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A new method of fault diagnosis is proposed. This method is called the complex fault diagnosis of based on federated kalman filter (FKF) and neural network (NN). It uses Kalman filter to estimate the measurable parameters' variations of car engine multi-sensor, and processes fault signal by information reconstruction, and then trains and corrects noise error by neural networks. According to these,...
For the sampling frequency is different between the quality indexes and the auxiliary variables in the soft sensing training patterns, a new neural network soft sensing method based on the multi-resolution data fusion is presented in the paper. In the presented method, the neural network is trained based on the quality indexes and auxiliary variables with low sampling frequency first and the high...
In this paper, we propose a human daily activity recognition method by fusing the data from two wearable inertial sensors attached on one foot and the waist of the subject, respectively. We developed a multi-sensor fusion scheme for activity recognition. First, data from these two sensors are fused for coarse-grained classification in order to determine the type of the activity: zero displacement...
The future of robotics is not limited to factories and homes, and is extending to robot-assisted urban search and rescue. This paper proposes a new application of neural networks in this emerging field of research. Specifically, a neural network with feedforward architecture using the backpropagation learning algorithm is implemented in order to determine the positioning of a robotic gripper that...
For target tracking in Interference Environments of cognitive radar problem, Extended Karman, Particle filter algorithms etc. are generally used to be regarded as usual solutions to state estimation. Many techniques have been developed to improve performance of target tracking. In this paper, we set the structure and key features of target's tracking design for cognitive radar, and newly propose cognitive...
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