Stable Electromyographic Sequence Prediction During Movement Transitions using Temporal Convolutional Networks

Accurate load forecasting is essential for ensuring safe, stable, and economical operation of energy internet. Temporal convolutional networks (TCNs) have demonstrated superior performance, when compared to recurrent neural network models, since their introduction in electrical load forecasting. However, the current TCN‐based models are unable to obtain a large receptive field and strong long‐time feature extraction capability owing to the specific kernel size of the 1D convolution structure. This paper proposes a temporal inception convolutional network based on multi‐head attention (TICN‐Att) for ultra‐short‐term load prediction. By introducing an inception structure into the TCN, the proposed model can extract multi‐dimensional information from the input features, through the multiple hidden convolutional kernels of different scales, without stacking layers depth‐wise. Simultaneously, by introducing a multi‐head attention mechanism, the TICN‐Att model has a long time‐dependent extraction capability, similar to that of the long short‐term memory network models. The generalization and validity of the model are tested using the global energy forecasting competition (GEFCOM2014) dataset, electrical load data of a city in Jiangsu (China), and PJM power system dataset. The experimental results demonstrated that the proposed model has the best prediction effect, compared to the other state‐of‐the‐art models.

Data in time series format, such as biological signals from medical sensors or machine signals from sensors in industrial environments are rich sources of information that can give crucial insights on the present and future condition of a person or machine. The task of predicting future values of time series has been initially approached with simple machine learning methods, and lately with deep learning. Two models that have shown good performance in this task are the temporal convolutional network and the attention module. However, despite the promising results of deep learning methods, their black-box nature makes them unsuitable for real-world applications where the predictions need to be explainable in order to be trusted. In this paper we propose an architecture comprised of a temporal convolutional network with an attention mechanism that makes predictions while presenting the timesteps of the input that were most influential for future outputs. We apply it on two datasets and we show that we gain interpretability without degrading the accuracy compared to the original temporal convolutional models. We then go one step further and we combine our configuration with various machine learning methods on top, creating a pipeline that achieves interpretability both across timesteps and input features. We use it to forecast a different variable from one of the above datasets and we study how the accuracy is affected compared to the original black-box approach.

Abstract. The present work proposes a prediction model of significant wave height (SWH) and average wave period (APD) based on variational mode decomposition (VMD), temporal convolutional networks (TCNs), and long short-term memory (LSTM) networks. The wave sequence features were obtained using VMD technology based on the wave data from the National Data Buoy Center. Then the SWH and APD prediction models were established using TCNs, LSTM, and Bayesian hyperparameter optimization. The VMD–TCN–LSTM model was compared with the VMD–LSTM (without TCN cells) and LSTM (without VMD and TCN cells) models. The VMD–TCN–LSTM model has significant superiority and shows robustness and generality in different buoy prediction experiments. In the 3 h wave forecasts, VMD primarily improved the model performance, while the TCN had less of an influence. In the 12, 24, and 48 h wave forecasts, both VMD and TCNs improved the model performance. The contribution of the TCN to the improvement of the prediction result determination coefficient gradually increased as the forecasting length increased. In the 48 h SWH forecasts, the VMD and TCN improved the determination coefficient by 132.5 % and 36.8 %, respectively. In the 48 h APD forecasts, the VMD and TCN improved the determination coefficient by 119.7 % and 40.9 %, respectively.

Battery health prognosis using improved temporal convolutional network modeling

Multivariate time-series forecasting is one of the crucial and persistent challenges in time-series forecasting tasks. As a kind of data with multivariate correlation and volatility, multivariate time series impose highly nonlinear time characteristics on the forecasting model. In this paper, a new multivariate time-series forecasting model, multivariate temporal convolutional attention network (MTCAN), based on a self-attentive mechanism is proposed. MTCAN is based on the Convolution Neural Network (CNN) model, using 1D dilated convolution as the basic unit to construct asymmetric blocks, and then, the feature extraction is performed by the self-attention mechanism to finally obtain the prediction results. The input and output lengths of this network can be determined flexibly. The validation of the method is carried out with three different multivariate time-series datasets. The reliability and accuracy of the prediction results are compared with Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Long Short-Term Memory (ConvLSTM), and Temporal Convolutional Network (TCN). The prediction results show that the model proposed in this paper has significantly improved prediction accuracy and generalization.

