Multiple Parameter Sets Research Articles

Application of deep learning methods to estimate multiphase flow rate in producing wells using surface measurements

In the past, the problem of predicting the multiphase flow rate in producing wells has been tackled using the Gilbert correlation. The Gilbert correlation offers a quick solution and requires quantities that are readily available at the surface, making it a staple among solutions in many publications. However, the correlation is limited in applicability to local data, and in some cases, multiple parameter settings are required when flow conditions change. To address these points, we introduced a systematic comparison of applying Gilbert correlation and Deep Learning (DL) algorithms to the problem of liquid flow prediction. We determined that some of the issues, specifically the need for multiple models, can be alleviated using DL methods. In this work, available wellhead measurements that can be collected at surface were used in modeling their relationship to well production rate. In addition, features were extracted automatically using autoencoders to generate additional inputs. The algorithms used for forecasting were designed using standard and customized DL architectures. The results of both DL and existing solutions from the literature were compared systematically to determine the usefulness of the DL methodology developed. The study developed a novel methodology for forecasting well liquid and multiphase restricted flow rate using wellhead surface measurements and demonstrated the application to real field data. The method was found to be an efficient utilization of data that are readily available and do not require well intervention to measure.

PSR: Unified Framework of Parameter-Learning-Based MR Image Superresolution.

Magnetic resonance imaging has significant applications for disease diagnosis. Due to the particularity of its imaging mechanism, hardware imaging suffers from resolution and reaches its limit, and higher radiation intensity and longer radiation time will cause damage to the human body. The problem is expected to be solved by a superresolution algorithm, especially the image superresolution based on sparse reconstruction has good performance. Dictionary generation is a key issue that affects the performance of superresolution algorithms, and dictionary performance is affected by dictionary construction parameters: balance parameters, dictionary size, overlapping block size, and a number of training sample blocks. In response to this problem, we propose an optimal dictionary construction parameter search method through the experiment to find the optimal dictionary construction parameters on the MR image and compare them with the dictionary obtained by multiple sets of random dictionary construction parameters. The dictionary we searched for the optimal parameters of the dictionary construction training has more powerful feature expressions, which can improve the superresolution effect of MR images.

Normalized stochastic gradient descent learning of general complex‐valued models

The stochastic gradient descent (SGD) method is one of the most prominent first-order iterative optimisation algorithms, enabling linear adaptive filters as well as general nonlinear learning schemes. It is applicable to a wide range of objective functions, while featuring low computational costs for online operation. However, without a suitable step-size normalisation, the convergence and tracking behaviour of the stochastic gradient descent method might be degraded in practical applications. In this letter, a novel general normalisation approach is provided for the learning of (non-)holomorphic models with multiple independent parameter sets. The advantages of the proposed method are demonstrated by means of a specific widely-linear estimation example.

A New Data Transmission Method with Low System Overhead Based on Multiple TSV Sub-Arrays

<p indent=0mm>Lots of crosstalk avoidance codes have the forbidden pattern free (FPF) feature. The FPF code suppresses the worst crosstalk with the reasonable codec area, if it is applied to the small-scale through silicon via (TSV) array. The multi-sub-array joint data transmission method is proposed, in order to transmit the data with low system overhead in the large-scale TSV array. Aiming at optimal number of TSV sub-arrays participating in joint data transmission, an optimal design method is proposed based on the analysis of the relationship between the number of TSV sub-arrays and the corresponding system overhead of the joint data transimission scheme. The optimal solution is obtained by theoretical proof. And the codec algorithm of the FPF code in the multi-sub-array joint transmission is further proposed, utilizing the transformation of numeral system. The simulations on the effect of the crosstalk suppression techniques are conducted with multiple sets of electrical TSV parameters. The experimental results show that, the proposed method has not only the lower system overhead, but also advantages on the transmission delay, transmission power and codec area, compared with those of the crosstalk avoidance codes based on Fibonacci numeral system.

Sample size re-estimation for covariate-adaptive randomized clinical trials.

Covariate-adaptive randomization (CAR) procedures have been developed in clinical trials to mitigate the imbalance of treatments among covariates. In recent years, an increasing number of trials have started to use CAR for the advantages in statistical efficiency and enhancing credibility. At the same time, sample size re-estimation (SSR) has become a common technique in industry to reduce time and cost while maintaining a good probability of success. Despite the widespread popularity of combining CAR designs with SSR, few researchers have investigated this combination theoretically. More importantly, the existing statistical inference must be adjusted to protect the desired type I error rate when a model that omits some covariates is used. In this article, we give a framework for the application of SSR in CAR trials and study the underlying theoretical properties. We give the adjusted test statistic and derive the sample size calculation formula under the CAR setting. We can tackle the difficulties caused by the adaptive features in CAR and prove the asymptotic independence between stages. Numerical studies are conducted under multiple parameter settings and scenarios that are commonly encountered in practice. The results show that all advantages of CAR and SSR can be preserved and further improved in terms of power and sample size.

