Cognitive radio (CR) is a promising technology to improve the utilisation of wireless spectrum resources. Spectrum sensing is the core functionality in CR networks (CRN). When there exist malicious users (MUs) in CRN and MUs start to attack the network after accumulating reputation to some extent, the performance is deteriorated. In this paper, a scheme is proposed by employing Orthogonalized Gnanadesikan-Kettenring (OGK) to mitigate the effect of MUs without the assistance of trusted nodes, and it can improve the robustness of CRN. Simulations verify the effectiveness of the proposed scheme.

Summary Peak ground acceleration (PGA) is a key parameter used in earthquake early warning systems to measure the ground motion strength and initiate emergency protocols at major projects. The traditional P-wave peak displacement-dependent PGA prediction model (Pd-PGA model) tends to underestimate the PGA for large earthquakes because it cannot make full use of the fault continuity rupture information hidden in the time-varying process of ground motion. In this paper, a continuous PGA prediction long short-term memory (LSTM) neural network model is proposed. The model takes eight sequential features of stations that are proxies of the energy and other physical parameters as input and provides the recorded PGA at the station as the target output. A total of 5961 records from 119 earthquakes recorded by the Japanese Strong-Motion Earthquake Network (K-NET) in Japan are used to train the neural network, and 3433 records from 73 earthquakes are used as the test set to verify the model's generalization ability. The results show that within the same dataset, the residuals of the predicted PGA for the proposed model are smaller than those of the Pd-PGA model, and that the problem of PGA underestimation is resolved. The prediction accuracy also improves with increasing sequence length, which indicates that the LSTM neural network learns the rules hidden in the time series. To further verify the model's generalization ability, the model performance is analyzed for an M 7.3 earthquake that was not included in the training or test datasets. The results show that the residuals of the predicted PGA for the event are consistent with those for the test dataset, indicating that the model has good generalization ability.

Classification of local soil conditions is important for the interpretation of structural seismic damage, which also plays a vital role in site-specific seismic hazard analyses. In this study, we propose to classify sites as an image recognition task using a deep convolutional neural network (DCNN)-based technique. We design the input image as a combination of the topographic slope and the mean horizontal-to-vertical spectral ratio (HVSR) of earthquake recordings. A DCNN model with five convolutional layers is trained using 1649 sites in Japan. The recall rates for site classes C, D, and E using our DCNN classifier for Japanese sites are 82%, 70%, and 60%, respectively. When compared with existing site classification schemes relying on predefined standard HVSR curves, our proposed method achieves the highest total accuracy rate (between 73% and 75%). The generality and applicability of our trained classifier are further validated using sites in Europe with a total accuracy between 64% and 66%. The proposed data-driven approach could be extended to other types of site amplification functions in the future.

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Since ancient times, earthquake disasters have always been one of the most harmful natural disasters, and it is necessary to reduce the damage caused by earthquakes effectively. The earthquake early warning is a new technology that has been gradually mature in recent years and can effectively reduce earthquake disasters. The ability to accurately and quickly predict the earthquake magnitude has become an important but difficult part of the earthquake early warning technology. Currently, many countries in the world have been engaged in the projects of establishing and improving the system of earthquake early warning, and completed two methods to predict the earthquake magnitude as a kind of earthquake early warning, namely, the method of characteristic frequency and the method of characteristic amplitude. In recent years, with the constant improvement of deep learning, the application of machine learning in determining the earthquake magnitude as an early warning has been showing great development prospects and application possibility.