Automatic Matching Research Articles

Rapid productivity prediction method for frac hits affected wells based on gas reservoir numerical simulation and probability method

Abstract As an important unconventional resource, shale gas can alleviate energy shortage, and its efficient development ensures the long-term growth of oil and gas. The prediction of production levels and estimated ultimate recovery with high accuracy is necessary for shale gas development. Conventional methods are widely applied in the oil and gas industry owing to their simplicity and effectiveness; however, none of them can accurately predict the results for frac hits affected wells. In this work, a probability method based on the numerical model of shale gas reservoir has been formed. In view of the impact of frac hits on the productivity of production wells during the development of shale gas reservoirs, an embedded discrete fractured numerical simulation method for gas reservoirs is proposed to simulate the geological engineering parameter range of wells before frac. And aiming at the established numerical model of shale gas reservoir, this method adopts the ensemble smoother with multiple data assimilation automatic history matching technology to carry out the history matching process of the model. Based on the probability theory and numerical simulation results, this study analyses the influence of different distribution functions of parameters on the calculation results of reserves, and obtains the expected curve of reserves through combination calculation. Besides, the effectiveness of this method was verified by comparing with other traditional predicted method.

Exploring effects of vessels on walrus behaviors using telemetry, automatic identification system data and matching

AbstractArctic marine mammals have had little exposure to vessel traffic and potential associated disturbance, but sea ice loss has increased accessibility of Arctic waters to vessels. Vessel disturbance could influence marine mammal population dynamics by altering behavioral activity budgets that affect energy balance, which in turn can affect birth and death rates. As an initial step in studying these linkages, we conducted the first comprehensive analysis to evaluate the effects of vessel exposure on Pacific walrus (Odobenus rosmarus divergens) behaviors. We obtained >120,000 h of location and behavior (foraging, in‐water not foraging, and hauled out) data from 218 satellite‐tagged walruses and linked them to vessel locations from the marine automatic identification system (AIS). This yielded 206 vessel‐exposed walrus telemetry hours for comparison to unexposed hours, which we used to assess if vessel exposure altered walrus behavior. We developed a filter to account for misclassification of vessel exposure of telemetered walruses. Then we tested for an effect of vessel exposure on walrus behaviors using a combination of exact and propensity score‐based matching to account for confounding covariates, and we conducted statistical power analyses. We did not detect an effect of vessel exposure on walrus behaviors even when statistical power was high (i.e., for foraging walruses), which may have been due to the sample size‐driven need to define vessel presence within a larger than desired distance (15‐km measured radius) around a walrus. Although this study did not determine at what distance vessel exposure affects walrus behaviors, it provided an upper bound on the distance at which the vessels encountered may disturb foraging walruses. When more situation‐specific information is lacking, this distance could be used as a conservative buffer to maintain between vessels and areas of high use by foraging walruses. Studies on behavioral consequences of closer proximities between walruses and vessels are needed, and our assessments of misclassification rates and statistical power can be used for future studies. We demonstrated that analytical approaches such as matching, which are rarely used in wildlife studies, are particularly useful for testing hypotheses with observational data.

A novel approach for automatic annotation of human actions in 3D point clouds for flexible collaborative tasks with industrial robots.

Manual annotation for human action recognition with content semantics using 3D Point Cloud (3D-PC) in industrial environments consumes a lot of time and resources. This work aims to recognize, analyze, and model human actions to develop a framework for automatically extracting content semantics. Main Contributions of this work: 1. design a multi-layer structure of various DNN classifiers to detect and extract humans and dynamic objects using 3D-PC preciously, 2. empirical experiments with over 10 subjects for collecting datasets of human actions and activities in one industrial setting, 3. development of an intuitive GUI to verify human actions and its interaction activities with the environment, 4. design and implement a methodology for automatic sequence matching of human actions in 3D-PC. All these procedures are merged in the proposed framework and evaluated in one industrial Use-Case with flexible patch sizes. Comparing the new approach with standard methods has shown that the annotation process can be accelerated by 5.2 times through automation.

GIPMA: Global Intensity-Guided Peak Matching and Alignment for 2D 1H-13C HSQC-Based Metabolomics.

