| 1. |
- Wang, Jianfeng, 1984-, et al.
(författare)
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A Hybrid Statistical-Dynamical Downscaling of Air Temperature over Scandinavia Using the WRF Model
- 2020
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Ingår i: Advances in Atmospheric Sciences. - : Springer. - 0256-1530 .- 1861-9533. ; 37:1, s. 57-74
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Tidskriftsartikel (refereegranskat)abstract
- An accurate simulation of air temperature at local scales is crucial for the vast majority of weather and climate applications. In this work, a hybrid statistical–dynamical downscaling method and a high-resolution dynamical-only downscaling method are applied to daily mean, minimum and maximum air temperatures to investigate the quality of localscale estimates produced by downscaling. These two downscaling approaches are evaluated using station observation data obtained from the Finnish Meteorological Institute over a near-coastal region of western Finland. The dynamical downscaling is performed with the Weather Research and Forecasting (WRF) model, and the statistical downscaling method implemented is the Cumulative Distribution Function-transform (CDF-t). The CDF-t is trained using 20 years of WRF-downscaled Climate Forecast System Reanalysis data over the region at a 3-km spatial resolution for the central month of each season. The performance of the two methods is assessed qualitatively, by inspection of quantile-quantile plots, and quantitatively, through the Cramer-von Mises, mean absolute error, and root-mean-square error diagnostics. The hybrid approach is found to provide significantly more skillful forecasts of the observed daily mean and maximum air temperatures than those of the dynamical-only downscaling (for all seasons). The hybrid method proves to be less computationally expensive, and also to give more skillful temperature forecasts (at least for the Finnish near-coastal region).
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| 2. |
- Wang, Jianfeng, 1984-, et al.
(författare)
-
Bayesian sparsity estimation in compressive sensing with application to MR images
- 2019
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Ingår i: Communications in Statistics: Case Studies, Data Analysis and Applications. - : Taylor & Francis Group. - 2373-7484. ; 5:4, s. 415-431
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Tidskriftsartikel (refereegranskat)abstract
- The theory of compressive sensing (CS) asserts that an unknownsignal x ∈ CN can be accurately recovered from m measurements with m « N provided that x is sparse. Most of the recovery algorithms need the sparsity s = ||x||0 as an input. However, generally s is unknown, and directly estimating the sparsity has been an open problem. In this study, an estimator of sparsity is proposed by using Bayesian hierarchical model. Its statistical properties such as unbiasedness and asymptotic normality are proved. In the simulation study and real data study, magnetic resonance image data is used as input signal, which becomes sparse after sparsified transformation. The results from the simulation study confirm the theoretical properties of the estimator. In practice, the estimate from a real MR image can be used for recovering future MR images under the framework of CS if they are believed to have the same sparsity level after sparsification.
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| 3. |
- Wang, Jianfeng, 1984-, et al.
(författare)
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Contrast Agent Quantification by Using Spatial Information in Dynamic Contrast Enhanced MRI
- 2021
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Ingår i: Frontiers in Signal Processing. - : Frontiers Media S.A.. - 2673-8198. ; 1
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Tidskriftsartikel (refereegranskat)abstract
- The purpose of this work is to investigate spatial statistical modelling approaches to improve contrast agent quantification in dynamic contrast enhanced MRI, by utilising the spatial dependence among image voxels. Bayesian hierarchical models (BHMs), such as Besag model and Leroux model, were studied using simulated MRI data. The models were built on smaller images where spatial dependence can be incorporated, and then extended to larger images using the maximum a posteriori (MAP) method. Notable improvements on contrast agent concentration estimation were obtained for both smaller and larger images. For smaller images: the BHMs provided substantial improved estimates in terms of the root mean squared error (rMSE), compared to the estimates from the existing method for a noise level equivalent of a 12-channel head coil at 3T. Moreover, Leroux model outperformed Besag models with two different dependence structures. Specifically, the Besag models increased the estimation precision by 27% around the peak of the dynamic curve, while the Leroux model improved the estimation by 40% at the peak, compared with the existing estimation method. For larger images: the proposed MAP estimators showed clear improvements on rMSE for vessels, tumor rim and white matter.
