For autonomous vehicles driving on roads, future trajectories of surrounding traffic agents (e.g., vehicles, bicycles, pedestrians) are essential information. The prediction of future trajectories is challenging as the motion of traffic agents is constantly affected by spatial-temporal interactions from agents and road infrastructure. To take those interactions into account, this study proposes a Graph Attention Transformer (Gatformer) in which a traffic scene is represented by a sparse graph. To maintain the spatial and temporal information of traffic agents in a traffic scene, Convolutional Neural Networks (CNNs) are utilized to extract spatial features and a position encoder is proposed to encode the spatial features and the corresponding temporal features. Based on the encoded features, a Graph Attention Network (GAT) block is employed to model the agent-agent and agent-infrastructure interactions with the help of attention mechanisms. Finally, a Transformer network is introduced to predict trajectories for multiple agents simultaneously. Experiments are conducted over the Lyft dataset and state-of-the-art methods are introduced for comparison. The results show that the proposed Gatformer could make more accurate predictions while requiring less inference time than its counterparts.

Fault diagnosis is a crucial task in motor-based industrial systems, but traditional signal analysis-based methods face various challenges that make it difficult to achieve high-level performance. This paper proposes a novel approach called Residual Multi-Head Self-Attention Network using Multi-Modal Shallow Feature Fusion (ResMHA-MMSFF) for motor fault diagnosis. The method first applies an improved feature fusion technique based on a gating mechanism to combine the datasets provided by both vibration and current signals. The fused features are then introduced into the residual multi-head self-attention network to obtain more detailed information. Finally, a SoftMax function is adopted to classify faults and diagnose motor system faults. Comparative experiments under different loads and speed conditions are conducted to demonstrate the effectiveness of the proposed method, which outperforms existing single-signal or other deep learning-based methods.

The cubature Kalman filter (CKF) overcomes the limitations of the Kalman filter in strong nonlinear systems, which has been widely used in many fields. However, in practical engineering, the abnormal measurement information obtained by the sensor causes the measurement noise covariance to change, which may deteriorate the filtering performance and even cause the filter failure. The fault-tolerant filter can deal with the state estimation problem for the systems with abnormal measurements. The key of the fault-tolerant filter is to forcefully correct filter innovation by using a fading factor. The fault-tolerant filter technology has been extensively applied in many practical systems, but it is still lack of reasonable theoretical analysis. To this end, the measurement noise model is established and the magnitude of the noise deviation is analyzed. The filtering performance under abnormal measurement is analyzed by three mean squared errors (MSEs), which are the ideal MSE, the filter calculated MSE and the true MSE. In order to solve the influence of sampling approximation deviation of CKF on fault detection, an improved fault detection algorithm is proposed. The performance of fault-tolerant CKF is analyzed from two views. The first view is about comparing the filter calculated MSEs of CKF and of fault-tolerant CKF, the second view is about comparing the relative closeness of the filter calculated MSE to the true MSE for the two algorithms. Numerical examples further verify these conclusions.

In order to make full use of the absolute position information of fault signal, this paper designs a new multi-head attention (MHA) mechanism focusing on data positional information, proposes a novel MHA-based fault diagnosis method and extends it to the fault diagnosis scenario with missing information. Based on the absolute positional information and the trainable parameter matrix of the fault data, a novel attention weight matrix is generated, and the fault features are extracted by a fully connected network with the attention mechanism. By integrating the positional information into the weight matrix, the new MHA mechanism has the ability to extract more effective data features, compared with the traditional MHA method. Furthermore, the proposed method is also developed for the fault diagnosis scenarios with missing information. A special attention weight modified method is designed to reduce the impact of missing data on fault diagnosis results. In the experiment simulations, the data sampled from ZHS-2 multi-function motor flexible rotor test bed and the Tennessee- Eastman process data are utilized to test the performance of the algorithm. The results show that the proposed method can effectively extract fault features and reduce the impact of missing data.

This paper is concerned with the filtering problem caused by the inaccuracy variance of measurement noise in real nonlinear systems. A novel weighted fusion estimation method of multiple different variance estimators is presented to estimate the variance of the measurement noise. On this basis, a hybrid adaptive cubature Kalman filtering structure is proposed. Furthermore, the information filter of the hybrid adaptive cubature Kalman filter is also studied, and the stability and filtering accuracy of the filter are theoretically discussed. The final simulation examples verify the validity and effectiveness of the hybrid adaptive cubature Kalman filtering methods proposed in this paper.