Solid-state data interpretation system - A concept

Device, serving as substitute for cathode ray tubes, applies to computer input-output devices such as microfilm readers, data displays, and optical scanners. Each device operates at speed of modern computer

No-err typing aids

Device for aligning paper in typewriter to correct one letter or line of type is discussed. Two types of correcting devices are described and illustrations of the devices are provided

Guide for a typewriter

The invention relates to accessories for typewriters, and more particularly to an improved guide for use in aligning a sheet of paper preparatory to an application of typed indicia to selected spaces. The device includes an aligning plate pivotally mounted on a line guide having formed therein a plurality of aligned apertures. The plate is so positioned that an aperture is positioned immediately above a target area for a type slug so that a slug will imprint a character in selected spaces

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

Apollo/Saturn 5 postflight trajectory: AS-512

The postflight trajectory is presented for the Apollo/Saturn V AS-512 flight. An analysis is included of the orbital and powered flight trajectories of the launch vehicle and the free flight trajectories of the expended S-IC and S-II stages. Trajectory dependent parameters are provided in earth-fixed launch site, launch vehicle navigation, and geographic polar coordinate systems. The time history of the trajectory parameters for the launch vehicle is presented from guidance reference release to Command Service Module (CSM) separation. Tables of significant parameters at engine cutoff, stage separation, parking orbit insertion, and translunar injection are included. Figures of such parameters as altitude, surface and cross range, and the magnitude of total velocity and acceleration as a function of range time for the powered flight trajectories are presented

Using conditional kernel density estimation for wind power density forecasting

Of the various renewable energy resources, wind power is widely recognized as one of the most promising. The management of wind farms and electricity systems can benefit greatly from the availability of estimates of the probability distribution of wind power generation. However, most research has focused on point forecasting of wind power. In this paper, we develop an approach to producing density forecasts for the wind power generated at individual wind farms. Our interest is in intraday data and prediction from 1 to 72 hours ahead. We model wind power in terms of wind speed and wind direction. In this framework, there are two key uncertainties. First, there is the inherent uncertainty in wind speed and direction, and we model this using a bivariate VARMA-GARCH (vector autoregressive moving average-generalized autoregressive conditional heteroscedastic) model, with a Student t distribution, in the Cartesian space of wind speed and direction. Second, there is the stochastic nature of the relationship of wind power to wind speed (described by the power curve), and to wind direction. We model this using conditional kernel density (CKD) estimation, which enables a nonparametric modeling of the conditional density of wind power. Using Monte Carlo simulation of the VARMA-GARCH model and CKD estimation, density forecasts of wind speed and direction are converted to wind power density forecasts. Our work is novel in several respects: previous wind power studies have not modeled a stochastic power curve; to accommodate time evolution in the power curve, we incorporate a time decay factor within the CKD method; and the CKD method is conditional on a density, rather than a single value. The new approach is evaluated using datasets from four Greek wind farms

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

On machine learning-based techniques for future sustainable and resilient energy systems

Permanently increasing penetration of converter-interfaced generation and renewable energy sources (RESs) makes modern electrical power systems more vulnerable to low probability and high impact events, such as extreme weather, which could lead to severe contingencies, even blackouts. These contingencies can be further propagated to neighboring energy systems over coupling components/technologies and consequently negatively influence the entire multi-energy system (MES) (such as gas, heating and electricity) operation and its resilience. In recent years, machine learning-based techniques (MLBTs) have been intensively applied to solve various power system problems, including system planning, or security and reliability assessment. This paper aims to review MES resilience quantification methods and the application of MLBTs to assess the resilience level of future sustainable energy systems. The open research questions are identified and discussed, whereas the future research directions are identified