169,036 research outputs found

    Field Information Modeling (FIM)™: Best Practices Using Point Clouds

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    This study presented established methods, along with new algorithmic developments, to automate point cloud processing in support of the Field Information Modeling (FIM)™ framework. More specifically, given a multi-dimensional (n-D) designed information model, and the point cloud’s spatial uncertainty, the problem of automatic assignment of point clouds to their corresponding model elements was considered. The methods addressed two classes of field conditions, namely (i) negligible construction errors and (ii) the existence of construction errors. Emphasis was given to defining the assumptions, potentials, and limitations of each method in practical settings. Considering the shortcomings of current frameworks, three generic algorithms were designed to address the point-cloud-to-model assignment. The algorithms include new developments for (i) point cloud vs. model comparison (negligible construction errors), (ii) robust point neighborhood definition, and (iii) Monte-Carlo-based point-cloud-to-model surface hypothesis testing (existence of construction errors). The effectiveness of the new methods was demonstrated in real-world point clouds, acquired from construction projects, with promising results. For the overall problem of point-cloud-to-model assignment, the proposed point cloud vs. model and point-cloud-to-model hypothesis testing methods achieved F-measures of 99.3% and 98.4%, respectively, on real-world datasets

    Data Improving in Time Series Using ARX and ANN Models

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    Anomalous data can negatively impact energy forecasting by causing model parameters to be incorrectly estimated. This paper presents two approaches for the detection and imputation of anomalies in time series data. Autoregressive with exogenous inputs (ARX) and artificial neural network (ANN) models are used to extract the characteristics of time series. Anomalies are detected by performing hypothesis testing on the extrema of the residuals, and the anomalous data points are imputed using the ARX and ANN models. Because the anomalies affect the model coefficients, the data cleaning process is performed iteratively. The models are re-learned on “cleaner” data after an anomaly is imputed. The anomalous data are reimputed to each iteration using the updated ARX and ANN models. The ARX and ANN data cleaning models are evaluated on natural gas time series data. This paper demonstrates that the proposed approaches are able to identify and impute anomalous data points. Forecasting models learned on the unclean data and the cleaned data are tested on an uncleaned out-of-sample dataset. The forecasting model learned on the cleaned data outperforms the model learned on the unclean data with 1.67% improvement in the mean absolute percentage errors and a 32.8% improvement in the root mean squared error. Existing challenges include correctly identifying specific types of anomalies such as negative flows

    Modeling Heteroskedasticity of Crop Yield Distributions: Implications for Normality

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    The paper analyzes the extent to which ignorance of heteroskedasticity or its inadequate modeling would result in misleading statistical inferences about crop yield distribution. We follow the "detrending mean yield approach" in which we model the conditional mean yield using a panel data model. We assume alternative structures of variance-covariance matrix for the random component. Heteroskedasticity robust and non-robust estimation methods are used before performing a joint normality test on the random component of crop yield data. Our findings provide evidence against the claim that virtually all previous findings of non-normality in crop yields are infected because of the ignorance of heteroskedasticity or its inappropriate modeling. Accounting for heteroskedasticity in crop yield data would matter for validity of evidence against normality only to the extent that its proportion among the source of departure from normal distribution is relatively sizable.Research Methods/ Statistical Methods,
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