Two-dimensional (2D) 1H-13C heteronuclear single quantum coherence (HSQC) has been increasingly applied to metabolomics studies because it can greatly improve the resolving capability compared with one-dimensional (1D) 1H NMR. However, preprocessing methods such as peak matching and alignment tools for 2D NMR-based metabolomics have lagged behind similar methods for 1D 1H NMR-based metabolomics. Correct matching and alignment of 2D NMR spectral features across multiple samples are particularly important for subsequent multivariate data analysis. Considering different intensity dynamic ranges of a variety of metabolites and the chemical shift variation across the spectra of multiple samples, here, we developed an efficient peak matching and alignment algorithm for 2D 1H-13C HSQC-based metabolomics, called global intensity-guided peak matching and alignment (GIPMA). In GIPMA, peaks identified in all spectra are pooled together and sorted by intensity. Chemical shift of a stronger peak is regarded to be more accurate and reliable than that of a weaker peak. The strongest undesignated peak is chosen as the reference of a new cluster if it is not located within the chemical shift tolerance of any existing peak cluster (PC), or otherwise it is matched to an existing PC and the aligned chemical shift of the PC is updated as the intensity-weighted average of the chemical shifts of all peaks in the cluster. Setting an optimum chemical shift tolerance (Δδo) is critical for the peak matching and alignment across multiple samples. GIPMA dynamically searches for and intelligently selects the Δδo for peak matching to maximize the number of valid peak clusters (vPC), that is, spectral features, among multiple samples. By GIPMA, fully automatic peakwise matching and alignment do not require any spectrum as initial reference, while the chemical shift of each PC is updated as the intensity-weighted average of the chemical shifts of all peaks in the same PC, which is warranted to be statistically more accurate. Accurate chemical shifts for each representative spectral feature will facilitate subsequent peak assignment and are essential for correct metabolite identification and result interpretation. The proposed method was demonstrated successfully on the spectra of six model mixtures consisting of seven typical metabolites, yielding correct matching of all known spectral features. The performance of GIPMA was also demonstrated on 2D 1H-13C HSQC spectra of 87 real extracts of 29 samples of five Dendrobium species. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) of the 87 matched and aligned spectra by GIPMA generates correct classification of the 29 samples into five groups. In summary, the proposed algorithm of GIPMA provided a practical peak matching and alignment method to facilitate 2D NMR-based metabolomics studies.

Image color-based preset light matching algorithm for an electric vitrine

Recent advanced light systems offer light presets to enable users to navigate a proper light. Often, the presets are labeled with target ambiance or mood, yet ambiguous for users to predict the light. This study proposes an algorithm that matches the light presets based on the image color characteristics derived from a photograph taken by users. In particular, we developed an automatic match of a light preset for an electric vitrine in which 15 RGB LEDs were linearly arrayed beneath the cover. We conducted a creativity workshop with eight light designers and composed a pool of 22 light presets for the electric vitrine. The presets attempted to cover five standard illuminants, eleven chromatic lights, and six kinds of the color spectrum. The algorithm enables users to receive the optimally matched light presets in order of hue similarities between displayed objects and the light preset. Based on stakeholders’ feedback, the algorithm UX is designed to provide three alternatives made up of the best chromatic light color, the best standard illuminant, and the best color spectrum. We expect the algorithm to be applied to different contexts, where light needs to be optimally tuned by being aware of the context characteristics.

Reservoir automatic history matching: Methods, challenges, and future directions

Reservoir history matching refers to the process of continuously adjusting the parameters of the reservoir model, so that its dynamic response will match the historical observation data, which is a prerequisite for making forecasts based on the reservoir model. With the development of optimization theory and machine learning algorithms, automatic history matching has made numerous breakthroughs for practical applications. In this perspective, the existing automatic history matching methods are summarized and divided into model-driven and surrogate-driven history matching methods according to whether the reservoir simulator needs to be run during the automatic history matching process. Then, the basic principles of these methods and their limitations in practical applications are outlined. Finally, the future trends of reservoir automatic history matching are discussed. Cited as: Liu, P., Zhang, K., Yao, J. Reservoir automatic history matching: Methods, challenges, and future directions. Advances in Geo-Energy Research, 2023, 7(2): 136-140. https://doi.org/10.46690/ager.2023.02.07

Resource Matching in the Supply Chain Based on Environmental Friendliness under a Smart Contract

