Design of field trials for the evaluation of transmissible vaccines in animal populations. by Justin K Sheen, Lee Kennedy-Shaffer, Michael Z Levy, Charlotte Jessica E Metcalf

“…Here, we establish the conditions under which a two-stage randomized field trial can characterize the protective effects of a transmissible vaccine relative to a traditional vaccine. …”
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Failure Mechanism and Factor of Safety for Spatially Variable Undrained Soil Slope by Liang Li, Xuesong Chu

“…The undrained shear strength of cohesive soil slope is modeled by a one-dimensional random field in the vertical direction. The FS and FM for a specific realization of random field are determined by SRT embedded in FEM- and FDM-based software (e.g., Phase2 6.0 and FLAC) and LEM, respectively. …”
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Discriminant analysis of Gaussian spatial data with exponential covariance structure by Kęstutis Dučinskas

“… This paper considers the discrimination of the observation of the stationary Gaussian random field belonging to one of two populations with different means and covariance functions. …”
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Semisupervised Association Learning Based on Partial Differential Equations for Sparse Representation of Image Class Attributes by Wei Song, Guang Hu, Liuqing OuYang, Zhenjie Zhu

“…In this paper, we propose a multitask multiview semisupervised learning model based on partial differential equation random field and Hilbert independent standard probability image genus attribute model, i.e., shared semantics. …”
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Langmuir Adsorption on Fractal Surfaces by Volodymyr V. Kutarov, L. Zub Yu, Erich Robens, Shanath Amarasiri A. Jayaweera

“…In the second method, the Langmuir isotherm is calculated from measurements of the random field of the adsorbed molecules. Then the fractal dimension is determined. using the proposed methods, we calculated the fractal dimensions of samples of xerogel at low-temperature nitrogen adsorption, lunar regolith sample at a low temperature of krypton and n-heptanes, and water vapour adsorption at ambient temperature.…”
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Importance of Distribution Type on Bearing Capacity of an Embedded Foundation in Spatially Varying Soils by Jiwei Han, Xiaoming Liu, Yongxin Wu, Xuhui Zhou

“…The nonstationary undrained shear strength is simulated by a nonstationary random field generator based on the spectral representation method. …”
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CRF combined with ShapeBM shape priors for image labeling by Hao WANG, Lijun GUO, Yadong WANG, Rong ZHANG

“…Conditional random field (CRF) is a powerful model for image labeling,it is particularly well-suited to model local interactions among adjacent regions (e.g.superpixels).However,CRF doesn't consider the global constraint of objects.The overall shape of the object is used as a global constraint,the ShapeBM can be taken advantage of modeling the global shape of object,and then a new labeling model that combined the above two types of models was presented.The combination of CRF and ShapeBM was based on the superpixels,through the pooling technology was wed to establish the corresponding relationship between the CRF superpixel layer and the ShapeBM input layer.It enhanced the effectiveness of the combination of CRF and ShapeBM and improved the accuracy of the labeling.The experiments on the Penn-Fudan Pedestrians dataset and Caltech-UCSD Birds 200 dataset demonstrate that the model is more effective and efficient than others.…”
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Remote sensing image segmentation based on spatially constrained Gaussian mixture model with unknown class number by Quan-hua ZHAO, Xue SHI, Yu WANG, Yu LI

“…In view of the traditional Gaussian mixture model (GMM),it was difficult to obtain the number of classes and sensitive to the noise.A remote sensing image segmentation method based on spatially constrained GMM with unknown number of classes was proposed.First,in the built GMM,prior probability that represented the membership between a pixel and one class was modeled as a Markov random field (MRF).In order to improve the sensitivity of noise,the smoothing factor was defined by combining the a posterior probability and the prior probability of neighboring pixels.For estimating the number of classes and the parameters of model,the reversible jump Markov chain Monte Carlo (RJMCMC) and maximum likelihood (ML) estimation were employed,respectively.Finally,by minimizing the smoothing factor the final segmentation was obtained.In order to verify the proposed segmentation method,the synthetic and real panchromatic images were tested.The experimental results show that the proposed method is feasible and effective.…”
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A Prediction Method Based on Monte Carlo Simulations for Finite Element Analysis of Soil Medium considering Spatial Variability in Soil Parameters by Kedong Tang, Jialiang Wang, Lielie Li

“…The proposed method introduces a distance space to the Monte Carlo Method (MCM) random field instances and, considering the importance of a safety margin in structures, uses selected spatial interpolation to predict the MCM instances to be solved. …”
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ANALYSIS OF STOCHASTIC RELIABILITY AND FAILURE RISK OF COLUMN DISCONNECTOR UNDER TYPHOON BASED ON THE PROBABILITY DENSITY EVOLUTION MODEL by SHI ShouJian, CHEN WenTong, YANG HuanHong, ZHU LiangLong, WU XueFeng, XU ChengHao

“…First, the typhoon random field model was given based on the power spectral density, in which the uncertainties of ground roughness, average wind speed, and boundary wave number were incorporated. …”
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Random Characteristics of Hydraulic Gradient through Three-Dimensional Multilayer Embankment by Xiaoming Zhao, Yulong Niu, Dongbin Cui, Mingming Hu

