Identifying antibiotic-resistance genes (ARGs) in marine systems traditionally relies on extensive laboratory- based analysis. data-driven modelling of ARGs is an emerging and promising approach that could enable real...
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Identifying antibiotic-resistance genes (ARGs) in marine systems traditionally relies on extensive laboratory- based analysis. data-driven modelling of ARGs is an emerging and promising approach that could enable real-time water quality monitoring, reducing both field and lab work, and associated costs. This work demonstrates a combination of Squared Prediction Error (SPE), Kernel Principal Component Analysis (KPCA) and Gaussian Processes (GP) for marine monitoring. The work advances real-time decision-making for both when to sample and data-efficient modelling by identifying when environmental conditions are likely to be novel. This was quantified by the SPE-KPCA model as a difference between current water conditions and those previously sampled. By identifying already-sampled environmental conditions, the number of water samples that were collected was reduced. The modelling of the level of ARGs is based on the GP predictive model. The method adaptively updated SPE-KPCA and GP models as new data were collected. The proposed framework was validated on both synthetic and real-life data. The validation demonstrated that the GP model, using a subset of the data sequentially selected by the SPE-KPCA model, led to similar prediction accuracy to the case of using all data, showing the potential for reducing the costs of data acquisition in this problem.
ABS T R A C T The accurate characterization of volumetric efficiency is essential for modern combustion engines to achieve better performance, lower emissions, and reduced fuel consumption. To minimize experimental ef...
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ABS T R A C T The accurate characterization of volumetric efficiency is essential for modern combustion engines to achieve better performance, lower emissions, and reduced fuel consumption. To minimize experimental effort on sample collection and maintain high-precision volumetric efficiency characterization, this paper proposes a new methodology of fuzzy-tree-constructed data-efficient modelling to precisely quantify the air mass flow through the engine. Differing from conventional data-driven modelling, this methodology introduces a hierarchical fuzzy inference tree (HFIT) with three original topologies that accommodates simplicity by combining several low-dimensional fuzzy inference systems. Driven by two derivative-free optimization algorithms, a two-step tuning process is introduced to speed up the convergence process when traversing HFIT parameters. A Gaussian distributed resampling technique is developed to screen a small number of samples with diverse engine opera-tions to maintain sample diversity. The experimental dataset is obtained from steady-state tests carried out in a BYD 1.5L gasoline engine specially made for a hybrid powertrain. The results demonstrate that the proposed fuzzy-tree-constructed data-efficient modelling methodology performs with superior learning efficiency on volumetric efficiency characterization than those of a fuzzy inference system, a neural network, or an adaptive neuro-fuzzy inference system. Even when dataset split ratio downs to 0.2, the relative mean absolute error can be restricted to 3.18% with the help of Gaussian distributed resampling technique.
This article investigates the suitability of constrained Gaussian process regression in predicting nonlinear mechanical responses of material systems with notably reduced uncertainties. This study reinforces the conve...
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This article investigates the suitability of constrained Gaussian process regression in predicting nonlinear mechanical responses of material systems with notably reduced uncertainties. This study reinforces the conventional Gaussian processes with mechanics-informed prior knowledge observed in various kinematic responses. Stiffening and softening responses of material systems mostly demonstrate at least one of the boundedness, monotonicity and convexity conditions with respect to some kinematic variables. These relationships or impositions in turn are encoded into a constrained Gaussian process for prediction, uncertainty quantification and extrapolation. Using numerous examples and comparative studies, this article evidently proves that the use of constrained Gaussian processes is data-efficient, highly accurate, yields low uncertainties, recovers model overfitting and extrapolates very well compared to unconstrained or conventional Gaussian processes. Moreover, the usability of the proposed numerical method across various engineering modelling domains such as multiscale homogenisation, experimentation, structural optimisation, material constitutive modelling and structural idealisation is demonstrated.
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