This study aims to solve the problem of environmental pollution caused by industry through the upgrading and transformation of the supply chain, supply chain resource allocation, and related aspects. Specifically, environmental friendliness is added to the resource-matching problem of the cloud platform supply chain. Additionally, learning theory and dynamic evaluation systems are introduced when creating a preference sequence. The deferred-acceptance algorithm is used for matching. Finally, the automatic matching of blockchain smart contracts ensures the interests of both matching parties. Through the analysis of the example at the end of the study, we found that (1) the deviation table of demand side 5 and supply side 7 in the example shows that the deviation between demand side 5 as demand side and supply side 7 is only 11.55186, and the deviation between supply side 7 as demand side and demand side 5 is only 6.56778, and both sides form a high-quality pairing when matched with other partners. No excessive waste of its resources occurs. (2) Effectively ensure the openness and transparency of the supply chain production process; (3) The impact of environmental factors on enterprises is fully considered. In the analysis of the calculation cases, it can be found that demand side 10 has extremely high requirements for the environmental friendliness of its partners, and although supplier 2 has a very high preference for demand side 10, it is not successfully matched because the environmental friendliness of its own enterprise is not up to the standard, while supplier 1 has an environmental friendliness of up to 92 and is finally matched with Demand side 10; (4) Through the comparison test in the appendix, it can be found that the improved GS algorithm achieves the distinction between positive and negative partners. After multiple rounds of scoring, positive demand side 1, 3 was matched with positive supply side 2, 4, which can strengthen the enthusiasm of both partners and avoid negative cooperation.

Reservoir automatic history matching method using ensemble Kalman filter based on shrinkage covariance matrix estimation

ABSTRACT Because the geological conditions of the reservoir are complicated and involve many factors, the inversion of reservoir parameters is realized by using numerical simulation technology and history matching method. At present, Ensemble Kalman Filter method is widely used in history matching. But in the fact, the Ensemble Kalman Filter has problem such as inaccurate gradient calculation and pseudo correlation. In this paper, the Ensemble Kalman Filter based on shrinkage covariance matrix estimation is used to construct the localization matrix. By gradually matching production performance, the gradient of data assimilation method is corrected, the pseudo correlation is weakened, the reservoir model is updated, and the optimal estimate is obtained. By an example, we compare the Ensemble Kalman Filter and Ensemble Kalman Filter based on shrinkage covariance matrix estimation. The results show that Ensemble Kalman Filter based on shrinkage covariance matrix estimation is superior to Ensemble Kalman Filter in the accuracy of model production dynamic matching.

Online education based on distributed multi-layer data processing technology

In order to promote the intelligent development of online education and optimize the status quo of online education, an online education method based on distributed multi-layer data processing technology is proposed. The method integrates the database information and builds the BTREE index by calculating the distributed database resource information, the initial information sequence, the main information set and the trade-off coefficient of information attributes. The bidirectional index method is used to extract the core data segment of integrated data and establish the time series table. The schedule is used to constrain the data twice, and the coincidence degree of the initial data transmission path is compared with the actual path parameters after evaluation, so as to realize the automatic matching of the data transmission path. The experimental results show that compared with the traditional transmission path matching method, the designed automatic matching method improves the matching accuracy of the complete data transmission path by 24% and the broken data transmission path by 19%, which has practical advantages. It is proved that this method can promote the precise personality teaching of online education, so as to achieve the effect of optimizing online education. It can also provide reference for the intelligent development of other online education.

Robust ACO-Based Landmark Matching and Maxillofacial Anomalies Classification

Imagery assessment is an efficient method for detecting craniofacial anomalies. A cephalometric landmark matching approach may help in orthodontic diagnosis, craniofacial growth assessment and treatment planning. Automatic landmark matching and anomalies detection helps face the manual labelling limitations and optimize preoperative planning of maxillofacial surgery. The aim of this study was to develop an accurate Cephalometric Landmark Matching method as well as an automatic system for anatomical anomalies classification. First, the Active Appearance Model (AAM) was used for the matching process. This process was achieved by the Ant Colony Optimization (ACO) algorithm enriched with proximity information. Then, the maxillofacial anomalies were classified using the Support Vector Machine (SVM). The experiments were conducted on X-ray cephalograms of 400 patients where the ground truth was produced by two experts. The frameworks achieved a landmark matching error (LE) of 0.50 ± 1.04 and a successful landmark matching of 89.47% in the 2 mm and 3 mm range and of 100% in the 4 mm range. The classification of anomalies achieved an accuracy of 98.75%. Compared to previous work, the proposed approach is simpler and has a comparable range of acceptable matching cost and anomaly classification. Results have also shown that it outperformed the K-nearest neighbors (KNN) classifier.

Prerequisites for the development of the system of automatic comparison of video and audio tracks by the speaker’s articulation

Deep learning and reinforcement learning technologies are opening up new possibilities for the automatic matching of video and audio data. This article explores the key steps in developing such a system, from matching phonemes and lip movements to selecting appropriate machine-learning models. It also discusses the importance of getting the reward function right, the balance between exploitation and exploitation, and the complexities of collecting training data. The article emphasizes the importance of using pre-trained models and transfer learning, and the importance of correctly evaluating and interpreting results to improve the system and achieve high-quality content. The article focuses on the need to develop effective mapping quality metrics and visualization methods to fully analyze system performance and identify possible areas for improvement.