“…Based on Local Average Subdivision technique, a three-dimensional multilayer random field of embankment hydraulic conductivity was generated. …”
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The application of series multi-pooling convolutional neural networks for medical image segmentation by Feng Wang, Siwei Huang, Lei Shi, Weiguo Fan

“…The main contents of this article were studied as follows: the principle and operating approach of convolutional neural network on image processing was first introduced, and then 12-layer convolutions were skillfully set up for local pathways based on two-way convolutional neural network architectures; considering the inter-label dependency in pixel areas, the situation of conditional random field was simulated to design the input series connection structure; multi-pooling input series connection model was designed to solve the problem that the input pixel area is limited; finally, the classification accuracy upon experiments reached 83%, which has verified the effectiveness of model to improve.…”
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Unsupervised SAR Image Segmentation Based on a Hierarchical TMF Model in the Discrete Wavelet Domain for Sea Area Detection by Jiajing Wang, Shuhong Jiao, Lianyang Shen, Zhenyu Sun, Lin Tang

“…The multiscale likelihood and the multiscale energy function are constructed to capture the intrascale and intrascale dependencies in a random field (X,U). To model the SAR data related to radar backscattering sources, the Gaussian distribution is utilized. …”
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Cyber security entity recognition method based on residual dilation convolution neural network by Bo XIE, Guowei SHEN, Chun GUO, Yan ZHOU, Miao YU

“…In recent years,cybersecurity threats have increased,and data-driven security intelligence analysis has become a hot research topic in the field of cybersecurity.In particular,the artificial intelligence technology represented by the knowledge graph can provide support for complex cyberattack detection and unknown cyberattack detection in multi-source heterogeneous threat intelligence data.Cybersecurity entity recognition is the basis for the construction of threat intelligence knowledge graphs.The composition of security entities in open network text data is very complex,which makes traditional deep learning methods difficult to identify accurately.Based on the pre-training language model of BERT (pre-training of deep bidirectional transformers),a cybersecurity entity recognition model BERT-RDCNN-CRF based on residual dilation convolutional neural network and conditional random field was proposed.The BERT model was used to train the character-level feature vector representation.Combining the residual convolution and the dilation neural network model to effectively extract the important features of the security entity,and finally obtain the BIO annotation of each character through CRF.Experiments on the large-scale cybersecurity entity annotation dataset constructed show that the proposed method achieves better results than the LSTM-CRF model,the BiLSTM-CRF model and the traditional entity recognition model.…”
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Named Entity Recognition Method Based on Multi-Feature Fusion by Weidong Huang, Xinhang Yu

“…The model also leverages multi-head attention for feature fusion, and the final results are decoded using a Conditional Random Field (CRF) layer. The model achieves an F1 score of 86.8383% on a collected dataset of online reviews containing eight entity categories. …”
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A Probabilistic Assessment Model for Train-Bridge Systems: Special Attention on Track Irregularities by Dejun Liu, Lifeng Xin, Xiaozhen Li, Jiaxin Zhang

“…In this paper, a probabilistic model devoted to investigating the dynamic behaviors of train-bridge systems subjected to random track irregularities is presented, in which a train-ballasted track-bridge coupled model with nonlinear wheel-rail contacts is introduced, and then a new approach for simulating a random field of track irregularities is developed; moreover, the probability density evolution method is used to describe the probability transmission from excitation inputs to response outputs; finally, extended analysis from three aspects, that is, stochastic analysis, reliability analysis, and correlation analysis, are conducted on the evaluation and application of the proposed model. …”
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Multiscale Region-Level VHR Image Change Detection via Sparse Change Descriptor and Robust Discriminative Dictionary Learning by Yuan Xu, Kun Ding, Chunlei Huo, Zisha Zhong, Haichang Li, Chunhong Pan

“…Robust change decision is conducted by multiscale region-consistent fusion, which is implemented by the superpixel-level cosparse representation with robust discriminative dictionary and the conditional random field model. Experimental results confirm the effectiveness of the proposed change detection technique.…”
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Multifeature Contrast Enhancement Algorithm for Digital Media Images Based on the Diffusion Equation by Jijun Wang, Yi Yuan, Guoxiang Li

“…In this paper, the proposed algorithm experiments with several sets of Kor Kor resolution remote sensing images, respectively, and the Markov random field model and fully convolutional network (FCN) algorithm are used as the comparison algorithm. …”
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Probabilistic Risk Assessment of Slope Failure in 3-D Spatially Variable Soils by Finite Element Method by Ya-Nan Ding, Zu-Fang Qi, Miao Hu, Jin-Zhu Mao, Xiao-Cheng Huang

“…Based on Monte Carlo simulation and the random field method, a quantitative risk evaluation method for slope failure considering the spatial variability of soil parameters is proposed in the study. …”
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Multilevel Attention and Multiscale Feature Fusion Network for Author Classification of Chinese Ink-Wash Paintings by Wei Jiang, Xianglian Meng, Ji Xi

“…Moreover, the conditional random field module is adopted to fuse the optimized three-scale feature maps, and the channel attention module is followed to refine the features. …”
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