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Short-range MEMS-based (Micro Electronical Mechanical System) LiDAR provides precise point cloud datasets for rock fragment surfaces. However, there is more vibrational noise in MEMS-based LiDAR signals, which cannot guarantee that the reconstructed point cloud data are not distorted with a high compression ratio. Many studies have illustrated that wavelet-based clustered compressive sensing can improve reconstruction precision. The k-means clustering algorithm can be conveniently employed to obtain clusters; however, estimating a meaningful k value (i.e., the number of clusters) is challenging. An excessive quantity of clusters is not necessary for dense point clouds, as this leads to elevated consumption of memory and CPU resources. For sparser point clouds, fewer clusters lead to more distortions, while excessive clusters lead to more voids in reconstructed point clouds. This study proposes a local clustering method to determine a number of clusters closer to the actual number based on GMM (Gaussian Mixture Model) observation distances and density peaks. Experimental results illustrate that the estimated number of clusters is closer to the actual number in four datasets from the KEEL public repository. In point cloud compression and recovery experiments, our proposed approach compresses and recovers the Bunny and Armadillo datasets in the Stanford 3D repository; the experimental results illustrate that our proposed approach improves reconstructed point clouds' geometry and curvature similarity. Furthermore, the geometric similarity increases to 0.9 above in our complete rock fragment surface datasets after selecting a better wavelet basis for each dimension of MEMS-based LiDAR signals. In both experiments, the sparsity of signals was 0.8 and the sampling ratio was 0.4. Finally, a rock outcrop point cloud data experiment is utilized to verify that the proposed approach is applicable for large-scale research objects. All of our experiments illustrate that the proposed adaptive clustered compressive sensing approach can better reconstruct MEMS-based LiDAR point clouds with a lower sampling ratio. [ABSTRACT FROM AUTHOR]

AbstractEarly detection of plum bruising is important in the online postharvest quality sorting process. This paper explores the rapid detection of bruises in plum at five stages (1, 3, 6, 24, and 48 h after bruising) using hyperspectral imaging system. Spectral preprocessing was performed using five methods (unit vector normalization, multiplicative scattering correction, standard normal variate, detrending, and baseline). The support vector machine was established to discriminate the spectral samples of healthy and bruised plums at stage 1 on full wavelengths. The results indicated that the spectral data pretreated by multiple scattering correction yielded better results. The characteristic wavelengths of the spectra were selected by six algorithms (random frog, competitive adaptive reweighted sampling, variable combination population analysis, successive projection algorithm, uninformative variable elimination and bootstrapping soft shrinkage). Subsequently, the support vector machine was established at stage 1 based on these selected characteristic wavelengths. Comparing the results of models, the support vector machine based on the characteristic wavelengths selected by competitive adaptive reweighted sampling generated a satisfied effect with an accuracy of 95% for the calibration set and 98% for the prediction set. This model was selected for bruise detection in plums at the residual stage. The characteristic wavelength grayscale images selected based on the model were used to successfully visualize the plum bruise regions in five stages using minimum noise fraction transformation and the principal component analysis method. The results showed that the hyperspectral imaging technique combined with machine learning algorithm could be used to identify plum bruises at different stages. This study contributes to the development of an online detection system for bruises in plum. [ABSTRACT FROM AUTHOR]

The method of adaptive cluster sampling (ACS) is useful for the surveys when the characteristic of interest is sparsely distributed but highly aggregated. In ACS, the initial sample is selected by simple random sampling (SRS). The final sample is obtained by adapting the nearby units which satisfy the pre-specified condition. This sampling can further be improved when the appropriate control is imposed to select the initial sample. In this article, Balanced Sampling plan Excluding Contiguous units (BSEC plan) has been used to select the initial sample for estimating population mean in ACS. The purpose is to ignore non preferred or contiguous units and include more preferred units in the final sample. The unbiased estimators of population mean based on the initial sample and modified Hansen-Hurwitz (HH) type estimators are discussed. It has been illustrated theoretically and empirically that the modified HH type estimator based on BSEC plan is more precise than that based on SRS. The proposed method provides the opportunity to select the non contiguous units in initial sample, which makes the design more informative and efficient in comparison to ACS. The proposed design may be helpful in improving the ACS estimates in many real-life situations. [ABSTRACT FROM AUTHOR]

Purpose: Parameter estimation in complex engineering structures typically necessitates repeated calculations using simulation models, leading to significant computational costs. This paper aims to introduce an adaptive multi-output Gaussian process (MOGP) surrogate model for parameter estimation in time-consuming models. Design/methodology/approach: The MOGP surrogate model is established to replace the computationally expensive finite element method (FEM) analysis during the estimation process. We propose a novel adaptive sampling method for MOGP inspired by the traditional expected improvement (EI) method, aiming to reduce the number of required sample points for building the surrogate model. Two mathematical examples and an application in the back analysis of a concrete arch dam are tested to demonstrate the effectiveness of the proposed method. Findings: The numerical results show that the proposed method requires a relatively small number of sample points to achieve accurate estimates. The proposed adaptive sampling method combined with the MOGP surrogate model shows an obvious advantage in parameter estimation problems involving expensive-to-evaluate models, particularly those with high-dimensional output. Originality/value: A novel adaptive sampling method for establishing the MOGP surrogate model is proposed to accelerate the procedure of solving large-scale parameter estimation problems. This modified adaptive sampling method, based on the traditional EI method, is better suited for multi-output problems, making it highly valuable for numerous practical engineering applications. [ABSTRACT FROM AUTHOR]

The key innovation lies in the incorporation of an adaptive cluster sampling strategy and a randomized response model based on the Poisson distribution. This integration aims to overcome shortcomings inherent in conventional models, providing a more robust framework for research area. In this paper, an adaptive cluster sampling randomized response model with Poisson distribution using a randomized response strategy was proposed. The proposed cluster randomized response model has improved efficiency and a large gain in precision. Conditions were obtained under which the proposed model is more efficient than the existing models. To validate the effectiveness of our approach, numerical computations were conducted, offering concrete illustrations of the model's performance. The results underscore the significant gains in efficiency and precision achieved by the proposed adaptive cluster sampling randomized response model. [ABSTRACT FROM AUTHOR]

