Electroencephalogram (EEG) signals are electrical signals generated by changes in brain potential. As a significant physiological signal, EEG signals have been applied in various fields, including emotion recognition. However, current deep learning methods based on EEG signals for emotion recognition lack consideration of important aspects and comprehensive analysis of feature extraction interactions. In this paper, we propose a novel model named ECA-CRNN for emotion recognition using EEG signals. Our model integrates the efficient channel attention (ECA-Net) module into our modified combination of a customized convolutional neural network (CNN) and gated circulation unit (GRU), which enables more comprehensive feature extraction, enhances the internal relationship between frequency bands and improves recognition performance. Additionally, we utilize four-dimensional data as input to our model, comprising temporal, spatial and frequency information. The test on the DEAP dataset demonstrates that it enhances the recognition accuracy of EEG signals in both arousal and valence to 95.70% and 95.33%, respectively, while also reducing the standard deviation during five-fold cross-validation to 1.16 and 1.45 for arousal and valence, respectively, surpassing most methods.

During industrial production activities, industrial products serve as critical resources whose performance is subject to various external factors and usage conditions. To ensure uninterrupted production processes and to guarantee the safety of the production personnel, a real-time analysis of the industrial product quality and subsequent decision making are essential. Conventional detection methods have inherent limitations in meeting the real-time demands of processing large volumes of data and achieving high response speeds. For instance, the regular inspection and maintenance of cars can be time-consuming and labor-intensive if performed manually. Furthermore, monitoring the damage situation of bearings in real time through a manual inspection may lead to delays and may hinder production efficiency. Therefore, this paper presents online machine-learning-based methods to address these two practical problems and simulates them on various datasets to meet the requirements of efficiency and speed. Prior to being fed into the network for training, the data undergo identity parsing to transform them into easily identifiable streaming data. The training process demonstrates that online machine learning ensures timely model updates as small batches of data are sent to the network. The test results indicate that the online learning method exhibits highly stable and effective performance, optimizing the training process.

Industrial gateways serve as critical data aggregation points within the Industrial Internet of Things (IIoT), enabling seamless data interoperability that empowers enterprises to extract value from equipment data more efficiently. However, their role exposes a fundamental trade-off between computational efficiency and prediction accuracy—a contradiction yet to be fully resolved by existing approaches. The rapid proliferation of IoT devices has led to a corresponding surge in network traffic, posing significant challenges for traffic forecasting methods, while deep learning models like Transformers and GNNs demonstrate high accuracy in traffic prediction, their substantial computational and memory demands hinder effective deployment on resource-constrained industrial gateways, while simple linear models offer relative simplicity, they struggle to effectively capture the complex characteristics of IIoT traffic—which often exhibits high nonlinearity, significant burstiness, and a wide distribution of time scales. The inherent time-varying nature of traffic data further complicates achieving high prediction accuracy. To address these interrelated challenges, we propose the lightweight and theoretically grounded DOA-MSDI-CrossLinear framework, redefining traffic forecasting as a hierarchical decomposition–interaction problem. Unlike existing approaches that simply combine components, we recognize that industrial traffic inherently exhibits scale-dependent temporal correlations requiring explicit decomposition prior to interaction modeling. The Multi-Scale Decomposable Mixing (MDM) module implements this concept through adaptive sequence decomposition, while the Dual Dependency Interaction (DDI) module simultaneously captures dependencies across time and channels. Ultimately, decomposed patterns are fed into an enhanced CrossLinear model to predict flow values for specific future time periods. The Dream Optimization Algorithm (DOA) provides bio-inspired hyperparameter tuning that balances exploration and exploitation—particularly suited for the non-convex optimization scenarios typical in industrial forecasting tasks. Extensive experiments on real industrial IoT datasets thoroughly validate the effectiveness of this approach.

As the scale of the Industrial Internet continues to expand, the number of network connections and data traffic are experiencing explosive growth. Security threats and attack types targeting the Industrial Internet are becoming increasingly complex, rendering traditional firewalls and encryption/decryption technologies inadequate for addressing diverse and sophisticated attack scenarios. Furthermore, traffic characteristics within the Industrial Internet environment exhibit significant asymmetry, such as a highly imbalanced distribution between benign and malicious traffic. To address this challenge, this paper proposes CBiNet—a hybrid deep learning model that integrates a one-dimensional convolutional neural network (1D-CNN) with a bidirectional long short-term memory network (BiLSTM). Designed to effectively learn and leverage such asymmetric spatio-temporal patterns, experimental validation demonstrates that the CBiNet model can efficiently tackle complex traffic identification tasks in industrial internet environments. It provides a highly accurate, scalable intrusion detection method for securing industrial internet gateways.

Etching has become a critical step in semiconductor wafer fabrication, and its importance in semiconductor manufacturing highlights the fact that it directly determines the ability of the fab to produce high-process products, as well as the application performance of the chip. While the health of the etcher is a concern, especially for the cooling system, accurately predicting the remaining useful life (RUL) of the etcher cooling system is a critical task. Predictive maintenance (PDM) can be used to monitor the basic condition of the equipment by learning from historical data, and it can help solve the task of RUL prediction. In this paper, we propose the FECAM-WTCN-Informer model, which first obtains a new WTCN structure by inserting wavelet convolution into the TCN, and then combines the discrete cosine transform (DCT) and channel attention mechanism into the temporal neural network (TCN). Multidimensional feature extraction of time series data can be realized, and the processed features are input into the Informer network for prediction. Experimental results show that the method is significantly more accurate in terms of overall prediction performance (MSE, RMSE, and MAE), compared with other state-of-the-art methods, and is suitable for solving the problem of predictive maintenance of etching machine cooling systems.