Physical and chemical studies of tektites

Physical and chemical properties of tektites from various location

The Chemical Composition and Origin of Moldavites

Metal oxide and rubidium-strontium content of moldavite

Investigation of slosh anomaly in Apollo lunar module propellant gage

Analysis of propellant sloshing in lunar module during Apollo 14 flight and resultant erroneous indication of low level of propellan

Skill forecasting from ensemble predictions of wind power

International audienceOptimal management and trading of wind generation calls for the providing of uncertainty estimates along with the commonly provided short-term wind power point predictions. Alternative approaches for the use of probabilistic forecasting are introduced. More precisely, focus is given to prediction risk indices aiming to give a comprehensive signal on the expected level of forecast uncertainty. Ensemble predictions of wind generation are used as input. A proposal for the definition of prediction risk indices is given. Such skill forecasts are based on the spread of ensemble forecasts (i.e. a set of alternative scenarios for the coming period) for a single prediction horizon or over a look-ahead period. It is shown on the test case of a Danish offshore wind farm how these prediction risk indices may be related to several levels of forecast uncertainty (and potential energy imbalances). Wind power ensemble predictions are derived from the conversion of ECMWF and NCEP ensemble forecasts of meteorological variables to wind power ensemble forecasts, as well as by a lagged average approach alternative. The ability of prediction risk indices calculated from the various types of ensembles forecasts to resolve among situations with different levels of uncertainty is discussed

Demand Forecasting at Low Aggregation Levels using Factored Conditional Restricted Boltzmann Machine.

The electrical demand forecasting problem can be regarded as a non-linear time series prediction problem depending on many complex factors since it is required at various aggregation levels and at high resolution. To solve this challenging problem, various time series and machine learning approaches has been proposed in the literature. As an evolution of neural network-based prediction methods, deep learning techniques are expected to increase the prediction accuracy by being stochastic and allowing bi-directional connections between neurons. In this paper, we investigate a newly developed deep learning model for time series prediction, namely Factored Conditional Restricted Boltzmann Machine (FCRBM), and extend it for demand forecasting. The assessment is made on the EcoGrid EU dataset, consisting of aggregated electric power consumption, price and meteorological data collected from 1900 customers. The households are equipped with local generation and smart appliances capable of responding to real-time pricing signals. The results show that for the energy prediction problem solved here, FCRBM outperforms the benchmark machine learning approach, i.e. Support Vector Machine

Non-Destructive Testing Robots (NDTBOTS) for In-service Storage Tank Inspection

Petrochemical storage tanks are generally inspected when the tank is offline mostly to assess the extent of underside corrosion on the tank floor. Emptying, cleaning and opening a tank for inspection takes many months and is very expensive. Inspection costs can be reduced significantly by inserting robots through manholes on the tank roof to perform non-destructive testing. The challenge is to develop robots that can operate safely in explosive and hazardous environments and measure the thickness of floor plates using ultrasound sensors. This paper reports on the development of a small and inexpensive prototype robot (NDTBOT) which is designed to be intrinsically safe for zone zero operation. The robot “hops” across the floor to make measurements, without any external moving parts. The paper describes the design, experimental testing of the NDTBOT and presents results of steel plate thickness measurements made under water

Are the Tails of Percolation Thresholds Gaussians ?

The probability distribution of percolation thresholds in finite lattices were first believed to follow a normal Gaussian behaviour. With increasing computer power and more efficient simulational techniques, this belief turned to a stretched exponential behaviour, instead. Here, based on a further improvement of Monte Carlo data, we show evidences that this question is not yet answered at all.Comment: 7 pages including 3 figure