Research on smartphone-based human activity recognition (HAR) is prevalent in the field of healthcare, especially for elderly activity monitoring. Researchers usually propose to use of accelerometers, gyroscopes or magnetometers that are equipped in smartphones as an individual sensing modality for human activity recognition. However, any of these alone is limited in capturing comprehensive movement information for accurate human activity analysis. Thus, we propose a smartphone-based HAR approach by leveraging the inertial signals captured by these three sensors to classify human activities. These heterogeneous sensors deliver information on various aspects of nature, motion and orientation, offering a richer set of features for more accurate representations of the activities. Hence, a deep learning approach that amalgamates long short-term memory (LSTM) in temporal convolutional network (TCN) is proposed. We use independent temporal convolutional networks, coined as temporal convolutional streams, to independently analyse the temporal data of each sensing modality. We name this architecture multi-stream TC-LSTM. The performance of multi-stream TC-LSTM is assessed on the self-collected elderly activity database. Empirical results exhibit that multi-stream TC-LSTM outperforms the existing machine learning and deep learning models, with an F1 score of 98.3 %

The accuracy of short-term electricity price forecasting has profound influence on the business decisions made by the participants in the electricity market. Thus, we propose a short-term electricity price combination prediction model based on Complete Ensemble Empirical Mode Decomposition(CEEMD) as well as Temporal Convolutional Attention-based Network(TCAN). First, we defined the correlations among the features according to the Pearson Correlation Analysis and selected the desired features for our model. We then defined the single prediction period to be 24 hours based on the Unit Root test. In addition, we decomposed and denoised the data based on CEEMD and built the attention mechanism into the temporal convolution network. Using the expansion cascade network to extract the relationship between the inputs, combined with the self-attention mechanism to extract the internal information and learn the position dependence, effectively improve the model training speed and prediction accuracy. In order to verify the validity of the model, the actual electricity price data of DK1 in the Nordic electricity market from April to May 2020 were forecasted. The results show that the proposed method can effectively improve the accuracy and reliability of electricity price forecasting, and can provide reliable decision-making basis.

Locomotion mode recognition has been shown to substantially contribute to the precise control of robotic lower-limb prostheses under different walking conditions. In this study, we proposed a temporal convolutional capsule network (TCCN) which integrates the spatial-temporal-based, dilation-convolution-based, dyna- mic routing and vector-based features for recognizing locomotion mode recognition with small data rather than big-data-based neural networks for robotic prostheses. TCCN proposed in this study has four characteristics, which extracts the (1) spatial-temporal information in the data and then makes (2) dilated convolution to deal with small data, and uses (3) dynamic routing, which produces some similarities to the human brain to process the data as a (4) vector, which is different from other scalar-based networks, such as convolutional neural network (CNN). By comparison with a traditional machine learning, e.g., support vector machine(SVM) and big-data-driven neural networks, e.g., CNN, recurrent neural network(RNN), temporal convolutional network(TCN) and capsule network(CN). The accuracy of TCCN is 4.1% higher than CNN under 5-fold cross-validation of three-locomotion-mode and 5.2% higher under the 5-fold cross-validation of five-locomotion modes. The main confusion we found appears in the transition state. The results indicate that TCCN may handle small data balancing global and local information which is closer to the way how the human brain works, and the capsule layer allows for better processing vector information and retains not only magnitude information, but also direction information.

Wave runup prediction is necessary for offshore structure designs and early warnings. Data-driven methods based on machine learning have inspired reduced-order solutions for wave–structure interaction problems. This study provides the quantification of deep learning algorithms’ potential for wave runup prediction. Two prominent deep learning models were utilized to predict the wave runups along the fore column of semisubmersible under head seas. The long short-term memory (LSTM) and the temporal convolutional networks (TCNs) were comprehensively compared based on the datasets from a model test carried out in the deep ocean basin. The LSTM and TCN model structures were optimized to compare prediction accuracy and computational complexity reasonably. The results reveal that (1) both developed TCN and LSTM models had a satisfied prediction accuracy of over 90%. Their predictions were extended to 10 s into the future with accuracies over 80% and 45%, respectively. (2) With the noise-extended datasets, the LSTM model was robust to noises, while the TCN model showed better prediction performance on the extreme wave runup events. (3) The incident wave and dominant rotation provided the major information for wave runup prediction. TCN and LSTM models’ prediction accuracies were 91.5% and 89.3% based on the simplified input tensors composed of incident wave and pitch. The comparison showed the great potential of the TCN model to predict the nonlinear wave runup with less time and memory costs. The input tensors’ design and optimization based on physical understanding also play a significant role in the prediction performance.

