Random Field Model Research Articles

Evaluation of the shear size effect in glued laminated timber using a stochastic FE model calibrated on 17000 glue-line tests

Glue-line tests provide the shear resistance and failure mode of samples extracted from laminated timber products. Several papers questioned the practical use of such tests affirming the lack of correlation between the capacity of the beam and the outcomes of glue-line tests. However, no studies provide a probabilistic framework for interpreting glue-line tests. This paper presents and discusses the largest database of glue-line tests ever published, consisting of approximately 17000 tests on glued laminated timber (GLT) beams, carried out at the Treteknisk, the Norwegian Institute of Wood Technology (Norway). This database has been used to develop in Abaqus a stochastic FE model to predict the shear capacity of glued laminated timber beams (GLT) using advanced modelling concepts, including discrete cracking and plasticity. The authors examine the dependence of the shear capacity on the beam size to assess the shear size effect in GLT components. The methodology is based on Montecarlo simulations. The mechanical properties of the elementary volumes composing the beam are assigned using a random field model calibrated on the glue-line tests.

Long range order for random field Ising and Potts models

AbstractWe present a new and simple proof for the classic results of Imbrie (1985) and Bricmont–Kupiainen (1988) that for the random field Ising model in dimension three and above there is long range order at low temperatures with presence of weak disorder. With the same method, we obtain a couple of new results: (1) we prove that long range order exists for the random field Potts model at low temperatures with presence of weak disorder in dimension three and above; (2) we obtain a lower bound on the correlation length for the random field Ising model at low temperatures in dimension two (which matches the upper bound in Ding–Wirth (2020)). Our proof is based on an extension of the Peierls argument with inputs from Chalker (1983), Fisher–Fröhlich–Spencer (1984), Ding–Wirth (2020) and Talagrand's majorizing measure theory (1980s) (and in particular, our proof does not involve the renormalization group theory).

Fast reconstruction of scanning transmission electron microscopy images using Markov random field model

In this study, we proposed a fast method of reconstruction for scanning transmission electron microscopy images. The proposed method is based on the Markov random field model and Bayesian inference, and we found that the method can reconstruct such images of sizes 512 × 512 and 264 × 240 in less than 200 ms and 100 ms, respectively. Furthermore, we showed that the method of reconstruction from multiple images without averaging them has better reconstruction performance than that from the averaged image.

Computational characteristics of the random-field Ising model with long-range interaction

Computational characteristics of the random-field Ising model with long-range interaction

Light Pollution Index System Model Based on Markov Random Field

Light pollution is one of the environmental pollution problems facing the world. The research on the measurement standard of light pollution is not perfect at present. In this paper, we proposed a Markov random field model to determine the light pollution risk level of a site. Firstly, the specific data of 12 indicators of 5 typical cities were collected, and 10-factor indicators were screened using the R-type clustering algorithm. Then, the entropy weight method was used to determine the weight, and the light pollution measurement method of the Markov random field was established. The model was tested by five different data sets, and the test results show that the model is very effective. Three kinds of potential effects were proposed, and the relationship between the factor index and potential effects was established by using the partial least square method. Three possible intervention strategies for solving the problem of light pollution are pointed out: road lighting system planning, increasing vegetation coverage, and building system planning. Finally, a simulated annealing algorithm was used to determine the best intervention strategy, concluding that using strategy 1 in urban neighborhood 2 was the most effective measure, reducing the risk level of light pollution by 17.2%.

Rotation in Event Horizon Telescope Movies

The Event Horizon Telescope (EHT) has produced images of M87* and Sagittarius A*, and will soon produce time sequences of images, or movies. In anticipation of this, we describe a technique to measure the rotation rate, or pattern speed Ω p , from movies using an autocorrelation technique. We validate the technique on Gaussian random field models with a known rotation rate and apply it to a library of synthetic images of Sgr A* based on general relativistic magnetohydrodynamics simulations. We predict that EHT movies will have Ω p ≈ 1° per GMc −3, which is of order 15% of the Keplerian orbital frequency in the emitting region. We can plausibly attribute the slow rotation seen in our models to the pattern speed of inward-propagating spiral shocks. We also find that Ω p depends strongly on inclination. Application of this technique will enable us to compare future EHT movies with the clockwise rotation of Sgr A* seen in near-infrared flares by GRAVITY. Pattern speed analysis of future EHT observations of M87* and Sgr A* may also provide novel constraints on black hole inclination and spin, as well as an independent measurement of black hole mass.

