Characterizing large-scale weak interlayer shear zones using conditional random field theory

The shear behavior of large-scale weak intercalation shear zones(WISZs)often governs the stability of foundations,rock slopes,and underground structures.However,due to their wide distribution,undulating morphology,complex fabrics,and varying degrees of contact states,characterizing the shear behavior of natural and complex large-scale WISZs precisely is challenging.This study proposes an analytical method to address this issue,based on geological fieldwork and relevant experimental results.The analytical method utilizes the random field theory and Kriging interpolation technique to simplify the spatial uncertainties of the structural and fabric features for WISZs into the spatial correlation and variability of their mechanical parameters.The Kriging conditional random field of the friction angle of WISZs is embedded in the discrete element software 3DEC,enabling activation analysis of WISZ C2 in the underground caverns of the Baihetan hydropower station.The results indicate that the activation scope of WISZ C2 induced by the excavation of underground caverns is approximately 0.5e1 times the main powerhouse span,showing local activation.Furthermore,the overall safety factor of WISZ C2 follows a normal distribution with an average value of 3.697.

Power entity recognition based on bidirectional long short-term memory and conditional random fields

With the application of artificial intelligence technology in the power industry,the knowledge graph is expected to play a key role in power grid dispatch processes,intelligent maintenance,and customer service response provision.Knowledge graphs are usually constructed based on entity recognition.Specifically,based on the mining of entity attributes and relationships,domain knowledge graphs can be constructed through knowledge fusion.In this work,the entities and characteristics of power entity recognition are analyzed,the mechanism of entity recognition is clarified,and entity recognition techniques are analyzed in the context of the power domain.Power entity recognition based on the conditional random fields (CRF) and bidirectional long short-term memory (BLSTM) models is investigated,and the two methods are comparatively analyzed.The results indicated that the CRF model,with an accuracy of 83%,can better identify the power entities compared to the BLSTM.The CRF approach can thus be applied to the entity extraction for knowledge graph construction in the power field.

Rockhead profile simulation using an improved generation method of conditional random field

Rockhead profile is an important part of geological profiles and can have significant impacts on some geotechnical engineering practice,and thus,it is necessary to establish a useful method to reverse the rockhead profile using site investigation results.As a general method to reflect the spatial distribution of geo-material properties based on field measurements,the conditional random field(CRF)was improved in this paper to simulate rockhead profiles.Besides,in geotechnical engineering practice,measurements are generally limited due to the limitations of budget and time so that the estimation of the mean value can have uncertainty to some extent.As the Bayesian theory can effectively combine the measurements and prior information to deal with uncertainty,CRF was implemented with the aid of the Bayesian framework in this study.More importantly,this simulation procedure is achieved as an analytical solution to avoid the time-consuming sampling work.The results show that the proposed method can provide a reasonable estimation about the rockhead depth at various locations against measurement data and as a result,the subjectivity in determining prior mean can be minimized.Finally,both the measurement data and selection of hyper-parameters in the proposed method can affect the simulated rockhead profiles,while the influence of the latter is less significant than that of the former.

Conditional Random Field Tracking Model Based on a Visual Long Short Term Memory Network

In dense pedestrian tracking,frequent object occlusions and close distances between objects cause difficulty when accurately estimating object trajectories.In this study,a conditional random field tracking model is established by using a visual long short term memory network in the three-dimensional(3D)space and the motion estimations jointly performed on object trajectory segments.Object visual field information is added to the long short term memory network to improve the accuracy of the motion related object pair selection and motion estimation.To address the uncertainty of the length and interval of trajectory segments,a multimode long short term memory network is proposed for the object motion estimation.The tracking performance is evaluated using the PETS2009 dataset.The experimental results show that the proposed method achieves better performance than the tracking methods based on the independent motion estimation.