Compressive sensing (CS) is recognized for its adeptness at compressing signals, making it a pivotal technology in the context of sensor data acquisition. With the proliferation of image data in Internet of Things (IoT) systems, CS is expected to reduce the transmission cost of signals captured by various sensor devices. However, the quality of CS-reconstructed signals inevitably degrades as the sampling rate decreases, which poses a challenge in terms of the inference accuracy in downstream computer vision (CV) tasks. This limitation imposes an obstacle to the real-world application of existing CS techniques, especially for reducing transmission costs in sensor-rich environments. In response to this challenge, this paper contributes a CV-oriented adaptive CS framework based on saliency detection to the field of sensing technology that enables sensor systems to intelligently prioritize and transmit the most relevant data. Unlike existing CS techniques, the proposal prioritizes the accuracy of reconstructed images for CV purposes, not only for visual quality. The primary objective of this proposal is to enhance the preservation of information critical for CV tasks while optimizing the utilization of sensor data. This work conducts experiments on various realistic scenario datasets collected by real sensor devices. Experimental results demonstrate superior performance compared to existing CS sampling techniques across the STL10, Intel, and Imagenette datasets for classification and KITTI for object detection. Compared with the baseline uniform sampling technique, the average classification accuracy shows a maximum improvement of 26.23%, 11.69%, and 18.25%, respectively, at specific sampling rates. In addition, even at very low sampling rates, the proposal is demonstrated to be robust in terms of classification and detection as compared to state-of-the-art CS techniques. This ensures essential information for CV tasks is retained, improving the efficacy of sensor-based data acquisition systems. [ABSTRACT FROM AUTHOR]

Sensors are the main components in Cyber-Physical Systems (CPS), which transmit large amounts of physical values and big data to computing platforms for processing. On the other hand, the embedded processors (as edge devices in fog computing) spend most of their time reading the sensor signals as compared with computing time. The impact of sensors on the performance of fog computing is very great, thus, the enhancement of the reading time of sensors will positively affect the performance of fog computing, and solves the CPS challenges such as delay, timed precision, temporal behavior, energy, and cost. In this paper, we propose an algorithm based on the 1st derivative of the sensor signal to generate an adaptive sampling frequency. The proposed algorithm uses an adaptive frequency to capture the sudden and rapid change in sensor signal in the steady state. Finally, we realize and tested it using the Ptolemy II Modeling Environment. [ABSTRACT FROM AUTHOR]

The classification of imbalanced datasets is a prominent task in text mining and machine learning. The number of samples in each class is not uniformly distributed; one class contains a large number of samples while the other has a small number. Overfitting of the model occurs as a result of imbalanced datasets, resulting in poor performance. In this study, we compare different oversampling techniques like synthetic minority oversampling technique (SMOTE), support vector machine SMOTE (SVM-SMOTE), Border-line SMOTE, K-means SMOTE, and adaptive synthetic (ADASYN) oversampling to address the issue of imbalanced datasets and enhance the performance of machine learning models. Preprocessing significantly enhances the quality of input data by reducing noise, redundant data, and unnecessary data. This enables the machines to identify crucial patterns that facilitate the extraction of significant and pertinent information from the preprocessed data. This study preprocesses the data using various top-level preprocessing steps. Furthermore, two imbalanced Twitter datasets are used to compare the performance of oversampling techniques with six machine learning models including random forest (RF), SVM, K-nearest neighbor (KNN), AdaBoost (ADA), logistic regression (LR), and decision tree (DT). In addition, the bag of words (BoW) and term frequency and inverse document frequency (TF-IDF) features extraction approaches are used to extract features from the tweets. The experiments indicate that SMOTE and ADASYN perform much better than other techniques thus providing higher accuracy. Additionally, overall results show that SVM with 'linear' kernel tends to attain the highest accuracy and recall score of 99.67% and 1.00% on ADASYN oversampled datasets and 99.57% accuracy on SMOTE oversampled dataset with TF-IDF features. The SVM model using 10-fold cross-validation experiments achieved 97.40 mean accuracy with a 0.008 standard deviation. Our approach achieved 2.62% greater accuracy as compared to other current methods. [ABSTRACT FROM AUTHOR]

The pivotal problem in reliability analysis is how to use as few actual assessments as possible to obtain an accurate failure probability. Although adaptive Kriging provides a viable method to address this problem, unsatisfied Kriging surrogate accuracy and reliability modeling samples often lead to an unacceptable computing burden. In this paper, an adaptive optimized Kriging combining efficient sampling (AOK‐ES) is proposed: first, to enhance the Kriging approximation ability, a high‐fidelity optimized Kriging model (OKM) is established; further, to ensure the samples quality of OKM modeling and reliability calculation, an improved Latin hypercube sampling method and an optimized importance sampling approach are developed correspondingly. Six different types of case studies demonstrate the superiority of the proposed AOK‐ES. The analysis results demonstrate that the proposed AOK‐ES holds the potential to reduce computing cost while ensuring accuracy. [ABSTRACT FROM AUTHOR]

Data-driven surrogate model is extensively applied in the aerodynamics prediction for flight vehicle design. However, for three-dimensional problems, modeling costs hugely in acquiring adequate data. Adaptive and efficient sampling represents a promising approach, so we employ Active Learning (AL) to assess the sampling space. An aerodynamics prediction framework based on the Loss for Active Learning (LLAL) is proposed for capturing the sample insufficient space. The LLAL-based method refines the model by estimating the biases between the ground truths and the predictions as a measure of the high-value samples, then selecting top-K samples for infilling. We first validate our proposed method using an analytical benchmark two-dimensional function, followed by its application to aerodynamics prediction of spaceplane with the number of 60 and the deformation dimensions of 18. Through using both the Latin Hypercube Sampling and LLAL-based methods to infill samples, we observed the R 2 of lift-to-drag ratio improves from 0.82 to 0.85. The AL method can enhance the accuracy of models with a limited number of samples, thereby reducing sampling costs and improving the efficiency of aerodynamic design. [ABSTRACT FROM AUTHOR]

In engineering practice, liquefaction severity map is usually developed from liquefaction potential index (LPI) which is estimated using in-situ tests, such as cone penetration tests (CPTs). To efficiently perform in-situ tests for obtaining a reliable liquefaction severity map, it is advantageous to use adaptive sampling strategy, which uses results from initial measurements to sequentially decide CPT number and locations in a later stage. For example, the optimal number and locations of subsequent CPTs may be determined for maximising the reduction of overall uncertainty in the interpolated LPI data over the map obtained from a preliminary stage. However, uncertainty-based adaptive sampling might not provide optimal results for liquefaction severity mapping because the threshold of liquefaction severity classification is not considered. To properly evaluate the reliability of interpreted liquefaction severity map, a reliability index, $\beta$ β , is proposed in this study and further used to determine the optimal number and locations of in-situ tests. The proposed risk-informed adaptive sampling strategy is illustrated and compared with the uncertainty-based strategy. The example shows that the proposed method is more efficient than the uncertainty-based strategy. [ABSTRACT FROM AUTHOR]

The advection–dispersion equation has been widely used to analyze the intermediate field mixing of pollutants in natural streams. The dispersion coefficient, manipulating the dispersion term of the advection–dispersion equation, is a crucial parameter in predicting the transport distance and contaminated area in the water body. In this study, the transverse dispersion coefficient was estimated using machine learning regression methods applied to oversampled datasets. Previous research datasets used for this estimation were biased toward width-to-depth ratio ( W / H ) values ≤ 50, potentially leading to inaccuracies in estimating the transverse dispersion coefficient for datasets with W / H > 50. To address this issue, four oversampling techniques were employed to augment the dataset with W / H > 50, thereby mitigating the dataset's imbalance. The estimation results obtained from data resampling with nonlinear regression method demonstrated improved prediction accuracy compared to the pre-oversampling results. Notably, the combination of adaptive synthetic sampling (ADASYN) and eXtreme Gradient Boosting regression (XGBoost) exhibited improved accuracy compared to other combinations of oversampling techniques and nonlinear regression methods. Through the combined ADASYN–XGBoost approach, it is possible to enhance the transverse dispersion coefficient estimation performance using only two variables, W / H and bed friction effects ( U / U * ), without adding channel sinuosity; this represents the effects of secondary currents. [ABSTRACT FROM AUTHOR]