Considering multiple process observables to determine material model parameters for FE-cutting simulations

Analyzing the chip formation process by means of the finite element method (FEM) is an established procedure to understand the cutting process. For a realistic simulation, different input models are required, among which the material model is crucial. To determine the underlying material model parameters, inverse methods have found an increasing acceptance within the last decade. The calculated model parameters exhibit good validity within the domain of investigation, but suffer from their non-uniqueness. To overcome the drawback of the non-uniqueness, the literature suggests either to enlarge the domain of experimental investigations or to use more process observables as validation parameters. This paper presents a novel approach merging both suggestions: a fully automatized procedure in conjunction with the use of multiple process observables is utilized to investigate the non-uniqueness of material model parameters for the domain of cutting simulations. The underlying approach is two-fold: Firstly, the accuracy of the evaluated process observables from FE simulations is enhanced by establishing an automatized routine. Secondly, the number of process observables that are considered in the inverse approach is increased. For this purpose, the cutting force, cutting normal force, chip temperature, chip thickness, and chip radius are taken into account. It was shown that multiple parameter sets of the material model can result in almost identical simulation results in terms of the simulated process observables and the local material loads.

An efficient procedure for optimization design of anti-seepage curtains: a case study

In large-scale hydropower projects, the anti-seepage curtain is an important measure to reduce the leakage of reservoirs and ensure the stability of foundation rocks. In this study, an efficient procedure for the optimization design of an anti-seepage curtain was proposed given the drawback of the time-consuming and low efficiency in optimizing multiple parameters of the anti-seepage curtain. This procedure can dramatically shorten the time for numerical simulation, reduce the workload of planning multiple parameter sets, and effectively avert the probable distortion of optimized results to some extent. This is achieved by identifying and optimizing the parameter that has the greatest impact on the performance of the anti-seepage curtain on the premise of adequate geological investigation and proper qualification of the performance of the anti-seepage curtain. This procedure was applied to the optimization design of the anti-seepage curtain in the upper reservoir of a pumped storage power station. Four different depths were suggested for the anti-seepage curtain according to the optimization results. This procedure can serve as a reference for the optimization design of an anti-seepage curtain with high effectiveness and confidence.

LWRpro: An Energy-Efficient Configurable Crypto-Processor for Module-LWR

Saber, the only module-learning with rounding-based algorithm in NIST's third round of post-quantum cryptography (PQC) standardization process, is characterized by simplicity and flexibility. However, energy-efficient implementation of Saber is still under investigation since the commonly used number theoretic transform can not be utilized directly. In this manuscript, an energy-efficient configurable crypto-processor supporting multi-security-level key encapsulation mechanism of Saber, is proposed. First, an 8-level hierarchical Karatsuba framework is utilized to reduce degree-256 polynomial multiplication to the coefficient-wise multiplication. Second, a hardware-efficient Karatsuba scheduling strategy and an optimized pre-/post-processing structure is designed to reduce the area overheads of scheduling strategy. Third, a task-rescheduling-based pipeline strategy and truncated multipliers are proposed to enable fine-grained processing. Moreover, multiple parameter sets are supported in LWRpro to enable configurability among various security scenarios. Enabled by these optimizations, LWRpro requires 1066, 1456 and 1701 clock cycles for key generation, encapsulation, and decapsulation of Saber768. The post-layout version of LWRpro is implemented with TSMC 40 nm CMOS process within 0.38 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . The throughput for Saber768 is up to 275k encapsulation operations per second and the energy efficiency is 0.15 uJ/encapsulation while operating at 400 MHz, achieving nearly 50× improvement and 31× improvement, respectively compared with current PQC hardware solutions.