Fast Chinese syntactic parsing method based on conditional random fields

A fast method for phrase structure grammar analysis is proposed based on conditional ran- dom fields （CRF）. The method trains several CRF classifiers for recognizing the phrase nodes at dif- ferent levels, and uses the bottom-up to connect the recognized phrase nodes to construct the syn- tactic tree. On the basis of Beijing forest studio Chinese tagged corpus, two experiments are de- signed to select the training parameters and verify the validity of the method. The result shows that the method costs 78. 98 ms and 4. 63 ms to train and test a Chinese sentence of 17. 9 words. The method is a new way to parse the phrase structure grammar for Chinese, and has good generalization ability and fast speed.

Enhanced Identifying Gene Names from Biomedical Literature with Conditional Random Fields

Identifying gene names is an attractive research area of biology computing. However, accurate extraction of gene names is a challenging task with the lack of conventions for describing gene names. We devise a systematical architecture and apply the model using conditional random fields （CRFs） for extracting gene names from Medline. In order to improve the performance, biomedical ontology features are inserted into the model and post processing including boundary adjusting and word filter is presented to solve name overlapping problem and remove false positive single words. Pure string match method, baseline CRFs, and CRFs with our methods are applied to human gene names and HIV gene names extraction respectively in 1100 abstracts of Medline and their performances are contrasted. Results show that CRFs are robust for unseen gene names. Furthermore, CRFs with our methods outperforms other methods with precision 0.818 and recall 0.812.

A Remote Sensing Image Semantic Segmentation Method by Combining Deformable Convolution with Conditional Random Fields

Currently,deep convolutional neural networks have made great progress in the field of semantic segmentation.Because of the fixed convolution kernel geometry,standard convolution neural networks have been limited the ability to simulate geometric transformations.Therefore,a deformable convolution is introduced to enhance the adaptability of convolutional networks to spatial transformation.Considering that the deep convolutional neural networks cannot adequately segment the local objects at the output layer due to using the pooling layers in neural network architecture.To overcome this shortcoming,the rough prediction segmentation results of the neural network output layer will be processed by fully connected conditional random fields to improve the ability of image segmentation.The proposed method can easily be trained by end-to-end using standard backpropagation algorithms.Finally,the proposed method is tested on the ISPRS dataset.The results show that the proposed method can effectively overcome the influence of the complex structure of the segmentation object and obtain state-of-the-art accuracy on the ISPRS Vaihingen 2D semantic labeling dataset.

An Image Segmentation Algorithm Based on a Local Region Conditional Random Field Model

To reduce the computation cost of a combined probabilistic graphical model and a deep neural network in semantic segmentation, the local region condition random field (LRCRF) model is investigated which selectively applies the condition random field (CRF) to the most active region in the image. The full convolutional network structure is optimized with the ResNet-18 structure and dilated convolution to expand the receptive field. The tracking networks are also improved based on SiameseFC by considering the frame relations in consecutive-frame traffic scene maps. Moreover, the segmentation results of the greyscale input data sets are more stable and effective than using the RGB images for deep neural network feature extraction. The experimental results show that the proposed method takes advantage of the image features directly and achieves good real-time performance and high segmentation accuracy.

STABC-IR:An air target intention recognition method based on bidirectional gated recurrent unit and conditional random field with space-time attention mechanism

The battlefield environment is changing rapidly,and fast and accurate identification of the tactical intention of enemy targets is an important condition for gaining a decision-making advantage.The current Intention Recognition(IR)method for air targets has shortcomings in temporality,interpretability and back-and-forth dependency of intentions.To address these problems,this paper designs a novel air target intention recognition method named STABC-IR,which is based on Bidirectional Gated Recurrent Unit(Bi GRU)and Conditional Random Field(CRF)with Space-Time Attention mechanism(STA).First,the problem of intention recognition of air targets is described and analyzed in detail.Then,a temporal network based on Bi GRU is constructed to achieve the temporal requirement.Subsequently,STA is proposed to focus on the key parts of the features and timing information to meet certain interpretability requirements while strengthening the timing requirements.Finally,an intention transformation network based on CRF is proposed to solve the back-and-forth dependency and transformation problem by jointly modeling the tactical intention of the target at each moment.The experimental results show that the recognition accuracy of the jointly trained STABC-IR model can reach 95.7%,which is higher than other latest intention recognition methods.STABC-IR solves the problem of intention transformation for the first time and considers both temporality and interpretability,which is important for improving the tactical intention recognition capability and has reference value for the construction of command and control auxiliary decision-making system.

