Orbital Error Analysis Based on Historical Data

A generalized data preprocessing method is proposed in this paper to reduce the amount of outliers among historical data and further improve the power prediction accuracy. Historical data of wind farms are fit with an S-shape curve via Linear Regression Model. Based on this statistical curve, outliers can be identified considering different fitting error. Furthermore, the expansion of wind farm is identified through the number of outliers. Then a selection method for the allowed maximum fitting errors is recommended. The presented method has been integrated into the prediction system in Inner Mongolia of China with 36 farms. The actual application shows that the wind farm power prediction accuracy has been improved by at least 28% with this model. It is noteworthy that the proposed preprocessing method is just based on statistical analysis of historical data and thus compatible with various wind power prediction methods.

Comparison of Intraoperative Aberrometry, OCT-Based IOL Formula, Haigis-L, and Masket Formulae for IOL Power Calculation after Laser Vision Correction

In automation, the suited scheduled maintenance is one of the keys for plants' operation in order to minimize plants' shutdown. Usually, maintenance is time-interval based or operation-time based to prevent plant shutdowns by means of displacing aged automation devices early. This causes an increasing expenditure due to the fact that the replaced automation devices could be in operation longer and the plant has to be stopped for replacing the device. In this paper we present an approach focusing on combining engineering data and historical process data to extract additional information and applying analysis methods for diagnosis. We introduce how to find cause-effect dependencies of failures during abnormal plant situations to forecast abnormal and critical plant situations.

: Three specific issues are analyzed in a major study RESOLVE (Research on Operations-Limiting Visual Extinction) of atmospheric visibility in the California desert. (1) The issue of the contribution of nitrogen dioxide to light extinction is investigated using aircraft measurements taken as part of the California Air Resources Board (CARB) Los Angeles/desert transport study, routine historical data for nitrogen dioxide and visual range, and a small amount of data acquired with high-resolution research-type nitrogen dioxide monitoring. (2) An assessment is made of the adequacy of 24-hour particle samples as compared to 8-hour particle samples with respect to characterizing visibility during daylight hours. (3) An investigation is made into the propagation of measurement errors through to the final modeling results for extinction budgets and particle source apportionments for the RESOLVE program.

This report aims to analyze the relationship between global oil prices and various economic indexes by using linear regression and ARIMAX models. This study will predict global oil prices accurately and establish a reasonable system for regulating oil prices. The research uses the statistical approach to predict oil prices based on historical data (including independent variables and dependent variable). The study uses monthly average data of WTI crude oil prices from January 2000 to March 2023 and contains the analysis of various economic indicators such as Consumer Price Index (CPI), Personal Consumption Expenditures (PCE), Employment, Population, and Oil Price. The findings indicate that the linear regression model can explain about 40.89% of the variation in log oil price, with significant negative effects of log_PCE, log_EMPLOYMENT, and log_POPULATION, and a significant positive effect of CPI on log_price. However, there exists the probability that some other factors have impact on oil prices. In this study, the author employ the ARIMAX model with ARIMA(4,1,1) errors, which can describe a relatively good fit and small errors in training set measures. Overall, while the linear regression model partially explains the variability in global oil prices, further analysis on residuals is necessary. The study concludes that the ARIMAX model provides a better approach to capture the time-series nature of the data.

Rotation modulation (RM) technology can effectively improve the navigation accuracy of strapdown inertial navigation system (SINS) by the periodic rotation of the rotation mechanism. But the original error characteristics of SINS will be changed after introducing the rotation modulation technology. Aiming at the practical micro-electro-mechanical system RMSINS, the error characteristics are comprehensively analyzed, and then a complete error model is established. At the same time, a reasonable parameter calibration scheme and the compensation method are designed to compensate the error in this paper. On this basis, static and dynamic experiments are carried out. The position accuracies of the rotation modulation state in the static and dynamic experiments are about ten times and about three times than that of the non-rotation state. The experimental results show that the proposed error analysis and compensation method are feasible which have been applied to the practical engineering.

We aim to develop a quantitative method for recalibration of historic helicopter electromagnetic data sets. Recent research has shown that frequency-domain helicopter electromagnetic data collected over a conductive half-space such as calm seawater can be used to correct system calibration errors. However, most historic surveys consist only of data collected over land, where the conductive half-space assumption is rarely justified. We estimate the required recalibration parameters by analyzing systematic misfits in the inversion of statistically chosen measures of historic data. Our method requires the identification, within the survey area, of a zone of conductive responses that are reasonably uniform. From this zone, a set of altitude-corrected median responses are estimated. These are inverted using geologically specifiedconstraints to obtain a best-fit layered earth model. Systematic inconsistencies between the median measured altitude and the inverted depth to surface are attributed to altitude error. Remaining frequency-dependent fitting errors are assumed to be the calibration errors. We tested the method with partial success on helicopter electromagnetic data sets collected over uniform deep sediments where seawater data were also available and two different inland surveys over multiple lithologies in one general area. At high frequencies, our method works reliably. Recalibration of low-frequency data is not possible if the area used as a reference consists of moderate or poor conductors. In this case, data amplitudes are small and are greatly affected by imperfect drift and magnetic susceptibility corrections. Historic helicopter electromagnetic data may require amplitude rescaling up to 20%–30%, with phase shifts of up to 3°.

