109 research outputs found
Corrigendum: Proceedings of the 12th annual deep brain stimulation think tank: cutting edge technology meets novel applications
No full textIn the published article, there was an error in the author list and author Sarah-Anna Hescham was erroneously excluded. The corrected author list appears below. “Alfonso Enrique Martinez-Nunez 1*, Christopher J. Rozell 2, Simon Little 3, Huiling Tan 4, Stephen L. Schmidt 5, Warren M. Grill 5,6, Miroslav Pajic 5, Dennis A. Turner 5,6,7, Coralie de Hemptinne 1, Andre Machado 8,9, Nicholas D. Schiff 10, Abbey S. Holt-Becker 11, Robert S. Raike 11, Mahsa Malekmohammadi 12,13, Yagna J. Pathak 14, Lyndahl Himes 14, David Greene 15, Lothar Krinke 16,17, Mattia Arlotti 16, Lorenzo Rossi 16, Jacob Robinson 18,19, Bahne H. Bahners 20,21,22, Vladimir Litvak 23, Luka Milosevic 24,25, Saadi Ghatan 26,27, Frederic L. W. V. J. Schaper 20, Michael D. Fox 20, Nicholas M. Gregg 28, Cynthia Kubu 8, James J. Jordano 29,30,31, Nicola G. Cascella 32, YoungHoon Nho 33, Casey H. Halpern 33,34, Helen S. Mayberg 35,36,37, Ki Sueng Choi 35,36, Haneul Song 35, Jungho Cha 35, Sankaraleengam Alagapan 2, Nico U. F. Dosenbach 38,39,40,41,42,43, Evan M. Gordon 44, Jianxun Ren 45, Hesheng Liu 45,46, Lorraine V. Kalia 47,48, Sarah-Anna Hescham 49,50,51, Dorian M. Kusyk 1, Adolfo Ramirez-Zamora 1, Kelly D. Foote 1, Michael S. Okun 1 and Joshua K. Wong 1.” The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.</p
Wavelength-decoupled geometric metasurfaces by arbitrary dispersion control
No full textConventional multicolor metaholograms suffer from the fundamental limitations of low resolution and irreducible noise because the unit structure functionality is still confined to a single wavelength. Here, we propose wavelength-decoupled metasurfaces that enables to control chromatic phase responses independently in a full range from 0 to 2�� for each wavelength. The propagation phase associated with the geometric phase of rectangular dielectric nanostructures plays a critical role to embed a dual phase response into a single nanostructure. A multicolor metahologram is also demonstrated to verify the feasibility of our method that breaks through the fundamental constraints of conventional multicolor metaholograms. Our approach can be extended to achieve complete control of chromatic phase responses in the visible for general dual-wavelength diffractive optical elements. ? 2019, The Author(s).11Ysciescopu
PRESENTING SEMANTIC CHARACTERISTICS OF PERCEPTION IN VERBS IN DIFFERENT RUSSIAN LANGUAGE DICTIONARIES
No full textThe article examines main characteristics of definitions of Russian verbs with perception semantics through in Russian language dictionaries. The author characterizes verbs and their nominations in dictionaries to find similarities and differencies between word definition
Improvement of Tropical Cyclone Track Simulation Over the Western North Pacific Using Machine Learning Method
No full textImprovement of Tropical Cyclone Track Forecast over the Western North Pacific Using a Machine Learning Method
No full textAll-Sky 1 km MODIS Land Surface Temperature Reconstruction Considering Cloud Effects Based on Machine Learning
No full textOpen AccessArticle
All-Sky 1 km MODIS Land Surface Temperature Reconstruction Considering Cloud Effects Based on Machine Learning
by Dongjin ChoORCID,Dukwon Bae,Cheolhee YooORCID,Jungho Im *ORCID,Yeonsu LeeORCID andSiwoo LeeORCID
Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Anand Inamdar
Remote Sens. 2022, 14(8), 1815; https://doi.org/10.3390/rs14081815
Received: 9 February 2022 / Revised: 5 April 2022 / Accepted: 7 April 2022 / Published: 9 April 2022
(This article belongs to the Special Issue Land Surface Temperature Estimation Using Remote Sensing)
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Abstract
A high spatio-temporal resolution land surface temperature (LST) is necessary for various research fields because LST plays a crucial role in the energy exchange between the atmosphere and the ground surface. The moderate-resolution imaging spectroradiometer (MODIS) LST has been widely used, but it is not available under cloudy conditions. This study proposed a novel approach for reconstructing all-sky 1 km MODIS LST in South Korea during the summer seasons using various data sources, considering the cloud effects on LST. In South Korea, a Local Data Assimilation and Prediction System (LDAPS) with a relatively high spatial resolution of 1.5 km has been operated since 2013. The LDAPS model???s analysis data, binary MODIS cloud cover, and auxiliary data were used as input variables, while MODIS LST and cloudy-sky in situ LST were used together as target variables based on the light gradient boosting machine (LightGBM) approach. As a result of spatial five-fold cross-validation using MODIS LST, the proposed model had a coefficient of determination (R2) of 0.89???0.91 with a root mean square error (RMSE) of 1.11???1.39 ??C during the daytime, and an R2 of 0.96???0.97 with an RMSE of 0.59???0.60 ??C at nighttime. In addition, the reconstructed LST under the cloud was evaluated using leave-one-station-out cross-validation (LOSOCV) using 22 weather stations. From the LOSOCV results under cloudy conditions, the proposed LightGBM model had an R2 of 0.55???0.63 with an RMSE of 2.41???3.00 ??C during the daytime, and an R2 of 0.70???0.74 with an RMSE of 1.31???1.36 ??C at nighttime. These results indicated that the reconstructed LST has higher accuracy than the LDAPS model. This study also demonstrated that cloud cover information improved the cloudy-sky LST estimation accuracy by adequately reflecting the heterogeneity of the relationship between LST and input variables under clear and cloudy skies. The reconstructed all-sky LST can be used in a variety of research applications including weather monitoring and forecasting