Path sampling is one of the economical sampling designs for spatial populations since it provides the capability of sampling all units along a path traveled during a survey. Path sampling is a non-adaptive sampling design; however it is known that the adaptive sampling design provides more precise estimates of the population mean and total than conventional sampling designs. Therefore, the objective of this article is to propose a new sampling design called "adaptive cluster path sampling" using adaptive sampling strategy and conventional path sampling in order to obtain more precise parameter estimate. Two estimators of the population mean and their mean square error were obtained. The efficiency of adaptive cluster path sampling was examined by performing a simulation study, comparing adaptive cluster path sampling to conventional path sampling, adaptive cluster sampling and simple random sampling. According to the simulation results on the blue winged teal data, adaptive cluster path sampling is more efficient than path sampling and adaptive cluster sampling when equal sampling cost is considered. [ABSTRACT FROM AUTHOR]

In this paper, a Pareto front (PF)-based sampling algorithm, PF-Voronoi sampling method, is proposed to solve computationally intensive multi-objective problems of medium size. The Voronoi diagram is introduced to classify the region containing PF prediction points into Pareto front cells (PFCs). Valid PFCs are screened according to the maximum crowding criterion (MCC), Maximum leave-one-out error criterion (MLEC), and maximum mean mean-square-error (MSE) criterion (MMMSEC). Sampling points are selected among the valid PFCs based on the Euclidean distance. The PF-Voronoi sampling method is applied to the coupled Kriging and NSGA-II models, and its validity is verified on the ZDT mathematical cases. The results show that the MCC criterion helps to improve the distribution diversity of PF. The MLEC criterion and the MMMSEC criterion reduce the number of training samples by 38.9% and 21.7%, respectively. The computational cost of the algorithm is reduced by more than 44.2%, compared to EHVIMOPSO and SAO-MOEA algorithms. The algorithm can be applied to multidisciplinary, multi-objective, and computationally intensive complex systems. [ABSTRACT FROM AUTHOR]

Accurate surrogate models are essential for the application of computational methods such as Markov chain Monte Carlo (McMC) using numerical reservoir simulation. Previous studies have often focused on building surrogates to represent the misfit (or likelihood) function. However, building an accurate constrained surrogate for the likelihood/misfit is difficult for higher dimensions unless an overly large number of samples is simulated first. Fortunately, functional data analysis can provide a set of ensemble-based statistical tools which can be utilized to emulate the full simulation output (timeseries) rather than the misfit function itself. Consequently, the misfit can be easily calculated by the simulated timeseries. In this study, functional principal component analysis (fPCA) is utilized to reduce the dimensionality of the timeseries. In other words, each simulation output (e.g., oil rate) is represented in terms of a few optimal functional principal component scores (fPCS), which can be readily inverted to reconstruct the original timeseries. fPCA is employed herein to develop a new and efficient Bayesian history-matching workflow in that it is used to iteratively update the fPCA-based local timeseries surrogates and search for the extrema of the likelihood function. In this proposed process, a few initial random samples are generated, and the corresponding timeseries are processed by fPCA. The resulting fPCSs for each simulation output are modelled individually using random variable proxies such as kriging or random forest to relate the uncertain variables to projected scores. These proxies are then used to calculate the likelihood and optimized to suggest the next best samples until a convergence criterion is met. This workflow is applied to a set of data obtained from a near-critical gas condensate well from a Canadian shale reservoir. The proposed history-matching workflow results in a nearly 8-times faster average convergence rate compared to other population-based algorithms studied. The history-matching samples are combined with additional adaptive samples to enhance the exploration aspect and predictability of the local surrogate models. This leads to an improvement of the local timeseries proxies for different outputs (from history-matching) and helps to generate global timeseries proxies that can be used for the entire parameter space. The results demonstrate that clustering the timeseries and applying fPCA to each cluster separately can significantly improve the accuracy of the global surrogates. Finally, the global surrogates are utilized to obtain accurate posterior distributions quickly through an McMC algorithm. This study introduces an adaptive sampling method for the first time that can be used to generate a highly accurate surrogate for timeseries and conduct the optimization efficiently. [ABSTRACT FROM AUTHOR]

A modified reliability-based design optimization (RBDO) technique was developed and applied to optimal designs of composite patches of a perforated base plate. The modified RBDO technique uses adaptive sampling strategies for the response surface method (RSM) to enhance the computational efficiency of the traditional RSM-based RBDO technique. Optimal design of composite patches was conducted for a perforated base plate using the newly proposed failure criteria which state that two conditions must be satisfied simultaneously for failure to occur. One condition is based on stress magnitude while the second condition is associated with the stress gradient. In other words, the failure criteria suggests that failure is more likely for higher stresses and lower stress gradients. Therefore, the design optimization of the composite patch was conducted to decrease the stress and to increase the stress gradient simultaneously at the edge of the perforated hole of the base plate in order to optimize the probability of failure of the base plate with composite patches. [ABSTRACT FROM AUTHOR]

Image-based monitoring techniques have achieved great success in many engineering applications. However, most existing image monitoring methods require fully observed images to implement modeling/monitoring. This requirement limits the application of these techniques for images with large size and/or cameras with limited scanning area. In this article, we propose to solve this issue by splitting the large image into multiple sub-images and using partially observed sub-images to implement monitoring of the whole large image. More specifically, the two-dimensional multivariate functional principal component analysis (2D-MFPCA) is proposed to model the cross-and-within correlation among sub-images, which facilitates the information augmentation from partially observed sub-images. The asymptotic properties of the 2D-MFPCA estimators are investigated to justify the use of MFPC scores as the monitoring statistics, which are fed into a multivariate CUSUM control chart to implement adaptive sampling and monitoring of image data. The developed chart can dynamically select locations of partially observed sub-images and adaptively focus on the most suspicious sub-images. The proposed method is validated and compared with various benchmark methods in both numerical and case studies. The results demonstrate the proposed method can achieve superior monitoring performance for large images when only partially observed sub-images are available. [ABSTRACT FROM AUTHOR]