3-D Adaptive AUV Sampling for Classification of Water Masses

Autonomous underwater vehicles with onboard computing units foster innovative approaches for sampling oceanographic phenomena. Feedback of observations via the onboard model for planning algorithms enable adaptive sampling for such robotic units. In this work, we develop, implement, and test an adaptive sampling algorithm for efficient sampling of water masses in a 3-D frontal system. Focusing on a river plume, salinity variations are used to characterize the water masses. A threshold in salinity is assumed to distinguish the ocean and river waters, so that excursions below the threshold define river waters. The onboard model builds on a Gaussian random field representation of the salinity variations in (north, east, depth) coordinates. This model is initially trained from numerical ocean model data, and then updated with data gathered by the vehicle sensor. The Gaussian random field model further allows closed-form expressions of the expected spatially integrated Bernoulli variance of the salinity excursion set, which is used to reward sampling efforts. Combining these results with forward-looking planning algorithms, we suggest a workflow for 3-D adaptive sampling to map river plume systems. Simulation studies are used to compare the suggested approach with others. Results of field trials in the Nidelva river plume in Norway are presented and discussed.

Research on Chinese Semantic Named Entity Recognition in Marine Engine Room Systems Based on BERT

With the development of intelligentization in maritime vessels, the pursuit of an organized and scalable knowledge storage approach for marine engine room systems has become one of the current research hotspots. This study addressed the foundational named entity recognition (NER) task in constructing a knowledge graph for marine engine rooms. It proposed an entity recognition algorithm for Chinese semantics in marine engine rooms that integrates language models. Firstly, the bidirectional encoder representation from transformers (BERT) language model is used to extract text features and obtain word-level granularity vector matrices. Secondly, the trained word embeddings are fed into a bidirectional long short-term memory network (BiLSTM) to extract contextual information. It considers the surrounding words and their sequential relationships, enabling a better understanding of the context. Additionally, the conditional random field (CRF) model was used to extract the globally optimal sequence of named entities in the marine engine room semantic. The CRF model considered the dependencies between adjacent entities that ensured a coherent and consistent final result for entity recognition in marine engine room semantics. The experiment results demonstrate that the proposed algorithm achieves superior F1 scores for all three entity types. Compared with BERT, the overall precision, recall, and F1 score of the entity recognition are improved by 1.36%, 1.41%, and 1.38%, respectively. Future research will be carried out on named entity recognition of a small sample set to provide basic support for more efficient entity relationship extraction and construction of a marine engine room knowledge graph.

Improving part-of-speech tagging in Amharic language using deep neural network

To date, several POS taggers have been introduced to facilitate the success of semantic analysis for different languages. However, the task of POS tagging becomes a bit intricate in morphologically complex languages, like Amharic. In this paper, we evaluated different models such as bidirectional long short term memory, convolutional neural network in combination with bidirectional long short term memory, and conditional random field for Amharic POS tagging. Various features, both language-dependent and -independent, have been explored in a conditional random field model. Besides, word-level and character-level features are analyzed in deep neural network models. A convolutional neural network is utilized for encoding features at the word and character level. Each model's performance has evaluated on the dataset that contained 321 K tokens and manually tagged with 31 POS tags. Lastly, the best performance obtained by an end-to-end deep neural network model, convolutional neural network in combination with bidirectional long term short memory and conditional random field, is 97.23% accuracy. This is the highest accuracy for Amharic POS tagging task and is competent with contemporary taggers currently existing in different languages.