Automatic segmentation algorithm for high-spatial-resolution remote sensing images based on self-learning super-pixel convolutional network

ABSTRACT Super-pixel algorithms based on convolutional neural networks with fuzzy C-means clustering are widely used for high-spatial-resolution remote sensing images segmentation. However, this model requires the number of clusters to be set manually, resulting in a low automation degree due to the complexity of the iterative clustering process. To address this problem, a segmentation method based on a self-learning super-pixel network (SLSP-Net) and modified automatic fuzzy clustering (MAFC) is proposed. SLSP-Net performs feature extraction, non-iterative clustering, and gradient reconstruction. A lightweight feature embedder is adopted for feature extraction, thus expanding the receiving range and generating multi-scale features. Automatic matching is used for non-iterative clustering, and the overfitting of the network model is overcome by adaptively adjusting the gradient weight parameters, providing a better irregular super-pixel neighborhood structure. An optimized density peak algorithm is adopted for MAFC. Based on the obtained super-pixel image, this maximizes the robust decision-making interval, which enhances the automation of regional clustering. Finally, prior entropy fuzzy C-means clustering is applied to optimize the robust decision-making and obtain the final segmentation result. Experimental results show that the proposed model offers reduced experimental complexity and achieves good performance, realizing not only automatic image segmentation, but also good segmentation results.

An Efficient Approach for Automatic Complex Fractured Networks Parameter Inversion Based on Surrogate Model and Deep Reinforcement Learning

AbstractComplex fracture networks are commonly generated in subsurface reservoirs, and the first and primary task is to understand their transport characteristics and properties. Like pumping test interpretation, the well‐testing interpretation is an effective means for transport characteristics analysis and parameter estimations, this work attempts to propose a parameter inversion approach for complex fracture networks based on well‐testing interpretation. Given that non‐unique solutions and computational inefficiencies are obstacles to practical interpretation, especially when complex fractures are existing, a new deep reinforcement learning (DRL) based approach is proposed for automatic curve matching on complex fractured networks well‐testing interpretation. Based on twin delayed deep deterministic policy gradient (TD3) algorithm, the proposed DRL approach is successfully applied to automatic matching of complex fractured networks' well test curves. In addition, to improve the training efficiency, a surrogate model of the well test model based on bidirectional GRU (Bi‐GRU) neural network was established. After episodic training, the agent finally converged to an optimal curve matching policy through interaction with the surrogate model. The results show that the average relative error of the curve's parameter interpretation is 4.34%. Additionally, the results from the case studies show that the proposed DRL approach has a high calculation speed.

TODIM method based on the CRITIC method for multi-attribute group decision making with dual probabilistic linguistic information

The main research of this paper is decision making under the dual probabilistic linguistic term sets (DPLTSs). This paper introduces a method, which combined TODIM method and CRITIC method. In this research, the CRITIC method is used to determine the weight, and the distance formula of TODIM method has been modified in order to adapt to DPLTS situation. Then, the TODIM method is used for multi-attribute group decision making (MAGDM) problem. Finally, a case study concerning investment project selection is given to demonstrate the merits of the developed methods. This combined method can be used for the automatic areal feature matching, medical quality assessment, and ranking of matching processes. There are very few papers about using TODIM method under DPLTS situation at present, so this is a new perspective on MAGDM. The DPLTS-TODIM-CRITIC method was compared with correlation coefficient method and closeness coefficient method, and it is easy to find the advantage of this new method over the other two existing methods.

Synthetic sparse nested array with extended aperture and reduced mutual coupling for DOA estimation

To obtain a larger aperture for improving direction of arrival (DOA) estimation performance, we present a synthetic sparse nested array consisting of three subarrays with dual-polarization antennas, and then propose a cascade method composing of three steps for super-resolution angle estimation with range of 180 degree. There is a systematic procedure to determine the number and positions of antennas in each single-polarization subarray used for the synthetic sparse nested array, in order to efficiently reduce mutual coupling and obtain increased degrees of freedom and aperture for difference coarray with limited physical antennas. By exploiting the configuration and fully utilizing the aperture of the proposed array, two sparse uniform linear arrays are achieved in difference coarray domain, for giving coarse estimation and fine but ambiguous estimation in the first and second steps, respectively. Then an automatic pair matching based on pseudo ambiguous angles is provided to obtain unique DOA estimation in the third step. Theoretical analysis and simulation results are given to demonstrate the superiority of the proposed array and method for high-accuracy DOA estimation in the presence of mutual coupling.

