Deep Learning Methods for EEG Signals Classification of Motor Imagery in BCI

EEG signals are obtained from an EEG device after recording the user's brain signals. EEG signals can be generated by the user after performing motor movements or imagery tasks. Motor Imagery (MI) is the task of imagining motor movements that resemble the original motor movements. Brain Computer Interface (BCI) bridges interactions between users and applications in performing tasks. Brain Computer Interface (BCI) Competition IV 2a was used in this study. A fully automated correction method of EOG artifacts in EEG recordings was applied in order to remove artifacts and Common Spatial Pattern (CSP) to get features that can distinguish motor imagery tasks. In this study, a comparative studies between two deep learning methods was explored, namely Deep Belief Network (DBN) and Long Short Term Memory (LSTM). Usability of both deep learning methods was evaluated using the BCI Competition IV-2a dataset. The experimental results of these two deep learning methods show average accuracy of 50.35% for DBN and 49.65% for LSTM

Automatic detection, segmentation and classification of abdominal aortic aneurysm using deep learning

This study is focused on developing an automated algorithm for the detection and segmentation of Abdominal Aortic Aneurysm (AAA) region in CT Angiography images. The outcome of this research will offer great assistance for radiologists to detect the AAA region and estimate its volume in CT datasets efficiently. In addition, suitable treatment strategies can also be suggested based on the classification of the AAA severity and measurement of the aorta diameter. This research takes the initiative by exploring and applying deep learning architecture in the study of AAA detection and segmentation, which has never been done by other researchers before in AAA problems. The findings from this study will also act as a reference for other similar future works. Deep Belief Network (DBN) is applied for the purpose of AAA detection and severity classification in this study. Optimum parameters for training the DBN are determined for the training data from the selected dataset. AAA region can be successfully segmented from the CT images and the result is comparable to the existing method with advantage over the existing method that the proposed method is fully automatic and added with auto detection and classification features. The limitation of the trained DBN in AAA detection accuracy can be improved by incorporating and adjusting the detection probability threshold and shape constraints. In future, the DBN can be further enhanced by adding and training it with more data which covers a wider variety of features, as well as extending its capability to perform detailed segmentation on AAA region

Improving Phoneme Sequence Recognition using Phoneme Duration Information in DNN-HSMM

Improving phoneme recognition has attracted the attention of many researchers due to its applications in various fields of speech processing. Recent research achievements show that using deep neural network (DNN) in speech recognition systems significantly improves the performance of these systems. There are two phases in DNN-based phoneme recognition systems including training and testing. Most previous research attempted to improve training phase such as training algorithms, different types of network, network architecture, feature type, etc. But in this study, we focus on test phase which is related to generate phoneme sequence that is also essential to achieve good phoneme recognition accuracy. Past research used Viterbi algorithm on hidden Markov model (HMM) to generate phoneme sequences. We address an important problem associated with this method. To deal with the problem of considering geometric distribution of state duration in HMM, we use real duration probability distribution for each phoneme with the aid of hidden semi-Markov model (HSMM). We also represent each phoneme with only one state to simply use phonemes duration information in HSMM. Furthermore, we investigate the performance of a post-processing method, which corrects the phoneme sequence obtained from the neural network, based on our knowledge about phonemes. The experimental results using the Persian FarsDat corpus show that using extended Viterbi algorithm on HSMM achieves phoneme recognition accuracy improvements of 2.68% and 0.56% over conventional methods using Gaussian mixture model-hidden Markov models (GMM-HMMs) and Viterbi on HMM, respectively. The post-processing method also increases the accuracy compared to before its application

Computational intelligence techniques for energy storage management

Ph. D. ThesisThe proliferation of stochastic renewable energy sources (RES) such as photovoltaic and wind power in the power system has made the balancing of generation and demand challenging for the grid operators. This is further compounded with the liberalization of electricity market and the introduction of real-time electricity pricing (RTP) to reflect the dynamics in generation and demand. Energy storage sources (ESS) are widely seen as one of the keys enabling technology to mitigate this problem. Since ESS is a costly and energy-limited resource, it is economical to provide multiple services using a single ESS. This thesis aims to investigate the operation of a single ESS in a grid-connected microgrid with RES under RTP to provide multiple services. First, artificial neural network is proposed for day-ahead forecasting of the RES, demand and RTP. After the day-ahead forecast is obtained, the day-ahead schedule of energy storage is formulated into a mixed-integer linear programming and implemented in AMPL and solved using CPLEX. This method considers the impact of forecasting errors in the day-ahead scheduling. Empirical evidence shows that the proposed nearoptimal day-ahead scheduling of ESS can achieve a lower operating cost and peak demand. Second, a fuzzy logic-based energy management system (FEMS) for a grid-connected microgrid with RES and ESS is proposed. The objectives of the FEMS are energy arbitrage and peak shaving for the microgrid. These objectives are achieved by controlling the charge and discharge rate of the ESS based on the state-of-charge (SoC) of ESS, the power difference between RES and demand, and RTP. Instead of using a forecasting-based approach, the proposed FEMS is designed with the historical data of the microgrid. It determines the charge and discharge rate of the ESS in a rolling horizon. A comparison with other controllers with the same objectives shows that the proposed controller can operate at a lower cost and reduce the peak demand of the microgrid. Finally, the effectiveness of the FEMS greatly depends on the membership functions. The fuzzy membership functions of the FEMS are optimized offline using a Pareto based multi-objective evolutionary algorithm, nondominated sorting genetic algorithm- II (NSGA-II). The best compromise solution is selected as the final solution and implemented in the fuzzy logic controller. A comparison was made against other control strategies with similar objectives are carried out at a simulation level. Empirical evidence shows that the proposed methodology can find more solutions on the Pareto front in a single run. The proposed FEMS is experimentally validated on a real microgrid in the energy storage test bed at Newcastle University, UK. Furthermore, reserve service is added on top of energy arbitrage and peak shaving to the energy management system (EMS). As such multi-service of a single ESS which benefit the grid operator and consumer is achieved