The fluctuation and intermittence of wind power bring great challenges to the operation and control of the distribution network. Accurate short-term prediction for wind power is helpful to avoid the ...risk caused by the uncertainties of wind powers. To improve the accuracy of short-term prediction for wind power, the temporal convolutional network (TCN) is proposed in this paper. The proposed method solves the problem of long-term dependencies and performance degradation of deep convolutional model in sequence prediction by dilated causal convolutions and residual connections. The simulation results show that the training process of TCN is very stable and it has strong generalization ability. Furthermore, TCN shows higher forecasting accuracy than existing predictors such as the support vector machine, multi-layer perceptron, long short-term memory network, and gated recurrent unit network.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Predicting history-dependent materials’ responses is crucial, as path-dependent behavior appears while characterizing or geometrically designing many materials (e.g., metallic and polymeric cellular ...materials), and it takes place in manufacturing and processing of many materials (e.g., metal solidification). Such phenomena can be computationally intensive and challenging when numerical schemes such as the finite element method are used. Here, we have applied a variety of sequence learning models to almost instantly predict the history-dependent responses (stresses and energy) of a class of cellular materials as well as the multiphysics problem of steel solidification with multiple thermo-viscoplasticity constitutive models accounting for substantial temperature, time, and path dependencies, and phase transformation. We have shown the gated recurrent unit (GRU) as well as the temporal convolutional network (TCN), can both accurately learn and almost instantly predict these irreversible, and history- and time-dependent phenomena, while TCN is more computationally efficient during the training process. This work may open the door for the broader adoption of data-driven models in similar computationally challenging constitutive models in plasticity and inelasticity.
•Several sequence learning methods are applied and compared for path-dependent plasticity and thermo-viscoplasticity.•GRU and TCN sequence learning models accurately predict the complex behavior of history-dependent materials.•Once properly trained, these deep learning models can instantly inference good quality results for unseen input data.•This work opens the door to the broader adoption of data-driven models in studying computationally challenging materials.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Machinery remaining useful life (RUL) prediction plays a pivotal role in modern industrial maintenance. Traditional methods entail the manual selection of useful features, which requires prior ...knowledge and lack adaptability to diverse cases. Moreover, as features may have different relevance to the degradation process at various stages, the prognostic performance will be limited by the utilization of fixed features throughout the full life-time. Additionally, most deep learning methods lack the perception of global information of features, which is critical to RUL prediction. To tackle these issues, an adaptive feature utilization method with separate gating mechanism and global temporal convolutional network (SGGTCN) is proposed in this paper. First, a separate gating mechanism is proposed to adaptively model temporal information within each feature individually through a series of designed separate gated residual modules. Second, an adaptive feature utilization method is proposed to evaluate and dynamically weight feature importance. Third, a global temporal convolutional network is proposed to model and fuse global temporal information for comprehensive sequential modeling. The effectiveness and superiority of the proposed method are validated by two prognostic case studies of turbofan engines and bearings.
Recently, the boom in wind power industry has called for the accurate and stable wind speed forecasting, on which reliable wind power generation systems depend heavily. Due to the intermittency and ...complexity of wind, an appropriate decomposition is proved as a pivotal part in the precise wind speed prediction. On this account, this paper constructs a hybrid decomposition method coupling the ensemble patch transform (EPT) and the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), where EPT is utilized to extract the trend of wind speed, then CEEMDAN is employed to divide the volatility into several fluctuation components with different frequency characteristics. Subsequently, the proposed decomposition method is combined with temporal convolutional networks (TCN) for the individual prediction of the trend and fluctuation components. Ultimately, the forecasted values for the wind speed prediction are obtained by reconstructing the prediction results of all the components. To evaluate the performance of the proposed EPT-CEEMDAN-TCN model, the historical wind speed data from three wind farms across China are used. The experimental results verify the notable effectiveness and necessity of the proposed EPT-CEEMDAN decomposition. In the meanwhile, the results demonstrate the significant superiority of the proposed EPT-CEEMDAN-TCN model on accuracy and stability.
