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A computer model can be used for predicting an output only after specifying the values of some unknown physical constants known as calibration parameters. The unknown calibration parameters can be estimated from real data by conducting physical experiments. This paper presents an approach to optimally design such a physical experiment. The problem of optimally designing physical experiment, using a computer model, is similar to the problem of finding optimal design for fitting nonlinear models. However, the problem is more challenging than the existing work on nonlinear optimal design because of the possibility of model discrepancy, that is, the computer model may not be an accurate representation of the true underlying model. Therefore, we propose an optimal design approach that is robust to potential model discrepancies. We show that our designs are better than the commonly used physical experimental designs that do not make use of the information contained in the computer model and other nonlinear optimal designs that ignore potential model discrepancies. We illustrate our approach using a toy example and a real example from industry., 25 pages, 10 figures

A manufacturing system collects big and heterogeneous data for tasks such as product quality modeling and data-driven decision-making. However, as the size of data grows, timely and effective data utilization becomes challenging. We propose an unsupervised data filtering method to reduce manufacturing big data sets with multi-variate continuous variables into informative small data sets. Furthermore, to determine the appropriate proportion of data to be filtered, we propose a filtering information criterion (FIC) to balance the tradeoff between the filtered data size and the information preserved. The case study of a babycare manufacturing and a simulation study have shown the effectiveness of the proposed method.

Observational and morphological study with three-dimensional (3D) computed tomography (CT) analysis.To discover the morphology and configuration deformities of craniovertebral junction (CVJ) and upper cervical spine in children with unilateral hemifacial microsomia (HFM). To determine whether there are specific HFM patients who are at higher risk of certain cervical vertebral anomaly.The evaluation for cervical vertebrae anomaly in HFM children, especially in CVJ region, is underreported.Eighty-eight unilateral HFM children (64 males, 24 females) with four Pruzansky-Kaban types (I, Ila, Ilb, and III) underwent cranial and cervical CT scanning from skull to C5 in neutral position. The 3D morphology and configuration of the occipital condyle, atlas, and axis, etc. were evaluated on the presence of deformed detailed structures of CVJ region.No C1 deformation was found in type I group. Six (14.3%) type Ila cases, seven (33.3%) type IIb cases, and six (37.5%) type Ill cases had lateral masses asymmetry of C1 (P 0.05). Five (55.6%) type I cases, 17 (40.5%) type Ila cases, 12 (57.1%) type Ilb cases, and 10 (62.5%) type Ill cases had C2 anomaly (P 0.05). The incidence rate of C1-C2 instability for four groups were 33.3% (type I), 33.3% (type IIa), 33.3% (type IIb), and 31.3% (type Ill), respectively (P 0.05).For HFM children, the incidence of C1 deformation increased from type I to type Ill. The probability of C2 anomaly and C1-C2 instability in children with different types of HFM is nearly the same. The craniovertebral junction of every HFM child must be monitored carefully for C1-C2 instability before any surgical procedure to avoid atlantoaxial dislocation and spinal cord injury.Level of Evidence: N/A.

A/B tests have been widely adopted across industries as the golden rule that guides decision making. However, the long-term true north metrics we ultimately want to drive through A/B test may take a long time to mature. In these situations, a surrogate metric which predicts the long-term metric is often used instead to conclude whether the treatment is effective. However, because the surrogate rarely predicts the true north perfectly, a regular A/B test based on surrogate metrics tends to have high false positive rate and the treatment variant deemed favorable from the test may not be the winning one. In this paper, we discuss how to adjust the A/B testing comparison to ensure experiment results are trustworthy. We also provide practical guidelines on the choice of good surrogate metrics. To provide a concrete example of how to leverage surrogate metrics for fast decision making, we present a case study on developing and evaluating the predicted confirmed hire surrogate metric in LinkedIn job marketplace.

Computer experiments may involve not only continuous input factors but also nominal factors, discrete numeric factors, and ordinal factors. Most existing literature in designing computer experiment...