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| 4. |
- Wang, Jianfeng, 1984-, et al.
(författare)
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Effects of winter climate on delays of high speed passenger trains in Botnia-Atlantica region
- 2021
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Ingår i: Journal of Rail Transport Planning & Management. - : Elsevier. - 2210-9706 .- 2210-9714. ; 18
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Tidskriftsartikel (refereegranskat)abstract
- Harsh winter climate can cause various problems for both public and private sectors in Sweden, especially in the northern part for railway industry. To have a better understanding of winter climate impacts, this study investigates effects of the winter climate including ice/snow precipitation on the performance of high speed passenger trains in the Botnia-Atlantica region. The investigation is done with train operational data together with simulated weather data fromthe Weather Research and Forecast model over January–February 2017.Two different measurements of the train performance are analysed. One is primary delay which measures the increment in delay in terms of running time within two consecutive measuring spots, the other is arrival delay which is the delay in terms of arrival time at each measuring spot compared to the schedule. Primary delay is investigated through a Cox model and the arrival delay is studied using a Markov chain model.The results show that the weather variables have impacts on the train performance. Therein temperature and humidity have significant impacts on both the occurrence of primary delay and the transition intensities between arrival delay and non-delay.
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| 5. |
- Wang, Jianfeng, 1984-, et al.
(författare)
-
Effects of winter climate on high speed passenger trains in Botnia-Atlantica region
- 2020
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Harsh winter climate can cause various problems for both public and private sectors in Sweden, especially in the northern part for railway industry. To have a better understanding of winter climate impacts, this study investigates effects of the winter climate including atmospheric icing on the performance of high speed passenger trains in the Botnia-Atlantica region. The investigation is done with train operational data together with simulated weather data from the Weather Research and Forecast model over January - February 2017.Two different measurements of the train performance are analysed. One is cumulative delay which measures the increment in delay in terms of running time within two consecutive measuring spots, the other is current delay which is the delay in terms of arrival time at each measuring spot compared to the schedule. Cumulative delay is investigated through a Cox model and the current delay is studied using a Markov chain model.The results show that the weather factors have impacts on the train performance. Therein temperature and humidity have significant impacts on both the occurrence of cumulative delay and the transition probabilities between (current) delayed and non-delayed states.
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| 6. |
- Wang, Jianfeng, 1984-
(författare)
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Enhanced block sparse signal recovery and bayesian hierarchical models with applications
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis is carried out within two projects ‘Statistical modelling and intelligentdata sampling in Magnetic resonance imaging (MRI) and positron-emission tomography(PET) measurements for cancer therapy assessment’ and ‘WindCoE -Nordic Wind Energy Center’ during my PhD study. It mainly focuses on applicationsof Bayesian hierarchical models (BHMs) and theoretical developments ofcompressive sensing (CS). Under the first project, Paper I improves the quantityestimation of MRI parametric imaging by utilizing inherent dependent structure inthe image through BHMs; Paper III constructs a theoretically unbiased and asymptoticallynormal estimator of sparsity of a sparsified MR image by using a BHM;Paper IV extends block sparsity estimation from real-valued signal recovery tocomplex-valued signal recovery. It also demonstrates the importance of accuratelyestimating the block sparsity through a sensitivity analysis; Paper V proposes anew measure, i.e. q-ratio block constrained minimal singular value, of measurementmatrix for block sparse signal recovery. An algorithm for computing thisnew measure is also presented. In the second project, Paper II estimates the uncertaintyof Weather Research and Forecasting (WRF) model’s daily-mean 2-metertemperature in a cold region by using a BHM. It is a computationally cheaper andfaster alternative to traditional ensemble approach. In summary, this thesis makessignificant contributions in improving and optimizing the estimation proceduresof parameters of interest in MRI and WRF in practice, and developing the novelestimators and measure under the framework of CS in theory.
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| 7. |
- Wang, Jianfeng, 1984-, et al.
