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Special load customers such as electric vehicles are emerging in modern power systems. They lead to a higher penetration of special load patterns, raising difficulty for short-term load forecasting (STLF). We propose a hierarchical STLF framework to improve load forecasting accuracy. An improved adaptive K-means clustering algorithm is designed for load pattern recognition and avoiding local sub-optimal clustering centroids. We also design bi-directional long-short-term memory neural networks with an attention mechanism to filter important load information and perform load forecasting for each recognized load pattern. The numerical results on the public load dataset show that our proposed method effectively forecasts the residential load with a high accuracy. [ABSTRACT FROM AUTHOR]

Automatic seizure detection using electroen-cephalogram (EEG) can significantly expedite the diagnosis of epilepsy, thereby facilitating prompt treatment and reducing the risk of future seizures and associated complications. While most existing EEG-based epilepsy detection studies employ deep learning models, they often ignore the chronological relationships between different EEG channels. To tackle this limitation, a novel automatic epilepsy detection method is proposed, which leverages path signature and Bidirectional Long Short-Term Memory (Bi-LSTM) neural network with an attention mechanism. The path signature algorithm is used to extract discriminative features for capturing the dynamic dependencies between different channels of EEG, while Bi-LSTM with attention further analyzes the inherent temporal dependencies hidden in EEG signal features. Our method is evaluated on two public EEG databases with different sizes (CHB-MIT and TUEP) and a private database from a local hospital. Two experimental settings are used, i.e., five-fold cross-validation and leave-one-out cross-validation. Experimental results show that our method achieves 99.09%, 95.60%, and 99.87% average accuracies on CHB-MIT, TUEP with 250Hz, and TUEP with 256Hz, respectively. On the private dataset, our method also achieves 99.40% average accuracy, which outperforms other methods. Furthermore, our method exhibits robustness in patients, as demonstrated by the evaluation results of cross-patient experiments.

Special load customers such as electric vehicles are emerging in modern power systems. They lead to a higher penetration of special load patterns, raising difficulty for short-term load forecasting (STLF). We propose a hierarchical STLF framework to improve load forecasting accuracy. An improved adaptive K-means clustering algorithm is designed for load pattern recognition and avoiding local sub-optimal clustering centroids. We also design bi-directional long-short-term memory neural networks with an attention mechanism to filter important load information and perform load forecasting for each recognized load pattern. The numerical results on the public load dataset show that our proposed method effectively forecasts the residential load with a high accuracy.

Bearings as the key component of most rotating machinery, responsible for major breakdowns. Therefore, this paper addresses intelligent prognostics involving remaining useful life estimation. The proposed framework is based on a deep learning model to learn the bearing degradation from vibration responses. A comprehensive feature selection strategy involving PSO (particle swarm optimization) optimization technique and feature transformations is carried out. The sensitive prognostic features set are then input to BiLSTM (bi-directional long short-term memory) network to learn long-term time dependencies in two directions. Furthermore, the attention mechanism is integrated with BiLSTM enables selective processing of information. The experimental validation is carried out by acquiring data from a high-speed rotor supported on the bearings. The results achieved higher prediction accuracy. Also, the generalization on IEEE PHM data achieves higher RUL (remaining useful life) prediction accuracy as compared to state-of-art methods. Hence, the results proved the high performance and feasibility of the proposed RUL prognostic method. [ABSTRACT FROM AUTHOR]

Data-driven prognostic techniques have been widely implemented to accurately predicting the remaining useful life (RUL) of high-speed bearings. Accurate RUL estimation attributes to determining underlying relationship between bearing degradation progression and current health status. To achieve this, effective feature compression and optimal feature selection are significant challenges to be overcome. Therefore, in this paper a framework is developed which utilizes the ' Stacked Bi-directional Long Short Term Memory ' (SBiLSTM) with attention mechanism (A-SBiLSTM) to determine the underlying relationship between the health status and RUL of bearing. Bi-directional LSTM is capable of processing the past and the future states simultaneously thereby mining the useful degradation information from time-series data. Additionally, fitness analysis is carried out using feature ranking metrics to select features highly correlated with bearing degradation. The effectiveness and feasibility of the proposed methodology are experimentally validated and generalized on dataset from PRONOSTIA platform. The comparison results based on root mean square error (RMSE), and mean absolute error (MAE) achieved superior performance compared to state-of-art methods. The experimental results revealed that the A-SBiLSTM with attention mechanism and fitness analysis achieved superior performance than other advanced methods for accurate RUL prediction. • Develop a framework to relate health status and RUL of bearing using (SBiLSTM) with an attention mechanism. • The features are extracted from raw vibration data using the wavelet denoising technique to remove unwanted noise. • A fitness analysis is done to select the highly sensitive features to enhance the prediction accuracy of the network further. • Performance of the proposed method is validated using experimental and vibration responses from the PRONOSTIA platform. [ABSTRACT FROM AUTHOR]