3D Heterogeneous Device Arrays for Multiplexed Sensing Platforms Using Transfer of Perovskites
No full textDespite recent substantial advances in perovskite materials, their 3D integration capability for next-generation electronic devices is limited owing to their inherent vulnerability to heat and moisture with degradation of their remarkable optoelectronic properties during fabrication processing. Herein, a facile method to transfer the patterns of perovskites to planar or nonplanar surfaces using a removable polymer is reported. After fabricating perovskite devices on this removable polymer film, the conformal attachment of this film on target surfaces can place the entire devices on various substrates by removing this sacrificial film. This transfer method enables the formation of a perovskite image sensor array on a soft contact lens, and in vivo tests using rabbits demonstrate its wearability. Furthermore, 3D heterogeneous integration of a perovskite photodetector array with an active-matrix array of pressure-sensitive silicon transistors using this transfer method demonstrates the formation of a multiplexed sensing platform detecting distributions of light and tactile pressure simultaneously.11Nsciescopu
Comparative Assessment of Various Machine Learning‐Based Bias Correction Methods for Numerical Weather Prediction Model Forecasts of Extreme Air Temperatures in Urban Areas
No full textForecasts of maximum and minimum air temperatures are essential to mitigate the damage of extreme weather events such as heat waves and tropical nights. The Numerical Weather Prediction (NWP) model has been widely used for forecasting air temperature, but generally it has a systematic bias due to its coarse grid resolution and lack of parametrizations. This study used random forest (RF), support vector regression (SVR), artificial neural network (ANN) and a multi-model ensemble (MME) to correct the Local Data Assimilation and Prediction System (LDAPS; a local NWP model over Korea) model outputs of next-day maximum and minimum air temperatures ( Tmaxt+1 and Tmint+1) in Seoul, South Korea. A total of 14 LDAPS model forecast data, the daily maximum and minimum air temperatures of in-situ observations, and five auxiliary data were used as input variables. The results showed that the LDAPS model had an R-2 of 0.69, a bias of -0.85 degrees C and an RMSE of 2.08 degrees C for Tmaxt+1 forecast, whereas the proposed models resulted in the improvement with R-2 from 0.75 to 0.78, bias from -0.16 to -0.07 degrees C and RMSE from 1.55 to 1.66 degrees C by hindcast validation. For forecasting Tmint+1, the LDAPS model had an R-2 of 0.77, a bias of 0.51 degrees C and an RMSE of 1.43 degrees C by hindcast, while the bias correction models showed R-2 values ranging from 0.86 to 0.87, biases from -0.03 to 0.03 degrees C, and RMSEs from 0.98 to 1.02 degrees C. The MME model had better generalization performance than the three single machine learning models by hindcast validation and leave-one-station-out cross-validation
Improved Tropical Cyclone Track Simulation over the Western North Pacific using the WRF Model and a Machine Learning Method
No full textAccurate tropical cyclone (TC) track simulations are required to mitigate property damage and casualties. Previous studies have generally simulated TC tracks using numerical models, which tend to experience systematic errors due to model imperfections, although the model accuracy has improved over time. Recently, machine-learning methods have been applied to correct such errors. In this study, we used an artificial neural network (ANN) to correct TC tracks hindcasted by the Weather Research and Forecasting (WRF) model from 2006 to 2018 over the western North Pacific. TC categories that are stronger than tropical depressions (i.e., tropical storms, severe tropical storms, and typhoons) were selected from June to November, and a bias correction was made to target TC positions at 72 h. The WRF-simulated tracks were used as input variables for training and testing the ANN using the best track and reanalysis data. To obtain a reliable corrected result, the number of neurons in the ANN structure was optimized for TCs during 2006???2015, and the optimized ANN was verified for TCs from 2016???2018. Because the performance of the numerical model differed according to the TC track, the ANN was assessed by cluster analysis. The results of the ANN were analyzed using k-means clustering to classify TCs into eight clusters. Overall, ANN with post-processing improved the WRF performance by 4.34%. The WRF error was corrected by 8.81% for clusters where the ANN was most applicable
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