Fatigued driving is one of the main causes of traffic accidents. The electroencephalogram (EEG)-based mental state analysis method is an effective and objective way of detecting fatigue. However, as EEG shows significant differences across subjects, effectively “transfering” the EEG analysis model of the existing subjects to the EEG signals of other subjects is still a challenge. Domain-Adversarial Neural Network (DANN) has excellent performance in transfer learning, especially in the fields of document analysis and image recognition, but has not been applied directly in EEG-based cross-subject fatigue detection. In this paper, we present a DANN-based model, Generative-DANN (GDANN), which combines Generative Adversarial Networks (GAN) to enhance the ability by addressing the issue of different distribution of EEG across subjects. The comparative results show that in the analysis of cross-subject tasks, GDANN has a higher average accuracy of 91.63% in fatigue detection across subjects than those of traditional classification models, which is expected to have much broader application prospects in practical brain–computer interaction (BCI).

Electroencephalogram (EEG) is an effective indicator for the detection of driver fatigue. Due to the significant differences in EEG signals across subjects, and difficulty in collecting sufficient EEG samples for analysis during driving, detecting fatigue across subjects through using EEG signals remains a challenge. EasyTL is a kind of transfer-learning model, which has demonstrated better performance in the field of image recognition, but not yet been applied in cross-subject EEG-based applications. In this paper, we propose an improved EasyTL-based classifier, the InstanceEasyTL, to perform EEG-based analysis for cross-subject fatigue mental-state detection. Experimental results show that InstanceEasyTL not only requires less EEG data, but also obtains better performance in accuracy and robustness than EasyTL, as well as existing machine-learning models such as Support Vector Machine (SVM), Transfer Component Analysis (TCA), Geodesic Flow Kernel (GFK), and Domain-adversarial Neural Networks (DANN), etc.

Satellite telemetry data plays an ever-important role in both the safety and the reliability of a satellite. These two factors are extremely significant in the field of space systems and space missions. Since it is challenging to repair space systems in orbit, health monitoring and early anomaly detection approaches are crucial for the success of space missions. A large number of efficient and accurate methods for health monitoring and anomaly detection have been proposed in aerospace systems but without showing enough concern for the patterns that can be mined from normal operational telemetry data. Concerning this, the present paper proposes DCLOP, an intelligent Deep Clustering-based Local Outlier Probabilities approach that aims at detecting anomalies alongside extracting realistic and reasonable patterns from the normal operational telemetry data. The proposed approach combines (i) a new deep clustering method that uses a dynamically weighted loss function with (ii) the adapted version of Local Outlier Probabilities based on the results of deep clustering. The DCLOP approach effectively monitors the health status of a spacecraft and detects the early warnings of its on-orbit failures. Therefore, this approach enhances the validity and accuracy of anomaly detection systems. The performance of the suggested approach is assessed using actual cube satellite telemetry data. The experimental findings prove that the suggested approach is competitive to the currently used techniques in terms of effectiveness, viability, and validity.

Frequent itemset mining (FIM) is the crucial task in mining association rules that finds all frequent k-itemsets in the transaction dataset from which all association rules are extracted. In the big-data era, the datasets are huge and rapidly expanding, so adding new transactions as time advances results in periodic changes in correlations and frequent itemsets present in the dataset. Re-mining the updated dataset is impractical and costly. This problem is solved via incremental frequent itemset mining. Numerous researchers view the new transactions as a distinct dataset (partition) that may be mined to obtain all of its frequent item sets. The extracted local frequent itemsets are then combined to create a collection of global candidates, where it is possible to estimate the support count of the combined candidates to avoid re-scanning the dataset. However, these works are hampered by the growth of a huge number of candidates, and the support count estimation is still imprecise. In this paper, the Closed Candidates-based Incremental Frequent Itemset Mining approach, or CC-IFIM, has been proposed to decrease candidate generation and improve the accuracy of the global frequent itemsets that are retrieved. The proposed approach is able to prune several produced candidates in earlier steps before performing any further computations. To improve the accuracy of the computation of the support count of the produced candidates, the similarity between partitions has been evaluated using just the local closed candidates rather than all candidates. The experimental findings demonstrated that the CC-IFIM approach is superior to its competitors in terms of efficiency and accuracy.