Horizontal convergence reconstruction in the longitudinal direction for shield tunnels based on conditional random field

Tunnel horizontal convergence monitoring is essential to ensure the operation safety.However,only a few representative tunnel sec-tions are chosen for monitoring due to the cost limitation.It is difficult to capture the horizontal convergence of each tunnel ring with limited measurements.Confronted with this difficulty,the paper proposes a horizontal convergence reconstruction method based on the measurements of deployed sensors.The tunnel horizontal convergence along the longitudinal direction is seen as a one-dimensional sta-tionary and ergodic random field.The reconstruction problem is then transformed into the generation of conditional random fields.Monte Carlo simulation is adopted to generate possible realizations and the mean of realizations is considered as the maximum likeli-hood reconstruction.Error analysis proves the effectiveness of the proposed reconstruction method.The proposed method is proved to be applicable in reconstructing the time-variant horizontal convergence and is verified by the monitoring results of the shield tunnel of Shanghai Metro Line 2.The effect of sensor numbers is parametrically studied,and an optimal sensor placement scheme is decided.Additional sensors placed at the deformation drastically changed location can significantly improve the performance of the proposed method.

Extracting 3D model feature lines based on conditional random fields

We propose a 3D model feature line extraction method using templates for guidance. The 3D model is first projected into a depth map, and a set of candidate feature points are extracted. Then, a conditional random fields (CRF) model is established to match the sketch points and the candidate feature points. Using sketch strokes, the candidate feature points can then be connected to obtain the feature lines, and using a CRF-matching model, the 2D image shape similarity features and 3D model geometric features can be effectively integrated. Finally, a relational metric based on shape and topological similarity is proposed to evaluate the matching results, and an iterative matching process is applied to obtain the globally optimized model feature lines. Experimental results showed that the proposed method can extract sound 3D model feature lines which correspond to the initial sketch template.

Scaling Conditional Random Fields by One-Against-the-Other Decomposition

As a powerful sequence labeling model, conditional random fields （CRFs） have had successful applications in many natural language processing （NLP） tasks. However, the high complexity of CRFs training only allows a very small tag （or label） set, because the training becomes intractable as the tag set enlarges. This paper proposes an improved decomposed training and joint decoding algorithm for CRF learning. Instead of training a single CRF model for all tags, it trains a binary sub-CRF independently for each tag. An optimal tag sequence is then produced by a joint decoding algorithm based on the probabilistic output of all sub-CRFs involved. To test its effectiveness, we apply this approach to tackling Chinese word segmentation （CWS） as a sequence labeling problem. Our evaluation shows that it can reduce the computational cost of this language processing task by 40-50% without any significant performance loss on various large-scale data sets.

Temporally Consistent Depth Map Prediction Using Deep Convolutional Neural Network and Spatial-Temporal Conditional Random Field

Deep convolutional neural networks （DCNNs） based methods recently keep setting new records on the tasks of predicting depth maps from monocular images. When dealing with video-based applications such as 2D （2-dimensional） to 3D （3-dimensional） video conversion, however, these approaches tend to produce temporally inconsistent depth maps, since their CNN models are optimized over single frames. In this paper, we address this problem by introducing a novel spatial-temporal conditional random fields （CRF） model into the DCNN architecture, which is able to enforce temporal consistency between depth map estimations over consecutive video frames. In our approach, temporally consistent superpixel （TSP） is first applied to an image sequence to establish the correspondence of targets in consecutive frames. A DCNN is then used to regress the depth value of each temporal superpixel, followed by a spatial-temporal CRF layer to model the relationship of the estimated depths in both spatial and temporal domains. The parameters in both DCNN and CRF models are jointly optimized with back propagation. Experimental results show that our approach not only is able to significantly enhance the temporal consistency of estimated depth maps over existing single-frame-based approaches, but also improves the depth estimation accuracy in terms of various evaluation metrics.

