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"Identification methods"
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The Study on Identification Method of oil Bearing Property in Oil-water Transition Zone of X Development Zone
The Oilfield where the development zone is located has a unified oil-water contact. The oil-water transition zone in LSX Oilfield is about 24m thick, and the oil layer thickness is about 10m/5m. The OOIP are about 250 million tons. At the early stage of development, about the upper 1/3 section of transition zone was perforated, and the rest lower 2/3 section was not perforated due to high water content, which has become one of the replacement potential in the oilfield. In order to effectively develop this potential, it is necessary to establish the identification standard of oil bearing in the transition zone and explain the oil content at different depths in order to accurately tap the potential of transition zone. Based on the core well data, oil test data, liquid production profile data, closed core well and well logging data, the identification method of oil bearing in the transition zone is determined. The oil content interpretation standard in SPG oil layer is established by using the parameters of mobile water saturation and bound water saturation, and the longitudinal and plane distribution characteristics of oil-prone or water-prone layers in O/W transition zone are implemented. The transition zone was divided into four levels. They are oil-prone, water-prone, oil-bearing, and aquifers respectively, which provide geological basis for exploring the potential of transition zone in different water cut stages in future.
Journal Article
State estimation-based parameter identification for a class of nonlinear fractional-order systems
Parametric identification is an important part of system theory since knowledge of the parameters allows the analysis and control of the system. The aim of this paper is to propose a novel robust (against measurement noise) parameter identification method for a class of nonlinear fractional-order systems. In order to solve the parametric identification we carry out this problem to a state estimation problem, we introduce a Fractional Algebraic Identifiability (FAI) property which allows to represent the system parameters as a function of the inputs and outputs of the system, this parameter identification method provides an on-line identification process (while the system is operating), we also propose a fractional-order differentiator which allows to reduce the effect of measurement noise as well as to provide the estimation of a fractional-order derivative of the system output. Moreover, we use the Mittag–Leffler boundedness to demonstrate the convergence of this method, a different approach for this stability analysis method is given in this paper. Finally, we illustrate the accuracy and robustness of our proposed method by means of the parametric identification of two nonlinear fractional-order systems: a time-varying nonlinear fractional-order system and a nonlinear fractional-order mathematical model of a simple pendulum.
Journal Article
Unified Identification Method for Different Types of Traffic Conflicts Based on Vehicle Projection
As a common method for identifying traffic risks, the traffic conflict technique needs to be more uniform in identifying different types of traffic conflicts and comparing the effectiveness of different conflict identification methods. To establish a unified identification method for traffic conflicts, this study describes the conditions under which vehicles collide through vehicle projection and constructs the Traffic Conflict of Unified Identification (TU) based on the time of vehicle collision to estimate the degree of risk of collision between vehicles. Using HighD high‐resolution traffic flow trajectory data with traffic conflict counts and cumulative exposure collision times, the differences and causes of different methods for all‐type and subtype conflict recognition were comparatively analyzed. From the perspective of a single traffic conflict, the correlation between conflict severity and the intensity of risk‐avoidance behaviors is proposed to compare and analyze the capability of the identification methods. The results show that (1) the unified identification method can better identify traffic conflicts without distinguishing the type of traffic conflicts, reducing the misjudgment of following conflicts and omission of lane changing conflicts to a greater extent, and being less affected by the predetermination of conflict types. (2) Compared with the traditional time to collision (TTC), TU has a better correlation between conflict severity and the intensity of vehicle‐avoidance behaviors, regardless of whether the conflict severity is characterized by the minimum collision time or the cumulative exposure collision time. The unified identification method improves the accuracy of traffic conflict identification, expands the scenario applicability of traffic conflict technology, and provides a new way for real‐time traffic conflict technology risk prediction for autonomous driving.
Journal Article
Mechanical Identification Method of Amplitude Warning False Alarm Points Based on Dynamical Time–Frequency Domain Analysis
HighlightsBased on the dynamical principle and the time–frequency domain analysis method, the FAPMIM is developed.The index change of rock mass damage is analyzed from the perspective of energy and dynamics.The proposed method can identify all the noise points in the test and reduce the false alarm rate from 2.82% to 0.
