Wafer surface defect detection plays an important role in controlling product quality in semiconductor manufacturing, which has become a research hotspot in computer vision. However, the induction and summary of wafer defect detection methods in the existing review literature are not thorough enough and lack an objective analysis and evaluation of the advantages and disadvantages of various techniques, which is not conducive to the development of this research field. This paper systematically analyzes the research progress of domestic and foreign scholars in the field of wafer surface defect detection in recent years. Firstly, we introduce the classification of wafer surface defect patterns and their causes. According to the different methods of feature extraction, the current mainstream methods are divided into three categories: the methods based on image signal processing, the methods based on machine learning, and the methods based on deep learning. Moreover, the core ideas of representative algorithms are briefly introduced. Then, the innovations of each method are compared and analyzed, and their limitations are discussed. Finally, we summarize the problems and challenges in the current wafer surface defect detection task, the future research trends in this field, and the new research ideas.

Federated learning (FL) supports distributed training of a global machine learning model across multiple Internet of Things (IoT) devices with the help of a central server. However, data heterogeneity across different IoT devices leads to the client model drift issue and results in model performance degradation and poor model fairness. To address the issue, we design federated learning with global–local knowledge fusion (FedKF) scheme in this article. The key idea in FedKF is to let the server return the global knowledge to be fused with the local knowledge in each training round so that the local model can be regularized toward the global optima. Therefore, the client model drift issue can be mitigated. In FedKF, we first propose the active–inactive model aggregation technique that supports a precise global knowledge representation. Then, we propose a data-free knowledge distillation (KD) approach to enable each client model to learn the global knowledge (embedded in the global model) while each client model can still learn the local knowledge (embedded in the local data set) simultaneously, thereby realizing the global–local knowledge fusion process. The theoretical analysis and intensive experiments demonstrate the superiority of FedKF over previous solutions.

In recent years, air pollutants have become an important issue in meteorological research and an indispensable part of air quality forecasting. To improve the accuracy of the Chinese Unified Atmospheric Chemistry Environment (CUACE) model’s air pollutant forecasts, this paper proposes a solution based on ensemble learning. Firstly, the forecast results of the CUACE model and the corresponding monitoring data are extracted. Then, using feature analysis, we screen the correction factors that affect air quality. The random forest algorithm, XGBoost algorithm, and GBDT algorithm are employed to correct the prediction results of PM_2.5, PM₁₀, and O₃. To further optimize the model, we introduce the grid search method. Finally, we compare and analyze the correction effect and determine the best correction model for the three air pollutants. This approach enhances the precision of the CUACE model’s forecast and improves our understanding of the factors that affect air quality. The experimental results show that the model has a better prediction error correction effect than the traditional machine learning statistical model. After the algorithm correction, the prediction accuracy of PM_2.5 and PM₁₀ is increased by 60%, and the prediction accuracy of O₃ is increased by 70%.

With the technological progress in the field of remote health monitoring and the wide application of wearable ECG monitoring equipment, it is necessary to develop ECG model to analyze the massive ECG data in real time, and the segmentation of ECG signal feature wave is the primary work of the automatic diagnosis system of heart disease. The traditional machine learning algorithm relies too much on the empirical parameters and the feature engineering to segment the feature waves, and the robustness is poor. The emergence of deep learning automates the segmentation process in medical imaging, but it requires the use of a large amount of labeled data to train the model. The labeling of such a large amount of data requires professional biomedical analysts, which is not only cumbersome and expensive, but also easy to be affected by the subjectivity of ECG experts. To address these problems, this paper proposes a self-supervised learning algorithm (SimCLR-Unet) for ECG feature wave segmentation, and experiments on LUDB show that the proposed algorithm is better than other state-of-the-art supervised segmentation methods in feature wave detection accuracy. In particular, the F1-score with only 25% of the labeled data for model tuning and used to detect initiation of P and T waves and QRS waves was at least 97.78%, 98.97%, and 99.98%, respectively.