Search · four archives
Search · four archives
13 papers · ranked by Valyu relevance
Ping Lou, Liang Shi, Xiaomei Zhang, Zheng Xiao + 1 more
The rise of edge computing has promoted the development of the industrial internet of things (IIoT). Supported by edge computing technology, data acquisition can also support more complex and perfect application requirements in industrial field. Most of traditional sampling methods use constant sampling frequency and…
Cathleen Balantic, Therese Donovan
1. Acoustic recordings of the environment can produce species presence-absence data for characterizing populations of sound-producing wildlife over multiple spatial scales. If a species is present at a site but does not vocalize during a scheduled audio recording survey, researchers may incorrectly conclude that the…
Suryateja Ravutla, Andrew Bai, Matthew J. Realff, Fani Boukouvala
Hybridization and Adaptive Sampling for Simulation-Based Optimization Authors: ['Suryateja Ravutla' 'Andrew Bai' 'Matthew J. Realff' 'Fani Boukouvala'] Process simulators are essential for modeling of complex processes; however, optimization of expensive models remains challenging due to lack of equations, simulation…
Ricardo Andrade-Pacheco, Francois Rerolle, Jean Lemoine, Leda Hernandez + 6 more
'Leda Hernandez' 'Aboulaye Meïté' 'Lazarus Juziwelo' 'Aurélien F. Bibaut' 'Mark J. van der Laan' 'Benjamin F. Arnold' 'Hugh J. W. Sturrock'] The identification of disease hotspots is an increasingly important public health problem. While geospatial modeling offers an opportunity to predict the locations of hotspots…
Haizhou Yang, Seong Hyeon Hong, Rei ZhG, Yi Wang
This paper presents a surrogate-based optimization (SBO) method with adaptive sampling for designing microfluidic concentration gradient generators (μCGGs) to meet prescribed concentration gradients (CGs). An efficient physics-based component model (PBCM) is used to generate data for Kriging-based surrogate model…
Josefine Asmus, Christian L. Müller, Ivo F. Sbalzarini
The design of systems or models that work robustly under uncertainty and environmental fluctuations is a key challenge in both engineering and science. This is formalized in the design-centering problem, which is defined as finding a design that fulfills given specifications and has a high probability of still doing so…
Félix Mercier, Nizar Bouhlel, Angelina El Ghaziri, Joseph Ly Vu + 2 more
Digital phenotyping is rapidly advancing, generating increasing amounts of data, particularly in the case of temporal monitoring. We propose an adaptive sampling method that optimizes sampling, thereby reducing costs associated with data production, processing, and storage. The proposed method is based on Bayesian…
Mateu Sbert, László Szirmay-Kalos, Chun-Hung Liu, Jwo-Yuh Wu + 1 more
'Peter Y. Hong'] Multiple Importance Sampling (MIS) combines the probability density functions (pdf) of several sampling techniques. The combination weights depend on the proportion of samples used for the particular techniques. Weights can be found by optimization of the variance, but this approach is costly and…
Fulvia Mecatti, Charalambos Sismanidis, Emanuela Furfaro, Pier Luigi Conti
'Pier Luigi Conti'] A new class of sampling strategies is proposed that can be applied to population-based surveys targeting a rare trait that is unevenly spread over an area of interest. Our proposal is characterised by the ability to tailor the data collection to specific features and challenges of the survey at…
Benjamin Ballnus, Sabine Hug, Kathrin Hatz, Linus Görlitz + 2 more
'Jan Hasenauer' 'Fabian J. Theis'] Background In quantitative biology, mathematical models are used to describe and analyze biological processes. The parameters of these models are usually unknown and need to be estimated from experimental data using statistical methods. In particular, Markov chain Monte Carlo (MCMC)…
Javier Garcia-Barcos, Ruben Martinez-Cantin, Eduardo C. Garrido-Merchán
'Eduardo C. Garrido-Merchán'] Optimizing complex systems usually involves costly and time-consuming experiments, where selecting the experiments to perform is fundamental. Bayesian optimization (BO) has proved to be a suitable optimization method in these situations thanks to its sample efficiency and principled way of…
Sabina J. Sloman, Daniel R. Cavagnaro, Stephen B. Broomell
Adaptive design optimization (ADO) is a state-of-the-art technique for experimental design (Cavagnaro et al., [9]). ADO dynamically identifies stimuli that, in expectation, yield the most information about a hypothetical construct of interest (e.g., parameters of a cognitive model). To calculate this expectation, ADO…
Maliki Moustapha, Alina Galimshina, Guillaume Habert, Bruno Sudret
Explicitly accounting for uncertainties is paramount to the safety of engineering structures. Optimization which is often carried out at the early stage of the structural design offers an ideal framework for this task. When the uncertainties are mainly affecting the objective function, robust design optimization is…