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- Molinder, Anna, et al.
(författare)
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Validity and reliability of the medial temporal lobe atrophy scale in a memory clinic population
- 2021
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Ingår i: BMC Neurology. - : Springer Science and Business Media LLC. - 1471-2377. ; 21:1
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Tidskriftsartikel (refereegranskat)abstract
- Background Visual rating of medial temporal lobe atrophy (MTA) is often performed in conjunction with dementia workup. Most prior studies involved patients with known or probable Alzheimer's disease (AD). This study investigated the validity and reliability of MTA in a memory clinic population. Methods MTA was rated in 752 MRI examinations, of which 105 were performed in cognitively healthy participants (CH), 184 in participants with subjective cognitive impairment, 249 in subjects with mild cognitive impairment, and 214 in patients with dementia, including AD, subcortical vascular dementia and mixed dementia. Hippocampal volumes, measured manually or using FreeSurfer, were available in the majority of cases. Intra- and interrater reliability was tested using Cohen's weighted kappa. Correlation between MTA and quantitative hippocampal measurements was ascertained with Spearman's rank correlation coefficient. Moreover, diagnostic ability of MTA was assessed with receiver operating characteristic (ROC) analysis and suitable, age-dependent MTA thresholds were determined. Results Rater agreement was moderate to substantial. MTA correlation with quantitative volumetric methods ranged from -0.20 (p< 0.05) to -0.68 (p < 0.001) depending on the quantitative method used. Both MTA and FreeSurfer are able to distinguish dementia subgroups from CH. Suggested age-dependent MTA thresholds are 1 for the age group below 75 years and 1.5 for the age group 75 years and older. Conclusions MTA can be considered a valid marker of medial temporal lobe atrophy and may thus be valuable in the assessment of patients with cognitive impairment, even in a heterogeneous patient population.
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| 2. |
- Molinder, Jennie
(författare)
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Forecasting of Icing Related Wind Energy Production Losses : Probabilistic and Machine Learning Approaches
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Icing on wind turbine blades causes significant production losses for wind energy in cold climate. Next-day forecasts of these production losses are crucial for the power balance in the electrical grid and for the trading process, but they are uncertain due to lack of understanding of, and simplifications, in the modelling chain. In the present work, uncertainties in the modelling chain for icing related production losses are addressed with the aim to increase the utility of next-day production loss forecasts. Probabilistic and machine learning methods are applied both to improve the forecast skill and to estimate reliable forecast uncertainties. The different methods enable uncertainties in different parts of the chain to be addressed. A Numerical Weather Prediction (NWP) ensemble captures uncertainties in the initial conditions of the forecasts while a neighbourhood method describes uncertainties in the spatial representation of the NWP forecast at the exact locations of the wind parks. An icing model ensemble is generated in order to address uncertainties in the icing model parameters. Finally, machine learning approaches are employed to both deterministically and probabilistically address uncertainties in the modelling chain. Production data from wind parks in Sweden were used to evaluate all methods. The physically based probabilistic methods; the NWP ensemble, the neighbourhood method and the icing model ensemble, increase the forecast skill and provide valuable uncertainty estimations. The largest forecast improvement is obtained when the different probabilistic approaches are combined. On the other hand, machine learning approaches for icing related production losses demonstrate large potential. The probabilistic machine learning method employed generally outperforms every other single probabilistic method mentioned above. By applying the different methods of uncertainty quantification, the utility of icing related production loss forecast in the trading process is improved since related costs can be reduced and usage of the produced power can be optimised. These methods can also be beneficial when planning for site maintenance and for the use of de-icing systems, since icing on the wind turbines are directly or indirectly forecasted. Thus, the improved representations of uncertainties in the modelling chain contributes to an enhanced usage of wind power in cold climates.
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| 3. |
- Molinder, Jennie, et al.
(författare)
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Probabilistic Forecasting of Wind Turbine Icing Related Production Losses Using Quantile Regression Forests
- 2021
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Ingår i: Energies. - BASEL, SWITZERLAND : MDPI. - 1996-1073. ; 14:1
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Tidskriftsartikel (refereegranskat)abstract
- A probabilistic machine learning method is applied to icing related production loss forecasts for wind energy in cold climates. The employed method, called quantile regression forests, is based on the random forest regression algorithm. Based on the performed tests on data from four Swedish wind parks available for two winter seasons, it has been shown to produce valuable probabilistic forecasts. Even with the limited amount of training and test data that were used in the study, the estimated forecast uncertainty adds more value to the forecast when compared to a deterministic forecast and a previously published probabilistic forecast method. It is also shown that the output from a physical icing model provides useful information to the machine learning method, as its usage results in an increased forecast skill when compared to only using Numerical Weather Prediction data. A potential additional benefit in machine learning for some stations was also found when using information in the training from other stations that are also affected by icing. This increases the amount of data, which is otherwise a challenge when developing forecasting methods for wind energy in cold climates.
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