Beijing University of Technology

About: Beijing University of Technology is a education organization based out in Beijing, Beijing, China. It is known for research contribution in the topics: Microstructure & Computer science. The organization has 31929 authors who have published 31987 publications receiving 352112 citations. The organization is also known as: Běijīng Gōngyè Dàxué & Beijing Polytechnic University.

A Multi-view Deep Learning Method for Epileptic Seizure Detection using Short-time Fourier Transform

Abstract: With the advances in pervasive sensor technologies, physiological signals can be captured continuously to prevent the serious outcomes caused by epilepsy. Detection of epileptic seizure onset on collected multi-channel electroencephalogram (EEG) has attracted lots of attention recently. Deep learning is a promising method to analyze large-scale unlabeled data. In this paper, we propose a multi-view deep learning model to capture brain abnormality from multi-channel epileptic EEG signals for seizure detection. Specifically, we first generate EEG spectrograms using short-time Fourier transform (STFT) to represent the time-frequency information after signal segmentation. Second, we adopt stacked sparse denoising autoencoders (SSDA) to unsupervisedly learn multiple features by considering both intra and inter correlation of EEG channels, denoted as intra-channel and cross-channel features, respectively. Third, we add an SSDA-based channel selection procedure using proposed response rate to reduce the dimension of intra-channel feature. Finally, we concatenate the learned multi-features and apply a fully-connected SSDA model with softmax classifier to jointly learn the cross-patient seizure detector in a supervised fashion. To evaluate the performance of the proposed model, we carry out experiments on a real world benchmark EEG dataset and compare it with six baselines. Extensive experimental results demonstrate that the proposed learning model is able to extract latent features with meaningful interpretation, and hence is effective in detecting epileptic seizure.

Mg Doped Perovskite LaNiO3 Nanofibers as an Efficient Bifunctional Catalyst for Rechargeable Zinc–Air Batteries

Abstract: Rational design of efficient and durable bifunctional catalysts toward oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) is important for rechargeable zinc–air batteries. Herein, ...

Some error analysis on virtual element methods

Abstract: Some error analyses on virtual element methods (VEMs) including inverse inequalities, norm equivalence, and interpolation error estimates are developed for polygonal meshes, each element of which admits a virtual quasi-uniform triangulation. This sub-mesh regularity covers the usual ones used for theoretical analysis of VEMs, and the proofs are presented by means of standard technical tools in finite element methods.

A novel deep learning framework: Prediction and analysis of financial time series using CEEMD and LSTM

Abstract: Deep learning is well-known for extracting high-level abstract features from a large amount of raw data without relying on prior knowledge, which is potentially attractive in forecasting financial time series. Long short-term memory (LSTM) networks are deemed as state-of-the-art techniques in sequence learning, which are less commonly applied to financial time series predictions, yet inherently suitable for this domain. We propose a novel methodology of deep learning prediction, and based on this, construct a deep learning hybrid prediction model for stock markets—CEEMD-PCA-LSTM. In this model, complementary ensemble empirical mode decomposition (CEEMD), as a sequence smoothing and decomposition module, can decompose the fluctuations or trends of different scales of time series step by step, generating a series of intrinsic mode functions (IMFs) with different characteristic scales. Then, with retaining the most of information on raw data, PCA reduces dimension of the decomposed IMFs component, eliminating the redundant information and improving prediction response speed. After that, high-level abstract features are separately fed into LSTM networks to predict closing price of the next trading day for each component. Finally, synthesizing the predicted values of individual components is utilized to obtain a final predicted value. The empirical results of six representative stock indices from three types of markets indicate that our proposed model outperforms benchmark models in terms of predictive accuracy, i.e., lower test error and higher directional symmetry. Leveraging key research findings, we perform trading simulations to validate that the proposed model outperforms benchmark models in both absolute profitability performance and risk-adjusted profitability performance. Furthermore, model robustness test unveils the more stable robustness compared to benchmark models.