1,556 research outputs found
Gibbs Max-margin Topic Models with Data Augmentation
Max-margin learning is a powerful approach to building classifiers and
structured output predictors. Recent work on max-margin supervised topic models
has successfully integrated it with Bayesian topic models to discover
discriminative latent semantic structures and make accurate predictions for
unseen testing data. However, the resulting learning problems are usually hard
to solve because of the non-smoothness of the margin loss. Existing approaches
to building max-margin supervised topic models rely on an iterative procedure
to solve multiple latent SVM subproblems with additional mean-field assumptions
on the desired posterior distributions. This paper presents an alternative
approach by defining a new max-margin loss. Namely, we present Gibbs max-margin
supervised topic models, a latent variable Gibbs classifier to discover hidden
topic representations for various tasks, including classification, regression
and multi-task learning. Gibbs max-margin supervised topic models minimize an
expected margin loss, which is an upper bound of the existing margin loss
derived from an expected prediction rule. By introducing augmented variables
and integrating out the Dirichlet variables analytically by conjugacy, we
develop simple Gibbs sampling algorithms with no restricting assumptions and no
need to solve SVM subproblems. Furthermore, each step of the
"augment-and-collapse" Gibbs sampling algorithms has an analytical conditional
distribution, from which samples can be easily drawn. Experimental results
demonstrate significant improvements on time efficiency. The classification
performance is also significantly improved over competitors on binary,
multi-class and multi-label classification tasks.Comment: 35 page
Personnel recognition and gait classification based on multistatic micro-doppler signatures using deep convolutional neural networks
In this letter, we propose two methods for personnel recognition and gait classification using deep convolutional neural networks (DCNNs) based on multistatic radar micro-Doppler signatures. Previous DCNN-based schemes have mainly focused on monostatic scenarios, whereas directional diversity offered by multistatic radar is exploited in this letter to improve classification accuracy. We first propose the voted monostatic DCNN (VMo-DCNN) method, which trains DCNNs on each receiver node separately and fuses the results by binary voting. By merging the fusion step into the network architecture, we further propose the multistatic DCNN (Mul-DCNN) method, which performs slightly better than VMo-DCNN. These methods are validated on real data measured with a 2.4-GHz multistatic radar system. Experimental results show that the Mul-DCNN achieves over 99% accuracy in armed/unarmed gait classification using only 20% training data and similar performance in two-class personnel recognition using 50% training data, which are higher than the accuracy obtained by performing DCNN on a single radar node
Fortissat minewater geothermal district heating project: case study
This study summarises the findings of a feasibility assessment for a potential minewater geothermal energy system in the vicinity of the James Hutton Institute’s Hartwood Home Farm, North Lanarkshire in Scotland’s Central Belt. This study aimed to assess the potential for Scotland’s first minewater geothermal scheme in a rural area with social deprivation. While focused on the specifics of the location, the project is conceived as a readily replicable and fully operational minewater geothermal district heating system demonstrator project that would act as proof of concept for duplication elsewhere. This study concluded that, with current support mechanisms, the scheme is technically and financially viable, breaking even or better with a network scenario covering the ‘representative’ communities of Allanton and Hartwood. As might be expected, there are considerable economies of scale, with the scheme becoming commercially viable when the network is extended to the town of Shotts
Elastic-plastic large deformation response of clay to footing loads, Davidson\u27s Ph.D., February 1974 NTIS number: PB237524/AS (77-26)
Dynamic Hand Gesture Classification Based on Multistatic Radar Micro-Doppler Signatures Using Convolutional Neural Network
We propose a novel convolutional neural network
(CNN) for dynamic hand gesture classification based on
multistatic radar micro-Doppler signatures. The timefrequency
spectrograms of micro-Doppler signatures at all the
receiver antennas are adopted as the input to CNN, where data
fusion of different receivers is carried out at an adjustable
position. The optimal fusion position that achieves the highest
classification accuracy is determined by a series of experiments.
Experimental results on measured data show that 1) the
accuracy of classification using multistatic radar is significantly
higher than monostatic radar, and that 2) fusion at the middle
of CNN achieves the best classification accuracy
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