Forced Oscillation Source Location via Multivariate Time Series Classification

Precisely locating low-frequency oscillation sources is the prerequisite of suppressing sustained oscillation, which is an essential guarantee for the secure and stable operation of power grids. Using synchrophasor measurements, a machine learning method is proposed to locate the source of forced oscillation in power systems. Rotor angle and active power of each power plant are utilized to construct multivariate time series (MTS). Applying Mahalanobis distance metric and dynamic time warping, the distance between MTS with different phases or lengths can be appropriately measured. The obtained distance metric, representing characteristics during the transient phase of forced oscillation under different disturbance sources, is used for offline classifier training and online matching to locate the disturbance source. Simulation results using the four-machine two-area system and IEEE 39-bus system indicate that the proposed location method can identify the power system forced oscillation source online with high accuracy.Comment: 5 pages, 3 figures. Accepted by 2018 IEEE/PES Transmission and Distribution Conferenc

Traumatic Brain Injury Induces Genome-Wide Transcriptomic, Methylomic, and Network Perturbations in Brain and Blood Predicting Neurological Disorders.

The complexity of the traumatic brain injury (TBI) pathology, particularly concussive injury, is a serious obstacle for diagnosis, treatment, and long-term prognosis. Here we utilize modern systems biology in a rodent model of concussive injury to gain a thorough view of the impact of TBI on fundamental aspects of gene regulation, which have the potential to drive or alter the course of the TBI pathology. TBI perturbed epigenomic programming, transcriptional activities (expression level and alternative splicing), and the organization of genes in networks centered around genes such as Anax2, Ogn, and Fmod. Transcriptomic signatures in the hippocampus are involved in neuronal signaling, metabolism, inflammation, and blood function, and they overlap with those in leukocytes from peripheral blood. The homology between genomic signatures from blood and brain elicited by TBI provides proof of concept information for development of biomarkers of TBI based on composite genomic patterns. By intersecting with human genome-wide association studies, many TBI signature genes and network regulators identified in our rodent model were causally associated with brain disorders with relevant link to TBI. The overall results show that concussive brain injury reprograms genes which could lead to predisposition to neurological and psychiatric disorders, and that genomic information from peripheral leukocytes has the potential to predict TBI pathogenesis in the brain

Joint Rigid Registration of Multiple Generalized Point Sets With Anisotropic Positional Uncertainties in Image-Guided Surgery

In medical image analysis (MIA) and computer-assisted surgery (CAS), aligning two multiple point sets (PSs) together is an essential but also a challenging problem. For example, rigidly aligning multiple point sets into one common coordinate frame is a prerequisite for statistical shape modelling (SSM). Accurately aligning the pre-operative space with the intra-operative space in CAS is very crucial to successful interventions. In this article, we formally formulate the multiple generalized point set registration problem (MGPSR) in a probabilistic manner, where both the positional and the normal vectors are used. The six-dimensional vectors consisting of both positional and normal vectors are called as generalized points. In the formulated model, all the generalized PSs to be registered are considered to be the realizations of underlying unknown hybrid mixture models (HMMs). By assuming the independence of the positional and orientational vectors (i.e., the normal vectors), the probability density function (PDF) of an observed generalized point is computed as the product of Gaussian and Fisher distributions. Furthermore, to consider the anisotropic noise in surgical navigation, the positional error is assumed to obey a multi-variate Gaussian distribution. Finally, registering PSs is formulated as a maximum likelihood (ML) problem, and solved under the expectation maximization (EM) technique. By using more enriched information (i.e., the normal vectors), our algorithm is more robust to outliers. By treating all PSs equally, our algorithm does not bias towards any PS. To validate the proposed approach, extensive experiments have been conducted on surface points extracted from CT images of (i) a human femur bone model; (ii) a human pelvis bone model. Results demonstrate our algorithm's high accuracy, robustness to noise and outliers