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Full-Text Articles in Physical Sciences and Mathematics
Research On Radar Signal Sorting Based On Ensemble Deep Learning, Weidong Jin, Chunli Chen
Research On Radar Signal Sorting Based On Ensemble Deep Learning, Weidong Jin, Chunli Chen
Journal of System Simulation
Abstract: In view of the fact it is difficult to extract the appropriate features quickly and present signal sorting method’s accuracy is low, a signal sorting method based on ensemble deep learning model is proposed. This method stacks different types of deep belief network for radar emitter signal feature learning to improve algorithm. After learning the characteristics of the radar emitter signals deeply, the posterior probability of each model is linearly integrated and learned and the final classification results are determined by the decision layer to further improve the signal recognition rate. The method is used to separate different …
Investigating The Impact Of Unsupervised Feature-Extraction From Multi-Wavelength Image Data For Photometric Classification Of Stars, Galaxies And Qsos, Annika Lindh
Conference papers
Accurate classification of astronomical objects currently relies on spectroscopic data. Acquiring this data is time-consuming and expensive compared to photometric data. Hence, improving the accuracy of photometric classification could lead to far better coverage and faster classification pipelines. This paper investigates the benefit of using unsupervised feature-extraction from multi-wavelength image data for photometric classification of stars, galaxies and QSOs. An unsupervised Deep Belief Network is used, giving the model a higher level of interpretability thanks to its generative nature and layer-wise training. A Random Forest classifier is used to measure the contribution of the novel features compared to a set …
Investigating The Impact Of Unsupervised Feature-Extraction From Multi-Wavelength Image Data For Photometric Classification Of Stars, Galaxies And Qsos, Annika Lindh
Dissertations
This thesis reviews the current state of photometric classification in Astronomy and identifies two main gaps: a dependence on handcrafted rules, and a lack of interpretability in the more successful classifiers. To address this, Deep Learning and Computer Vision were used to create a more interpretable model, using unsupervised training to reduce human bias.
The main contribution is the investigation into the impact of using unsupervised feature-extraction from multi-wavelength image data for the classification task. The feature-extraction is achieved by implementing an unsupervised Deep Belief Network to extract lower-dimensionality features from the multi-wavelength image data captured by the Sloan Digital …