In computer simulation and optimal design, sequential batch sampling offers an appealing way to iteratively stipulate optimal sampling points based upon existing selections and efficiently construct surrogate modeling. Nonetheless, the issue of near duplicates poses tremendous quandary for sequential learning. It refers to the situation that selected critical points cluster together in each sampling batch, which are individually but not collectively informative towards the optimal design. Near duplicates severely diminish the computational efficiency as they barely contribute extra information towards update of the surrogate. To address this issue, we impose a dispersion criterion on concurrent selection of sampling points, which essentially forces a sparse distribution of critical points in each batch, and demonstrate the effectiveness of this approach in adaptive contour estimation. Specifically, we adopt Gaussian process surrogate to emulate the simulator, acquire variance reduction of the critical region from new sampling points as a dispersion criterion, and combine it with the modified expected improvement (EI) function for critical batch selection. The critical region here is the proximity of the contour of interest. This proposed approach is vindicated in numerical examples of a two‐dimensional four‐branch function, a four‐dimensional function with a disjoint contour of interest and a time‐delay dynamic system. [ABSTRACT FROM AUTHOR]

Background: Stable Isotope Resolved Metabolomics (SIRM) is a new biological approach that uses stable isotope tracers such as uniformly 13 C -enriched glucose ( 13 C 6 -Glc) to trace metabolic pathways or networks at the atomic level in complex biological systems. Non-steady-state kinetic modeling based on SIRM data uses sets of simultaneous ordinary differential equations (ODEs) to quantitatively characterize the dynamic behavior of metabolic networks. It has been increasingly used to understand the regulation of normal metabolism and dysregulation in the development of diseases. However, fitting a kinetic model is challenging because there are usually multiple sets of parameter values that fit the data equally well, especially for large-scale kinetic models. In addition, there is a lack of statistically rigorous methods to compare kinetic model parameters between different experimental groups. Results: We propose a new Bayesian statistical framework to enhance parameter estimation and hypothesis testing for non-steady-state kinetic modeling of SIRM data. For estimating kinetic model parameters, we leverage the prior distribution not only to allow incorporation of experts' knowledge but also to provide robust parameter estimation. We also introduce a shrinkage approach for borrowing information across the ensemble of metabolites to stably estimate the variance of an individual isotopomer. In addition, we use a component-wise adaptive Metropolis algorithm with delayed rejection to perform efficient Monte Carlo sampling of the posterior distribution over high-dimensional parameter space. For comparing kinetic model parameters between experimental groups, we propose a new reparameterization method that converts the complex hypothesis testing problem into a more tractable parameter estimation problem. We also propose an inference procedure based on credible interval and credible value. Our method is freely available for academic use at https://github.com/xuzhang0131/MCMCFlux. Conclusions: Our new Bayesian framework provides robust estimation of kinetic model parameters and enables rigorous comparison of model parameters between experimental groups. Simulation studies and application to a lung cancer study demonstrate that our framework performs well for non-steady-state kinetic modeling of SIRM data. [ABSTRACT FROM AUTHOR]

Self-rated health (SRH) serves as an important indicator for measuring the physical and mental well-being of older adults, holding significance for their health management and disease prevention. In this paper, we introduce a novel classification method based on oversampling and neural network with the objective of enhancing the accuracy of predict the SRH of older adults. Utilizing data from the 2020 China Family Panel Studies (CFPS), we included a total of 6596 participants aged 60 years and above in our analysis. To mitigate the impact of imbalanced data, an improved oversampling was proposed, known as weighted Tomek-links adaptive semi-unsupervised weighted oversampling (WTASUWO). It firstly removes the features that are not relevant to the classification by ReliefF. Consequently, it combines undersampling and oversampling. To improve the prediction accuracy of the classifier, an improved multi-layer perception (IMLP) for predicting the SRH was constructed based on bagging and adjusted learning rate. Referring to the experimental results, WTASUWO can effectively improve the prediction performance of a classifier when being applied on an imbalanced dataset, and the IMLP using WTASUWO achieves a higher accuracy. This method can more objectively and accurately assess the health status and identify factors affecting the SRH of older adults. By mining relevant information related the health status of older adults and constructing the prediction model, we can provide policymakers and healthcare professionals with targeted intervention techniques to focus on the health needs of older adults. Meanwhile, this method provides a practical research basis for improving the health level of older adults in China. [ABSTRACT FROM AUTHOR]

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 hand. It is based on integrating an adaptive component into a sequential selection, which aims both to intensify the detection of positive cases, upon exploiting the spatial clustering, and to provide a flexible framework to manage logistics and budget constraints. A class of estimators is also proposed to account for the selection bias, that are proved unbiased for the population mean (prevalence) as well as consistent and asymptotically Normal distributed. Unbiased variance estimation is also provided. A ready-to-implement weighting system is developed for estimation purposes. Two special strategies included in the proposed class are presented, that are based on the Poisson sampling and proved more efficient. The selection of primary sampling units is also illustrated for tuberculosis prevalence surveys, which are recommended in many countries and supported by the World Health Organisation as an emblematic example of the need for an improved sampling design. Simulation results are given in the tuberculosis application to illustrate the strengths and weaknesses of the proposed sequential adaptive sampling strategies with respect to traditional cross-sectional non-informative sampling as currently suggested by World Health Organisation guidelines. [ABSTRACT FROM AUTHOR]

Recent progress has been made in defect detection using methods based on deep learning, but there are still formidable obstacles. Defect images have rich semantic levels and diverse morphological features, and the model is dynamically changing due to ongoing learning. In response to these issues, this article proposes a shunt feature fusion model (ST-YOLO) for steel-defect detection, which uses a split feature network structure and a self-correcting transmission allocation method for training. The network structure is designed to specialize the process of classification and localization tasks for different computing needs. By using the self-correction criteria of adaptive sampling and dynamic label allocation, more sufficiently high-quality samples are utilized to adjust data distribution and optimize the training process. Our model achieved better performance on the NEU-DET datasets and the GC10-DET datasets and was validated to exhibit excellent performance. [ABSTRACT FROM AUTHOR]

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Fostered by technological and theoretical developments, deep neural networks (DNNs) have achieved great success in many applications, but their training via mini-batch stochastic gradient descent (SGD) can be very costly due to the possibly tens of millions of parameters to be optimized and the large amounts of training examples that must be processed. The computational cost is exacerbated by the inefficiency of the uniform sampling typically used by SGD to form the training mini-batches: since not all training examples are equally relevant for training, sampling these under a uniform distribution is far from optimal, making the case for the study of improved methods to train DNNs. A better strategy is to sample the training instances under a distribution where the probability of being selected is proportional to the relevance of each individual instance; one way to achieve this is through importance sampling (IS), which minimizes the gradients' variance w.r.t. the network parameters, consequently improving convergence. In this paper, an IS-based adaptive sampling method to improve the training of DNNs is introduced. This method exploits side information to construct the optimal sampling distribution and is dubbed regularized adaptive sampling (RAS). Experimental comparison using deep convolutional networks for classification of the MNIST and CIFAR-10 datasets shows that when compared against SGD and against another sampling method in the state of the art, RAS produces improvements in the speed and variance of the training process without incurring significant overhead or affecting the classification. [ABSTRACT FROM AUTHOR]