Journal Article
Closed-loop subspace identification methods: an overview
In this study, the authors present an overview of closed-loop subspace identification methods found in the recent literature. Since a significant number of algorithms has appeared over the last decade, the authors highlight some of the key algorithms that can be shown to have a common origin in autoregressive modelling. Many of the algorithms found in the literature are variants on the algorithms that are discussed here. In this study, the aim is to give a clear overview of some of the more successful methods presented throughout the last decade. Furthermore, the authors retrace these methods to a common origin and show how they differ. The methods are compared both on the basis of simulation examples and real data. Although the main focus in the literature has been on the identification of discrete-time models, identification of continuous-time models is also of practical interest. Hence, the authors also provide an overview of the continuous-time formulation of the identification framework.
Journal Article
Stepwise Identification Method of Thermal Load for Box Structure Based on Deep Learning
Accurate and rapid thermal load identification based on limited measurement points is crucial for spacecraft on-orbit monitoring. This study proposes a stepwise identification method based on deep learning for identifying structural thermal loads that efficiently map the local responses and overall thermal load of a box structure. To determine the location and magnitude of the thermal load accurately, the proposed method segments a structure into several subregions and applies a cascade of deep learning models to gradually reduce the solution domain. The generalization ability of the model is significantly enhanced by the inclusion of boundary conditions in the deep learning models. In this study, a large simulated dataset was generated by varying the load application position and intensity for each sample. The input variables encompass a small set of structural displacements, while the outputs include parameters related to the thermal load, such as the position and magnitude of the load. Ablation experiments are conducted to validate the effectiveness of this approach. The results show that this method reduces the identification error of the thermal load parameters by more than 45% compared with a single deep learning network. The proposed method holds promise for optimizing the design and analysis of spacecraft structures, contributing to improved performance and reliability in future space missions.
Journal Article
Density Functional Theory–Spectroscopy Integrated Identification Method Encompassing Experimental and Theoretical Analyses for Designer Drug Stimulants
Increases in synthetic drug production and distribution pose significant risks to public health and safety. Traditional detection methods often fail to accurately identify these complex chemicals, particularly when they are mixed. Accordingly, Herein, an advanced density functional theory spectroscopy integrated identification method (D‐SIIM) comprising a combination of experimental and theoretical analyses is introduced. D‐SIIM is helpful in correcting the erroneous information provided by gas chromatography–mass spectrometry during the analysis of a mixture of three synthetic drug stimulants. Furthermore, the application of a denoising mechanism to the experimental Raman data considerably aligns experimental results with theoretical predictions, thereby augmenting the accuracy and reliability of D‐SIIM. Moreover, the potential of employing hyperpolarized nuclear magnetic resonance (NMR) spectroscopy to enhance NMR signals at low concentrations is explored. Current approach provides a robust and adaptable framework for identifying synthetic drugs in complex mixtures and will play critical roles in forensic investigations and drug enforcement strategies. A novel approach integrates gas chromatography‐mass spectrometry with infrared, Raman, and nuclear magnetic resonance spectroscopy plus density functional theory to detect synthetic drugs. Quantum hyperpolarization further boosts NMR sensitivity for low‐concentration samples, improving detection accuracy in complex mixtures and offering promising forensic applications.
Journal Article
Computer-Assisted Photo Identification Outperforms Visible Implant Elastomers in an Endangered Salamander, Eurycea tonkawae
Despite recognition that nearly one-third of the 6300 amphibian species are threatened with extinction, our understanding of the general ecology and population status of many amphibians is relatively poor. A widely-used method for monitoring amphibians involves injecting captured individuals with unique combinations of colored visible implant elastomer (VIE). We compared VIE identification to a less-invasive method - computer-assisted photographic identification (photoID) - in endangered Jollyville Plateau salamanders (Eurycea tonkawae), a species with a known range limited to eight stream drainages in central Texas. We based photoID on the unique pigmentation patterns on the dorsal head region of 1215 individual salamanders using identification software Wild-ID. We compared the performance of photoID methods to VIEs using both 'high-quality' and 'low-quality' images, which were taken using two different camera types and technologies. For high-quality images, the photoID method had a false rejection rate of 0.76% compared to 1.90% for VIEs. Using a comparable dataset of lower-quality images, the false rejection rate was much higher (15.9%). Photo matching scores were negatively correlated with time between captures, suggesting that evolving natural marks could increase misidentification rates in longer term capture-recapture studies. Our study demonstrates the utility of large-scale capture-recapture using photo identification methods for Eurycea and other species with stable natural marks that can be reliably photographed.
Journal Article