Influence of stochastic chlorine environment on the spatiotemporal deterioration of marine RC structures

The stochastic nature of the chloride environment plays an instrumental role in the variability of the deterioration processes afflicting marine reinforced concrete structures. The objective of this study is to investigate the influence of the stochastic chloride environment on the spatiotemporal variability of deterioration processes based on spatiotemporal reliability analysis. Based on long-term detection data from the Zhapu Port of Hangzhou Bay, the scale of fluctuation of chloride concentration and chloride diffusion coefficient in the depth dimension was obtained. The effect of the spatiotemporal variability of chloride environmental impacts and structural resistance on structural deterioration was evaluated in terms of both extent and probability based on three random field models. The results demonstrate that appropriate access to information on the scale of fluctuation in terms of both environmental impacts and structural resistance can construct more robust random fields. Moreover, the effect of the previous limit state on cover spalling is greater than the effect on cover cracking and structural collapse. In addition, the relationship between corrosion failure and structural failure was obtained, and the extent and duration of cover spalling constitute an important indicator of structural reliability.

Correlation length of the two-dimensional random field Ising model via greedy lattice animal

For the two-dimensional random field Ising model where the random field is given by independent and identically distributed mean zero Gaussian variables with variance ε2, we study (one natural notion of) the correlation length, which is the critical size of a box at which the influences of the random field and of the boundary condition on the spin magnetization are comparable. We show that as ε→0, at zero temperature the correlation length scales as eΘ(ε−4∕3) (and our upper bound applies for all positive temperatures).

Nonsteady dynamics at the dynamic depinning transition in the two-dimensional Gaussian random-field Ising model.

With large-scale Monte Carlo simulations, we investigate the nonsteady relaxation at the dynamic depinning transition in the two-dimensional Gaussian random-field Ising model. The dynamic scaling behavior is carefully analyzed, and the transition fields as well as static and dynamic exponents are accurately determined based on the short-time dynamic scaling form. Different from the usual assumption, two distinguished growth processes of spatial correlation lengths for the velocity and height of the domain wall are found. Thus, the universality class of the depinning transition is established, which significantly differs from that of the quenched disorder equationbut agrees with that of the recent experiment as well as other simulations works. Under the influence of the mesoscopic time regime, the crossover from the second-order phase transition to the first-order one is confirmed in the weak-disorder regime, yielding an abnormal disorder-dependent nature of the criticality.

Automatic knowledge extraction from Chinese electronic medical records and rheumatoid arthritis knowledge graph construction.

Knowledge graphs are a powerful tool for organizing knowledge, processing information and integrating scattered information, effectively visualizing the relationships among entities and supporting further intelligent applications. One of the critical tasks in building knowledge graphs is knowledge extraction. The existing knowledge extraction models in the Chinese medical domain usually require high-quality and large-scale manually labeled corpora for model training. In this study, we investigate rheumatoid arthritis (RA)-related Chinese electronic medical records (CEMRs) and address the automatic knowledge extraction task with a small number of annotated samples from CEMRs, from which an authoritative RA knowledge graph is constructed. After constructing the domain ontology of RA and completing manual labeling, we propose the MC-bidirectional encoder representation from transformers-bidirectional long short-term memory-conditional random field (BERT-BiLSTM-CRF) model for the named entity recognition (NER) task and the MC-BERT + feedforward neural network (FFNN) model for the entity extraction task. The pretrained language model (MC-BERT) is trained with many unlabeled medical data and fine-tuned using other medical domain datasets. We apply the established model to automatically label the remaining CEMRs, and then an RA knowledge graph is constructed based on the entities and entity relations, a preliminary assessment is conducted, and an intelligent application is presented. The proposed model achieved better performance than that of other widely used models in knowledge extraction tasks, with mean F1 scores of 92.96% in entity recognition and 95.29% in relation extraction. This study preliminarily confirmed that using a pretrained medical language model could solve the problem that knowledge extraction from CEMRs requires a large number of manual annotations. An RA knowledge graph based on the above identified entities and extracted relations from 1,986 CEMRs was constructed. Experts verified the effectiveness of the constructed RA knowledge graph. In this paper, an RA knowledge graph based on CEMRs was established, the processes of data annotation, automatic knowledge extraction, and knowledge graph construction were described, and a preliminary assessment and an application were presented. The study demonstrated the viability of a pretrained language model combined with a deep neural network for knowledge extraction tasks from CEMRs based on a small number of manually annotated samples.