• A new adaptive polynomial chaos expansion technique for surrogate modelling. • Novel basis selection strategy comprising basis expansion, pruning, and refinement. • Automatic and simultaneous selection of training samples and truncation degree. • Sparse representation along with sequential sampling and adaptive basis selection. • Validation using benchmarks and physical model and comparison with existing methods. In this paper, a novel adaptive modelling framework for sparse polynomial chaos expansion is proposed, which can automatically determine adequate truncation degree and training sample set simultaneously. Moreover, the curse of dimensionality issue in polynomial chaos expansion, which generally arises in dealing with high input dimension or large truncation degree, can be alleviated to a large extent. In this framework, a new basis selection strategy, which leverages basis expansion, pruning and refinement, is pursued to adaptively select the polynomial terms of proper degree during the modelling process. Besides, an outstanding sequential sampling strategy is adopted to collect samples of high quality and in relatively small quantity for training polynomial chaos expansion model, and a sparse representation method, Bayesian compressive sensing, is employed for regression calculation. To reconcile the sequential sampling and adaptive basis selection in a consistent framework, a stability evaluation process which works in parallel with the sequential sampling process is performed. The performance of the proposed adaptive modelling framework is evaluated on two benchmark functions and a physical model through comparison with an existing adaptive polynomial chaos expansion modelling technique, a sequential sampling-only approach, and several basis adaptivity strategies. Results demonstrate that the proposed method has high adaptiveness in building surrogate models for various problems. It outperforms the existing adaptive polynomial chaos expansion modelling technique in terms of modelling precision and convergence rate, and it has similar performance with the sequential sampling-only method while lessening the burden in regression calculation and enabling adaptive determination of the truncation degree. Limitations of the proposed method are also summarized after comparing it with various basis adaptivity strategies. [ABSTRACT FROM AUTHOR]

Multi-profile data can provide within-and-between profile information for efficiently modeling and monitoring system status. In practice, however, acquisition of such data requires large number of sensors, which raises various concerns and difficulties, for example, cost, energy, and data transmission bandwidth, in accessing the full data from each sensor. In this article, we propose an adaptive sampling strategy for multi-profile monitoring by using limited portion of data. The proposed sampling and monitoring scheme incorporates the within-and-between profile correlation and features the balance between random search and greedy search in identifying the most informative profiles. More specifically, the multivariate functional principal component analysis (MFPCA) is used to capture the within-and-between profile correlation, and the MFPC scores are augmented for unobservable profiles to feed into a multivariate CUSUM chart. Two properties of the proposed method for allocating sampling resources among sensors are investigated. Numerical and case studies are conducted under various scenarios to demonstrate the effectiveness of the method. [ABSTRACT FROM AUTHOR]

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Neural network (NN) ensembles can reduce large prediction variance of NN and improve prediction accuracy. For highly non-linear problems with insufficient data set, the prediction accuracy of NN models becomes unstable, resulting in a decrease in the accuracy of ensembles. Therefore, this study proposes a prediction frequency-based ensemble that identifies core prediction values, which are core prediction members to be used in the ensemble and are expected to be concentrated near the true response. The prediction frequency-based ensemble classifies core prediction values supported by multiple NN models by conducting statistical analysis with a frequency distribution, which is a collection of prediction values obtained from various NN models for a given prediction point. The prediction frequency-based ensemble searches for a range of prediction values that contains prediction values above a certain frequency, and thus the predictive performance can be improved by excluding prediction values with low accuracy and coping with the uncertainty of the most frequent value. An adaptive sampling strategy that sequentially adds samples based on the core prediction variance calculated as the variance of the core prediction values is proposed to improve the predictive performance of the prediction frequency-based ensemble efficiently. Results of various case studies show that the prediction accuracy of the prediction frequencybased ensemble is higher than that of Kriging and other existing ensemble methods. In addition, the proposed adaptive sampling strategy effectively improves the predictive performance of the prediction frequency-based ensemble compared with the previously developed space-filling and prediction variance-based strategies. [ABSTRACT FROM AUTHOR]

Precise emission estimation of vehicle exhausts is crucial to urban traffic pollution prevention and control. Existing methods utilize the widely distributed and large number of GPS data to estimate the emission distribution of vehicles in the road network. However, the emission features are insufficient to decrease the estimation accuracy when the data distribution is uneven and sparse in the spatial and temporal domains. To address this problem, we propose a two-step emission estimation model under incomplete information, which exploits the spatiotemporal propagation features of emission information. Specifically, an adaptive smoothing strategy reconstructs a second-by-second emission rate field by modeling the correlation of neighboring traffic states, to address the inconsistency of time intervals between emission models and sampling intervals. Then a spatiotemporal convolutional GAN (ST-CGAN) is proposed, which introduces the spatiotemporal convolution operator to generate the traffic emission by temporal features and structural similarity. We evaluated the proposed method using the GPS trajectory data on Didi Chuxing GAIA Open Dataset. The framework aligns GPS data and emission models and reconstructs effectively the high-emission features in traffic networks. The proposed ST-CGAN generates a more reasonable spatial and temporal distribution of vehicle emissions than state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In the task of knowledge distillation, a dual-source adaptive knowledge distillation(DSAKD)method is proposed to address the issues of feature information loss during the feature alignment process and the lack of consideration for the differences in samples in the soft label distillation method. The DSAKD method extracts more discriminative knowledge from both the feature layer and soft labels of the teacher network, which enhances the performance of the lightweight student network. An attention-based feature adaptive fusion module was proposed to integrate the intermediate layer features of the teacher network and the student network, and then the feature embedding contrastive distillation strategy was used to optimize the features of the student network. An adaptive temperature distillation strategy was also proposed, which assigned different temperature coefficients to all training samples adaptively based on the prediction confidence of the teacher network. The experimental results show that our proposed method achieves the optimal distillation effect on three benchmark datasets, significantly improving the classification performance of lightweight student networks. Specifically, compared with the best-performing method, the proposed method improves the average top-1 validation accuracy on CIFAR10, CIFAR100, and ImageNet datasets by 0.46%, 0.41%, and 0.59%, respectively. [ABSTRACT FROM AUTHOR]

The response to a natural disaster ultimately depends on credible and real-time information regarding impacted people and areas. Nowadays, social media platforms such as Twitter have emerged as the primary and fastest means of disseminating information. Due to the massive, imprecise, and redundant information on Twitter, efficient automatic sentiment analysis (SA) plays a crucial role in enhancing disaster response. This paper proposes a novel methodology to efficiently perform SA of Twitter data during a natural disaster. The tweets during a natural calamity are biased toward the negative polarity, producing imbalanced data. The proposed methodology has reduced the misclassification of minority class samples through the adaptive synthetic sampling technique. A binary modified equilibrium optimizer has been used to remove irrelevant and redundant features. The k-nearest neighbor has been used for sentiment classification with the optimized value of k. The nine datasets on natural disasters have been used for evaluation. The performance of the proposed methodology has been validated using the Friedman mean rank test against nine state-of-the-art techniques, including two optimized, one transfer learning, one deep learning, two ensemble learning, and three baseline classifiers. The results show the significance of the proposed methodology through the average improvement of 6.9%, 13.3%, 20.2%, and 18% for accuracy, precision, recall, and F1-score, respectively, as compared to nine state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