2D Correlative-Chain Conditional Random Fields for Semantic Annotation of Web Objects

Semantic annotation of Web objects is a key problem for Web information extraction. The Web contains an abundance of useful semi-structured information about real world objects, and the empirical study shows that strong two-dimensional sequence characteristics and correlative characteristics exist for Web information about objects of the same type across different Web sites. Conditional Random Fields （CRFs） are the state-of-the-art approaches taking the sequence characteristics to do better labeling. However, as the appearance of correlative characteristics between Web object elements, previous CRFs have their limitations for semantic annotation of Web objects and cannot deal with the long distance dependencies between Web object elements efficiently. To better incorporate the long distance dependencies, on one hand, this paper describes long distance dependencies by correlative edges, which are built by making good use of structured information and the characteristics of records from external databases; and on the other hand, this paper presents a two-dimensional Correlative-Chain Conditional Random Fields （2DCC-CRFs） to do semantic annotation of Web objects. This approach extends a classic model, two-dimensional Conditional Random Fields （2DCRFs）, by adding correlative edges. Experimental results using a large number of real-world data collected from diverse domains show that the proposed approach can significantly improve the semantic annotation accuracy of Web objects.

Accurate prediction of protein dihedral angles through conditional random field

Identifying local conformational changes induced by subtle differences on amino acid sequences is critical in exploring the functional variations of the proteins. In this study, we designed a computational scheme to predict the dihedral angle variations for different amino acid sequences by using conditional random field. This computational tool achieved an accuracy of 87% and 84% in 10-fold cross validation in a large data set for φ and ψ, respectively. The prediction accuracies of φand ψ are positively correlated to each other for most of the 20 types of amino acids. Helical amino acids can achieve higher prediction accuracy in general, while amino acids in beet sheet have higher accuracy at specific angular regions. The prediction accuracy of φ is negatively correlated with amino acid flexibility represented by Vihinen Index. The prediction accuracy of φ can also be negatively correlated with angle distribution dispersion.

Fine-Grained Opinion Mining on Chinese Car Reviews with Conditional Random Field

Nowadays,the Internet has penetrated into all aspects of people's lives.A large number of online customer reviews have been accumulated in several product forums,which are valuable resources to be analyzed.However,these customer reviews are unstructured textual data,in which a lot of ambiguities exist,so analyzing them is a challenging task.At present,the effective deep semantic or fine-grained analysis of customer reviews is rare in the existing literature,and the analysis quality of most studies is also low.Therefore,in this paper a fine-grained opinion mining method is introduced to extract the detailed semantic information of opinions from multiple perspectives and aspects from Chinese automobile reviews.The conditional random field (CRF) model is used in this method,in which semantic roles are divided into two groups.One group relates to the objects being reviewed,which includes the roles of manufacturer,the brand,the type,and the aspects of cars.The other group of semantic roles is about the opinions of the objects,which includes the sentiment description,the aspect value,the conditions of opinions and the sentiment tendency.The overall framework of the method includes three major steps.The first step distinguishes the relevant sentences with the irrelevant sentences in the reviews.At the second step the relevant sentences are further classified into different aspects.At the third step fine-grained semantic roles are extracted from sentences of each aspect.The data used in the training process is manually annotated in fine granularity of semantic roles.The features used in this CRF model include basic word features,part-of-speech (POS) features,position features and dependency syntactic features.Different combinations of these features are investigated.Experimental results are analyzed and future directions are discussed.