Maritime vessel movement prediction: A temporal convolutional network model with optimal look-back window size determination

Cardiovascular diseases remain a major cause of death in the world, and thus it is imperative to have correct and timely diagnoses of cardiac abnormalities. The direct interpretation of the electrocardiogram (ECG) signals is manual and is time consuming and prone to human error, especially when there is constant monitoring. The following paper is a review of a Temporal Convolutional Networks (TCN)-based real-time ECG anomaly detection framework using AI. The system reviewed has ECG signals obtained in the MIT-BIH Arrhythmia Database and applies noise reduction and heartbeat segmentation as preprocessing procedures to enhance the quality of the signal. It uses a powerful multi-feature extraction framework based on Fractional Discrete Cosine Transform (FrDCT), Radon Wavelet Transform (RWT) and Fractional Wavelet Transform (FrWT) to ensure that the ECG data is represented in a holistic manner. A Concrete Autoencoder (CAE) is used to reduce the dimensions by picking out the most significant features. TL-MobileNet-ED A new Transformer-based MobileNet Encoder-Decoder that incorporates Temporal Convolutional Networks (TL-MobileNet-ED) is proposed to perform efficient and accurate anomaly classification. Experimental findings indicate that the classification accuracy is 98.43 with a matching high precision, recall and F1-score as compared to current machine learning and deep learning benchmarks. The paper summarizes the methodology, presents the analysis of the experimental findings, and explains the general implications of the framework to real-time intelligent cardiac monitoring. Keywords: Electrocardiogram (ECG), ECG Anomaly Detection, Artificial Intelligence (AI), Deep Learning, Temporal Convolutional Networks (TCN), MobileNet, Transformer Learning, Feature Extraction, Concrete Autoencoder (CAE), Real-Time Monitoring.

Veg-W2TCN: A parallel hybrid forecasting framework for non-stationary time series using wavelet and temporal convolution network model

In this study, we introduce a novel approach to speech enhancement through the design of a complex temporal convolutional network (Complex-TCN). This model leverages the power of complex networks, enabling the simultaneous capture of both magnitude and phase information inherent in speech signals. By employing a temporal convolutional network, the Complex-TCN excels at extracting contextual information within the time domain of speech. Our findings underscore the substantial performance improvements achieved through the synergistic use of the temporal convolutional network and the incorporation of complex representations.

In modern power system, along with the developments of the data collecting technologies, the intensive and high-dimensional load data collection can be achieved. Therefore, to deeply reveal the patterns and behaviors hidden in the load dataset using load classification is of great significance for improving the service quality and the user experience of the power system. However, inevitable issues, for example the data missing and class imbalance are frequently reported in the present load dataset, which deteriorates the performance of the classification algorithms. Also, due to the special features, for example the time series, periodicity, and fluctuation of the load data, the traditional data classification algorithms also encounter performance defects. Therefore, this paper presents a data augmentation based enhanced temporal convolutional network (TCN) algorithm in enabling load classification. In the data augmentation phase, first an LRTC-TSVD algorithm is presented to implement the missing data completion. Second, a WGAN based class balancing approach is further presented to solve the class imbalance issue. Then, in the enhanced TCN phase, a WeightNorm, exponential linear unit (ELU) activation function, residual connection, and bidirectional feature fusion techniques based improved TCN (ITCN) algorithm is presented to carry out the accurate load data classification. Combining the data augmentation and the enhanced TCN phases, the ITCN algorithm is finally conducted. Based on the benchmark load datasets, the performances of the presented ITCN are evaluated. The experimental results report that the presented data augmentations can improve the quality of the dataset, moreover the classification algorithm is able to achieve the satisfied classification accuracy.

Motor imagery (MI) is a cognitive process wherein an individual mentally rehearses a specific movement without physically executing it. Recently, MI-based brain-computer interface (BCI) has attracted widespread attention. However, accurate decoding of MI and understanding of neural mechanisms still face huge challenges. These seriously hinder the clinical application and development of BCI systems based on MI. Thus, it is very necessary to develop new methods to decode MI tasks. In this work, we propose a multi-branch convolutional neural network (MBCNN) with a temporal convolutional network (TCN), an end-to-end deep learning framework to decode multi-class MI tasks. We first used MBCNN to capture the MI electroencephalography signals information on temporal and spectral domains through different convolutional kernels. Then, we introduce TCN to extract more discriminative features. The within-subject cross-session strategy is used to validate the classification performance on the dataset of BCI Competition IV-2a. The results showed that we achieved 75.08% average accuracy for 4-class MI task classification, outperforming several state-of-the-art approaches. The proposed MBCNN-TCN-Net framework successfully captures discriminative features and decodes MI tasks effectively, improving the performance of MI-BCIs. Our findings could provide significant potential for improving the clinical application and development of MI-based BCI systems.