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Scanner circuit sequentially samples the ac signals from accelerometers used in conducting noise vibration tests, and provides a time-averaged output signal. The scanner is used in conjunction with other devices for random noise vibration tests.
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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.
Computer experiments (numerical simulations) are widely used in scientific research to study and predict the behavior of complex systems, which usually have responses consisting of a set of distinct outputs. The computational cost of the simulations at high resolution are often expensive and become impractical for parametric studies at different input values. To overcome these difficulties we develop a Bayesian treed multivariate Gaussian process (BTMGP) as an extension of the Bayesian treed Gaussian process (BTGP) in order to model and evaluate a multivariate process. A suitable choice of covariance function and the prior distributions facilitates the different Markov chain Monte Carlo (MCMC) movements. We utilize this model to sequentially sample the input space for the most informative values, taking into account model uncertainty and expertise gained. A simulation study demonstrates the use of the proposed method and compares it with alternative approaches. We apply the sequential sampling technique and BTMGP to model the multiphase flow in a full scale regenerator of a carbon capture unit. The application presented in this paper is an important tool for research into carbon dioxide emissions from thermal power plants.
Analyzing the phosphoproteome at nanoscale poses a significant challenge, mainly due to the substantial sample loss from non-specific surface adsorption during the enrichment of low stoichiometric phosphopeptides. Here, we describe a tandem tip-based phosphoproteomics sample preparation method capable of sequential sample cleanup and enrichment without the need for additional sample transfer, thereby minimizing sample loss. Integration of this method to our recently developed SOP (Surfactant-assisted One-Pot sample preparation) and iBASIL (improved Boosting to Amplify Signal with Isobaric Labeling) approaches creates a streamlined workflow, enabling sensitive, high-throughput nanoscale phosphoproteomics measurements.
Efficient analysis of computer models is essential for the validation and uncertainty quantification of those models. Surrogate models, and Gaussian processes in particular, are a common and powerful approach to analyzing computer models that treat computer models as a black-box function. Gaussian processes form a Bayesian model over a space of functions that gives a measure of uncertainty about the computer model output at unobserved locations and a framework for sequential sampling. This tutorial will show how to fit Gaussian processes on a series of test functions and apply sequential design techniques to estimate extrema, level sets, and reliabilities of those test functions.
Electric vehicles (EVs) hold promise to improve the energy efficiency and environmental impacts of transportation. However, widespread EV use can impose significant stress on electricity-distribution systems due to their added charging loads. This paper proposes a centralized EV charging-control model, which schedules the charging of EVs that have flexibility. This flexibility stems from EVs that are parked at the charging station for a longer duration of time than is needed to fully recharge the battery. The model is formulated as a two-stage stochastic optimization problem. The model captures the use of distributed energy resources and uncertainties around EV arrival times and charging demands upon arrival, non-EV loads on the distribution system, energy prices, and availability of energy from the distributed energy resources. We use a Monte Carlo-based sample-average approximation technique and an L-shaped method to solve the resulting optimization problem efficiently. We also apply a sequential sampling technique to dynamically determine the optimal size of the randomly sampled scenario tree to give a solution with a desired quality at minimal computational cost. Here, we demonstrate the use of our model on a Central-Ohio-based case study. We show the benefits of the model in reducing charging costs, negative impacts on the distribution system, and unserved EV-charging demand compared to simpler heuristics. Lastly, we also conduct sensitivity analyses, to show how the model performs and the resulting costs and load profiles when the design of the station or EV-usage parameters are changed.
Computer simulation experiments design for industrial systems, considering variance techniques, multiple ranking procedures, sequential sampling and spectral analysis
Purpose: Thin sheets, of a high sensitivity formulation of radiochromic dosimeter, Presage were prepared and evaluated for optical readout. Methods: Sheets of radiochromic polyurethane, 12 cm long, 10 cm wide and 0.2 cm thick were prepared with leuco crystal violet as the reporter molecule. Sample transmission was evaluated at a wavelength of 590 nm with in-house constructed instruments: optical cone beam laser CT scanner, fixed and scanning spot densitometers. Sample sequential irradiations to a total dose of 40 Gy were conducted with a modified, Theratron 60, cobalt radiotherapy machine at dose rates of 1 or 0.25 Gy per minute. Exposure to ambient and readout light was minimized to limit background photochromic signals. Samples were stored at 4°C. Optical activity was assessed from linearly polarized transmission images. Comparison sensitivity measurements with EBT3 film were conducted. Results: Samples were transparent, smooth and pale purple before irradiation. Radiochromic reaction was completed in less than 5 minutes. A linear dose response with a sensitivity of 0.5 cm-1Gy-1 was observed. Micrometer measurements found sheet thickness variations up to 20%. Uniform dose, 2 Gy attenuation images, correlated with local sheet thicknesses. Comparable measurements with EBT3 film were 3 times more sensitive at 1 Gy but above 15 Gy, EBT3 film had lower sensitivity than 0.2 cm thick Presage sheet dosimeter due to its non-linear response. Conclusion: Dose sensitivity provided a 10% decrease in transmission for a 1 Gy dose. Improvements in mold design are expected to allow production of sheets with less than 5% variation in thickness. Above, 10 Gy, Presage sheet dosimeter performance expected to exceed EBT3 film based on linearity, sensitivity, transparency and smoothness of samples. J Adamovics is owner of Heuris Inc.