In the last decade, advances in molecular dynamics (MD) and Markov State Model (MSM) methodologies have made possible accurate and efficient estimation of kinetic rates and reactive pathways for complex biomolecular dynamics occurring on slow time scales. A promising approach to enhanced sampling of MSMs is to use "adaptive" methods, in which new MD trajectories are "seeded" preferentially from previously identified states. Here, we investigate the performance of various MSM estimators applied to reseeding trajectory data, for both a simple 1D free energy landscape and mini-protein folding MSMs of WW domain and NTL9(1–39). Our results reveal the practical challenges of reseeding simulations and suggest a simple way to reweight seeding trajectory data to better estimate both thermodynamic and kinetic quantities. [ABSTRACT FROM AUTHOR]

Automatic cell type annotation methods are increasingly used in single-cell RNA sequencing (scRNA-seq) analysis due to their fast and precise advantages. However, current methods often fail to account for the imbalance of scRNA-seq datasets and ignore information from smaller populations, leading to significant biological analysis errors. Here, we introduce scBalance, an integrated sparse neural network framework that incorporates adaptive weight sampling and dropout techniques for auto-annotation tasks. Using 20 scRNA-seq datasets with varying scales and degrees of imbalance, we demonstrate that scBalance outperforms current methods in both intra- and inter-dataset annotation tasks. Additionally, scBalance displays impressive scalability in identifying rare cell types in million-level datasets, as shown in the bronchoalveolar cell landscape. scBalance is also significantly faster than commonly used tools and comes in a user-friendly format, making it a superior tool for scRNA-seq analysis on the Python-based platform. An integrated sparse neural network framework scBalance incorporates adaptive weight sampling and dropout techniques to handle noise and imbalanced datasets and achieves high classification accuracy for both rare and major cell types. [ABSTRACT FROM AUTHOR]

The aim of this study is to create a digital twin of a commercial absorption chiller for control and optimization purposes. The chiller is a complex system that is affected by solar intermittency and non-linearities. The authors use Adaptive Neuro-fuzzy Inference System (ANFIS) to model the chiller's behavior during transients and part-load events. The chiller is divided into four sub-models, each modeled by ANFIS, and trained and validated using data from 15 days of operation. The ANFIS models are precise, accurate, and fast, with a worst-case Mean Absolute Percentage Error (MAPE) of 3.30% and reduced error dispersion ( σ E = 0. 88) and Standard Error (SE=0.01). The models outperformed literature models in terms of MAPE, with MAPEs of 1.12%, 2.21%, and 3.24% for the High Temperature Generator (HTG), absorber + condenser, and evaporator outlet temperatures, respectively. The computational execution time of the model is also a valuable asset, with an average simulation step taking less than 0.20 ms and a total simulation time of 8.9 s for three days of operation. The resulting digital twin is suitable for Model Predictive Control applications and fast what-if analysis and optimization due to its gray-box representation and computational speed. • Four adaptive neuro-fuzzy inference systems describe a commercial absorption chiller. • The learning considers 15 days of continuous measurement and sampling time of 20 s. • The dynamic model has generalized adaptive learning despite sun intermittency. • The model is accurate and precise — worst error of 0.09 ± 3.6 °C (95%). • The model is fast — takes 8.9 s to simulate three days of operation. [ABSTRACT FROM AUTHOR]

The imbalance and concept drift problems in data streams become more complex in multi-class environment, and extreme imbalance and variation in class ratio may also exist. To tackle the above problems, Hybrid Sampling and Dynamic Weighted-based classification method for Multi-class Imbalanced data stream (HSDW-MI) is proposed. The HSDW-MI algorithm deals with imbalance and concept drift problems through the hybrid sampling and dynamic weighting phases, respectively. In the hybrid sampling phase, adaptive spectral clustering is proposed to sample the data after clustering, which can maintain the original data distribution; then the sample safety factor is used to determine the samples to be sampled for each class; the safe samples are oversampled and the unsafe samples are under-sampled in each cluster. If the data stream is extremely imbalanced, the sample storage pool is used to extract samples with a high safety factor to add to the data stream. In the dynamic weighting phase, a dynamic weighting method based on the G-mean value is proposed. The G-mean values are used as the weights of each base classifier in the ensemble and the ensemble is dynamically updated during the processing of the data stream to accommodate the occurrence of concept drift. Experiments were conducted with LB, OAUE, ARF, BOLE, MUOB, MOOD, CALMID, and the proposed HSDW-MI on 10 multi-class synthetic data streams with different class ratios and concept drifts and 3 real multi-class imbalanced streams with unknown drifts, and the results show that the proposed HSDW-MI has better classification capabilities and performs more consistently compared to all other algorithms. [ABSTRACT FROM AUTHOR]

While numerical models have been developed for several years, some of these have been applied to ocean-state sampling. Adaptive sampling deploys limited assets using prior information; then, observation assets are concentrated in areas of greater sampling value, which is very suitable for an extensive and dynamic marine environment. The improved resolution allows numerical models to be used on mobile platforms. However, the existing adaptive sampling framework for mobile platforms lacks regular interaction with the numerical model. And the observation scheme is easy to deviate from the optimal. This study sets up a closed-loop adaptive sampling framework for mobile platforms that realizes the optimization of model → sampling → model. Linking a coupled model with the sampling points of the mobile platforms, the adaptive method configures key sampling locations to determine when and where the sampling schemes are adjusted. With the aid of a coupled model, we selected an optimization algorithm for the framework and simulated the process under the twin experimental framework. This research provides theoretical technical support for the combination of model and mobile sampling platforms. Significance Statement: The ocean is very difficult to observe because it is so vast. How to deploy observing assets is a question worth investigating. We designed an adaptive sampling framework for mobile observing platforms to improve the prediction accuracy of numerical models. This framework uses the prediction to design the observation scheme, then the observation to update the prediction, and then the updated prediction to adjust the sampling scheme, which achieves closed-loop optimization. We first selected an optimization algorithm for this framework by comparing experiments. Based on the optimization algorithm, we simulated the closed-loop optimization process. Simulation results show that this framework can significantly increase the efficiency of long-term ocean observations with mobile devices. [ABSTRACT FROM AUTHOR]

Adaptive sampling near a limit state is important for metamodeling-based reliability analysis of structures involving an implicit limit state function. Active learning based on the posterior mean and standard deviation provided by a chosen metamodel is widely used for such adaptive sampling. Most studies on active learning-based reliability estimation methods use the Kriging approach, which provides prediction along with its variance. As with the Kriging approach, sparse Bayesian learning-based regression also provides posterior mean and standard deviation. Due to the sparsity involved in learning, it is expected to be computationally faster than the Kriging approach. Motivated by this, active learning-enhanced adaptive sampling-based sparse Bayesian regression is explored in the present study for reliability analysis. In doing so, polynomial basis functions, which do not involve free parameters, are chosen for the sparse Bayesian regression to avoid computationally expensive parameter tuning. The convergence of the proposed approach is attained based on the stabilization of 10 consecutive failure estimates. The effectiveness of the proposed adaptive sparse Bayesian regression approach is illustrated numerically with five examples. [ABSTRACT FROM AUTHOR]