Probabilistic Failure Envelopes of Monopiles in Scoured Seabed Based on a New Nonstationary Random Field Model

Monopiles, which have been most extensively used in the offshore wind industries, are usually subject to threats from local scour. The effects of local scour on the bearing capacity of monopiles have been studied based on deterministic soil properties and local scour dimensions. However, the properties of the seabed soil and the dimensions of the local scour both have uncertainty. In this study, a new nonstationary random field model, which can better simulate the nonstationary characteristics of the seabed clayey soil and result in a safer probabilistic design of foundations, is proposed to characterize the spatial variability of the seabed clayey soil. Then, the effects of the spatial variability of the seabed clayey soil and the uncertainty of the local scour depth on the bearing capacity of monopiles are investigated under both uniaxial loading and vertical-horizontal-moment (VHM) combined loading using the random finite-element method combined with kriging metamodeling technique and Monte Carlo simulation. The undrained shear strength of the seabed clayey soil was simulated with a lognormally distributed nonstationary random field, and the local scour depth was treated as a uniform random variable. The negative correlation between them was also considered. The results show that the factor of safety required for the local scoured seabed decreased from 3.3 to 2.6 as the negative correlation was enhanced, greater than or equal to that [factor of safety (FS)=2.6] for the intact seabed. The procedure of analyzing the design failure envelop of a monopile in spatially variable clayey soil with a local scour proposed in this study can provide a reference for geotechnical engineers.

Tile low-rank approximations of non-Gaussian space and space-time Tukey g-and-h random field likelihoods and predictions on large-scale systems

Large-scale statistical modeling has become necessary with the vast flood of geospace data coming from various sources. In space statistics, the Maximum Likelihood Estimation (MLE) is widely considered for modeling geospace data by estimating a set of statistical parameters related to a predefined covariance function. This covariance function describes the correlation between a set of geospace locations where the main goal is to model given data samples and impute missing data. Climate/weather modeling is a prevalent application for the MLE operation where data interpolation and forecasting are highly required. In the literature, the Gaussian random field is often used to describe geospace data as one of the most popular models for MLE. However, real-life datasets are often skewed and/or have extreme values, and non-Gaussian random field models are more appropriate for capturing such features. In this work, we provide an exact and approximate parallel implementation of the well-known Tukey g-and-h (TGH) non-Gaussian random field in the context of climate/weather applications. The proposed implementation alleviates the computation complexity of the log-likelihood function, which requires O(n2) storage and O(n3) operations, where N is the number of geospace locations, M is the number of time slots, and n=N×M. Based on tile low-rank (TLR) approximations, our implementation of the TGH model can tackle large-scale problems. Furthermore, we rely on task-based programming models and dynamic runtime systems to provide fast execution for the MLE operation in space and space-time cases. We assess the performance and accuracy of the proposed implementations using synthetic space and space-time datasets up to 800K. We also consider a 12-month precipitation dataset in Germany to demonstrate the advantage of using non-Gaussian over Gaussian random field models. We evaluate the prediction accuracy of the TGH model on the precipitation dataset using the Probability Integral Transformation (PIT) tool showing that the TGH model outperforms the Gaussian modeling in the real dataset. Moreover, our performance assessment indicates that TLR computations allow solving larger matrix sizes while preserving the required accuracy for prediction. The TLR-based approximation shows a speedup up to 7.29X and 2.96X over the exact solution.