Prediction of High-ozone Events Using GAM, SMOTE, and Tail Dependence Approaches in Texas (2005–2019)

We test three methods for ozone prediction in the El Paso (ELP) and Houston-Galveston-Brazoria (HGB) regions of Texas from 2005–2019: (1) a Generalized Additive Model (GAMs) approach; (2) a GAM approach with the addition of the Synthetic Minority Over-sampling TEchnique (SMOTE) and (3) a tail dependence modeling approach based in extreme value theory (EVT). We also compare the feature selection capabilities of the tail dependence approach to other feature selection methods. We find that the GAM+SMOTE model outperformed the GAM-only model when predicting ozone values for the root mean square error metric, particularly with regard to the above-threshold ozone values, which may be of particularly useful for extreme ozone event prediction. In addition, we find that the improvement of above-threshold MDA8 O3 prediction for the GAM+SMOTE method tends to come at the cost of below-threshold prediction, which is particularly important if MDA8 O3 trends are of interest. We also find that the tail dependence approach is capable of predicting extreme ozone events, but algorithmic stability and configuration complexity can make this approach difficult to operationalize on a broad scale and that the selection of the threshold needs to be carefully considered. Finally, the feature selection via the tail dependence method performs comparably to other forms of machine learning-based feature selection and we find that there are multiple parameter sets that can predict MDA8 O3 with equal success.

Optimization of electron beam-deposited silver nanoparticles on zinc oxide for maximally surface enhanced Raman spectroscopy.

Surface enhanced Raman spectroscopy enables robust, rapid analysis on highly dilute samples. To be useful, the technique needs sensing substrates that will enhance intrinsically weak Raman signals of trace analytes. In particular, three-dimensional substrates such as zinc oxide nanowires decorated with electron-beam deposited silver nanoparticles are easily fabricated and serve the dual need of structural stability and detection sensitivity. However, little has been done to optimize electron beam-deposited silver nanoparticles for maximal surface enhancement in the unique dielectric environment of the zinc oxide substrate. Herein, fabrication and anneal parameters of electron beam-deposited silver nanoparticles were examined for the purpose of maximizing surface enhancement. Specifically, this work explored the effect of changing film thickness, deposition rate, anneal temperature, and anneal time on the surface plasmon resonance of Ag nanoparticles. In this study, multiple sets of fabrication and annealing parameters were discovered that optimized surface plasmon resonance for maximal enhancement to Raman signals acquired with a 532 nm laser. This work represents the first characterization of the fabrication and annealing parameters for electron beam-deposited silver nanoparticles on zinc oxide.

Supergap and subgap enhanced currents in asymmetric S1FS2 Josephson junctions

We have theoretically studied the supercurrent profiles in three-dimensional normal metal and ferromagnetic Josephson configurations, where the magnitude of the superconducting gaps in the superconducting leads are unequal, i.e., $\Delta_1\neq \Delta_2$, creating asymmetric $S_1NS_2$ and $S_1FS_2$ systems. Our results reveal that by increasing the ratio of the superconducting gaps $\Delta_2/\Delta_1$, the critical supercurrent in a ballistic $S_1NS_2$ system can be enhanced by more than $100\%$, and reaches a saturation point, or decays away, depending on the junction thickness, magnetization strength, and chemical potential. The total critical current in a diffusive $S_1NS_2$ system was found to be enhanced by more than $50\%$ parabolically, and reaches saturation by increasing one of the superconducting gaps. In a uniform ferromagnetic junction, the supercurrent undergoes reversal by increasing $\Delta_2/\Delta_1>1$. Through decomposing the total supercurrent into its supergap and subgap components, our results illustrate their crucial relative contributions to the Josephson current flow. It was found that the competition of subgap and supergap currents in a $S_1FS_2$ junction results in the emergence of second harmonics in the current-phase relation. In contrast to a diffusive asymmetric Josephson configuration, the behavior of the supercurrent in a ballistic system with $\Delta_2/\Delta_1=1$ can be properly described by the subgap current component only, in a wide range of parameter sets, including Fermi level mismatch, magnetization strength, and junction thickness. Interestingly, when $\Delta_2/\Delta_1>1$, our results have found multiple parameter sets where the total supercurrent is driven by the supergap component. Therefore, our comprehensive study highlights the importance of subgap and supergap supercurrent components in both the ballistic and diffusive regimes.

A multi-task learning convolutional neural network for source localization in deep ocean

A multi-task learning (MTL) method with adaptively weighted losses applied to a convolutional neural network (CNN) is proposed to estimate the range and depth of an acoustic source in deep ocean. The network input is the normalized sample covariance matrices of the broadband data received by a vertical line array. To handle the environmental uncertainty, both the training and validation data are generated by an acoustic propagation model based on multiple possible sets of environmental parameters. The sensitivity analysis is investigated to examine the effect of mismatched environmental parameters on the localization performance in the South China Sea environment. Among the environmental parameters, the array tilt is found to be the most important factor on the localization. Simulation results demonstrate that, compared with the conventional matched field processing (MFP), the CNN with MTL performs better and is more robust to array tilt in the deep-ocean environment. Tests on real data from the South China Sea also validate the method. In the specific ranges where the MFP fails, the method reliably estimates the ranges and depths of the underwater acoustic source.