Chelonians (turtles, tortoises, and sea turtles) grow scute keratin in sequential layers over time. Once formed, scute keratin acts as an inert reservoir of environmental information. For chelonians inhabiting areas with legacy or modern nuclear activities, their scute has the potential to act as a time-stamped record of radionuclide contamination in the environment. Here, we measure bulk (i.e., homogenized scute) and sequential samples of chelonian scute from the Republic of the Marshall Islands and throughout the United States of America, including at the Barry M. Goldwater Air Force Range, southwestern Utah, the Savannah River Site, and the Oak Ridge Reservation. We identify legacy uranium ( 235 U and 236 U) contamination in bulk and sequential chelonian scute that matches known nuclear histories at these locations during the 20th century. Our results confirm that chelonians bioaccumulate uranium radionuclides and do so sequentially over time. This technique provides both a time series approach for reconstructing nuclear histories from significant past and present contexts throughout the world and the ability to use chelonians for long-term environmental monitoring programs (e.g., sea turtles at Enewetok and Bikini Atolls in the Republic of the Marshall Islands and in Japan near the Fukushima Daiichi reactors).
We report the development and experimental implementation of the automated experiment workflows for the identification of the best predictive channel for a phenomenon of interest in spectroscopic measurements. The approach is based on the combination of ensembled deep kernel learning for probabilistic predictions and a basic reinforcement learning policy for channel selection. It allows the identification of which of the available observational channels, sampled sequentially, are most predictive of selected behaviors, and hence have the strongest correlations. We implement this approach for multimodal imaging in piezoresponse force microscopy (PFM), with the behaviors of interest manifesting in piezoresponse spectroscopy. We illustrate the best predictive channel for polarization-voltage hysteresis loop and frequency-voltage hysteresis loop areas is amplitude in the model samples. The same workflow and code are applicable for any multimodal imaging and local characterization methods.
Filtering, prediction, and smoothing procedures for multi-stage linear dynamic systems using sampled data with sequentially correlated noise
The results of studying the surface Si layer and precipitate formation in CZ n-Si(100) samples sequentially implanted with {sup 64}Zn{sup +} ions with a dose of 5 × 10{sup 16} cm{sup 2} and energy of 100 keV and {sup 16}O{sup +} ions with the same dose but an energy of 33 keV at room temperature so that their projection paths R{sub p} = 70 nm would coincide are presented. The post-implantation samples are annealed for 1 h in an inert Ar medium in the temperature range of 400–900°C with a step of 100°C. The profiles of the implanted impurities are studied by time-of-flight secondary ion mass spectrometry. The Si surface is visualized using a scanning electron microscope, while the near-surface layer is visualized with the help of maps of elements formed by Auger electron spectroscopy with profiling over depth. The ZnO(002) texture is formed in an amorphized Si layer after the implantation of Zn and O ions. ZnO(102) crystallites of 5 nm in size are found in a recrystallized single-crystalline Si layer after annealing in Ar at 700°C.
The lack of well-developed techniques for modeling changing statistical moments in our observations has stymied the application of stochastic process theory for many scientific and engineering applications. Non linear effects of the observation methodology is one of the most perplexing aspects to modeling non stationary processes. This perplexing problem was encountered when modeling the effect of non stationary receiver fluctuations on the performance of radiometer calibration architectures. Existing modeling approaches were found not applicable; particularly problematic is modeling processes across scales over which they begin to exhibit non stationary behavior within the time interval of the calibration algorithm. Alternatively, the radiometer output is modeled as samples from a sequence random variables; the random variables are treated using a conditional probability distribution function conditioned on the use of the variable in the calibration algorithm. This approach of treating a process as a sequence of random variables with non stationary stochastic moments produce sensible predictions of temporal effects of calibration algorithms. To test these model predictions, an experiment using the Millimeter wave Imaging Radiometer (MIR) was conducted. The MIR with its two black body calibration references was configured in a laboratory setting to observe a third ultra-stable reference (CryoTarget). The MIR was programmed to sequentially sample each of the three references in approximately a 1 second cycle. Data were collected over a six-hour interval. The sequence of reference measurements form an ensemble sample set comprised of a series of three reference measurements. Two references are required to estimate the receiver response. A third reference is used to estimate the uncertainty in the estimate. Typically, calibration algorithms are designed to suppress the non stationary effects of receiver fluctuations. By treating the data sequence as an ensemble collection, it is possible to apply temporal algorithms which exacerbate the non stationary effects. By varying the algorithm, information about the properties of the non stationary receiver fluctuations is obtained. Comparisons of analytical calculations and statistical analysis of data demonstrate impressive agreement.