The age of Internet of Things (IoT) has witnessed the rapid development of modern data acquisition devices and communicating-actuating networks, which enables the generation of big data streams shared across platforms for remote and efficient decision making of many critical systems. The monitoring of big data streams remains a challenging task in various practical applications mainly due to their complexity in interrelationships, large volume, and high velocity, which places prohibitive demands on monitoring methodologies and resources. To tackle the challenges of monitoring unexchangeable and correlated big data streams with only partial observations available under resource constraints, we propose a method by incorporating spatial rank-based statistics with effective data augmentation techniques for the online unobservable data streams that can analytically inform the monitoring and sampling decisions based only on partially observed data streams. By exploiting historical data, the proposed method preserves strong descriptive power of general big data streams under partial observations and can explicitly use the correlation among data streams, and thus allows effective monitoring and equitable sampling over general heterogeneous and correlated big data streams, which is free of simplified assumptions (e.g., exchangeability) compared to existing methods. Theoretical investigations are carried out to evaluate the effectiveness of the augmentation statistics as well as the sampling strategy, which guarantee the superiority of the sampling performance over existing methods. Simulations under various scenarios and two real case studies are also conducted to evaluate and validate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

Determining the aero-icing characteristics is key for safety assurance in aviation, but it may be a computationally expensive task. This work presents a framework for the development of low-dimensional models for application to aerofoil icing. The framework builds on: an adaptive sampling strategy to identify the local, nonlinear features across the icing envelope for continuous intermittent icing; a classic technique based on Proper Orthogonal Decomposition, and a modern Neural Network architecture. The extreme diversity in simulated ice shapes, from smooth and streamlined to rugged and irregular shapes, motivated the use of an unsupervised classification of the ice shapes. This allowed deploying the Proper Orthogonal Decomposition locally within each sub-region, sensibly improving the prediction accuracy over the global model. On the other hand, the Neural Network architecture and the convolutional auto-encoder were found insensitive to the complexity in ice shapes. A strong correlation was found to exist between the ice shape, resulting ice mass and aerodynamic performance of the iced aerofoil, both in terms of the average and variance. On average, rime ice causes a loss of maximum lift coefficient of 21.5% compared to a clean aerofoil, and the average ice thickness is 0.9% of the aerofoil chord. For glaze ice, the average loss of maximum lift coefficient is 46.5% and the average ice thickness is 2.1%. Glaze ice was also found to have three times more surface coverage than rime ice. [ABSTRACT FROM AUTHOR]

Non‐invasive genetic sampling (NGS) methods are becoming a mainstay in wildlife monitoring and can be used with spatial capture‐recapture (SCR) methods to estimate population density. Yet SCR based on NGS remains relatively underused for ungulate population monitoring, despite the importance of robust density estimates for this ecologically and economically important group of species. This may be in part attributed to biological characteristics of ungulate species and data collection methods that lead to violations of SCR model assumptions. We conducted a simulation study to evaluate the robustness of SCR methods to spatially heterogeneous density (i.e., configuration of individuals into groups of variable sizes and composition), individual heterogeneity in space‐use patterns, and adaptive sampling (i.e., variation in detectability across space that correlates with density). We evaluated each violation separately and in combination. We parameterized our simulations based on published information and preliminary analyses of NGS data sets of 3 ungulate species: chamois (Rupicapra rupicapra), red deer (Cervus elaphus), and wild boar (Sus scrofa). While SCR estimates were robust to grouping and adaptive sampling, abundance estimates could be negatively biased (up to 10% in our simulations) in the presence of unaccounted individual heterogeneity in space use. The degree to which abundance estimates were underestimated depended mostly on the amount of variation in space use and detectability among age classes. This bias was also accompanied by a reduction in precision and coverage probability of the SCR estimators. We discuss the implications of these findings, possible approaches to identify problematic violations in available data sets (goodness‐of‐fit tests), and potential further developments of SCR models to ensure reliable abundance estimates for ungulate populations from NGS data. [ABSTRACT FROM AUTHOR]

In complex survey design, the resampling method is often used for assessing the variability and confidence interval of non-linear estimators which is a function of several estimated means or totals. A well-known resampling method, called the re-scaling bootstrap technique was originated by Rao and Wu (1988). This article is an attempt to propose a re-scaling bootstrap technique in estimating the parameters of rare and clustered population for a complex survey design. Adaptive cluster sampling design is a probabilistic approach to reach out to the rare and clustered units. Thompson (1990) introduced this design. Chaudhuri (2000) extended this design for varying probability sampling. In practice, the final sample size of adaptive sampling may be exorbitantly large. From this realistic point of view, Chaudhuri, Bose, and Dihidar (2005) developed the size-constrained adaptive sampling design, in varying probability sampling. We first describe here, how this well-known re-scaling bootstrap technique may be employed for non-linear estimators of the population total, in the case of rare and clustered population. It has been found that there is a need to develop an alternative re-scaling bootstrap procedure to avoid complications in computation to cover Chaudhuri, Bose, and Dihidar (2005). A simulation study has been carried out to demonstrate the proposed methods. [ABSTRACT FROM AUTHOR]

One way to improve the value of citizen science data for a specific aim is through promoting adaptive sampling, where the marginal value of a citizen science observation is dependent on existing data collected to address a specific question. Adaptive sampling could increase sampling at places or times—using a dynamic and updateable framework—where data are expected to be most informative for a given ecological question or conservation goal. We used an experimental approach to test whether the participants in a popular Australian citizen science project—FrogID—would follow an adaptive sampling protocol aiming to maximize understanding of frog diversity. After a year, our results demonstrated that these citizen science participants were willing to adopt an adaptive sampling protocol, improving the sampling of biodiversity consistent with a specific aim. Such adaptive sampling can increase the value of citizen science data for biodiversity research and open up new avenues for citizen science project design. [ABSTRACT FROM AUTHOR]