The need to develop tools for historical and regional variations is becoming more urgent in natural language processing. In this paper, we present two candidate systems for lemmatising historical East Slavic lects (Late Old East Slavic and Middle Russian), as well as modern regional East Slavic lects (Belogornoje and Megra): BERT-based end-to-end pipeline with language-specific heuristics and sequence-to-sequence BART-based encoderdecoder. To evaluate their predictions, we use accuracy score and string similarity measures, such as Levenshtein distance. The BERT-based model is more suitable for the regional data, achieving 85% accuracy score, and only 74% on the historical data. BART-based model climbs up to 92.6% accuracy score on the historical data, yet gets only 80% on the regional data. We provide an error analysis and discuss ways to enhance models, such as dictionary lookup and spellchecker.

Reservoir fluid composition is an essential element in the planning and development of petroleum fields. Experimental analysis and computational modeling are performed on reservoir fluid samples to find molar compositions and to understand fluid behavior throughout the life cycle of the reservoir. There are various computational practices to simulate reservoir fluids behavior applying equation of states and other approaches. However, with the aid of the 4th industrial revolution, and with many successful applications of artificial intelligence in the oil and gas industry, an artificial neural network model was built to predict molar compositions for different reservoir fluids. The expert system described in this work is a tool which can be used to predict molar compositions of reservoir fluids. One of the main challenges in predicting fluid compositions is its complexity and its wide differences. There are huge composition variations between different formations and sometimes within the same formation due to possible existing compartments in the reservoir. Moreover, the compositions vary with several factors such as location, reservoir conditions, and time. Therefore, the inputs to the ANN model should include well location 3D coordinates, reservoir conditions, API gravity, gas specific gravity, average molecular weight, sampling date, as well as reservoir and field information. The ANN model was trained based on historical experimental data of fluids from different fields and formations. Different cases and error analysis were performed to optimize the prediction accuracy of the developed tool. A feedforward back-propagation algorithm was applied in building the model. Results indicated that a more accurate model could be obtained without grouping any hydrocarbon components. A successful prediction of molar composition was achieved using the built ANN model. This method can add a great value to the reservoir characterization process as the fluid molar composition can now be predicted using simple measurements of fluid properties and other reservoir data that can be easily obtained. Introducing this ANN model into the process of fluid compositional analysis will optimize the operational process by reducing the time and cost associated with the experimental analysis.

Petroleum economic evaluation involves estimating revenues from forecasted production profiles and field costs including capital expenditures (CAPEX), drilling expenses (DRILLEX), and operating expenses (OPEX). The existing cost-estimating tool requires several inputs making it time-intensive and difficult to use with few data during the early stages of projects. Majority of the previously developed time saving cost estimations proxy models rely on unrealistic assumptions that include uniform operational costs for different fields with a different number of wells, casings, and drilled depths. This work focused at developing proxy models that consider the variability of the development costs with different parameters. The developed models benefited from a three-step approach for CAPEX, DRILLEX, and OPEX estimations based on datasets from three wells from a gas field in southern coastal Tanzania. Firstly, cost sensitivity analysis was performed using QUE$TOR v15.1.0.18, a cost estimating commercial software to determine the most influential field parameters of the field costs. Secondly, the field cost models were generated based on historical cost data from the gas field using multivariable regression analysis with the help of Statistical Package for the Social Sciences software (SPSS) v22.0. Lastly, errors analysis was done for checking the predictive reliability of the models. Based on the analysis, the CAPEX and OPEX were found to be strongly linearly dependent on the size of processing facilities, number of producing wells in the gas field and production capacity, respectively. On the other hand, a nonlinearity relation was revealed on of the DRILLEX which was strongly dependent on drilled well depth and number of installed well casing. Results show that the developed models are useful and their reliability becomes robust when more data is used. A stochastic modeling approach was further recommended for the models to incorporate uncertainties associated with the parameters used to quantify the cost estimates.