Graft block polymers are defined by several architectural parameters, including backbone flexibility, graft density, backbone length, side-chain composition, and side-chain length. In this work we probe the impacts of each of these parameters on the phase behavior, rheological properties, and mechanical performance of these materials. Specifically, we examine two sets of materials prepared from backbones of different inherent flexibility. One set was prepared from poly[(n-butyl acrylate)-co-(2-hydroxyethyl acrylate)] (B$_x$E$_y$) copolymers; the other was prepared from hydroxypropyl methyl cellulose (HPMC) samples. Sequential ring-opening transesterification polymerization from these hydroxyl-functionalized macroinitiatiors yielded a diblock graft architecture containing a rubbery interior block and semicrystalline exterior blocks tethered to a flexible (B$_x$E$_y$) or rigid (HPMC) backbone. Good control over side-chain molar mass and composition and judicious choice of the graft block segments enabled the preparation of materials that were either ordered or disordered in the melt state. In the former case, crystallization destroys existing order in the material; in the latter case crystallization induces new microphase separation in the bulk. Many of the structure–mechanical property relationships observed for graft block copolymers with rigid backbones are maintained for graft block polymers with semiflexible backbones, including the tendency for samples to remain transparent when stretched. However, interestingly, we note the effects of graft density and backbone length are quite different depending on the rigidity of the backbone.
Sequential scattering measurements are often performed under a fixed experimental-time budget, even though the expected count rate varies strongly across the measured coordinate. When the dwell time at each measurement position can be controlled independently, this variation creates a general resource-allocation problem: how should the available time be distributed to minimize the uncertainty of the reconstructed profile? We formulate this problem as a constrained variational optimization for measurements governed by Poisson counting statistics. When each measurement is treated independently, minimizing the averaged squared relative uncertainty yields an inverse-square-root intensity allocation. The formulation is then generalized to include correlations between neighboring measurements and an instrumental resolution operator, leading to an allocation criterion that equalizes the marginal reduction in posterior uncertainty per unit measurement time. Bonse–Hart ultra-small-angle neutron scattering (USANS), in which reciprocal space is sampled sequentially through analyzer-angle stepping, provides an experimentally grounded application. Computational benchmarking shows that the optimized allocation outperforms uniform-time and constant-relative-error strategies, while application to an experimentally measured graphite USANS profile from the Spallation Neutron Source, using Poisson resampling under alternative schedules, demonstrates how counting time should be redistributed toward weak-intensity regions under an identical total duration. The resulting framework applies to sequential scattering and related scanning measurements whenever local dwell times are adjustable and directly determine the measurement uncertainties, and when the relevant correlation and instrumental-response models are available.
Power converters are pervasive in modern electronic component design. They can be found in all electronic devices from household appliances and cellphone chargers to vehicles. Currently, designing new circuit topologies is hard because it requires human expertise based on experience and is difficult to automate. However, artificial-intelligence-assisted design can significantly facilitate the development of new power converters and/or improve the final result. Intelligently designed highly efficient power converters can have a significant effect on many important attributes, such as power efficiency, layout size, cost, heat dissemination, energy requirements, etc. We propose Autonomous Topology Generator (AutoTG), a reinforcement-learning-based framework that generates power converter topology candidates based on user specifications, optimized for user preferences. By modeling power converter design as a symbolic optimization problem, we sequentially sample components in an autoregressive manner until new topologies are formed, providing both the topology specification and the sizing (magnitude of each component parameter) of the proposed power converter. Here, we provide an empirical evaluation and show that AutoTG is able to generate varied high-efficiency topologies within component restrictions based on user input and show that previously unknown topologies can be found for further evaluation.
Tuning algorithmic parameters to optimize the performance of large, complicated computational codes is an important problem involving finding the optima and identifying regimes defined by non-smooth boundaries in black-box functions. Within the Bayesian optimization framework, the Gaussian process surrogate model produces smooth mean functions, but functions in the tuning problem are often non-smooth, which is exacerbated by the fact that we usually have limited sequential samples from the black-box function. Here, motivated by these issues encountered in tuning, we propose a novel Gaussian process model called a clustered Gaussian process (cGP), where the components are dynamically updated by clustering. In our studies, the performance of cGP can be better than stationary GPs in nearly 90% of the experiments and better than non-stationary GPs in nearly 70% of the repeated experiments while requiring less computational cost. cGP provides a novel approach for dynamic GP, computes more efficiently than recursive partitioning, and discovers non-smoothness regimes. We provide extensive experiments including high-performance computing (HPC) and industrial simulation functions to show the effectiveness of our methods.