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Scientific partial discharge (PD) severity evaluation is highly important to the safe operation of gas-insulated switchgear. However, describing PD severity with only a few statistical features such as discharge time and discharge amplitude is unreliable. Hence, a deep-learning neural network model called stacked sparse auto-encoder (SSAE) is proposed to realise feature extraction from the middle...
Human-powered lower exoskeletons have gained considerable interests from both academia and industry over the past few decades, and thus have seen increasing applications in areas of human locomotion assistance and strength augmentation. One of the most important aspects in those applications is to achieve robust control of lower exoskeletons, which, in the first place, requires the proactive modeling...
Sensitivity Amplification Control (SAC) algorithm was first proposed in the augmentation applications of Berkeley Lower Extremity Exoskeleton (BLEEX). The SAC algorithm is widely used in human augmentation applications since it just need the information from the exoskeleton robot, so that the complexity of exoskeleton system can be reduced greatly. However, the SAC algorithm has two main drawbacks:...
In this paper, we propose a novel unsupervised clustering method for feature space analysis. We combine mean shift with a transductive learning method, semi-supervised discriminant analysis (SDA), in an incremental learning scheme. We use mean shift clustering to generate the class label, and use SDA to do subspace selection. Both these steps are performed alternately. Our clustering result could...
Option has proven useful in discovering hierarchical structure in reinforcement learning to fasten learning. The key problem of automatic option discovery is to find subgoals. Though approaches based on visiting-frequency have gained much research focuses, many of them fail to distinguish subgoals from their nearby states. Based on the action-restricted property of subgoals we find, subgoals can be...
In this paper, an adaptive neural network nonlinear control method is developed based on trajectory linearization control (TLC). The adaptive neural network TLC control (ANNTLC) compensates the model nonlinear uncertainty adaptively, and improves controller performance. ANNTLC can also be used to simplify the TLC control design procedure by using a simplified model. A stable neural network learning...
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