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Future capacity prediction of lithium-ion batteries is a highly researched topic in the field of battery management systems, owing to the gradual degradation of battery capacity over time due to various factors such as chemical changes within the battery, usage patterns, and operating conditions. The accurate prediction of battery capacity can aid in optimizing its usage, extending its lifespan, and mitigating the risk of unforeseen failures. In this paper, we proposed a novel fine-tuning model based on a deep learning model with a transfer learning approach comprising of two key components: offline training and online prediction. Model weights and prediction parameters were transferred from offline training using source data to the online prediction stage. The transferred Bi-directional Long Short-Term Memory with an Attention Mechanism model weights and prediction parameters were utilized to fine-tune the model by partial target data in the online prediction phase. Three battery batches with different charging policy were used to evaluate the proposed approach’s robustness, reliability, usability, and accuracy for the three charging policy batteries’ real-world data. The experiment results show that the proposed method’s efficacy improved, with an increase in the cycle number of the starting point, exhibiting a linear relationship with the starting point. The proposed method yields relative error values of 8.70%, 6.38%, 9.52%, 7.58%, 1.94%, and 2.29%, respectively, for the six target batteries in online prediction. Thus, the proposed method is effective in predicting the future capacity of lithium-ion batteries and holds potential for use in predictive maintenance applications.

Different deep convolution neural network (DCNN) models have been proposed for wafer map pattern identification and classification tasks in previous studies. However, factors such as the effect of input image resolution on the classification performance of the proposed models and class imbalance in the training set after splitting the data into training and test sets have not been considered in the previous studies. This study proposes a DCNN model with residual blocks, called Opt-ResDCNN model, for wafer map defect pattern identification and classification by considering 26 * 26 input image resolutions and class imbalance issues during the model training. The proposed model is compared with the previously published defect pattern recognition and classification models in terms of accuracy, precision, recall, and F1 score for 26 * 26 input image size. Using a publicly available wafer map dataset (WM-811K), the proposed method can obtain an average accuracy, precision, recall, and F1 score results of 99.672%, 99.664%, 99.695%, 99.692%, respectively for the 26 * 26 input image resolution.

Proton exchange membrane fuel cell (PEMFC) is a clean and efficient alternative technology for transport applications. The degradation analysis of the PEFMC stack plays a vital role in electric vehicles. We propose a hybrid method based on a deep neural network model, which uses the Monte Carlo dropout approach called MC-DNN and a sparse autoencoder model to analyze the power degradation trend of the PEMFC stack. The sparse autoencoder can map high-dimensional data space to low-dimensional latent space and significantly reduce noise data. Under static and dynamic operating conditions, using two experimental PEMFC stack datasets the predictive performance of our proposed model is compared with some published models. The results show that the MC-DNN model is better than other models. Regarding the remaining useful life (RUL) prediction, the proposed model can obtain more accurate results under different training lengths, and the relative error between 0.19% and 1.82%. In addition, the prediction interval of the predicted RUL is derived by using the MC dropout approach.

Sustainability in operations and supply chains is becoming more popular among academics and practitioners through Sustainable Supplier Selection (SSS). In addition to balancing economic, social, and environmental factors, the awareness of the United Nations Sustainable Development Goals 2030 has affected the selection of long-term suppliers, ensuring green operations and sustainable supply chains. The criteria for SSS have multiple dimensions and are interdependent; this mimics the real-world scenario rather than assuming independently from an analytic hierarchy process. We use the multi-criteria decision-making (MCDM) model, combining DEMATEL-based on ANP (called DANP) with VIKOR, to solve the SSS problem. The DANP method is used to model and assess the interdependent relationships between criteria. Then, ranking the available alternatives and selecting the best one can be accomplished using the VIKOR method. We consider the electronic manufacturing industry in Taiwan as an empirical case. This study, in addition to selecting the best sustainable supplier, demonstrates the use of influential network relationship maps to analyze and improve the gaps in each dimension and criterion.

Different deep convolution neural network (DCNN) models have been proposed for wafer map pattern identification and classification tasks in previous studies. However, factors such as the effect of input image resolution on the classification performance of the proposed models and class imbalance in the training set after splitting the data into training and test sets have not been considered in the previous studies. This study proposes a DCNN model with residual blocks, called Opt-ResDCNN model, for wafer map defect pattern identification and classification by considering 26 * 26 input image resolutions and class imbalance issues during the model training. The proposed model is compared with the previously published defect pattern recognition and classification models in terms of accuracy, precision, recall, and F1 score for 26 * 26 input image size. Using a publicly available wafer map dataset (WM-811K), the proposed method can obtain an average accuracy, precision, recall, and F1 score results of 99.672%, 99.664%, 99.695%, 99.692%, respectively for the 26 * 26 input image resolution.