Domain Setup Research Articles

Abstract. There is evidence of an increased frequency of rapid intensification events of tropical cyclones (TCs) in global offshore regions. This will not only result in increased peak wind speeds but may lead to more intense heavy precipitation events, leading to flooding in coastal regions. Therefore, high impacts are expected for urban agglomerations in coastal regions such as the densely populated Pearl River Delta (PRD) in China. Regional climate models (RCMs) such as the Weather Research and Forecasting (WRF) model are state-of-the-art tools commonly applied to predict TCs. However, typhoon simulations are connected with high uncertainties due to the high number of parameterization schemes of relevant physical processes (including possible interactions between the parameterization schemes) such as cumulus (CU) and microphysics (MP), as well as other crucial model settings such as domain setup, initial times, and spectral nudging. Since previous studies mostly focus on either individual typhoon cases or individual parameterization schemes, in this study a more comprehensive analysis is provided by considering four different typhoons of different intensity categories with landfall near the PRD, i.e. Typhoon Neoguri (2008), Typhoon Hagupit (2008), Typhoon Hato (2017), and Typhoon Usagi (2013), as well as two different schemes for CU and MP, respectively. Moreover, the impact of the model initialization and the driving data is studied by using three different initial times and two spectral nudging settings. Compared with the best-track reference data, the results show that the four typhoons show some consistency. For track bias, nudging only horizontal wind has a positive effect on reducing the track distance bias; for intensity, compared with a model explicitly resolving cumulus convection, i.e. without cumulus parameterization (CuOFF; nudging potential temperature and horizontal wind; late initial time), using the Kain–Fritsch scheme (KF; nudging only horizontal wind; early initial time) configuration shows relatively lower minimum sea level pressures and higher maximum wind speeds, which means stronger typhoon intensity. Intensity shows less sensitivity to two MP schemes compared with the CuOFF, nudging, and initial time settings. Furthermore, we found that compared with the CuOFF, using the KF scheme shows a relatively larger latent heat flux and higher equivalent potential temperature, providing more energy to typhoon development and inducing stronger TCs. This study could be used as a reference to configure WRF with the model's different combinations of schemes for historical and future TC simulations and also contributes to a better understanding of the performance of principal TC structures.

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Accurate indoor crowd counting (ICC) is a key enabler to many smart home/office applications. Recent development of the WiFi-based ICC technology relies on detecting the variation of wireless channel state information (CSI) caused by human motions and has gained increasing popularity due to its low hardware cost, reliability under all lighting conditions, and privacy preservation in sensing data processing. To attain high estimation accuracy, existing WiFi-based ICC methods often require a large amount of labeled CSI training data samples for each application domain, i.e., a particular WiFi transceiver or background deployment. This makes large-scale deployment of the WiFi-based ICC technology across dissimilar domains extremely difficult and costly. In this article, we propose a Domain-Agnostic and Sample-Efficient wireless indoor crowd Counting (DASECount) framework that suffices to attain robust cross-domain detection accuracy given very limited data samples in new domains. DASECount leverages the wisdom of the few-shot learning (FSL) paradigm consisting of two major stages: 1) source domain meta training and 2) target domain meta testing. Specifically, in the meta-training stage, we design and train two separate convolutional neural network (CNN) modules on the source domain data set to fully capture the implicit amplitude and phase features of CSI measurements related to human activities. A subsequent knowledge distillation procedure is designed to iteratively update the CNN parameters for better generalization performance. In the meta-testing stage, we use the partial CNN modules to extract low-dimension features out of the high-dimension input target domain CSI data. With the obtained low-dimension CSI features, we can even use very few amounts of target domain data samples (e.g., 5-shot samples) to train a lightweight logistic regression (LR) classifier, and attain very high cross-domain ICC accuracy. Experiment results show that the proposed DASECount method achieves over 92.68%, and on average 96.37% detection accuracy in a 0–8 people counting task under various domain setups, which significantly outperforms the other representative benchmark methods considered.

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Domain Setup Research Articles

Articles published on Domain Setup

A p-Multigrid Hybrid-Spectral Model for Nonlinear Water Waves

Key ingredients in regional climate modelling for improving the representation of typhoon tracks and intensities

DASECount: Domain-Agnostic Sample-Efficient Wireless Indoor Crowd Counting via Few-Shot Learning

Blade-Resolved CFD Simulations of a Periodic Array of NREL 5 MW Rotors with and without Towers

Demystifying hepatitis B vaccination for public: Aspiration for global eradication

Insights from training drive for coronavirus disease-19 vaccination in India

Solving inverse wave scattering with deep learning

A sensitivity study of the WRF model in offshore wind modeling over the Baltic Sea

Managing Illicit Drug Use and Gambling Cases in Poor Countries: Fixing Accountabilities and Finding Solutions

A pilot study to analyze the quality of sleep by pittsburgh sleep quality index in patients with polycystic ovary syndrome

Needle stick injuries and postexposure prophylaxis for hepatitis B infection

Poverty, gambling, and illicit drug use perpetuate each other in a bidirectional cycle: An analysis from a case study

A comparative study on the large-scale-resolving capability of wall-modeled large-eddy simulation

Applying the FDS pyrolysis model to predict heat release rate in small-scale forced ventilation tunnel experiments

Post-COVID-19 sequelae-issue which remain unanswered

National digital health blueprint of India: A need for implementation research

Diet in celiac disease

Call for integration of basic and applied sciences with clinical practice

COVID 19 knowledge assessment: A step towards protecting health care workers

Cardiovascular disease risk prediction among employees registered in staff clinic of a tertiary care institute of northern india using available risk scoring charts

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Domain Setup Research Articles

Articles published on Domain Setup

A p-Multigrid Hybrid-Spectral Model for Nonlinear Water Waves

Key ingredients in regional climate modelling for improving the representation of typhoon tracks and intensities

DASECount: Domain-Agnostic Sample-Efficient Wireless Indoor Crowd Counting via Few-Shot Learning

Blade-Resolved CFD Simulations of a Periodic Array of NREL 5 MW Rotors with and without Towers

Demystifying hepatitis B vaccination for public: Aspiration for global eradication

Insights from training drive for coronavirus disease-19 vaccination in India

Solving inverse wave scattering with deep learning

A sensitivity study of the WRF model in offshore wind modeling over the Baltic Sea

Managing Illicit Drug Use and Gambling Cases in Poor Countries: Fixing Accountabilities and Finding Solutions

A pilot study to analyze the quality of sleep by pittsburgh sleep quality index in patients with polycystic ovary syndrome

Needle stick injuries and postexposure prophylaxis for hepatitis B infection

Poverty, gambling, and illicit drug use perpetuate each other in a bidirectional cycle: An analysis from a case study

A comparative study on the large-scale-resolving capability of wall-modeled large-eddy simulation

Applying the FDS pyrolysis model to predict heat release rate in small-scale forced ventilation tunnel experiments

Post-COVID-19 sequelae-issue which remain unanswered

National digital health blueprint of India: A need for implementation research

Diet in celiac disease

Call for integration of basic and applied sciences with clinical practice

COVID 19 knowledge assessment: A step towards protecting health care workers

Cardiovascular disease risk prediction among employees registered in staff clinic of a tertiary care institute of northern india using available risk scoring charts