In multistage manufacturing systems, modeling multiple quality indices based on the process sensing variables is important. However, the classic modeling technique predicts each quality variable one at a time, which fails to consider the correlation within or between stages. We propose a deep multistage multi-task learning framework to jointly predict all output sensing variables in a unified end-to-end learning framework according to the sequential system architecture in the MMS. Our numerical studies and real case study have shown that the new model has a superior performance compared to many benchmark methods as well as great interpretability through developed variable selection techniques., Comment: Accepted by Journal of Quality Technology

To identify the robust settings of the control factors, it is very important to understand how they interact with the noise factors. In this article, we propose space-filling designs for computer experiments that are more capable of accurately estimating the control-by-noise interactions. Moreover, the existing space-filling designs focus on uniformly distributing the points in the design space, which are not suitable for noise factors because they usually follow non-uniform distributions such as normal distribution. This would suggest placing more points in the regions with high probability mass. However, noise factors also tend to have a smooth relationship with the response and therefore, placing more points towards the tails of the distribution is also useful for accurately estimating the relationship. These two opposing effects make the experimental design methodology a challenging problem. We propose optimal and computationally efficient solutions to this problem and demonstrate their advantages using simulated examples and a real industry example involving a manufacturing packing line.

The purpose of model calibration is to make the model predictions closer to reality. The classical Kennedy-O'Hagan approach is widely used for model calibration, which can account for the inadequacy of the computer model while simultaneously estimating the unknown calibration parameters. In many applications, the phenomenon of censoring occurs when the exact outcome of the physical experiment is not observed, but is only known to fall within a certain region. In such cases, the Kennedy-O'Hagan approach cannot be used directly, and we propose a method to incorporate the censoring information when performing model calibration. The method is applied to study the compression phenomenon of liquid inside a bottle. The results show significant improvement over the traditional calibration methods, especially when the number of censored observations is large.

Sliced Latin hypercube designs (SLHDs) have important applications in designing computer experiments with continuous and categorical factors. However, a randomly generated SLHD can be poor in terms of space-filling, and based on the existing construction method that generates the SLHD column by column using sliced permutation matrices, it is also difficult to search for the optimal SLHD. In this article, we develop a new construction approach that first generates the small Latin hypercube design in each slice and then arranges them together to form the SLHD. The new approach is intuitive and can be easily adapted to generate orthogonal SLHDs and orthogonal array-based SLHDs. More importantly, it enables us to develop general algorithms that can search for the optimal SLHD efficiently.

Space-filling properties are important in designing computer experiments. The traditional maximin and minimax distance designs consider only space-filling in the full-dimensional space; this can result in poor projections onto lower-dimensional spaces, which is undesirable when only a few factors are active. Restricting maximin distance design to the class of Latin hypercubes can improve one-dimensional projections but cannot guarantee good space-filling properties in larger subspaces. We propose designs that maximize space-filling properties on projections to all subsets of factors. We call our designs maximum projection designs. Our design criterion can be computed at no more cost than a design criterion that ignores projection properties.

Problem: The prediction of cutting forces is very important in designing mechanical micromachining to ensure geometrical accuracy of the machined feature and avoid tool breakage. These predictions can be done either via analytical models or finite-element models. The finite-element models are precise but usually time consuming to run. Analytical models, on the other hand, are less accurate but computationally much cheaper. The problem here is to integrate these two types of physics-based models and obtain an easy-to-evaluate statistical model that can approximate the machining forces. Approach: We propose performing a sensitivity analysis using the computationally cheap analytical models prior to conducting the computationally intensive finite-element simulations. With the elicited prior knowledge from the sensitivity analysis, a two-stage strategy is presented for designing the finite-element simulations in which customized number of levels can be assigned for each input factor. The finite-element simulation data can then be integrated with the analytical models in developing the final metamodel. Results: We show that the initial sensitivity analysis can reveal critical information about the underlying system and guide us to more efficiently extract information from the finite-element models. The proposed design for the finite-element simulations is comprised of two subarrays and overall can achieve desirable orthogonality and space-filling properties. Compared with using n different levels for all input factors, as in the traditional space-filling design, the new design is more capable for estimating factor interactions while still maintaining the ability to capture necessary nonlinear effects. By using fewer levels, it can also improve the efficiency of estimating the effects when the simulation is subject to stochastic noise. In model validation, our numerical results indicate that the fitted integrated metamodel can more precisely approximate the machining forces than either using the analytical models or the traditional metamodel based on the finite-element simulations alone.