Background: Stable Isotope Resolved Metabolomics (SIRM) is a new biological approach that uses stable isotope tracers such as uniformly 13 C -enriched glucose ( 13 C 6 -Glc) to trace metabolic pathways or networks at the atomic level in complex biological systems. Non-steady-state kinetic modeling based on SIRM data uses sets of simultaneous ordinary differential equations (ODEs) to quantitatively characterize the dynamic behavior of metabolic networks. It has been increasingly used to understand the regulation of normal metabolism and dysregulation in the development of diseases. However, fitting a kinetic model is challenging because there are usually multiple sets of parameter values that fit the data equally well, especially for large-scale kinetic models. In addition, there is a lack of statistically rigorous methods to compare kinetic model parameters between different experimental groups. Results: We propose a new Bayesian statistical framework to enhance parameter estimation and hypothesis testing for non-steady-state kinetic modeling of SIRM data. For estimating kinetic model parameters, we leverage the prior distribution not only to allow incorporation of experts' knowledge but also to provide robust parameter estimation. We also introduce a shrinkage approach for borrowing information across the ensemble of metabolites to stably estimate the variance of an individual isotopomer. In addition, we use a component-wise adaptive Metropolis algorithm with delayed rejection to perform efficient Monte Carlo sampling of the posterior distribution over high-dimensional parameter space. For comparing kinetic model parameters between experimental groups, we propose a new reparameterization method that converts the complex hypothesis testing problem into a more tractable parameter estimation problem. We also propose an inference procedure based on credible interval and credible value. Our method is freely available for academic use at https://github.com/xuzhang0131/MCMCFlux. Conclusions: Our new Bayesian framework provides robust estimation of kinetic model parameters and enables rigorous comparison of model parameters between experimental groups. Simulation studies and application to a lung cancer study demonstrate that our framework performs well for non-steady-state kinetic modeling of SIRM data. [ABSTRACT FROM AUTHOR]

The viscoelastic behavior of short fiber reinforced polymers (SFRPs) partly depends on different microstructural parameters such as the local fiber orientation distribution. To account for this by simulation on component level, two‐scale methods couple simulations on the micro‐ and macroscale, which involve considerable computational costs. To circumvent this problem, the generation of a viscoelastic surrogate model is presented here. For that purpose, an adaptive sampling technique is investigated and data are obtained by creep simulations of representative volume elements (RVEs) using a fast Fourier transform (FFT) based homogenization method. Numerical tests confirm the high accuracy of the surrogate model. The possibility of using that model for efficient material optimization is shown. [ABSTRACT FROM AUTHOR]

As a source of vital nutrients for the normal functioning of the body, chicken meat plays an important role in promoting good health. This study examines the occurrence of total volatile basic nitrogen (TVB-N) as an index for evaluating freshness, using novel colorimetric sensor arrays (CSA) in combination with linear and nonlinear regression models. Herein, the TVB-N was determined by steam distillation, and the CSA was fabricated via the use of nine chemically responsive dyes. The corresponding dyes utilized, and the emitted volatile organic compounds (VOCs) were found to be correlated. Afterwards, the regression algorithms were applied, assessed, and compared, with the result that a nonlinear model based on competitive adaptive reweighted sampling coupled with support vector machines (CARS-SVM) achieved the best results. Accordingly, the CARS-SVM model provided improved coefficient values (Rc = 0.98 and Rp = 0.92) based on the figures of merit used, as well as root mean square errors (RMSEC = 3.12 and RMSEP = 6.75) and a ratio of performance deviation (RPD) of 2.25. Thus, this study demonstrated that the CSA paired with a nonlinear algorithm (CARS-SVM) could be employed for fast, noninvasive, and sensitive detection of TVB-N concentration in chicken meat as a major indicator of freshness in meat. [ABSTRACT FROM AUTHOR]

In this article, an optimization procedure of composite structures is proposed and applied to a vehicle subframe redesign. The process is divided into two stages and surrogate models are built by Gaussian Process Regression. The modified Fast Flexible Filling designs is used to deal with the non-rectangular design region problem. And combined with the adaptive sampling method, uniform training and test points which increase the accuracy of surrogate models are obtained. The two established surrogate models are solved by non-dominated sorting genetic algorithm-II and particle swarm optimization algorithm, respectively. The redesigned subframe exhibits significantly weight reduction and performance improvement. [ABSTRACT FROM AUTHOR]

In many real-world problems of real-time monitoring high-dimensional streaming data, one wants to detect an undesired event or change quickly once it occurs, but under the sampling control constraint in the sense that one might be able to only observe or use selected components data for decision-making per time step in the resource-constrained environments. In this article, we propose to incorporate multi-armed bandit approaches into sequential change-point detection to develop an efficient bandit change-point detection algorithm based on the limiting Bayesian approach to incorporate a prior knowledge of potential changes. Our proposed algorithm, termed Thompson-Sampling-Shiryaev-Roberts-Pollak (TSSRP), consists of two policies per time step: the adaptive sampling policy applies the Thompson Sampling algorithm to balance between exploration for acquiring long-term knowledge and exploitation for immediate reward gain, and the statistical decision policy fuses the local Shiryaev–Roberts–Pollak statistics to determine whether to raise a global alarm by sum shrinkage techniques. Extensive numerical simulations and case studies demonstrate the statistical and computational efficiency of our proposed TSSRP algorithm. [ABSTRACT FROM AUTHOR]

The Bayesian support vector regression (BSVR) metamodel is widely used in various engineering fields to analyze the uncertainty arising from uncertain parameters. However, the accuracy of the BSVR metamodel based on the traditional one-shot sampling method fails to meet the requirements of the uncertainty analysis of complex systems. To this end, an error-pursing adaptive uncertainty analysis method based on the BSVR metamodel is presented by combining a new adaptive sampling scheme. This new sampling scheme was improved by a new error-pursuing active learning function that is named, herein, adjusted mean square error (AMSE), which guides the adaptive sampling of the BSVR metamodel's design of experiments (DoE). During the sampling process, AMSE combines mean square error and leave-one-out cross-validation error to estimate the prediction error of the metamodel in the entire design space. Stepwise refinement of the metamodel was achieved by placing the sampled regions at locations with large prediction errors. Six benchmark analytical functions featuring different dimensions were used to validate the proposed method. The effectiveness of the method was then further illustrated by a more realistic application of an overhung rotor system. [ABSTRACT FROM AUTHOR]

Collaborative Filtering (CF) recommendation algorithms are a popular solution to the information overload problem, aiding users in the item selection process. Relevant research has long focused on refining and improving these models to produce better (more effective) recommendations, and has converged on a methodology to predict their effectiveness on target datasets by evaluating them on random samples of the latter. However, predicting the efficiency of the solutions—especially with regard to their time- and resource-hungry training phase, whose requirements dwarf those of the prediction/recommendation phase—has received little to no attention in the literature. This article addresses this gap for a number of representative and highly popular CF models, including algorithms based on matrix factorization, k-nearest neighbors, co-clustering, and slope one schemes. To this end, we first study the computational complexity of the training phase of said CF models and derive time and space complexity equations. Then, using characteristics of the input and the aforementioned equations, we contribute a methodology for predicting the processing time and memory usage of their training phase. Our contributions further include an adaptive sampling strategy, to address the tradeoff between resource usage costs and prediction accuracy, and a framework that quantifies both the efficiency and effectiveness of CF. Finally, a systematic experimental evaluation demonstrates that our method outperforms state-of-the-art regression schemes by a considerable margin, with an overhead that is a small fraction of the overall requirements of CF training. [ABSTRACT FROM AUTHOR]