Improving conditional random field model for prediction of protein-RNA residue-base contacts

Background： For understanding biological cellular systems, it is important to analyze interactions between protein residues and RNA bases. A method based on conditional random fields （CRFs） was developed for predicting contacts between residues and bases, which receives multiple sequence alignments for given protein and RNA sequences, respectively, and learns the model with many parameters involved in relationships between neighboring residue-base pairs by maximizing the pseudo likelihood function. Methods： In this paper, we proposed a novel CRF-based model with more complicated dependency relationships between random variables than the previous model, but which takes less parameters for the sake of avoidance of overfitting to training data. Results： We performed cross-validation experiments for evaluating the proposed model, and took the average of AUC （area under receiver operating characteristic curve） scores. The result suggests that the proposed CRF-based model without using Ll-norm regularization （lasso） outperforms the existing model with and without the lasso under several input observations to CRFs. Conclusions： We proposed a novel stochastic model for predicting protein-RNA residue-base contacts, and improved the prediction accuracy in terms of the AUC score. It implies that more dependency relationships in a CRF could be controlled by less parameters.

高级地图匹配算法:研究现状和趋势

地图匹配是许多位置服务与轨迹挖掘应用的基础.随着定位技术和位置服务应用的发展,地图匹配研究不断演进,从早期基于高采样率GPS(Global Position System)的实时匹配,到近期基于低采样率GPS轨迹的离线匹配、再到当前非GPS定位数据或高精度地图匹配。迄今已有许多地图匹配算法相继提出,但鲜有研究对这些算法进行全面总结.为此,对近十年提出的地图匹配算法进行调研,归纳出地图匹配算法的统一框架及常用时空特征.从模型或实现技术角度分类发现:现有算法大都采用HMM(Hidden Markov Model)模型,其次是最大权重模型;深度学习技术近期开始用于地图匹配,将是未来高精度地图匹配研究的趋势.

Brain Tumor Segmentation using Multi-View Attention based Ensemble Network

Astrocytoma IV or glioblastoma is one of the fatal and dangerous types of brain tumors.Early detection of brain tumor increases the survival rate and helps in reducing the fatality rate.Various imaging modalities have been used for diagnosing by expert radiologists,and Medical Resonance Image(MRI)is considered a better option for detecting brain tumors as MRI is a non-invasive technique and provides better visualization of the brain region.One of the challenging issues is to identify the tumorous region from the MRI scans correctly.Manual segmentation is performed by medical experts,which is a time-consuming task and got chances of errors.To overcome this issue,automatic segmentation is performed for quick and accurate results.The proposed approach is to capture inter-slice information and reduce the outliers.Deep learning-based brain tumor segmentation techniques proved best among available segmentation techniques.However,deep learning may miss some preliminary info while using MRI images during segmentation.As MRI volumes are volumetric,3D U-Net-based models are used but complex.Combinations of multiple 2D U-Net predictions in axial,sagittal,and coronal views help to capture inter-slice information.This approach may reduce the system complexity.Moreover,the Conditional Random Fields(CRF)reduce the predictions’false positives and improve the segmentation results.This model is applied to Brain Tumor Segmentation(BraTS)2019 dataset,and cross-validation is performed to check the accuracy of results.The proposed approach achieves Dice Similarity Score(DSC)of 0.77 on Enhancing Tumor(ET),0.90 on Whole Tumor(WT),and 0.84 on Tumor Core(TC)with reduced Hausdorff Distance(HD)of 3.05 on ET,5.12 on WT and 3.89 on TC.

Integrating Multi-Source Web Records into Relational Database

How to integrate heterogeneous semi-structured Web records into relational database is an important and challengeable research topic. An improved model of conditional random fields was presented to combine the learning of labeled samples and unlabeled database records in order to reduce the dependence on tediously hand-labeled training data. The pro- posed model was used to solve the problem of schema matching between data source schema and database schema. Experimental results using a large number of Web pages from diverse domains show the novel approach＇s effectiveness.