SONG which is the GPU version of
HNSW), which are popular in industrial practice, e.g., search and ads
(advertising).
We experiment with the proprietary ads targeting application, as well as
benchmark public datasets. For ads targeting, we train embeddings with the
standard ``cosine two-tower'' model and we also develop the ``chi-square
two-tower'' model. Both models produce (highly) sparse embeddings when they are
integrated with the ``ReLu'' activation function. In EBR (embedding-based
retrieval) applications, after we the embeddings are trained, the next crucial
task is the approximate near neighbor (ANN) search for serving. While there are
many ANN algorithms we can choose from, in this study, we focus on the
graph-based ANN algorithm (e.g., HNSW-type).
Sparse embeddings should help improve the efficiency of EBR. One benefit is
the reduced memory cost for the embeddings. The other obvious benefit is the
reduced computational time for evaluating similarities, because, for
graph-based ANN algorithms such as HNSW, computing similarities is often the
dominating cost. In addition to the effort on leveraging data sparsity for
storage and computation, we also integrate ``sign cauchy random projections''
(SignCRP) to hash vectors to bits, to further reduce the memory cost and speed
up the ANN search. In NIPS'13, SignCRP was proposed to hash the chi-square
similarity, which is a well-adopted nonlinear kernel in NLP and computer
vision. Therefore, the chi-square two-tower model, SignCRP, and HNSW are now
tightly integrated
TransFA: Transformer-based Representation for Face Attribute Evaluation
Face attribute evaluation plays an important role in video surveillance and
face analysis. Although methods based on convolution neural networks have made
great progress, they inevitably only deal with one local neighborhood with
convolutions at a time. Besides, existing methods mostly regard face attribute
evaluation as the individual multi-label classification task, ignoring the
inherent relationship between semantic attributes and face identity
information. In this paper, we propose a novel \textbf{trans}former-based
representation for \textbf{f}ace \textbf{a}ttribute evaluation method
(\textbf{TransFA}), which could effectively enhance the attribute
discriminative representation learning in the context of attention mechanism.
The multiple branches transformer is employed to explore the inter-correlation
between different attributes in similar semantic regions for attribute feature
learning. Specially, the hierarchical identity-constraint attribute loss is
designed to train the end-to-end architecture, which could further integrate
face identity discriminative information to boost performance. Experimental
results on multiple face attribute benchmarks demonstrate that the proposed
TransFA achieves superior performances compared with state-of-the-art methods
K-BERT: Enabling Language Representation with Knowledge Graph
Pre-trained language representation models, such as BERT, capture a general
language representation from large-scale corpora, but lack domain-specific
knowledge. When reading a domain text, experts make inferences with relevant
knowledge. For machines to achieve this capability, we propose a
knowledge-enabled language representation model (K-BERT) with knowledge graphs
(KGs), in which triples are injected into the sentences as domain knowledge.
However, too much knowledge incorporation may divert the sentence from its
correct meaning, which is called knowledge noise (KN) issue. To overcome KN,
K-BERT introduces soft-position and visible matrix to limit the impact of
knowledge. K-BERT can easily inject domain knowledge into the models by
equipped with a KG without pre-training by-self because it is capable of
loading model parameters from the pre-trained BERT. Our investigation reveals
promising results in twelve NLP tasks. Especially in domain-specific tasks
(including finance, law, and medicine), K-BERT significantly outperforms BERT,
which demonstrates that K-BERT is an excellent choice for solving the
knowledge-driven problems that require experts.Comment: 8 pages, 2019091
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