20,583 research outputs found
3DCFS : Fast and robust joint 3D semantic-instance segmentation via coupled feature selection
We propose a novel fast and robust 3D point clouds segmentation framework via coupled feature selection, named 3DCFS, that jointly performs semantic and instance segmentation. Inspired by the human scene perception process, we design a novel coupled feature selection module, named CFSM, that adaptively selects and fuses the reciprocal semantic and instance features from two tasks in a coupled manner. To further boost the performance of the instance segmentation task in our 3DCFS, we investigate a loss function that helps the model learn to balance the magnitudes of the output embedding dimensions during training, which makes calculating the Euclidean distance more reliable and enhances the generalizability of the model. Extensive experiments demonstrate that our 3DCFS outperforms state-of-the-art methods on benchmark datasets in terms of accuracy, speed and computational cost
Predicting Blood Glucose with an LSTM and Bi-LSTM Based Deep Neural Network
A deep learning network was used to predict future blood glucose levels, as
this can permit diabetes patients to take action before imminent hyperglycaemia
and hypoglycaemia. A sequential model with one long-short-term memory (LSTM)
layer, one bidirectional LSTM layer and several fully connected layers was used
to predict blood glucose levels for different prediction horizons. The method
was trained and tested on 26 datasets from 20 real patients. The proposed
network outperforms the baseline methods in terms of all evaluation criteria.Comment: 5 pages, submitted to 2018 14th Symposium on Neural Networks and
Applications (NEUREL
A novel monitoring system for fall detection in older people
Indexación: Scopus.This work was supported in part by CORFO - CENS 16CTTS-66390 through the National Center on Health Information Systems, in part by the National Commission for Scientific and Technological Research (CONICYT) through the Program STIC-AMSUD 17STIC-03: ‘‘MONITORing for ehealth," FONDEF ID16I10449 ‘‘Sistema inteligente para la gestión y análisis de la dotación de camas en la red asistencial del sector público’’, and in part by MEC80170097 ‘‘Red de colaboración cientÃfica entre universidades nacionales e internacionales para la estructuración del doctorado y magister en informática médica en la Universidad de ValparaÃso’’. The work of V. H. C. De Albuquerque was supported by the Brazilian National Council for Research and Development (CNPq), under Grant 304315/2017-6.Each year, more than 30% of people over 65 years-old suffer some fall. Unfortunately, this can generate physical and psychological damage, especially if they live alone and they are unable to get help. In this field, several studies have been performed aiming to alert potential falls of the older people by using different types of sensors and algorithms. In this paper, we present a novel non-invasive monitoring system for fall detection in older people who live alone. Our proposal is using very-low-resolution thermal sensors for classifying a fall and then alerting to the care staff. Also, we analyze the performance of three recurrent neural networks for fall detections: Long short-term memory (LSTM), gated recurrent unit, and Bi-LSTM. As many learning algorithms, we have performed a training phase using different test subjects. After several tests, we can observe that the Bi-LSTM approach overcome the others techniques reaching a 93% of accuracy in fall detection. We believe that the bidirectional way of the Bi-LSTM algorithm gives excellent results because the use of their data is influenced by prior and new information, which compares to LSTM and GRU. Information obtained using this system did not compromise the user's privacy, which constitutes an additional advantage of this alternative. © 2013 IEEE.https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=842305
Toward Abstraction from Multi-modal Data: Empirical Studies on Multiple Time-scale Recurrent Models
The abstraction tasks are challenging for multi- modal sequences as they
require a deeper semantic understanding and a novel text generation for the
data. Although the recurrent neural networks (RNN) can be used to model the
context of the time-sequences, in most cases the long-term dependencies of
multi-modal data make the back-propagation through time training of RNN tend to
vanish in the time domain. Recently, inspired from Multiple Time-scale
Recurrent Neural Network (MTRNN), an extension of Gated Recurrent Unit (GRU),
called Multiple Time-scale Gated Recurrent Unit (MTGRU), has been proposed to
learn the long-term dependencies in natural language processing. Particularly
it is also able to accomplish the abstraction task for paragraphs given that
the time constants are well defined. In this paper, we compare the MTRNN and
MTGRU in terms of its learning performances as well as their abstraction
representation on higher level (with a slower neural activation). This was done
by conducting two studies based on a smaller data- set (two-dimension time
sequences from non-linear functions) and a relatively large data-set
(43-dimension time sequences from iCub manipulation tasks with multi-modal
data). We conclude that gated recurrent mechanisms may be necessary for
learning long-term dependencies in large dimension multi-modal data-sets (e.g.
learning of robot manipulation), even when natural language commands was not
involved. But for smaller learning tasks with simple time-sequences, generic
version of recurrent models, such as MTRNN, were sufficient to accomplish the
abstraction task.Comment: Accepted by IJCNN 201
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