Susceptibility Ranking of Electrical Feeders: A Case Study

Ranking problems arise in a wide range of real world applications where an ordering on a set of examples is preferred to a classification model. These applications include collaborative filtering, information retrieval and ranking components of a system by susceptibility to failure. In this paper, we present an ongoing project to rank the feeder cables of a major metropolitan area's electrical grid according to their susceptibility to outages. We describe our framework and the application of machine learning ranking methods, using scores from Support Vector Machines (SVM), RankBoost and Martingale Boosting. Finally, we present our experimental results and the lessons learned from this challenging real-world application

Estimation of system reliability using a semiparametric model

An important problem in reliability engineering is to predict the failure rate, that is, the frequency with which an engineered system or component fails. This paper presents a new method of estimating failure rate using a semiparametric model with Gaussian process smoothing. The method is able to provide accurate estimation based on historical data and it does not make strong a priori assumptions of failure rate pattern (e.g., constant or monotonic). Our experiments of applying this method in power system failure data compared with other models show its efficacy and accuracy. This method can be used in estimating reliability for many other systems, such as software systems or components

Physiological Adjustments to Stress Measures Following Massage Therapy: A Review of the Literature

Use of massage therapy by the general public has increased substantially in recent years. In light of the popularity of massage therapy for stress reduction, a comprehensive review of the peer-reviewed literature is important to summarize the effectiveness of this modality on stress-reactive physiological measures. On-line databases were searched for articles relevant to both massage therapy and stress. Articles were included in this review if (i) the massage therapy account consisted of manipulation of soft tissues and was conducted by a trained therapist, and (ii) a dependent measure to evaluate physiological stress was reported. Hormonal and physical parameters are reviewed. A total of 25 studies met all inclusion criteria. A majority of studies employed a 20–30 min massage administered twice-weekly over 5 weeks with evaluations conducted pre-post an individual session (single treatment) or following a series of sessions (multiple treatments). Single treatment reductions in salivary cortisol and heart rate were consistently noted. A sustained reduction for these measures was not supported in the literature, although the single-treatment effect was repeatable within a study. To date, the research data is insufficient to make definitive statements regarding the multiple treatment effect of massage therapy on urinary cortisol or catecholamines, but some evidence for a positive effect on diastolic blood pressure has been documented. While significant improvement has been demonstrated following massage therapy, the general research body on this topic lacks the necessary scientific rigor to provide a definitive understanding of the effect massage therapy has on many physiological variables associated with stress

Ground vibrations produced by surface and near-surface explosions

Measurements of seismic signatures produced by airborne, near-surface detonations of explosive charges over a variety of ground types show two distinct ground vibration arrivals. In all cases, the earlier arrival (precursor), has a time of arrival consistent with a predominantly underground path and coupling of blast sound to the ground close to the source and is always much smaller than the later vibration, the time of arrival of which is consistent with coupling from the air blast arrival at the receiver. The ratio of the seismic particle velocity to the acoustic pressure at the surface for the air-coupled seismic wave is constant with respect to distance and maximum pressure at a given location, but varies from site to site, with values usually between 1 and 13 μm s-1 Pa-1. For the precursor seismic wave, a coupling coefficient of 0.16 μm s-1 Pa-1 was measured. A numerical code enabling calculations of the fields due to an impulsive source above a layered poroelastic ground is described. Predictions of the air pressure spectrum above ground and the vertical and radial components of solid particle velocity near the ground surface are found to compare tolerably well with the measured spectra and waveforms of acoustic and seismic pulses at about 100 m range in seismically- hard and -soft soils and with a snow cover present. The predicted seismic responses in ‘soft’ soil confirm that the existence of a near-surface S-wave speed less than that in air is responsible for the observed ‘ringing’, i.e. a long low-frequency wavetrain associated with coupling to the dispersive Rayleigh wave. The predicted seismic pulses in the presence of the shallow snow cover explain the observed phenomenon whereby a high frequency ground vibration is modulated by a lower frequency layer resonance. An empirical equation relating ground vibration from explosions to distance predicts that the commonly- used vibrational damage peak velocity criterion of 12 or 25 mm s-1 will be exceeded when the peak positive pressure exceeds 480 Pa (147.6 dB) or 1 kPa (154.0 dB), respectively. Either of these levels is much higher than the current U.S. Army overpressure damage criterion of 159 Pa (138 dB). Thus in most situations damage from blast overpressure will occur long before damaging levels of ground vibration are reached, so it is likely that civilian perceptions of vibration are produced by coupling from the airblast

Forecasting Energy Demand in Large Commercial Buildings Using Support Vector Machine Regression

