Lake shoreline detection plays an important role in hydrological structure analysis and urban ecology governance but is a challenging task in synthetic aperture radar (SAR) image interpretation. Due to the complex shoreline environment, the preservation of weak boundaries and fitting of global information in large-scale SAR images deserve further research. Thus, this letter proposes a novel coarse-to-fine lake shoreline detection approach for SAR images based on modified region-scalable fitting (RSF) model combined with edge energy and global energy. In this approach, SAR images are despeckled by the block-matching 3-D (BM3D) filter. Then a novel energy term based on Laplacian of Gaussian (LoG) operator and ratio of exponentially weighted averages (ROEWA) operator is constructed to accurately locate the boundary and reduce false boundary. Additionally, the global energy term is adopted to fit the global information well. The experimental results based on real data demonstrate that the proposed approach has a stronger ability to maintain weak edges compared with RSF model, which exhibits better effectiveness and reliability.
A free-vortex wake (FVW) model is developed in this paper to analyse the unsteady aerodynamic performance of offshore floating wind turbines. A time-marching algorithm of third-order accuracy is applied in the FVW model. Owing to the complex floating platform motions, the blade inflow conditions and the positions of initial points of vortex filaments, which are different from the fixed wind turbine, are modified in the implemented model. A three-dimensional rotational effect model and a dynamic stall model are coupled into the FVW model to improve the aerodynamic performance prediction in the unsteady conditions. The effects of floating platform motions in the simulation model are validated by comparison between calculation and experiment for a small-scale rigid test wind turbine coupled with a floating tension leg platform (TLP). The dynamic inflow effect carried by the FVW method itself is confirmed and the results agree well with the experimental data of a pitching transient on another test turbine. Also, the flapping moment at the blade root in yaw on the same test turbine is calculated and compares well with the experimental data. Then, the aerodynamic performance is simulated in a yawed condition of steady wind and in an unyawed condition of turbulent wind, respectively, for a large-scale wind turbine coupled with the floating TLP motions, demonstrating obvious differences in rotor performance and blade loading from the fixed wind turbine. The non-dimensional magnitudes of loading changes due to the floating platform motions decrease from the blade root to the blade tip.
Massive amount of water level data has been collected by using Internet of Things (IoT) techniques in the Yangtze River and other rivers. In this paper, utilizing these data to construct deep neural network models for water level prediction is focused. To achieve higher accuracy, both the factors of time and locations of data collection sensors are considered to perform prediction. And the network structures of gated recurrent unit (GRU) and convolutional neural network (CNN) are combined to build a CNN-GRU model in which the GRU part learns the changing trend of water level, and the CNN part learns the spatial correlation among water level data observed from adjacent water stations. The CNN-GRU model that using data from multiple locations to predict the water level of the middle location has higher accuracy than the model only based on GRU and other state-of-the-art methods including autoregressive integrated moving average model (ARIMA), wavelet-based artificial neural network (WANN) and long-short term memory model (LSTM), because of its ability to decrease the affections of abnormal value and data randomness of a single water station to some extent. The results are verified on an experiment dataset that including 30-year observed data of water level at several collection stations in the Yangtze River. For forecasting the 8-o'clock water levels of future 5 days, accuracy of the CNN-GRU model is better than that of ARIMA, WANN and LSTM models with three evaluation factors including Nash-Sutcliffe efficiency coefficient (NSE), average relative error (MRE) and root mean square error (RMSE).
320 Slice Dynamic Volume CT provides intracranial aneurysm patients with 4D digital subtraction angiography (DSA) imaging and the Key technology of which are the wide detection platform and ConeXact Cone-beam reconstruction algorithm. In this thesis, some familiar cone-beam scan reconstruction algorithms are analyzed and compared, especially those algorithms currently developed such as Feldkamp-type, Grangeant, Katsevich and ConeXact cone-beam reconstruction algorithm. In clinical applications, thirty-four aneurysm patients have through the dynamic volume scan with wide detection platform by TOSHIBA 320 Slice CT and obtained fine single-pass imaging results all of which had no phase artifacts. The quality of scanning images were evaluated and compared with computed tomography angiography (CTA) and convention cerebral vascular DSA angiography. Comparative analysis shows that the 320 slice CT has great advantages in dynamic volume function imaging compared to CTA and it is of a great value to screen out patients with susceptible intracranial aneurysm from the following re-examination after the surgery for the patients with aneurysm.
Battery technology is the bottleneck of the electric vehicles (EVs). It is important, both in theory and practical application, to do research on the modeling and state estimation of batteries, which is essential to optimizing energy management, extending the life cycle, reducing cost, and safeguarding the safe application of batteries in EVs. However, the batteries, with strong time-variables and nonlinear characteristics, are further influenced by such random factors such as driving loads, operational conditions, in the application of EVs. The real-time, accurate estimation of their state is challenging. The classification of the estimation methodologies for estimating state-of-charge (SoC) of battery focusing with the estimation method/algorithm, advantages, drawbacks, and estimation error are systematically and separately discussed. Especially for the battery packs existing of the inevitable inconsistency in cell capacity, resistance and voltage, the advanced characterizing monomer selection, and bias correction-based method has been described and discussed. The review also presents the key feedback factors that are indispensable for accurate estimation of battery SoC, it will be helpful for ensuring the SoC estimation accuracy. It will be very helpful for choosing an appropriate method to develop a reliable and safe battery management system and energy management strategy of the EVs. Finally, the paper also highlights a number of key factors and challenges, and presents the possible recommendations for the development of next generation of smart SoC estimation and battery management systems for electric vehicles and battery energy storage system.
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