In this brief, in order to optimize the tracking accuracy and enhance the robustness of the tracking process for permanent magnet synchronous motor (PMSM), an adaptive dynamic programming(ADP)-based optimal control strategy combining with anti-disturbance control method is proposed. We consider the unknown, mismatched and time-varying load torque as a disturbance and a disturbance observer is then designed. Based on the output of the disturbance observer, a feedforward control can compensate the disturbance in the PMSM in real time. For the easy implementation of optimal control, this brief adopts the actor-critic neural network to approximate the value function and the optimal controller, respectively. A composite controller is then proposed to ensure that the speed can track the reference signal in an optimal and robust way. Finally, an experiment based on a motor towed platform is given to prove the optimality and robustness.

Our understanding of the structure of the brain and its relationships with human traits is largely determined by how we represent the structural connectome. Standard practice divides the brain into regions of interest (ROIs) and represents the connectome as an adjacency matrix having cells measuring connectivity between pairs of ROIs. Statistical analyses are then heavily driven by the (largely arbitrary) choice of ROIs. In this article, we propose a human trait prediction framework utilizing a tractography-based representation of the brain connectome, which clusters fiber endpoints to define a data-driven white matter parcellation targeted to explain variation among individuals and predict human traits. This leads to Principal Parcellation Analysis (PPA), representing individual brain connectomes by compositional vectors building on a basis system of fiber bundles that captures the connectivity at the population level. PPA eliminates the need to choose atlases and ROIs a priori, and provides a simpler, vector-valued representation that facilitates easier statistical analysis compared to the complex graph structures encountered in classical connectome analyses. We illustrate the proposed approach through applications to data from the Human Connectome Project (HCP) and show that PPA connectomes improve power in predicting human traits over state-of-the-art methods based on classical connectomes, while dramatically improving parsimony and maintaining interpretability. Our PPA package is publicly available on GitHub, and can be implemented routinely for diffusion image data.

The partial discharge (PD) detection is of critical importance in the stability and continuity of power distribution operations. Although several feature engineering methods have been developed to refine and improve PD detection accuracy, they can be suboptimal due to several major issues: 1) failure in identifying fault-related pulses; 2) the lack of inner-phase temporal representation; and 3) multiscale feature integration. The aim of this article is to develop a learning-based multiscale feature engineering (LMFE) framework for PD detection of each signal in a three-phase power system, while addressing the above issues. The three-phase measurements are first preprocessed to identify the pulses together with the surrounded waveforms. Next, our feature engineering is conducted to extract the global-scale features, i.e., phase-level and measurement-level aggregations of the pulse-level information, and the local-scale features focusing on waveforms and their inner-phase temporal information. A recurrent neural network (RNN) model is trained, and intermediate features are extracted from this trained RNN model. Furthermore, these multiscale features are merged and fed into a classifier to distinguish the different patterns between faulty and nonfaulty signals. Finally, our LMFE is evaluated by analyzing the VSB ENET dataset, which shows that LMFE outperforms existing approaches and provides the state-of-the-art solution in PD detection.

The brain connectome maps the structural and functional connectivity that forms an important neurobiological basis for the analysis of human cognitive traits while the genetic predisposition and our cognition ability are frequently found in close association. The issue of how genetic architecture and brain connectome causally affect human behaviors remains unknown. To seek for the potential causal relationship, in this paper, we carried out the causal pathway analysis from single nucleotide polymorphism (SNP) data to four common human cognitive traits, mediated by the brain connectome. Specifically, we selected 942 SNPs that are significantly associated with the brain connectome, and then estimated the direct and indirect effect on the human traits for each SNP. We found out that a majority of the selected SNPs have significant direct effects on human traits and discussed the trait-related brain regions and their implications.