A comparison of linear interpolation and spline interpolation for turbine efficiency curves in short-term hydropower scheduling problems

Skjelbred, Hans Ivar; Kong, Jiehong

doi:10.1088/1755-1315/240/4/042011

Cited by 8 publications

(3 citation statements)

References 5 publications

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“…It provides an estimated value between two or more points (Franke, 1982). The assumption in the method is that the two supporting points are nearly linear (Perperoglou et al, 2019;Skjelbred and Kong, 2019). It is used to obtain the gradually changing very smooth surface showing monsoon onset and withdrawal dates for India at 0.25 °× 0.25 °spatial resolution.…”

Section: Methodsmentioning

confidence: 99%

Unraveling Intricacies of Monsoon Attributes in Homogenous Monsoon Regions of India

et al. 2022

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India observes the summer monsoon in June–July–August–September (JJAS) season, and the livelihood security of a huge population depends on it. The impact of the monsoon onset timing, length of monsoon season, rainfall amount, and related extreme events is huge on the Indian economy. Therefore, understanding the inherent intricacies needed a detailed investigation. In five homogenous monsoon regions of India, the trend of monsoon onset and the length of monsoon season are examined. The association between 1) monsoon onset ∼ rainfall amount, 2) length of monsoon season ∼ rainfall amount, and 3) monsoon onset ∼ length of monsoon season is investigated. Subsequently, the behavior of rainfall and extreme excess days in the ±1 standard deviation (SD) length of monsoon season is also examined in detail. The trend for monsoon onset shows late onset in all the homogenous monsoon regions except the northeast region. The length of monsoon season is found increasing significantly with high magnitude in west central and northwest regions. A significantly strong negative correlation (∼−0.6) for monsoon onset timing ∼ length of monsoon season is observed. Therefore, the change in rainfall anomaly, extreme excess days, and rainy days is done concerning the length of the monsoon season. In the cases of the −1 SD (+1 SD) length of monsoon season, rainfall anomaly and extreme excess days are low (high) in most parts of the homogenous monsoon regions. Extreme excess days showed a significant association with rainy days, which indicates a high possibility of rainy days converting into extreme excess days. However, the increase in extreme excess days in the +1 SD length of monsoon season is limited to a great extent in JJAS and June only. Morlet wavelet power spectrum shows the delay (advance) of power in −1SD (+1 SD) length of monsoon season.

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Section: Methodsmentioning

confidence: 99%

Unraveling Intricacies of Monsoon Attributes in Homogenous Monsoon Regions of India

et al. 2022

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“…Before segmentation, all images were resampled to a common voxel spacing of 1 mm × 1 mm × 1 mm by using the linear interpolation algorithm to construct new data points within the range of discrete datasets of known data points to standardize spacing across all images ( 25 ).…”

Section: Methodsmentioning

confidence: 99%

CT-Based Peritumoral and Intratumoral Radiomics as Pretreatment Predictors of Atypical Responses to Immune Checkpoint Inhibitor Across Tumor Types: A Preliminary Multicenter Study

Feng

Lin

et al. 2021

Front. Oncol.

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ObjectiveTo develop and validate a new strategy based on radiomics features extracted from intra- and peritumoral regions on CT images for the prediction of atypical responses to the immune checkpoint inhibitor (ICI) in cancer patients.MethodsIn total, 135 patients derived from five hospitals with pathologically confirmed malignancies receiving ICI were included in this retrospective study. Atypical responses including pseudoprogression (PsP) and hyperprogression disease (HPD) were identified as their definitions. A subgroup of standard progression disease (sPD) in 2018 was also involved in this study. Based on pretreatment CT imaging, a total of 107 features were extracted from intra- and peri-tumoral regions, respectively. The least absolute shrinkage and selection operator (Lasso) algorithm was used for feature selection, and multivariate logistic analysis was used to develop radiomics signature (RS). Finally, a total of nine RSs, derived from intra-tumoral, peri-tumoral, and combination of both regions, were built respectively to distinguish PsP vs. HPD, PsP vs. sPD, and HPD vs. sPD. The performance of the RSs was evaluated with discrimination, calibration, and clinical usefulness.ResultsNo significant difference was found when compared in terms of clinical characteristics of PsP, HPD, and sPD. RS based on combined regions outperformed those from either intra-tumoral or peri-tumoral alone, yielding an AUC (accuracy) of 0.834 (0.827) for PsP vs. HPD, 0.923 (0.868) for PsP vs. sPD, and 0.959 (0.894) for HPD vs. sPD in the training datasets, and 0.835 (0.794) for PsP vs. HPD, 0.919 (0.867) for PsP vs. sPD, and 0.933 (0.842) for HPD vs. sPD in the testing datasets. The combined RS showed good fitness (Hosmer–Lemeshow test p > 0.05) and provided more net benefit than the treat-none or treat-all scheme by decision curve analysis in both training and testing datasets.ConclusionPretreatment radiomics are helpful to predict atypical responses to ICI across tumor types. The combined RS outperformed those from either intra- or peri-tumoral alone which may provide a more comprehensive characterization of atypical responses to ICI.

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“…Basic methods such as mean imputation and last observation carry forward (LOCF) are known to bias estimates and distort variance even when data are MCAR ( Little & Rubin, 2019 ). Linear and spline interpolation have been shown to produce accurate estimates when used for imputation in covariates ( Skjelbred & Kong, 2019 ; Terry et al, 1986 ) but likely bias autocorrelation estimates due to their imposition on the autocorrelation structure. Wijesekara and Liyanage (2020) showed under MCAR that Kalman smoothing ( Moritz & Bartz-Beielstein, 2017 ) achieves high accuracy when imputing values in a univariate time series, however the method has not been evaluated on other missing mechanisms.…”

Section: Introductionmentioning

confidence: 99%

Testing unit root non-stationarity in the presence of missing data in univariate time series of mobile health studies

Fowler,

Cai,

Baker

et al. 2024

Journal of the Royal Statistical Society Series C: Applied Statistics

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The use of digital devices to collect data in mobile health studies introduces a novel application of time series methods, with the constraint of potential data missing at random or missing not at random (MNAR). In time-series analysis, testing for stationarity is an important preliminary step to inform appropriate subsequent analyses. The Dickey–Fuller test evaluates the null hypothesis of unit root non-stationarity, under no missing data. Beyond recommendations under data missing completely at random for complete case analysis or last observation carry forward imputation, researchers have not extended unit root non-stationarity testing to more complex missing data mechanisms. Multiple imputation with chained equations, Kalman smoothing imputation, and linear interpolation have also been used for time-series data, however such methods impose constraints on the autocorrelation structure and impact unit root testing. We propose maximum likelihood estimation and multiple imputation using state space model approaches to adapt the augmented Dickey–Fuller test to a context with missing data. We further develop sensitivity analyses to examine the impact of MNAR data. We evaluate the performance of existing and proposed methods across missing mechanisms in extensive simulations and in their application to a multi-year smartphone study of bipolar patients.

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A comparison of linear interpolation and spline interpolation for turbine efficiency curves in short-term hydropower scheduling problems

Cited by 8 publications

References 5 publications

Unraveling Intricacies of Monsoon Attributes in Homogenous Monsoon Regions of India

Unraveling Intricacies of Monsoon Attributes in Homogenous Monsoon Regions of India

CT-Based Peritumoral and Intratumoral Radiomics as Pretreatment Predictors of Atypical Responses to Immune Checkpoint Inhibitor Across Tumor Types: A Preliminary Multicenter Study

Testing unit root non-stationarity in the presence of missing data in univariate time series of mobile health studies

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