Toward Robust Partial-Image Based Template Matching Techniques for MRI-Guided Interventions.

We have developed an MRI-safe needle guidance toolkit for MRI-guided interventions intended to enable accurate positioning for needle-based procedures. The toolkit allows intuitive and accurate needle angulation and entry point positioning according to an MRI-based plan, using a flexible, patterned silicone 2D grid. The toolkit automatically matches the grid on MRI planning images with a physical silicon grid placed conformally on the patient's skin and provides the Interventional Radiologist an easy-to-use guide showing the needle entry point on the silicon grid as well as needle angle information. The radiologist can use this guide along with a 2-degree-of-freedom (rotation and angulation relative to the entry point) hand-held needle guide to place the needle into the anatomy of interest. The initial application that we are considering for this toolkit is arthrography, a diagnostic procedure to evaluate the joint space condition. However, this toolkit could be used for any needle-based and percutaneous procedures such as MRI-guided biopsy and facet joint injection. For matching the images, we adopt a transformation parameter estimation technique using the phase-only correlation method in the frequency domain. We investigated the robustness of this method against rotation, displacement, and Rician noise. The algorithm was able to successfully match all the dataset images. We also investigated the accuracy of identifying the entry point from registered template images as a prerequisite for a future targeting study. Application of the template matching algorithm to locate the needle entry points within the MRI dataset resulted in an average entry point location estimation accuracy of 0.12 ±0.2 mm. This promising result motivates a more detailed assessment of this algorithm in the future including a targeting study on a silicon phantom with embedded plastic targets to investigate the end-to-end accuracy of this automatic template matching algorithm in the interventional MRI room.

The Vision of the FAIR Digital Object Machine and Ubiquitous FDO Services

The Vision of the FAIR Digital Object Machine and Ubiquitous FDO Services

Automatic Color-Matching Model of Environmental Art Based on Visual Matching by Using IoT

In order to improve the matching effect of environmental art color automatic matching, a model of environmental art color automatic matching based on visual matching is designed. Firstly, it preprocesses the color matching of environmental art, then extracts the color space and its main color, and analyzes the color deviation. At the same time, the color model is established and the color quantization is processed, and finally the color matching is realized. The experimental results show that the matching time of the proposed model is less than that of the traditional model, and the wrong contour is less. Research Objective. A model of environmental art color-automated matching based on visual matching is developed in order to improve the matching effect of environmental art color matching. Current Challenges. Because of time constraints, the matching model still has certain drawbacks, necessitating more optimization research in the subsequent study. The main steps in the research are pretreatment before color matching of environmental art, color space and main color extraction, color deviation analysis, color modeling, color quantization processing, implementation of color matching, and experimental comparison.

Automatic Matching of Multimodal Remote Sensing Images via Learned Unstructured Road Feature

Automatic matching of multimodal remote sensing images remains a vital yet challenging task, particularly for remote sensing and computer vision applications. Most traditional methods mainly focus on key point detection and description of the original image, thus ignoring the deep semantic feature information such as semantic road features, with the result that the traditional method can not effectively resist nonlinear grayscale distortion, and has low matching efficiency and poor accuracy. Motivated by this, this paper proposes a novel automatic matching method named LURF via learned unstructured road features for the multimodal images. There are four main contributions in LURF. To begin with, the semantic road features were extracted from multimodal images based on segmentation model CRESIv2. Next, based on semantic road features, a stable and reliable intersection point detector has been proposed to detect unstructured key points. Moreover, a local entropy descriptor has been designed to describe key points with the local skeleton feature. Finally, a global optimization strategy is adopted to achieve the correct matching. The extensive experimental results demonstrate that the proposed LURF outperforms other state-of-the-art methods in terms of both accuracy and efficiency on different multimodal image data sets.

Automatic matching of multi-scale road network under the constraint of small-scale road mesh

Aiming at the problem of road network multi-scale matching, a multi-scale road matching method under the constraint of road mesh of small-scale data has been proposed. First, two road meshes with different scale data are constructed; Secondly, under the constraint of the small-scale road mesh, the composite mesh composed of several road meshes in the large-scale road is extracted, and the mesh matching with the small-scale road mesh is completed; Then, many-to-many matching of road meshes with different scales is realized; finally, the matching relationship between composite mesh and small-scale road mesh is transformed into the matching between multi-scale road mesh boundary roads and internal roads, and the matching of the whole road network is completed. The experimental results show that this method can better realize the matching of multi-scale road network.