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•The ensemble patch transformation (EPT) is established to extract the daily trend.•The CEEMDAN is applied to decompose the volatility of wind speed series.•The temporal convolutional networks (TCN) are adopted for prediction.•The EPT-CEEMDAN-TCN model is developed for multi-step-ahead wind speed forecasting.•The superiority of the proposed model is verified on datasets from diverse areas.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Accurate PV power forecasting is becoming a mandatory task to integrate the PV plant into the electrical grid, scheduling and guaranteeing the safety of the power grid. In this paper, a novel model ...to forecast the PV power using LSTM-TCN has been proposed. It consists of a combination between Long Short Term Memory and Temporal Convolutional Network models. LSTM is used to extract the temporal features from input data, then combined with TCN to build the connection between features and outputs. The proposed model has been tested using a dataset that includes historical time series of measured PV power. The accuracy of this model is then compared to LSTM and TCN models in different seasons, time periods forecast, cloudy, clear, and intermittent days. For one step forecasting, the results show that our proposed model outperforms the LSTM and TCN model. It has carried out a reduction of 8.47%, 14.26% for the autumn season, 6.91%,15.18 for the winter season, 10.22%,14.26% for spring season and 14.26%, 14.23% for the summer season on the Mean Absolute Error compared with LSTM, TCN. For multistep forecasting, LSTM-TCN surpassed all compared models in different time periods forecast from 2 steps to 7 steps PV power forecasting.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Multimodal sentiment analysis aims to extract and integrate information from different modalities to accurately identify the sentiment expressed in multimodal data. How to effectively capture the ...relevant information within a specific modality and how to fully exploit the complementary information among multiple modalities are two major challenges in multimodal sentiment analysis. Traditional approaches fail to obtain the global contextual information of long time-series data when extracting unimodal temporal features, and they usually fuse the features from multiple modalities with the same method and ignore the correlation between different modalities when modeling inter-modal interactions. In this paper, we first propose an Attentional Temporal Convolutional Network (ATCN) to extract unimodal temporal features for enhancing the feature representation ability, then introduce a Multi-layer Feature Fusion (MFF) model to improve the effectiveness of multimodal fusion, which fuses the different-level features by different methods according to the correlation coefficient between the features, and cross-modal multi-head attention is used to fully explore the potential relationship between the low-level features. The experimental results on SIMS and CMU-MOSI datasets show that the proposed model achieves superior performance on sentiment analysis tasks compared to state-of-the-art baselines.
An accurate aging forecasting and state of health estimation is essential for a safe and economically valuable usage of lithium-ion batteries. However, the non-linear aging of lithium-ion batteries ...is dependent on various operating and environmental conditions wherefore the degradation estimation is a complex challenge. Moreover, for on-board estimations where only limited memory and computing power are available, a state of health estimation algorithm is needed that is able to process raw sensor data without complex preprocessing. This paper presents a data-driven state of health estimation algorithm for lithium-ion batteries using different segments of partial discharge profiles. Raw sensor data is directly input to a temporal convolutional neural network without the need of executing feature engineering steps. The neural network is able to process raw sensor data and estimate the state of health of battery cells for different aging and degradation scenarios. After executing Bayesian hyperparameter tuning together with a stratified cross validation approach for splitting the training and test data, the achieved generalized aging model estimates the state of health with an overall root mean squared error of 1.0%.
•Investigation of the influence of using partial load profiles on SOH Estimation.•SOH model that is able to process raw sensor data without preprocessing steps.•Generalized SOH model that accurately estimates the SOH for different aging histories.•Optimal model selection using Bayesian hyperparameter tuning.•Temporal convolutional neural network model with an overall SOH Estimation RMSE of 1%.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Vascular interventional surgery (VIS) robot is a surgical treatment plan that effectively protects surgeons from X-ray radiation. However, the master-slave control method cuts off the surgeons' ...natural force feedback, which increases the risk of surgical safety. Most VIS robotic systems use force sensors placed at the proximal end of guidewire to achieve force feedback, but due to the non-rigidity of the guidewire and the influence of mechanism friction, the proximal force collected has certain errors. In addition, the current VIS robotic systems are also insufficient in functionality, and cannot simultaneously complete the delivery of multiple surgical instruments. To solve the above mechanism design and force feedback challenges, a novel VIS robotic system equipped with force sensing mechanism is developed in this study. In addition, a temporal convolutional network (TCN) for the guidewire distal force prediction and an enhanced interactive force feedback strategy are proposed to improve the safety of the robotic system. Finally, combining the developed robotic system and the enhanced interactive force feedback strategy, a series of performance evaluations and model experiments are carried out. The results of the study demonstrate the effectiveness of the developed robotic system and the feasibility of the enhanced interactive force feedback strategy in improving surgical safety.
Prediction with high-precision of the performance degradation is crucial for the development of proton exchange membrane fuel cells (PEMFCs) with long lifetime. In this work, a new hybrid neural ...network model on fuel cell performance degradation prediction (TCN-LSTM) is developed by incorporating the advantages of temporal convolutional network (TCN) and long short-term memory (LSTM). The innovation of TCN-LSTM is that LSTM captures the long-term correlation of data from the high-level features of the original data extracted by TCN. On the available PEMFC datasets (IEEE 2014 PHM Data Challenge), the TCN-LSTM reduces the root mean square error (RMSE) by up to 94% compared with other state-of-the-art algorithms. In addition, the TCN-LSTM is also assessed on 2673 h self-tested fuel cell voltage degradation dataset recorded under dynamic cyclic load conditions with complicated decay mechanism. The RMSE is only 0.00205 even under the 50% training length, which further verifies the excellent prediction performance of TCN-LSTM. It can be concluded that the TCN-LSTM will be a promising fuel cell performance degradation prediction model.
•A highly precise PEMFCs performance degradation prediction model TCN-LSTM is proposed.•TCN-LSTM combines the advantages of TCN with local time feature extraction and LSTM with long-term correlation capture.•TCN-LSTM presents highly precise prediction in both available dataset and self-recorded dataset.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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