Wyniki wyszukiwania - BazTech

1

Remaining useful life prediction of a lithium-ion battery based on a temporal convolutional network with data extension

Zhao Jing, Liu Dayong, Meng Lingshuai

International Journal of Applied Mathematics and Computer Science

|

2024

|

Vol. 34, no. 1

105--117

EN

Unmanned underwater vehicles are typically deployed in deep sea environments, which present unique working conditions. Lithium-ion power batteries are crucial for powering underwater vehicles, and it is vital to accurately predict their remaining useful life (RUL) to maintain system reliability and safety. We propose a residual life prediction model framework based on complete ensemble empirical mode decomposition with an adaptive noise-temporal convolutional net (CEEMDAN-TCN), which utilizes dilated causal convolutions to improve the model’s ability to capture local capacity regeneration and enhance the overall prediction accuracy. CEEMDAN is employed to denoise the data and prevent RUL prediction errors caused by local regeneration, and feature expansion is utilized to extend the temporal dimension of the original data. The NASA and CALCE battery capacity datasets are used as input to train the network framework. The output is the current predicted residual capacity, which is compared with the real residual battery capacity. The MAE, RMSE and RE are used as the evaluation indexes of the RUL prediction performance. The proposed network model is verified on the NASA and CACLE datasets. The evaluation results show that our method has better life prediction performance. At the same time, it is proved that both feature expansion and modal decomposition can improve the generalization ability of the model, which is very useful in industrial scenarios.

2

Optimal Prediction of Air Quality Index in Metropolitan Cities Using Fuzzy Time Series with Deep Learning Approach

Unnikrishnan Asha, Rajeswari Subramanian

Ecological Engineering & Environmental Technology

|

2024

|

Vol. 25, iss. 1

341--359

EN

Predicting the air quality index (AQI) with high accuracy is just as crucial as predicting the weather. The research selected a few potential meteorological parameters and historical data after taking into account a variety of complex factors to accurately anticipate AQI. The dataset was gathered, pre-processed to substitute missing values (MV) and eliminate redundant information, and before being applied to predict the AQI. The data was collected from 2019 to 2022 to analyse the AQI founded on time series forecasting (TSF). Many AQI parameters, including accumulated precipitation, the daily normal temperature, and prevailing winds, are lacking in this study. To preserve the characteristics of the time series, kNN classification was implemented to fill in the MV and integrate Principal Component Analysis (PCA) to decrease the noise of data to recover the accuracy of AQI prediction. However, the majority of research is limited due to a lack of panel data, which means that characteristics such as seasonal behaviour cannot be taken into account. Consequently, the research introduced a TSF based on seasonal autoregressive integrated moving average (SARIMA) and stochastic fuzzy time series (SFTS). The stacked dilated convolution technique (SDCT) which effectively extracts the time autocorrelation, while the time attention module focuses on the time intervals that were significantly linked with each instant. To control the strongly connected features in the data set, the Spearman rank correlation coefficient (SRCC) was utilised. The selected features included SO2, CO and O3, NO2, PM10 and PM2.5, temperature, pressure, humidity, wind speed and weather, as well as rainfall. Additionally, to estimate the AQI and SO2, PM10, PM2.5, NO2, CO, and O3 concentration from 2019 to 2022, the data of climatological elements after PCA and historical AQI were input into the multiple linear regression (MLR) techniques with a temporal convolution network (TCN) built deep learning model (DLM). The proposed DLM springs a correct and detailed assessment for AQI prediction. The experimental results confirm that the expected background yields a stable forecasting result, that the pollutant concentration of the surrounding areas affects the AQI of a place, and that the planned model outperforms existing state-of-the-art models in terms of prediction of consequences. Consequently, utilising this presented innovative approach integrates fuzzy time series with deep learning, addressing missing values and noise reduction, incorporating seasonal behaviour, utilising the SRCC for feature control, employing a comprehensive set of meteorological parameters, and presenting a hybrid model that outperforms existing models. These aspects collectively contribute to the advancement of air quality prediction methodologies, particularly in metropolitan cities. However, this hybrid approach leverages the strengths of both traditional statistical methods and deep learning techniques, resulting in a robust and accurate assessment for AQI prediction as well as providing more stable and accurate forecasting results.

3

Prediction of the electromagnetic responses of geological bodies based on a temporal convolutional network model

Yuan Chongxin, Wang Xuben, Deng Fei, Wang Kunpeng, Yang Rui

Acta Geophysica

|

2022

|

Vol. 70, no 1

191--209

EN

The transient electromagnetic method employed in aeromagnetic surveys has been widely used for geophysical, petroleum, and engineering exploration because geophysical characteristics can be predicted as an inversion problem based on measured electromagnetic response data. However, this process requires uniformly and densely distributed electromagnetic response data, which are typically unavailable in actual TEM applications due to the high cost of the aeromagnetic surveys, which necessitates the use of large grid patterns to effectively map large areas. Therefore, developing methods for predicting missing electromagnetic response data based on the available data is essential for ensuring the accurate characterization of geological bodies. The present work addresses this issue by establishing an electromagnetic response curve prediction model based on a temporal convolutional network (TCN) architecture. Firstly, the electromagnetic response data is subjected to grey relational analysis to obtain correlations and reduce the data dimension. Secondly, the response data with correlation degrees greater than a threshold are selected as TCN model input. Finally, the TCN model establishes the nonlinear relationship between the electromagnetic response parameter sequence and its output sequence. The proposed model and other existing state-of-the-art prediction models are applied to actual electromagnetic prospecting data, and the results demonstrate that the proposed TCN model provides higher prediction accuracy and stronger robustness than the other models considered. Moreover, the proposed model is suitable for processing multiple series of related data, such as electromagnetic response prediction models. Therefore, the proposed model has good application prospects in electromagnetic response prediction and electromagnetic response recovery research.