Statistical Analysis of Disturbances in Power Transmission Systems

Disturbance analysis is essential to the study of the power transmission systems. Traditionally, disturbances are described as megawatt (MW) events, but the access to data is inefficient due to the slow installation and authorization process of the monitoring device. In this paper, we propose a novel approach to disturbance analysis conducted at the distribution level by exploiting the frequency recordings from Frequency Disturbance Recorders (FDRs) of the Frequency Monitoring Network (FNET/GridEye), based on the relationship between frequency change and the power loss of disturbances - linearly associated by the Frequency Response. We first analyze the real disturbance records of North America (1992 to 2009) and confirm the power law distribution; we discover that small disturbances are log-normal distributed. Then based on the real records from 2011 to 2013 (EI), the disturbances in megawatt and the corresponding frequency change records are studied in parallel. We prove that the frequency change of disturbances and its megawatt records share similar power law distribution when the disturbances are large; the frequency change can be delineated by a log-normal distribution with its numerically approximated coefficient when the disturbances are small. Meanwhile, activities like FIDVR in the power systems reflected as voltage signature patterns recorded at the transmission level are worth studying since each pattern corresponds to a certain type of behavior. Pattern recognition is used in this problem. Initially the records are preprocessed through eliminating ineligible records and rescaling. Feature extraction is applied to obtain a better representation of signature dataset by statistics of amplitude, wavelet transform and Fourier transform. With the extracted features, k-means, an unsupervised clustering algorithm is exploited to generate root patterns; furthermore we use heuristic selection to remove the mis-classified patterns. The extracted root patterns then serve as training dataset to train a support vector machine (SVM). After the parameters of kernel function in SVM is optimized, a subset of voltage signature records is generated as testing dataset, based on which the performance of SVM is evaluated. With all patterns we achieve an accuracy of 80.12% of multi-label classification; and if only considering dominant patterns, the accuracy reaches 86.20%

Hunting for Undetectable Metamorphic Viruses

Commercial anti-virus scanners are generally signature based, that is, they scan for known patterns to determine whether a file is infected by a virus or not. To evade signature-based detection, virus writers have adopted code obfuscation techniques to create highly metamorphic computer viruses. Since metamorphic viruses change their appearance from generation to generation, signature-based scanners cannot detect all instances of such viruses. To combat metamorphic viruses, detection tools based on statistical analysis have been studied. A tool based on hidden Markov models (HMMs) was previously developed and the results are encouraging—it has been shown that metamorphic viruses created by a well-designed metamorphic engine can be detected using an HMM. In this project, we explore whether there are any exploitable weaknesses in this HMM-based detection approach. We create a highly metamorphic virus generating tool designed specifically to evade HMM-based detection. We then test our engine, showing that we can generate viral copies that cannot be detected using previously-developed HMM-based detection techniques. Finally, we consider possible defenses against our approach

Distribution network topology detection with time-series measurements

This paper proposes a novel approach to detecting the topology of distribution networks based on the analysis of time series measurements. The analysis approach draws on data from high-precision phasor measurement units (PMUs or synchrophasors) for distribution systems. A key fact is that time-series data taken from a dynamic system show specific patterns regarding state transitions such as opening or closing switches, as a kind of signature from each topology change. The algorithm proposed here is based on the comparison of the actual signature of a recent state transition against a library of signatures derived from topology simulations. The IEEE 33-bus model is used for initial algorithm validation

Defining signatures of arm-wise copy number change and their associated drivers in kidney cancers

Using pan-cancer data from The Cancer Genome Atlas (TCGA), we investigated how patterns in copy number alterations in cancer cells vary both by tissue type and as a function of genetic alteration. We find that patterns in both chromosomal ploidy and individual arm copy number are dependent on tumour type. We highlight for example, the significant losses in chromosome arm 3p and the gain of ploidy in 5q in kidney clear cell renal cell carcinoma tissue samples. We find that specific gene mutations are associated with genome-wide copy number changes. Using signatures derived from non-negative matrix factorisation (NMF), we also find gene mutations that are associated with particular patterns of ploidy change. Finally, utilising a set of machine learning classifiers, we successfully predicted the presence of mutated genes in a sample using arm-wise copy number patterns as features. This demonstrates that mutations in specific genes are correlated and may lead to specific patterns of ploidy loss and gain across chromosome arms. Using these same classifiers, we highlight which arms are most predictive of commonly mutated genes in kidney renal clear cell carcinoma (KIRC)