(författare)
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Error bounds of block sparse signal recovery based on q-ratio block constrained minimal singular values
- 2019
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Ingår i: EURASIP Journal on Advances in Signal Processing. - : Springer. - 1687-6172 .- 1687-6180. ; 2019
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we introduce the q-ratio block constrained minimal singular values (BCMSV) as a new measure of measurement matrix in compressive sensing of block sparse/compressive signals and present an algorithm for computing this new measure. Both the mixed ℓ2/ℓq and the mixed ℓ2/ℓ1 norms of the reconstruction errors for stable and robust recovery using block basis pursuit (BBP), the block Dantzig selector (BDS), and the group lasso in terms of the q-ratio BCMSV are investigated. We establish a sufficient condition based on the q-ratio block sparsity for the exact recovery from the noise-free BBP and developed a convex-concave procedure to solve the corresponding non-convex problem in the condition. Furthermore, we prove that for sub-Gaussian random matrices, the q-ratio BCMSV is bounded away from zero with high probability when the number of measurements is reasonably large. Numerical experiments are implemented to illustrate the theoretical results. In addition, we demonstrate that the q-ratio BCMSV-based error bounds are tighter than the block-restricted isotropic constant-based bounds.
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| 8. |
- Wang, Jianfeng, 1984-, et al.
(författare)
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Statistical inference for block sparsity of complex signals
- 2019
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Block sparsity is an important parameter in many algorithms to successfully recover block sparse signals under the framework of compressive sensing. However, it is often unknown and needs to beestimated. Recently there emerges a few research work about how to estimate block sparsity of real-valued signals, while there is, to the best of our knowledge, no investigation that has been conductedfor complex-valued signals. In this paper, we propose a new method to estimate the block sparsity of complex-valued signal. Its statistical properties are obtained and verified by simulations. In addition,we demonstrate the importance of accurately estimating the block sparsity in signal recovery through asensitivity analysis.
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| 9. |
- Wang, Jianfeng, 1984-, et al.
(författare)
-
Statistical inference for block sparsity of complex-valued signals
- 2020
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Ingår i: IET Signal Processing. - : Institution of Engineering and Technology. - 1751-9675 .- 1751-9683. ; 14:3, s. 154-161
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Tidskriftsartikel (refereegranskat)abstract
- Block sparsity is an important parameter in many algorithms to successfully recover block-sparse signals under the framework of compressive sensing. However, it is often unknown and needs to be estimated. Recently there emerges a few research work about how to estimate block sparsity of real-valued signals, while there is, to the best of our knowledge, no research that has been done for complex-valued signals. In this study, we propose a method to estimate the block sparsity of complex-valued signal. Its statistical properties are obtained and verified by simulations. In addition, we demonstrate the importance of accurately estimating the block sparsity through a sensitivity analysis.
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| 10. |
- Wang, Jianfeng, 1984-, et al.
(författare)
-
Statistical learning for train delays and influence of winter climate and atmospheric icing
- 2023
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Ingår i: Journal of Rail Transport Planning & Management. - : Elsevier. - 2210-9706 .- 2210-9714. ; 26
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Tidskriftsartikel (refereegranskat)abstract
- This study investigated the climate effect under consecutive winters on the arrival delay of high-speed passenger trains. Inhomogeneous Markov chain model and stratified Cox model were adopted to account for the time-varying risks of train delays. The inhomogeneous Markov chain modelling used covariates weather variables, train operational direction, and findings from the primary delay analysis through stratified Cox model. The results showed that temperature, snow depth, ice/snow precipitation, and train operational direction significantly impacted the arrival delay. Further, by partitioning the train line into three segments as per transition intensity, the model identified that the middle segment had the highest chance of a transfer from punctuality to delay, and the last segment had the lowest probability of recovering from delayed state. The performance of the fitted inhomogeneous Markov chain model was evaluated by the walk-forward validation method, which indicated that approximately 9% of trains may be misclassified as having arrival delays by the fitted model at a measuring point on the train line. With the model performance, the fitted model could be beneficial for both travellers to plan their trips reasonably and railway operators to design more efficient and wiser train schedules as per weather condition.
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