scholarly journals IMPROVING THE ACCURACY OF SURGE MODELS USING SUBGRID CORRECTIONS

2018 ◽  
pp. 46
Author(s):  
Andrew Kennedy ◽  
Damrongsak Wirasaet ◽  
Diogo Bolster ◽  
J. Casey Dietrich
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Storm Surge ◽  
Human Activities ◽  
Water Levels ◽  
Low Resolution ◽  
Ensemble Forecasts

Modern storm surge models to predict hurricane water levels have gone in two opposite directions: (1) Low resolution, fast, models that may be run thousands of times as a storm approaches land; and (2) High resolution, more accurate, models that are largely used for planning and hindcasts, and are too slow for real-time ensemble forecasts. Differences in predictions between the two types of models are particularly large over flooded ground, which is most important for human activities.

scholarly journals An objective and efficient method for estimating probabilistic coastal inundation hazards

2019 ◽  
Vol 99 (2) ◽  
pp. 1105-1130 ◽  
Author(s):  
Kun Yang ◽  
Vladimir Paramygin ◽  
Y. Peter Sheng
Keyword(s):  
High Resolution ◽  
Storm Surge ◽  
Computational Cost ◽  
Joint Probability ◽  
Water Levels ◽  
Computational Time ◽  
Probability Method ◽  
Highly Efficient ◽  
Inundation Maps ◽  
Flood Elevation

Abstract The joint probability method (JPM) is the traditional way to determine the base flood elevation due to storm surge, and it usually requires simulation of storm surge response from tens of thousands of synthetic storms. The simulated storm surge is combined with probabilistic storm rates to create flood maps with various return periods. However, the map production requires enormous computational cost if state-of-the-art hydrodynamic models with high-resolution numerical grids are used; hence, optimal sampling (JPM-OS) with a small number of (~ 100–200) optimal (representative) storms is preferred. This paper presents a significantly improved JPM-OS, where a small number of optimal storms are objectively selected, and simulated storm surge responses of tens of thousands of storms are accurately interpolated from those for the optimal storms using a highly efficient kriging surrogate model. This study focuses on Southwest Florida and considers ~ 150 optimal storms that are selected based on simulations using either the low fidelity (with low resolution and simple physics) SLOSH model or the high fidelity (with high resolution and comprehensive physics) CH3D model. Surge responses to the optimal storms are simulated using both SLOSH and CH3D, and the flood elevations are calculated using JPM-OS with highly efficient kriging interpolations. For verification, the probabilistic inundation maps are compared to those obtained by the traditional JPM and variations of JPM-OS that employ different interpolation schemes, and computed probabilistic water levels are compared to those calculated by historical storm methods. The inundation maps obtained with the JPM-OS differ less than 10% from those obtained with JPM for 20,625 storms, with only 4% of the computational time.

scholarly journals Upsampling Real-Time, Low-Resolution CCTV Videos Using Generative Adversarial Networks

Electronics ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1312
Author(s):  
Debapriya Hazra ◽  
Yung-Cheol Byun
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Super Resolution ◽  
Poor Quality ◽  
Generative Adversarial Networks ◽  
Temporal Consistency ◽  
Low Resolution ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Benchmark Datasets

Video super-resolution has become an emerging topic in the field of machine learning. The generative adversarial network is a framework that is widely used to develop solutions for low-resolution videos. Video surveillance using closed-circuit television (CCTV) is significant in every field, all over the world. A common problem with CCTV videos is sudden video loss or poor quality. In this paper, we propose a generative adversarial network that implements spatio-temporal generators and discriminators to enhance real-time low-resolution CCTV videos to high-resolution. The proposed model considers both foreground and background motion of a CCTV video and effectively models the spatial and temporal consistency from low-resolution video frames to generate high-resolution videos. Quantitative and qualitative experiments on benchmark datasets, including Kinetics-700, UCF101, HMDB51 and IITH_Helmet2, showed that our model outperforms the existing GAN models for video super-resolution.

scholarly journals Binary defocusing technique based on complementary decoding with unconstrained dual projectors

2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Xuexing Li ◽  
Wenhui Zhang
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Three Dimensional ◽  
Low Resolution ◽  
Fusion Technique ◽  
Space Time Coding ◽  
Depth Extraction ◽  
Core Idea ◽  
3D Scene ◽  
Binary Defocusing

AbstractBinary defocusing technique can effectively break the limitation of hardware speed, which has been widely used in the real-time three-dimensional (3D) reconstruction. In addition, fusion technique can reduce captured images count for a 3D scene, which helps to improve real-time performance. Unfortunately, it is difficult for binary defocusing technique and fusion technique working simultaneously. To this end, our research established a novel system framework consisting of dual projectors and a camera, where the position and posture of the dual projectors are not strictly required. And, the dual projectors can adjust defocusing level independently. Based on this, this paper proposed a complementary decoding method with unconstrained dual projectors. The core idea is that low-resolution information is employed for high-resolution phase unwrapping. For this purpose, we developed the low-resolution depth extraction strategy based on periodic space-time coding patterns and the method from the low-resolution order to high-resolution order of fringe. Finally, experimental results demonstrated the performance of our proposed method, and the proposed method only requires three images for a 3D scene, as well as has strong robustness, expansibility, and implementation.