Space-filling designs such as Latin hypercube designs (LHDs) are widely used in computer experiments. However, finding an optimal LHD with good space-filling properties is computationally cumbersome. On the other hand, the well-established factorial designs in physical experiments are unsuitable for computer experiments owing to the redundancy of design points when projected onto a subset of factor space. In this work, we present a new class of space-filling designs developed by splitting two-level factorial designs into multiple layers. The method takes advantage of many available results in factorial design theory and therefore, the proposed multi-layer designs (MLDs) are easy to generate. Moreover, our numerical study shows that MLDs can have better space-filling properties than optimal LHDs.

Controlling the morphology of the as-synthesized nanostructures is usually challenging, and there lacks of a general theoretical guidance in experimental approach. In this study, a novel way of optimizing the aspect ratio of hydrothermally grown ZnO nanowire (NW) arrays is presented by utilizing a systematic statistical design and analysis method. In this work, we use pick-the-winner rule and one-pair-at-a-time main effect analysis to sequentially design the experiments and identify optimal reaction settings. By controlling the hydrothermal reaction parameters (reaction temperature, time, precursor concentration, and capping agent), we improved the aspect ratio of ZnO NWs from around 10 to nearly 23. The effect of noise on the experimental results was identified and successfully reduced, and the statistical design and analysis methods were very effective in reducing the number of experiments performed and in identifying the optimal experimental settings. In addition, the antireflection spectrum of the as-synthesized ZnO NWs clearly shows that higher aspect ratio of the ZnO NW arrays leads to about 30% stronger suppression in the UV-vis range emission. This shows great potential applications as antireflective coating layers in photovoltaic devices.

Computational fluid dynamics (CFD) was used to simulate air flow changes in reconstructed nasal passages based on magnetic resonance imaging (MRI) data from a previous clinical study of 0.05% Oxymetazoline (Vicks Sinex Micromist®). Total-pressure boundary conditions were uniquely applied to accommodate low patency subjects. Net nasal resistance, the primary simulation outcome, was determined using a parallel-circuit analogy and compared across treatments. Relative risk (RR) calculations show that for a 50% reduction in nasal resistance, subjects treated with Sinex® are 9.1 times more likely to achieve this after 8 hr, and 3.2 times more likely after 12 hr compared to Sham.

The influence of micro-structure on magnetic properties of amorphous powder core was investigated. The results show that the amorphous powders of the powder core become crystallized with the increase of annealing temperature, and the permeability decreases from 60 to 12, the core loss increases from 0.2 to 0.3 W °Cm−3, DC-bias characteristic was improved with further increase of annealing temperature, and the magnetic properties become deteriorated due to decrease of permeability and enhancement of coercive force resulting from the crystallization of amorphous powder.

During the past several years, giant magneto-impedance effect (GMI) in amorphous wires has generated growing interest in the science community because of their applications in sensors. The giant magneto-impedance effect in Co71.8Fe4.9Nb0.8Si7.5B15 amorphous glass-covered wires (AGCW) consists of strong changes in the high frequency impedance with a small DC magnetic field. When f = 1 MHz, there is no GMI effect due to the fact that the magnetic penetration depth is higher than their radius. As the frequency increases, the GMI effect becomes important in both the glass-covered wire and the wire after glass removal. Field dependence of the impedance has a similar behaviour to the AGGW ones, when a tensile stress is applied to the wire without glass cover.

In this paper we propose a novel feature extracting method based on spatiotemporal slice analyzing. To date, video features are focused on the character of every single video frame. With our method, the video content is no longer represented with every single frame. The temporal variation of visual information is taken as an important feature of video in our method. We examined this kind of feature with experiments in this paper. The experiment results show that the proposed feature is effective and robust for variant video content and format.

This paper presents a system for automatic detecting and recognizing complex individual actions in sports video to facilitate high-level content-based video indexing and retrieval. This is challenging due to the cluttered and dynamic background in sports video which makes object segmentation formidable. Another difficulty is to fully automatically and accurately detect desired actions from long video sequence. We propose three techniques to handle these challenges. Firstly, an efficient approach exploiting dominant motion and semantic color analysis is developed to detecting the highlight clips which contain athlete’s action from video sequences. Secondly, a robust object segmentation algorithm based on adaptive dynamic background construction is proposed to segment the athlete’s body from the clip. Finally, to recognize the segmented body shape sequences, the hidden markov models are slightly modified to make them suitable for noisy data processing. The proposed system for broadcast diving video analysis has achieved 96.6% detection precision; and 85% recognition accuracy for 13 kinds of diving actions.