Underwater terrain positioning method based on Markov random field for unmanned underwater vehicles

Underwater terrain-matching navigation technologies have become a popular topic for the high-precision positioning and navigation of autonomous underwater vehicles. This paper proposes an underwater terrain-matching positioning method based on a Markov random field model, which is based on real-time terrain data obtained using a multi-beam echo sounder. It focuses on the strong correlation between adjacent terrain data, which can improve terrain adaptability and matching accuracy. Playback simulation tests were conducted based on actual sea trial data, and the results showed that the proposed method has good positioning performance, which can correct the cumulative errors of inertial navigation systems. The results demonstrated the usability of the proposed method for positioning correction in underwater engineering applications.

Color design of modern architectural interior space based on environmental psychology

Abstract Due to the diversified development trend of modern architectural design, the functional design and application of houses are being presented in the interior space color layout of houses with its many advantages of interactivity, comprehensiveness, multi-functionality, and personalization. With this as the starting point, this paper analyzes the psychological perception of survey respondents on the same spatial color through big data, converts the perception into EEG signal for decoding, and after the decoding process of EEG data pre-processing, feature extraction, feature identification, and classification, calculates the significance of each element in the time-frequency matrix, which can get a homo-dimensional mask matrix. Then the conditional random field model is established on the random field theory to get the parameters of the model. Finally, the parameters of the model are obtained by maximizing the following entropy function, which is brought into the Lagrangian operator to obtain the pairwise Lagrangian operator. Finally, the EEG signal is decoded to realize the self-control training of color perception under different mentalities. The experimental results showed that by performing the intervention test on self-control and color insight, the mean value of the total self-control score in training was 61.99±11.45, and the intervention effect had stability. Therefore, improving self-control ability and forming correct tendency psychological perception plays a vital role in the color design of architectural space.

The self-control training method of vocal performance teaching in a new media environment

Abstract Due to the diverse development trend of modern media, new media arts and applications are being presented in the field of vocal performance teaching with its many advantages of interactivity, immediacy, sharing, comprehensiveness, versatility, community, and personalization. In this paper, by decoding the EEG signal, through the decoding process of EEG data pre-processing, feature extraction, feature identification, and classification, and calculating the significance of each element in the time-frequency matrix, an iso-dimensional mask matrix can be obtained. Then the conditional random field model is established on the random field theory to get the parameters of the model. Finally, the parameters of the model are obtained by maximizing the following entropy function, which is brought into the Lagrangian operator to obtain the pairwise Lagrangian operator. Finally, the EEG signal is decoded to realize the self-control training of vocal performance teaching in the new media environment. The experimental results show that by conducting the intervention test on self-control and vocal performance insight, the mean value of the total self-control score in self-control training is 61.99±11.45, and the intervention effect has stability. Therefore, improving self-control, forming correct expressions and forms, and enriching emotions are important for vocal performance.

Covariance models for multivariate random fields resulting from pseudo cross-variograms

So far, the pseudo cross-variogram is primarily used as a tool for the structural analysis of multivariate random fields. Mainly applying recent theoretical results on the pseudo cross-variogram, we use it as a cornerstone in the construction of valid covariance models for multivariate random fields. In particular, we extend known univariate constructions to the multivariate case, and generalize existing multivariate models. Furthermore, we provide a general construction principle for conditionally negative definite matrix-valued kernels, which we use to reinterpret previous modeling proposals.

Critical nematic correlations throughout the superconducting doping range in Bi2−zPbzSr2−yLayCuO6+x

Charge modulations have been widely observed in cuprates, suggesting their centrality for understanding the high-Tc superconductivity in these materials. However, the dimensionality of these modulations remains controversial, including whether their wavevector is unidirectional or bidirectional, and also whether they extend seamlessly from the surface of the material into the bulk. Material disorder presents severe challenges to understanding the charge modulations through bulk scattering techniques. We use a local technique, scanning tunneling microscopy, to image the static charge modulations on Bi2−zPbzSr2−yLayCuO6+x. The ratio of the phase correlation length ξCDW to the orientation correlation length ξorient points to unidirectional charge modulations. By computing new critical exponents at free surfaces including that of the pair connectivity correlation function, we show that these locally 1D charge modulations are actually a bulk effect resulting from classical 3D criticality of the random field Ising model throughout the entire superconducting doping range.