A Wireless Sensors Network System for Local Multipoint Storm Surge Measurements

2018 ◽  
Vol 52 (4) ◽  
pp. 32-41 ◽  
Author(s):  
Sunil Chintalapati ◽  
Chelakara S. Subramanian
Keyword(s):  
Real Time ◽  
Storm Surge ◽  
Wireless Sensors ◽  
Prediction Models ◽  
Water Levels ◽  
Time Data ◽  
Network Systems ◽  
Efficient System ◽  
Real Time Data ◽  
Wireless Sensors Network

AbstractReal-time data of storm surge are much needed for developing effective prediction models and nowcasting of impending hazard potential. The crucial aspect of monitoring and transmitting information for extreme weather conditions in near real time is vitally important and would benefit from accurate, robust, and relatively inexpensive wireless sensing network systems. This article presents a detailed overview of the design, operation modes, system performance, and field testing of a prototype wireless sensors network (WSN) system for local multipoint storm surge measurements. Key differentiators for the prototype WSN system when compared to the existing infrastructure for monitoring water levels used by National Oceanic and Atmospheric Administration are (1) real-time data transmission, (2) ultra low cost, and (3) power-efficient system. The WSN system offers reliable field measurement employing single or multiple sensors and with features to upload data to either a local laptop or to the cloud for easy concurrent access to data for users located in any part of the world.

Deforming a High-Resolution Mesh in Real-Time by Mapping onto a Low-Resolution Physical Model

2008 ◽  
pp. 135-146
Author(s):  
Hans de Visser ◽  
Olivier Comas ◽  
David Conlan ◽  
Sébastien Ourselin ◽  
Josh Passenger ◽  
...  
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Physical Model ◽  
Low Resolution

Real-time prediction of movement, intensity and storm surge of very severe cyclonic storm Hudhud over Bay of Bengal using high-resolution dynamical model

2016 ◽  
Vol 81 (3) ◽  
pp. 1771-1795 ◽  
Author(s):  
Raghu Nadimpalli ◽  
Krishna K. Osuri ◽  
Sujata Pattanayak ◽  
U. C. Mohanty ◽  
M. M. Nageswararao ◽  
...  
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Storm Surge ◽  
Bay Of Bengal ◽  
Dynamical Model ◽  
Cyclonic Storm ◽  
Time Prediction ◽  
Movement Intensity ◽  
Severe Cyclonic Storm

scholarly journals mGEODAR—A Mobile Radar System for Detection and Monitoring of Gravitational Mass-Movements

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6373
Author(s):  
Anselm Köhler ◽  
Lai Bun Lok ◽  
Simon Felbermayr ◽  
Nial Peters ◽  
Paul V. Brennan ◽  
...  
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Continuous Wave ◽  
Signal To Noise Ratio ◽  
Mass Movement ◽  
Radar System ◽  
Gravitational Mass ◽  
Mass Movements ◽  
Low Resolution ◽  
Wave Radar

Radar measurements of gravitational mass-movements like snow avalanches have become increasingly important for scientific flow observations, real-time detection and monitoring. Independence of visibility is a main advantage for rapid and reliable detection of those events, and achievable high-resolution imaging proves invaluable for scientific measurements of the complete flow evolution. Existing radar systems are made for either detection with low-resolution or they are large devices and permanently installed at test-sites. We present mGEODAR, a mobile FMCW (frequency modulated continuous wave) radar system for high-resolution measurements and low-resolution gravitational mass-movement detection and monitoring purposes due to a versatile frequency generation scheme. We optimize the performance of different frequency settings with loop cable measurements and show the freespace range sensitivity with data of a car as moving point source. About 15 dB signal-to-noise ratio is achieved for the cable test and about 5 dB or 10 dB for the car in detection and research mode, respectively. By combining continuous recording in the low resolution detection mode with real-time triggering of the high resolution research mode, we expect that mGEODAR enables autonomous measurement campaigns for infrastructure safety and mass-movement research purposes in rapid response to changing weather and snow conditions.

scholarly journals Active-Sensing Epidermal Stretchable Bioelectronic Patch for Noninvasive, Conformal, and Wireless Tendon Monitoring

Research ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Sheng Shu ◽  
Jie An ◽  
Pengfei Chen ◽  
Di Liu ◽  
Ziming Wang ◽  
...  
Keyword(s):  
High Resolution ◽  
Achilles Tendon ◽  
Real Time ◽  
Muscle Atrophy ◽  
Physical Condition ◽  
Human Activities ◽  
Muscle Injury ◽  
The Body ◽  
Normal Walking ◽  
Micro Controller Unit

Sensors capable of monitoring dynamic mechanics of tendons throughout a body in real time could bring systematic information about a human body’s physical condition, which is beneficial for avoiding muscle injury, checking hereditary muscle atrophy, and so on. However, the development of such sensors has been hindered by the requirement of superior portability, high resolution, and superb conformability. Here, we present a wearable and stretchable bioelectronic patch for detecting tendon activities. It is made up of a piezoelectric material, systematically optimized from architectures and mechanics, and exhibits a high resolution of 5.8×10−5 N with a linearity parameter of R2=0.999. Additionally, a tendon real-time monitoring and healthcare system is established by integrating the patch with a micro controller unit (MCU), which is able to process collected data and deliver feedback for exercise evaluation. Specifically, through the patch on the ankle, we measured the maximum force on the Achilles tendon during jumping which is about 16312 N, which is much higher than that during normal walking (3208 N) and running (5909 N). This work not only provides a strategy for facile monitoring of the variation of the tendon throughout the body but also throws light on the profound comprehension of human activities.

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