Unravelling the evolutionary history and future prospects of endemic species restricted to former glacial refugia

The contemporary distribution and genetic composition of biodiversity bear a signature of species’ evolutionary histories and the effects of past climatic oscillations. For many European species, the Mediterranean peninsulas of Iberia, Italy and the Balkans acted as glacial refugia and the source of range recolonization, and as a result, they contain disproportionately high levels of diversity. As these areas are particularly threatened by future climate change, it is important to understand how past climatic changes affected their biodiversity. We use an integrated approach, combining markers with different evolutionary rates and combining phylogenetic analysis with approximate Bayesian computation and species distribution modelling across temporal scales. We relate phylogeographic processes to patterns of genetic variation in Myotis escalerai, a bat species endemic to the Iberian Peninsula. We found a distinct population structure at the mitochondrial level with a strong geographic signature, indicating lineage divergence into separate glacial refugia within the Iberian refugium. However, microsatellite markers suggest higher levels of gene flow resulting in more limited structure at recent time frames. The evolutionary history of M. escalerai was shaped by the effects of climatic oscillations and changes in forest cover and composition, while its future is threatened by climatically induced range contractions and the role of ecological barriers due to competition interactions in restricting its distribution. This study warns that Mediterranean peninsulas, which provided refuge for European biodiversity during past glaciation events, may become a trap for limited dispersal and ecologically limited endemic species under future climate change, resulting in loss of entire lineages

Multivariate Analysis and Visualization of Splicing Correlations in Single-Gene Transcriptomes

BACKGROUND: RNA metabolism, through 'combinatorial splicing', can generate enormous structural diversity in the proteome. Alternative domains may interact, however, with unpredictable phenotypic consequences, necessitating integrated RNA-level regulation of molecular composition. Splicing correlations within transcripts of single genes provide valuable clues to functional relationships among molecular domains as well as genomic targets for higher-order splicing regulation. RESULTS: We present tools to visualize complex splicing patterns in full-length cDNA libraries. Developmental changes in pair-wise correlations are presented vectorially in 'clock plots' and linkage grids. Higher-order correlations are assessed statistically through Monte Carlo analysis of a log-linear model with an empirical-Bayes estimate of the true probabilities of observed and unobserved splice forms. Log-linear coefficients are visualized in a 'spliceprint,' a signature of splice correlations in the transcriptome. We present two novel metrics: the linkage change index, which measures the directional change in pair-wise correlation with tissue differentiation, and the accuracy index, a very simple goodness-of-fit metric that is more sensitive than the integrated squared error when applied to sparsely populated tables, and unlike chi-square, does not diverge at low variance. Considerable attention is given to sparse contingency tables, which are inherent to single-gene libraries. CONCLUSION: Patterns of splicing correlations are revealed, which span a broad range of interaction order and change in development. The methods have a broad scope of applicability, beyond the single gene – including, for example, multiple gene interactions in the complete transcriptome

Neural crest migration is driven by a few trailblazer cells with a unique molecular signature narrowly confined to the invasive front

Neural crest (NC) cell migration is crucial to the formation of peripheral tissues during vertebrate development. However, how NC cells respond to different microenvironments to maintain persistence of direction and cohesion in multicellular streams remains unclear. To address this, we profiled eight subregions of a typical cranial NC cell migratory stream. Hierarchical clustering showed significant differences in the expression profiles of the lead three subregions compared with newly emerged cells. Multiplexed imaging of mRNA expression using fluorescent hybridization chain reaction (HCR) quantitatively confirmed the expression profiles of lead cells. Computational modeling predicted that a small fraction of lead cells that detect directional information is optimal for successful stream migration. Single-cell profiling then revealed a unique molecular signature that is consistent and stable over time in a subset of lead cells within the most advanced portion of the migratory front, which we term trailblazers. Model simulations that forced a lead cell behavior in the trailing subpopulation predicted cell bunching near the migratory domain entrance. Misexpression of the trailblazer molecular signature by perturbation of two upstream transcription factors agreed with the in silico prediction and showed alterations to NC cell migration distance and stream shape. These data are the first to characterize the molecular diversity within an NC cell migratory stream and offer insights into how molecular patterns are transduced into cell behaviors