Earthquake prediction (EQP) is an extremely difficult task, which has been overcome by adopting various technologies, with no further transformation so far. The negative selection algorithm (NSA) is an artificial intelligence method based on the biological immune system. It is widely used in anomaly detection due to its advantages of requiring little normal data to detect anomalies, including historical seismic-events-based EQP. However, NSA can suffer from the undesirable effect of data drift, resulting in outdated normal patterns learned from data. To tackle this problem, the data changes must be detected and processed, stimulating fast algorithmic adaptation strategies. This study proposes a dendritic cell algorithm (DCA)-based adaptive learning method for drift detection and negative selection algorithm (DC-NSA) that dynamically adapts to new input data. First, this study adopts the Gutenberg–Richter (GR) law and other earthquake distribution laws to preprocess input data. Then, the NSA is employed for EQP, and then, the dendritic cell algorithm (DCA) is employed to detect changes to trigger gradient descent strategies and update the self-set in NSA. Finally, the proposed approach is implemented to predict the earthquakes of MW > 5 in Sichuan and the surroundings during the next month. The experimental results demonstrate that our proposed DC-NSA is superior to the existing state-of-the-art EQP approaches.

The best method to prevent error due to inhomogeneity is to use a new thermocouple design—the thermocouple with controlled temperature field (TCTF). It uses the auxiliary furnace to control the temperature field along its legs. Such a design allows setting and maintaining the temperature field along the thermocouple (TC) legs for the sensor. Error due to inhomogeneity of TCs cannot appear in a stable temperature field. However, the auxiliary furnace and TCs, to control the temperature field, have errors, so the temperature field along the main TC is maintained with some error. This leads to residual error due to acquired inhomogeneity of the TCTF. We constructed the mathematical models to fit the experimental data of error due to drift for the type K TC. The authors used the constructed models to study error due to inhomogeneity of the TCTF and the conventional type K TC under considerable changes in temperature field. The main results of modelling are as follows: (i) if the changes in temperature field exceed 7 °C, error due to inhomogeneity of the TCTF is lesser than that of the conventional TC; (ii) the maximum error due to inhomogeneity of the conventional type K TC is 10.75 °C; (iii) the maximum error due to inhomogeneity of the TCTF is below 0.2 °C.

In the case of traditional methods such as network models and algorithms are highly open source and highly bound to hardware, data processing has become an important method to optimize the performance of neural networks. In this paper, we combine traditional data processing methods and propose a method based on the mini dataset which is strictly randomly divided within the training process; and takes the calculation results of the cross-entropy loss function as the measurement standard, by comparing the mini dataset, screening, and processing to optimize the deep neural network. Using this method, each iteration training can obtain a relatively optimal result, and the optimization effects of each time are integrated to optimize the results of each epoch. Finally, in order to verify the effectiveness and applicability of this data processing method, experiments are carried out on MNIST, HAGRID, and CIFAR-10 datasets to compare the effects of using this method and not using this method under different hyper-parameters, and finally, the effectiveness of this data processing method is verified. Finally, we summarize the advantages and limitations of this method and look forward to the future improvement direction of this method.

Data processing is one of the essential methods to optimize the performance of neural networks. In this paper, we give up the traditional data processing method and propose a method to optimize the deep neural network by processing the mini data set based on the loss. Using this method, each Literation training can obtain a relatively optimal result, and the optimization effects of each time are integrated to optimize the results of each epoch finally At the same time, in order to verify the effectiveness of this data processing method, experiments are carried out on MNIST, HAGRID, and CIFAR-10 datasets to compare the effects of using this method and not using this method under different hyperparameters, and finally, the effectiveness of this data processing method is verified. On this basis, the advantages and disadvantages of this method are summarized. Finally, the future research direction has been prospected.

Accurate multi-step wind power forecasting is important for power-system scheduling and renewable energy integration, yet the strong nonstationarity and multi-scale structure of wind power remain challenging. This study evaluates a CEEMDAN-based empirical wavelet transform (EWT) and frequency-aligned gated recurrent unit (GRU) framework for direct six-step forecasting. Each monthly series is first partitioned chronologically into training, validation, and test sets at a 7:1:2 ratio; CEEMDAN and EWT are then applied independently within the fixed partitions used for comparative evaluation. The decomposition atoms are aggregated into fixed physical frequency bands, providing a common representation across partitions. The validation set selects the EWT band subset used to denoise the leading high-frequency representation, and independent single-channel GRUs with temporal attention forecast the retained frequency-aligned components. Component forecasts are summed and constrained to the physical operating range. On twelve monthly subsets of the T1 SCADA dataset, the block-wise framework achieves equal-month averages of 3.9846% NMAE, 236.8220 kW RMSE, and 143.4440 kW MAE, outperforming a persistence baseline and seven trained benchmarks. The paired one-sided Wilcoxon tests show significant improvements over both CEEMDAN-GRU and CEEMDAN-EWT-LSTM for all three metrics. A strict origin-wise implementation, AdaptiveEWT+SDG, recomputes features only from the history available at each forecast origin. Across six initializations, it achieves 6.0050% NMAE, 376.2803 kW mean-horizon RMSE, and 216.1808 kW MAE, versus 6.0149%, 376.9031 kW, and 216.5353 kW for Persistence, confirming the adaptive-frequency principle under strictly sequential information.