view Abstract Citations (9) References (86) Co-Reads Similar Papers Volume Content Graphics Metrics Export Citation NASA/ADS Comparison of the Plasma Tails of Four Comets: P/Halley, Okazaki-Levy-Rudenko, Austin, and Levy Farnham, Tony L. ; Meech, Karen J. Abstract Photographic and charge coupled device (CCD) plasma tail observations are compared for four comets: P/Halley (22 nights in 1985/1986), Okazaki-Levy-Rudenko 1989 XIX (1989 December 2), Austin 1990 V (nine nights in 1990), and Levy 1990 XX (two nights in 1991). We present a discussion of several image-processing techniques used to enhance the visibility of the plasma tail features in order to measure velocities, accelerations, and position angles. The data are used to assess the validity of various physical mechanisms proposed to explain plasma tail phenomena. Seven disconnection events were observed in the comet P/Halley data, two in the Austin data, and none for the other comets. Analysis of these data suggests that while the crossing of the solar neutral sheet (the sector boundary) is a prominent factor in the production of a disconnection event, it is likely that several mechanisms are at work. A sector boundary crossing has been ruled out as the cause of either the 1986 April 26 P/Halley disconnection or the 1990 May 5/6 Austin disconnection. The motions of the disconnection events, knots, and condensations in the tails were seen to increase from 30-60 km/s near the nucleus (within 106 km) to 80-100 km/s at 107 km, consistent with either bulk motion or Alfven waves. Distinguishing between the two cases is not possible with these data. It was found that although the tail ray rotation rate slows as the ray approaches the tail axis, it is not a good indicator of the solar wind speed. Historical plasma tail data are also used to look for clues as to why some comets form well-developed plasma tails and others do not. Publication: The Astrophysical Journal Supplement Series Pub Date: March 1994 DOI: 10.1086/191943 Bibcode: 1994ApJS...91..419F Keywords: Astronomical Models; Austin Comet; Comet Tails; Halley'S Comet; Image Enhancement; Image Processing; Magnetohydrodynamic Waves; Okazaki-Levy-Rudenko Comet; Tables (Data); Astronomical Photography; Charge Coupled Devices; Comet Nuclei; Error Analysis; Giacobini-Zinner Comet; Solar Wind Velocity; Astronomy; COMETS: GENERAL full text sources ADS |

Predicting housing prices in China based on modified Holt's exponential smoothing incorporating whale optimization algorithm

This article presents a novel approximate adder that is design-optimized and has optimized error metrics. Optimization in design translates into optimization of the design parameters such as delay, power, and area/resources, and optimization of the error metrics points to the usefulness of the proposed approximate adder for practical applications. We refer to the proposed approximate adder that is hardware optimized and having a near-normal error distribution as HOAANED, in short. We consider the implementation of HOAANED and the other approximate adders based on ASIC and FPGA design environments. We used a Xilinx Artix-7 device for a FPGA implementation, and a 32/28-nm CMOS standard digital cell library for an ASIC based implementation. To demonstrate the practical utility of HOAANED, we considered digital image processing as a practical application and performed experimentation with many images. The quality of the images reconstructed using the approximate adders was analyzed based on the peak signal-to-noise ratio (PSNR), which is used as a qualitative figure-of-merit to assess the images. It is found that HOAANED achieves an improved PSNR in comparison with the other approximate adders for image processing, and this is achieved with HOAANED simultaneously featuring optimized design metrics and error characteristics.

In this paper we address the use of satellite-based irradiance data as a proxy for the ground irradiance data to determine the intraday solar variability as a function of a finite difference of hourly clear sky index, here after called nominal variability. The satellite data based nominal variability is compared to that of the ground data based variability to determine the use of satellite data as replacement for ground data for this application. A mathematical relationship has been developed to predict nominal variability as a function of the day's clear sky index. The article also demonstrates the application of the intraday variability to predict day ahead hourly forecast variability range as a function of the day's clear sky index. The results show that the intraday solar irradiance variability can be calculated using historical satellite data and provides a similar result to that of variability computed using quality historical ground data. The results also show the potential of intraday solar variability to characterize day ahead forecast variability.

With increasing demands for precision in traffic control, evaluation, and other trajectory-based applications, reconstructing sparse trajectories has become a prominent topic. This study presents an innovative approach employing the particlike filter (PF) algorithm to directly output reconstructed second-by-second trajectories at signalized intersections, grounded in Markov assumptions. Instead of allocating travel time, this method incorporates the influence of signal status for trajectory reconstruction. By integrating historical trajectory data with real-time signal status, the current vehicle motion is categorized to estimate the expected position for the next second. These serve as referential data for generating particles and weight assignments in the PF algorithm. Numerical experiments using the NGSIM dataset confirmed the accuracy and stability of this approach through analysis of average error, comparisons with other methods, and parametric sensitivity analysis. The reconstruction accuracy is improved by 6.37% when the model processing is classified based on signal status.