On the Perception Analysis of User Feedback for Interactive Face Retrieval

In this article, we explore the coherence of face perception between human and machine in the scenario of interactive face retrieval. In the part of human perception, we collect user feedback to the stimuli of a target face and groups of displayed candidate face images in a face database with a large number of subjects. In the part of machine vision, we compare the benchmark features and general metrics to measure face similarity. We propose a series of coherence measurements to evaluate the statistic characteristic of human and machine face perception. We discover that despite the unfamiliarity of users to most faces in the database, the coherence between human and machine remains in a stable level across multiple variations in metrics, features, size of databases, and demographics. The simulation experiments with the coherence distributions demonstrate that the embedded information is valuable to speed up interactive retrieval. The comparisons over multiple parameter settings provide feasible instructions in designing the interactive face retrieval system with more consideration of human factors.

Bounding surface plasticity for sand using fractional flow rule and modified critical state line

Bounding surface plasticity has been widely used for capturing the stress–strain behaviour of geomaterials. However, it may require multiple sets of model parameters for constitutive modelling of sands with a wide range of initial states, because of the distinct critical state characteristics under low and high densities or pressures in the $$e-\ln {p}'$$ plane. In this study, an enhanced bounding surface plasticity approach for sand with a wide range of initial material states is developed. A fractional plastic flow rule and a modified critical state line are suggested, which ensures that without using any predefined state indices, the developed model can consider the state-dependent dilatancy and hardening behaviours of sand subjected to low and high pressures/densities. The approach is validated by simulating the well-documented test results of Toyoura sand and Sacramento River sand. For comparison, the original state-dependent dilatancy approach in Li and Dafalias (Geotechnique 50(4):449–460, 2000. https://doi.org/10.1680/geot.2000.50.4.449 ) is also adopted and implemented. It is found that the two approaches can reasonably capture the typical stress–strain behaviour, e.g. hardening/contraction, softening/dilation, liquefaction, quasi-steady state flow, and non-flow, of sands with different initial material states, by using a single set of model parameters. However, compared to the current work, Li and Dafalias (2000) model relied on a predefined state parameter, for capturing the state-dependent behaviour of sand under a wide range of initial states

Analysis of the Relaxometric Properties of Extremely Rapidly Exchanging Gd3+ Chelates: Lessons from a Comparison of Four Isomeric Chelates.

Relaxometric analyses and in particular the use of fast-field cycling techniques have become routine in the study of paramagnetic metal complexes. The field dependence of the solvent proton relaxation properties (nuclear magnetic relaxation dispersion, NMRD) can provide unparalleled insights into the chemistry of these complexes. However, analyzing NMRD data is a multiparametric problem, and some sets of variables are mutually compensatory. Specifically, when fitting NMRD profiles, the metal-proton distance and the rotational correlation time constant have a push-pull relationship in which a change to one causes a predictable compensation in the other. A relaxometric analysis of four isomeric chelates highlights the pitfalls that await when fitting the NMRD profiles of chelates for which dissociative water exchange is extremely rapid. In the absence of independently verified values for one of these parameters, NMRD profiles can be fitted to multiple parameter sets. This means that NMRD fitting can inadvertently be used to buttress a preconceived notion of how the complex should behave when a different parameter set may more accurately describe the actual behavior. These findings explain why the effect of very rapid dissociative exchange on the hydration state of Gd3+ has remained obscured until only recently.

Semi-Automated Operational Modal Analysis Methodology to Optimize Modal Parameter Estimation

Nowadays, long-term monitoring systems rely on the efficient implementation of automated methodologies to extract the modal parameters of buildings and bridges to assess their structural integrity. However, modal parameter estimation, usually, requires a certain level of user interaction, mainly when parametric system identification methods are used. Such procedures generally depend on the selection of a set of parameters, defined according to heuristic criteria, and kept constant during long monitoring campaigns. The main objective of this paper is to prove the necessity of abandoning identification approaches based on a single set of parameters for long-term monitoring campaigns and to propose a semi-automated modal identification tool, where the user-defined parameters vary within an established range of values, that can be set independently of the user’s expertise. The proposed method is validated with an application in the operational modal analysis of a historic civic tower, and its excellent results demonstrate the importance of considering multiple sets of parameters, mainly when dealing with complex structures and challenging monitoring conditions.