As our society gains a better understanding of how humans have negatively impacted the environment, research related to reducing carbon emissions and overall energy consumption has become increasingly important. One of the simplest ways to reduce energy usage is by making current buildings less wasteful. By improving energy efficiency, this method of lowering our carbon footprint is particularly worthwhile because it reduces energy costs of operating the building, unlike many environmental initiatives that require large monetary investments. In order to improve the efficiency of the heating, ventilation, and air conditioning (HVAC) system of a Manhattan skyscraper, 345 Park Avenue, a predictive computer model was designed to forecast the amount of energy the building will consume. This model uses Support Vector Machine Regression (SVMR), a method that builds a regression based purely on historical data of the building, requiring no knowledge of its size, heating and cooling methods, or any other physical properties. SVMR employs time-delay coordinates as a representation of the past to create the feature vectors for SVM training. This pure dependence on historical data makes the model very easily applicable to different types of buildings with few model adjustments. The SVM regression model was built to predict a week of future energy usage based on past energy, temperature, and dew point temperature data

Estimation of System Reliability Using a Semiparametric Model

An important problem in reliability engineering is to predict the failure rate, that is, the frequency with which an engineered system or component fails. This paper presents a new method of estimating failure rate using a semiparametric model with Gaussian process smoothing. The method is able to provide accurate estimation based on historical data and it does not make strong a priori assumptions of failure rate pattern (e.g., constant or monotonic). Our experiments of applying this method in power system failure data compared with other models show its efficacy and accuracy. This method can be used in estimating reliability for many other systems, such as software systems or components

Improving Efficiency and Reliability of Building Systems Using Machine Learning and Automated Online Evaluation

A high percentage of newly-constructed commercial office buildings experience energy consumption that exceeds specifications and system failures after being put into use. This problem is even worse for older buildings. We present a new approach, 'predictive building energy optimization', which uses machine learning (ML) and automated online evaluation of historical and real-time building data to improve efficiency and reliability of building operations without requiring large amounts of additional capital investment. Our ML approach uses a predictive model to generate accurate energy demand forecasts and automated analyses that can guide optimization of building operations. In parallel, an automated online evaluation system monitors efficiency at multiple stages in the system workflow and provides building operators with continuous feedback. We implemented a prototype of this application in a large commercial building in Manhattan. Our predictive machine learning model applies Support Vector Regression (SVR) to the building's historical energy use and temperature and wet-bulb humidity data from the building's interior and exterior in order to model performance for each day. This predictive model closely approximates actual energy usage values, with some seasonal and occupant-specific variability, and the dependence of the data on day-of-the-week makes the model easily applicable to different types of buildings with minimal adjustment. In parallel, an automated online evaluator monitors the building's internal and external conditions, control actions and the results of those actions. Intelligent real-time data quality analysis components quickly detect anomalies and automatically transmit feedback to building management, who can then take necessary preventive or corrective actions. Our experiments show that this evaluator is responsive and effective in further ensuring reliable and energyefficient operation of building systems

Accurate Galactic 21-cm H I measurements with the NRAO Green Bank Telescope

Aims: We devise a data reduction and calibration system for producing highly-accurate 21-cm H I spectra from the Green Bank Telescope (GBT) of the NRAO. Methods: A theoretical analysis of the all-sky response of the GBT at 21 cm is made, augmented by extensive maps of the far sidelobes. Observations of radio sources and the Moon are made to check the resulting aperture and main beam efficiencies. Results: The all-sky model made for the response of the GBT at 21 cm is used to correct for "stray" 21-cm radiation reaching the receiver through the sidelobes rather than the main beam. This reduces systematic errors in 21-cm measurements by about an order of magnitude, allowing accurate 21-cm H I spectra to be made at about 9' angular resolution with the GBT. At this resolution the procedures discussed here allow for measurement of total integrated Galactic H I line emission, W, with errors of 3 K km s^-1, equivalent to errors in optically thin N_HI of 5 x 10^18 cm^-2.Comment: 49 pages, 25 figures; A&A, in pres

Failure Analysis of the New York City Power Grid

As U.S. power grid transforms itself into Smart Grid, it has become less reliable in the past years. Power grid failures lead to huge financial cost and affect people’s life. Using a statistical analysis and holistic approach, this paper analyzes the New York City power grid failures: failure patterns and climatic effects. Our findings include: higher peak electrical load increases likelihood of power grid failure; increased subsequent failures among electrical feeders sharing the same substation; underground feeders fail less than overhead feeders; cables and joints installed during certain years are more likely to fail; higher weather temperature leads to more power grid failures. We further suggest preventive maintenance, intertemporal consumption, and electrical load optimization for failure prevention. We also estimated that the predictability of the power grid component failures correlates with the cycles of the North Atlantic Oscillation (NAO) Index