Engineering topics

Hafen, Ryan P

Publications and source records attributed to Hafen, Ryan P.

6 records

DOE OSTI2013

Challenges in Biomarker Discovery: Combining Expert Insights with Statistical Analysis of Complex Omics Data

The advent of high throughput technologies capable of comprehensive analysis of genes, transcripts, proteins and other significant biological molecules has provided an unprecedented opportunity for the identification of molecular markers of disease processes. However, it has simultaneously complicated the problem of extracting meaningful signatures of biological processes from these complex datasets. The process of biomarker discovery and characterization provides opportunities both for purely statistical and expert knowledge-based approaches and would benefit from improved integration of the two. Areas covered In this review we will present examples of current practices for biomarker discovery from complex omic datasets and the challenges that have been encountered. We will then present a high-level review of data-driven (statistical) and knowledge-based methods applied to biomarker discovery, highlighting some current efforts to combine the two distinct approaches. Expert opinion Effective, reproducible and objective tools for combining data-driven and knowledge-based approaches to biomarker discovery and characterization are key to future success in the biomarker field. We will describe our recommendations of possible approaches to this problem including metrics for the evaluation of biomarkers.

bioinformatics↗

DOE OSTI2012

Planning for Variable Generation Integration through Balancing Authorities Consolidation

As more and more variable generation is integrated into power grids, many challenges and concerns arise for an individual balancing authority (BA) to balance the system with limited resources. Consolidating balancing authorities provides a promising method to mitigate these problems by enabling the sharing of resources through operating different BAs as a single BA. The diversity in load and renewable generation over a wide area can be effectively leveraged, which makes it possible to achieve significant savings in balancing requirements. This paper develops a detailed procedure to compute savings in load following and regulation service requirements due to BAs consolidation. It proposes several evaluation metrics for demonstrating the benefits of BA consolidation. Several study scenarios are designed for a set of BAs in the western United States to test the proposed procedure. Results have shown significant savings in the capacity, ramp rate, and energy of balancing service requirements. Important factors affecting the savings, such as forecast accuracy and cross correlation between forecast errors, are also discussed.

Diao, Ruisheng↗

DOE OSTI2012

Speech information retrieval: a review

Audio is an information-rich component of multimedia. Information can be extracted from audio in a number of different ways, and thus there are several established audio signal analysis research fields. These fields include speech recognition, speaker recognition, audio segmentation and classification, and audio finger-printing. The information that can be extracted from tools and methods developed in these fields can greatly enhance multimedia systems. In this paper, we present the current state of research in each of the major audio analysis fields. The goal is to introduce enough back-ground for someone new in the field to quickly gain high-level understanding and to provide direction for further study.

Hafen, Ryan P↗

DOE OSTI2011

Atypical Behavior Identification in Large Scale Network Traffic

Cyber analysts are faced with the daunting challenge of identifying exploits and threats within potentially billions of daily records of network traffic. Enterprise-wide cyber traffic involves hundreds of millions of distinct IP addresses and results in data sets ranging from terabytes to petabytes of raw data. Creating behavioral models and identifying trends based on those models requires data intensive architectures and techniques that can scale as data volume increases. Analysts need scalable visualization methods that foster interactive exploration of data and enable identification of behavioral anomalies. Developers must carefully consider application design, storage, processing, and display to provide usability and interactivity with large-scale data. We present an application that highlights atypical behavior in enterprise network flow records. This is accomplished by utilizing data intensive architectures to store the data, aggregation techniques to optimize data access, statistical techniques to characterize behavior, and a visual analytic environment to render the behavioral trends, highlight atypical activity, and allow for exploration.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2011

Integration of Uncertainty Information into Power System Operations

Contemporary power systems face uncertainties coming from multiple sources, including forecast errors of load, wind and solar generation, uninstructed deviation and forced outage of traditional generators, loss of transmission lines, and others. With increasing amounts of wind and solar generation being integrated into the system, these uncertainties have been growing significantly. It is critical important to build knowledge of major sources of uncertainty, learn how to simulate them, and then incorporate this information into the decision-making processes and power system operations, for better reliability and efficiency. This paper gives a comprehensive view on the sources of uncertainty in power systems, important characteristics, available models, and ways of their integration into system operations. It is primarily based on previous works conducted at the Pacific Northwest National Laboratory (PNNL).

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2010

Wind and Load Forecast Error Model for Multiple Geographically Distributed Forecasts

The impact of wind and load forecast errors on power grid operations is frequently evaluated by conducting multi-variant studies, where these errors are simulated repeatedly as random processes based on their known statistical characteristics. To generate these errors correctly, we need to reflect their distributions (which do not necessarily follow a known distribution law), standard deviations, auto- and cross-correlations. For instance, load and wind forecast errors can be closely correlated in different zones of the system. This paper introduces a new methodology for generating multiple cross-correlated random processes to simulate forecast error curves based on a transition probability matrix computed from an empirical error distribution function. The matrix will be used to generate new error time series with statistical features similar to observed errors. We present the derivation of the method and present some experimental results by generating new error forecasts together with their statistics.

17 WIND ENERGY↗