Old-fashioned parametric models are still the best: a comparison of value-at-risk approaches in several volatility states

Numerous advances in the modeling techniques of value-at-risk (VaR) have provided financial institutions with a wide range of market risk approaches. However, which model to use depends on the state of volatility. We present backtesting results for 1% and 2.5% VaR of six indexes from emerging and developed countries using several of the best-known VaR models, including generalized autoregressive conditional heteroscedasticity (GARCH), extreme value theory (EVT), conditional autoregressive VaR (CAViaR) and filtered historical simulation (FHS) with multiple sets of parameters. The backtesting procedure is based on the excess ratio, Kupiec and Christoffersen tests for multiple thresholds and cost functions. The main contribution of this paper is that we compared the models in four different scenarios, with different states of volatility in the training and testing samples. The results indicate that the best of the models, ie, the least affected by changes in the volatility, is GARCH(1,1) with a standardized Student t distribution. Nonparametric techniques (eg, CAViaR with GARCH setup or FHS with a skewed normal distribution) have very prominent results in testing periods with low volatility, but they are worse in turbulent periods. We also discuss an automatic method to set an extreme distribution threshold for EVT models as well as several ensembling methods for VaR, of which the minimum VaR estimate from the best models – in particular, a minimum of GARCH(1,1) with a standardized Student t distribution and either the EVT or the CAViaR model – has been proven to give very good results.

Conceptual Models of Entrainment, Jet Lag, and Seasonality.

Understanding entrainment of circadian rhythms is a central goal of chronobiology. Many factors, such as period, amplitude, Zeitgeber strength, and daylength, govern entrainment ranges and phases of entrainment. We have tested whether simple amplitude-phase models can provide insight into the control of entrainment phases. Using global optimization, we derived conceptual models with just three free parameters (period, amplitude, and relaxation rate) that reproduce known phenotypic features of vertebrate clocks: phase response curves (PRCs) with relatively small phase shifts, fast re-entrainment after jet lag, and seasonal variability to track light onset or offset. Since optimization found multiple sets of model parameters, we could study this model ensemble to gain insight into the underlying design principles. We found complex associations between model parameters and entrainment features. Arnold onions of representative models visualize strong dependencies of entrainment on periods, relative Zeitgeber strength, and photoperiods. Our results support the use of oscillator theory as a framework for understanding the entrainment of circadian clocks.

Parameterized Anand constitutive model under a wide range of temperature and strain rate: experimental and theoretical studies

The inconsistency of constitutive parameters weakens the rationality and also impedes the application of the Anand model. In this paper, a modified framework of Anand model is proposed and the corresponding parameters are calibrated using two viscoplastic materials, that is, Sn-3.0Cu-0.5Ag and Sn37-Pb63 which are typical die-attach solder alloys in the electronic packaging industry. The stress–strain relationships under a wide range of temperature and strain rate are measured from the uniaxial tensile experiments for both materials by dog-bone specimens. The dependency on strain rate and temperature can be well described by reasonably parameterizing the proposed Anand model. Effects of strain rate and temperature on saturation stresses are emphasized. In order to highlight the necessity to account for strain rate and temperature effects, the plastic ball grid array packaging structures are adopted in the scenario of thermal cycling. Due to variation of strain rate and strain, different stresses are observed among different positions in the packaging structure under the applied cycled temperature field. The wide ranges of strain rate and temperature are satisfactorily described by the proposed Anand model with one set of parameters, rather than using multiple sets of parameters or even multiple types of constitutive models. Lastly, the accumulative plastic strain and stress of two materials are numerically predicted and discussed to reveal the relationship between strength, Young’s modulus and plastic deformation, which shows the proposed parameterized Anand model can be readily utilized for fatigue life estimations of packaging structures under thermal cycling.

The DQM system for the SND detector

The SND detector has been operating at the VEPP-2000 collider (BINP, Russia) for several years. We present its new DQM system.The system deals with multiple parameters being numbers, histograms or user opinions to form a data quality decision (good, bad, etc). It calculates SND subsystem and overall statuses and produces summaries available via web-interface. The system supports multiple parameter sets for different usages like daily checks, precise subsystem control, preparing data for processing, etc. The parameter values are analyzed using special ROOT macros yielding quality checks that could be supplemented or corrected by users.The system is a part of a new SND information system implemented as a web application using Node.js and MySQL. The ROOT macros are written by SND subsystems experts using a simple framework. Histograms are displayed using JSROOT.The system is put into production. It is consistent, however there are some improvements to implement.